jpad journal

AND option

OR option

THE ASSOCIATION BETWEEN PEANUT AND PEANUT BUTTER CONSUMPTION AND COGNITIVE FUNCTION AMONG COMMUNITY-DWELLING OLDER ADULTS

 

E.W. Katzman1, S.J. Nielsen2

 

1. Landmark Health, , USA; 2. Russell Sage College, USA

Corresponding Author: Elizabeth W. Katzman, Landmark Health, USA, ewkatzman@gmail.com

J Prev Alz Dis 2021;
Published online June 11, 2021, http://dx.doi.org/10.14283/jpad.2021.32

 


Abstract

Background: Many studies have focused on the association between diet and cognitive function. While a subset of these studies focused on a diet that includes tree nuts (TN), there is limited data on the association between peanut and peanut butter consumption (P/PB) and cognitive health.
Objective: This study investigated the association of P/PB consumption and cognitive function.
Design: This was a cross-sectional study using 2011-2014 NHANES data.
Participants/setting: Individuals 60-80 years old in 2011-2014 NHANES who had two 24-hour dietary recalls, cognitive function tests, and education level and with no history of stroke.
Measurements: P/PB and TN consumption was measured as well as participant performance on the CERAD Word Learning subtest (CERAD W-L), Animal Fluency test (AFT), and the Digit Symbol Substitution test (DSST). Scores from the three cognitive tests were dichotomized. Individuals were classified as either P/PB consumers or non-consumers and TN consumers or non-consumers. Logistic regression models examined associations between P/PB consumption, tree nut consumption, and cognitive function with adjusted models including age, sex, and education as covariates.
Results: A total of 2,454 adults, aged 60-80 years old (mean age=69.4) participated. Approximately half were male (48%), 18% were P/PB consumers, and 14% consumed TN. Participants who did not consume P/PB were more likely to do poorly on the CERAD W-L (adjusted OR=1.56, 95% CI 1.24-1.97; p<0.05), AFT (adjusted OR=1.29, 95% CI 1.03-1.61; p<0.05), and DSST (adjusted OR=1.43, 95% CI 1.12-1.82; p<0.05) when compared to those who did consume P/PB.
Conclusions: These findings suggest an association between P/PB consumption and cognitive function; however, this is a cross sectional study and a causal relationship cannot be established. More studies are needed to determine causality.

Key words: Peanuts, peanut butter, cognitive function, NHANES, diet.


 

Diets containing nuts have been associated with a reduced risk of cognitive decline (1-5). The Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet includes nuts and has been associated with slower cognitive decline (1) and better verbal memory (2). Consuming a Mediterranean diet supplemented with 30 grams of mixed nuts was found to improve performance on cognitive tests including the Rey Auditory Verbal Learning Test and the Color Trail Test compared to the control group (3). In a similar study, participants who consumed a Mediterranean diet supplemented with nuts also scored better on the Mini-Mental State Examination and Clock Drawing tests when compared to the control (4). A review of data from the Nurses’ Health Study associated long-term nut intake with improved overall cognitive function at baseline and highlighted a dose response between nut intake and cognitive function; however, unlike other studies, the cognitive benefit was not seen at follow-up (5).
Peanuts, though legumes, have a similar nutritional profile to tree nuts (TN). They are a common pantry item in many homes and are less expensive than TN. Although Valls-Pedret et al. demonstrated improved performance on cognitive tests with Mediterranean diets supplemented with extra virgin olive oil or mixed nuts compared to a baseline Mediterranean diet, the nut group consumed only walnuts, hazelnuts, and almonds (3). In a 2015 cross-sectional study, nut intake was associated with improved delayed memory and abstraction, while legumes may have benefited overall cognition (6). Similarly, Nooyens et al. noted higher nut intake was associated with better baseline cognitive function when compared to participants consuming less nuts, however, legumes demonstrated no significant change to cognition (7). In both studies, the authors do not indicate to which group peanuts were included thereby making it difficult to determine the association of peanuts and cognitive health.
Available research into peanut consumption and its association with cognitive function is limited. One study, examining P/PB intake and cognitive function did not find an association with Alzheimer’s Disease mortality (8). A randomized control trial noted increased cerebrovascular reactivity and a 5% improvement with short-term memory when participants consumed 56-84 grams of high-oleic peanuts six days per week for 24 weeks when compared to a nut free diet (9). This study does not compare outcomes with peanuts containing standard amounts of oleic acid. Given the limited data, the objective of this study is to investigate the association of peanut/peanut butter (P/PB) consumption with cognitive function.

 

Methods

NHANES Study Population

The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional survey designed to monitor the health and nutrition of the US population (10). Cognitive testing was administered to participants 60+ years of age in the 2011-2014 NHANES study during a single interview in the Mobile Examination Center (11). Testing consisted of the Consortium to Establish a Registry for Alzheimer’s Disease Word Learning subtest (CERAD W-L), the Animal Fluency test (AFT), and the Digit Symbol Substitution test (DSST). The CERAD W-L has been used in many epidemiological studies to assess learning ability of verbal information (12). The AFT assesses categorical verbal fluency, a component of executive function and has been shown to differentiate between normal cognitive function, mild cognitive impairment, and more severe forms of cognitive impairment including Alzheimer’s disease (13-16). The DSST is part of the Wechsler Adult Intelligence Scale (WAIS III) and relies on working memory, processing speed, and sustained attention (17). Cognitive tests were administered by interviewers fluent in English and Spanish and were available in Korean, Vietnamese, and Chinese for participants (11).

Dietary Intake Assessment

For this study, two 24-hour dietary recalls from the 2011-2014 NHANES data were analyzed. The recalls were from non-consecutive days. The first 24-hour recall was collected in person in the Mobile Exam Center (MEC) and the next 24-hour recall was 3 to 10 days later by phone. The 24-hour recalls were collected on both weekdays and weekend days (18). Individuals were classified as either being a P/PB consumer or not having consumed P/PB based upon two 24-hour recalls. In addition, individuals were classified as being a TN consumer or not having consumed TN based upon the dietary recalls.

Cognitive Function Assessment and Covariates

Scores were calculated by dichotomizing the results. For the word scores: 0-10 were possible scores and three sets of word score tests were performed. The results were dichotomized into 0-18, 19-30. For the animal scores: possible scores were 0-40. The results were dichotomized 0-15, 16-40. The DSST had scores that ranged from 0-105. The variable was dichotomized into 0-45, 46-105. The other covariates that were analyzed were age (dichotomized: 60-69; 70-80), sex, and education. Education was classified as less than high school (HS), graduated from HS and some college or more.

Statistical Methods

The analytical sample included 2,454 individuals who had 2 days of dietary data, cognitive function data and education level and had no history of stroke. Logistic regression models were used to calculate odds ratios and 95% confidence intervals and examined the association between P/PB consumption, and cognitive function with TN consumption, age, sex, and education as covariates. Adjusted models examined P/PB consumption and cognitive function controlling for TN consumption. All statistical analyses were completed in SAS© version 9.4 created by SAS Institute Inc. Significance was established at p<0.05.

Ethical Considerations

Data collection protocols for NHANES 2011-2014 were approved by the CDC National Center for Health Statistics Research Ethics Review Board, an equivalent of an Institutional Review Board. All adult participants provided written and informed consent.

 

Results

Of the 2,454 participants that met criteria from the 2011-2014 NHANES data, 18% were peanut butter consumers (n=451) and 14% were TN consumers (n=347). There were no significant differences in sex and age between P/PB consumers and non-consumers (Table 1). However, there was a significant difference in education between P/PB consumers and non-consumers. Those who ate P/PB and TN consumed significantly greater amounts of total fat, monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs) in their diets than those who do not include P/PB and TN in their diets (p<0.01).

Table 1. Sociodemographic and dietary characteristics (n=2454)

*Statistically significant difference (p<0.05) between peanut/PB consumers and non-peanut/PB consumers; PB=Peanut Butter, HS=high school

Table 2. Comparison of fat intake of consumers and non-consumers of peanuts/peanut butter and tree nuts

*Statistically significantly different from one another at p<0.01; PB=Peanut Butter, MUFA=monounsaturated fatty acid, PUFA=polyunsaturated fatty acids

 

Tables 3-5 highlight the association between P/PB consumption, TN consumption, and the three cognitive performance tests. Those who did not consume P/PB were more likely to do poorly on the CERAD W-L (adjusted OR=1.56, 95% CI 1.24-1.97; p<0.05), AFT (adjusted OR=1.29, 95% CI 1.03-1.61; p<0.05), and DSST (adjusted OR=1.43, 95% CI 1.12-1.82; p<0.05) when compared to those who did consume P/PB. Individuals who did not consume TN were also more likely to do poorly on the CERAD W-L (adjusted OR=1.33, 95% CI 1.03-1.73; p<0.05), AFT (adjusted OR=1.37, 95% CI 1.06-1.75; p<0.05), and DSST (adjusted OR=2.09, 95% CI 1.58-2.78; p<0.05) when compared to individuals who did consume TN.

Table 3. Relationship between peanut/peanut butter consumption, tree nut consumption and performance on CERAD Word Learning subtest

*Statistically significant difference (p<0.05) between peanut/PB consumers and non-peanut/PB consumers; Adjusted model controlled for TN consumption, age, sex, education; PB=Peanut Butter, OR=Odds Ratio, CI=Confidence Interval

Table 4. Relationship between peanut/peanut butter consumption, tree nut consumption and performance on the Animal Fluency Test

*Statistically significant difference (p<0.05) between peanut/PB consumers and non-peanut/PB consumers; Adjusted model controlled for TN consumption, age, education; PB=Peanut Butter, OR=Odds Ratio, CI=Confidence Interval

Table 5. Relationship between peanut/peanut butter consumption, tree nut consumption and performance on the Digit Symbol Substitution Test

*Statistically significant difference (p<0.05) between peanut/PB consumers and non-peanut/PB consumers; Adjusted model controlled for TN consumption, age, sex, education; PB=Peanut Butter, OR=Odds Ratio, CI=Confidence Interval

 

Discussion

Using the NHANES data from 2011-2014, we observed an association between P/PB consumption and cognitive function. Participants who did not consume P/PB in the two 24-hour dietary recalls were more likely to do poorly on cognitive tests. A similar relationship was found with TN consumption. Further research is needed to confirm the association between P/PB and cognitive function.
As the prevalence of neurodegenerative diseases increases, comes an increased interest in neuroprotective foods (19-21). TN and peanuts contain neuroprotective, antioxidant and anti-inflammatory properties, as well as a large amount of MUFAs and PUFAs (19, 22). Indeed, participants consumed more MUFAs and PUFAs with P/PB and TN consumption than non-consumers; however, our study does not investigate the individual nutrients in P/PB or TN to determine the basis for this relationship. Growing evidence supports a potential link between cardiovascular disease and an increased risk for dementia and Alzheimer’s disease (23-27) and both TN and peanuts have been associated with reduced risk of cardiovascular and coronary heart disease (8, 28-30). Thereby making it possible that TN and peanuts may influence cognitive function based on cardiovascular disease risk.
To our knowledge, this investigation is the first to examine the association between P/PB consumption and cognitive function, and while studies have examined various TN or a combination of TN and peanuts, they have not focused solely on P/PB. Arab et al. observed significantly higher cognitive test scores and faster response times among walnut consumers after adjusting for age, sex, race, education, BMI, smoking, alcohol, and physical activity using NHANES data (31). Similarly, using data from the Nurses’ Health Study, O’Brien et al. demonstrated women who consumed walnuts one to three times monthly were more likely to perform better on cognitive tests when compared to women who consumed walnuts less than once monthly (5). In one five-year study, participants scored better on the Mini-Mental State Examination and Clock Drawing tests when consuming a Mediterranean diet supplemented with a combination of walnuts, hazelnuts, and almonds when compared to a nut-free, low fat group (4).
Experimental studies have also demonstrated potential benefits of TN consumption on cognitive health but have not shown a consistent effect. Verbal fluency and constructional praxis subtests scores improved over time when supplemented with Brazil nuts, although this was a pilot study consisting of 20 participants (32). The Walnuts and Healthy Aging (WAHA) study, a large dual center, single-blind clinical trial, failed to demonstrate a significant impact on cognition among healthy seniors (aged 63-79) with walnut supplementation; however, MRI scans in a subset of participants suggested walnuts may attenuate memory decline common to aging (33). Whereas young adults given banana bread with a half cup of ground walnuts did not demonstrate a significant improvement in memory, mood, or non-verbal reasoning skills, but a modest increase in inferential reasoning was observed (34). Differences in outcomes of these studies may be related to several factors including amount of nut consumed, the participant’s history of nut consumption, and the individual’s age.
The results observed in our study for TN and the three cognitive tests is consistent with the relationship between tree nuts and cognitive function in these studies and our results for P/PB showed a similar relationship. It is plausible that P/PB consumption would benefit cognitive health due to the nutritional similarities between TN and peanuts; however, we recommend further research to fully understand the association between P/PB consumption and cognitive health.
Since neurodegenerative diseases have increased and neuroprotective foods are becoming more popular (19-21), peanuts are an inexpensive and easy way for anyone to potentially help protect their cognitive function as they age. From our results, P/PB consumption is more prevalent than TN consumption, and in general, more individuals consume peanuts than tree nuts. Knowing that not only may tree nuts help delay cognitive decline but also P/PB may help delay cognitive decline is important.

Limitations

Our study is cross sectional therefore a causal relationship cannot be established. Data could not be adjusted for intrapersonal variability. More studies are needed to determine causality. Long term consumption of peanuts may have a different association with cognitive function when compared with recent consumption of peanuts. Additionally, cognitive function questionnaires were only administered to 60-80 year olds in 2011-2014. It would be helpful to compare cognitive function across a greater span of ages since we know that cognitive decline is associated with age.

 

Conclusion

These findings suggest an association between P/PB consumption and cognitive function; however, more information is needed to fully understand the relationship.

 

Disclaimer Statements: This study was funded by The Peanut Institute. E.K. and S.J.N. report no conflicts of interest.

Conflicts of Interest: E.K. and S.J.N. report no conflicts of interest.

Ethical Statement: This study complies with current laws of the country in which it was performed.

 

References

1. Morris MC, Tangney CC, Wang Y, et al. MIND diet slows cognitive decline with aging. Alzheimers Dement. 2015 Sep;11(9):1015-22. doi: 10.1016/j.jalz.2015.04.011. Epub 2015 Jun 15.
2. Berendsen AM, Kang JH, Feskens EJM, de Groot CPGM, Grodstein F, van de Rest O. Association of Long-Term Adherence to the MIND Diet with Cognitive Function and Cognitive Decline in American Women. J Nutr Health Aging. 2018;22(2):222-229. doi: 10.1007/s12603-017-0909-0.
3. Valls-Pedret C, Sala-Vila A, Serra-Mir M, et al. Mediterranean diet and age-related cognitive decline: a randomized clinical trial. JAMA Intern Med. 2015 Jul;175(7):1094-1103. doi: 10.1001/jamainternmed.2015.1668.
4. Martínez-Lapiscina EH, Clavero P, Toledo E, et al. Mediterranean diet improves cognition: the PREDIMED-NAVARRA randomised trial. J Neurol Neurosurg Psychiatry. 2013 Dec;84(12):1318-25. doi: 10.1136/jnnp-2012-304792. Epub 2013 May 13.
5. O’Brien J, Okereke O, Devore E, Rosner B, Breteler M, Grodstein F. Long-term intake of nuts in relation to cognitive function in older women. J Nutr Health Aging. 2014 May;18(5):496-502. doi: 10.1007/s12603-014-0014-6.
6. Dong L, Xiao R, Cai C, et al. Diet, lifestyle and cognitive function in old Chinese adults. Arch Gerontol Geriatr. Mar-Apr 2016;63:36-42. doi: 10.1016/j.archger.2015.12.003. Epub 2015 Dec 17.
7. Nooyens ACJ, Bueno-de-Mesquita HB, van Boxtel MPJ, van Gelder BM, Verhagen H, Verschuren WMM. Fruit and vegetable intake and cognitive decline in middle-aged men and women: the Doetinchem Cohort Study. Br J Nutr. 2011 Sep;106(5):752-61. doi: 10.1017/S0007114511001024. Epub 2011 Apr 11.
8. Amba V, Murphy G, Etemadi A, Wang S, Abnet CC, Hashemian M. Nut and peanut butter consumption and mortality in the National Institutes of Health-AARP diet and health study. Nutrients. 2019 Jul 2;11(7):1508. doi: 10.3390/nu11071508.
9. Barbour JA, Howe PRC, Buckley JD, Bryan J, Coates AM. Cerebrovascular and cognitive benefits of high-oleic peanut consumption in healthy overweight middle-aged adults. Nutr Neurosci. 2017 Dec;20(10):555-562. doi: 10.1080/1028415X.2016.1204744. Epub 2016 Jul 7.
10. About the National Health and Nutrition Examination Survey. Centers for Disease Control and Prevention. https://www.cdc.gov/nchs/nhanes/about_nhanes.htm. Accessed April 18, 2021.
11. National Health and Nutrition Examination Survey 2011–2012 data documentation, codebook, and frequencies: cognitive functioning. Centers for Disease Control and Prevention. https://wwwn.cdc.gov/Nchs/Nhanes/2011-2012/CFQ_G.htm. Accessed April 17, 2021.
12. Morris JC, Heyman A, Mohs RC, et al. The consortium to establish a registry for Alzheimer’s disease (CERAD). Part 1. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology. 1989 Sep;39(9):1159-65. doi: 10.1212/wnl.39.9.1159.
13. Strauss E, Sherman EMS and Spreen O. (2006) A Compendium of Neuropsychological Tests: Administration, Norms and Commentary (3rd edition). New York: Oxford University Press.
14. Henry JP, Crawford JR, Phillips LH. Verbal fluency performance in dementia of the Alzheimer’s type: a meta-analysis. Neuropsychologia. 2004;42:1212-1222.
15. Clark LJ, Gatz M, Zheng L, et al. Longitudinal verbal fluency in normal aging, preclinical and prevalent Alzheimer’s disease. American Journal of Alzheimer’s Disease and Other Dementia. 2009;24:461-468.
16. Duff Canning SJ, Leach L, Stuss D, et al. Diagnostic utility of abbreviated fluency measures in Alzheimer disease and vascular dementia. Neurology. 2004;62(4):556-562.
17. Wechsler D. WAIS Manual – Third Edition. New York: Psychological Corporation. 1997.
18. National Health and Nutrition Examination Survey 2011–2012 data documentation, codebook, and frequencies: dietary interview—individual foods, first day. Centers for Disease Control and Prevention. https://wwwn.cdc.gov/Nchs/Nhanes/2011-2012/DR1IFF_G.htm. Accessed April 18, 2021.
19. Gonzalez-Sarrias A, Nunez-Sanchez MA, Tomas-Barberan FA, Espin JC. Neuroprotective effects of bioavailable polyphenol-derived metabolites against oxidative stress-induced cytotoxicity in human neuroblastoma SH-SY5Y Cells. J Agric Food Chem 2017; 65: 752-758.
20. Lahiri DK, Chen DM, Lahiri P, Bondy S, Greig NH. Amyloid, cholinesterase, melatonin, and metals and their roles in aging and neurodegenerative diseases. Ann N Y Acad Sci 2005; 1056: 430-449.
21. Miller MG, Thangthaeng N, Poulose SM, Shukitt-Hale B. Role of fruits, nuts, and vegetables in maintaining cognitive health. Exp Gerontol 2017; 94: 24-28.
22. Pribis P, Shukitt-Hale B. Cognition: the new frontier for nuts and berries. Am J Clin Nutr 2014; 100 Suppl 1: 347S-352S.
23. Wanleenuwat P, Iwanowski P, Kozubski W. Alzheimer’s dementia: pathogenesis and impact of cardiovascular risk factors on cognitive decline. Postgrad Med. 2019 Sep;131(7):415-422. doi: 10.1080/00325481.2019.1657776. Epub 2019 Aug 27.
24. Stampfer MJ. Cardiovascular disease and Alzheimer’s disease: common links. J Intern Med. 2006 Sep;260(3):211-23. doi: 10.1111/j.1365-2796.2006.01687.x.
25. Newman AB, Fitzpatrick AL, Lopez O, et al. Dementia and Alzheimer’s disease incidence in relationship to cardiovascular disease in the Cardiovascular Health Study cohort. J Am Geriatr Soc. 2005 Jul;53(7):1101-7. doi: 10.1111/j.1532-5415.2005.53360.x.
26. Qiu C, Winblad B, Marengoni M, Klarin I, Fastbom J, Fratiglioni L. Heart failure and risk of dementia and Alzheimer disease: a population-based cohort study. Arch Intern Med. 2006 May 8;166(9):1003-8. doi: 10.1001/archinte.166.9.1003.
27. Eriksson U, Bennet A, Gatz M, Dickman P, Pedersen N. Non-stroke cardiovascular disease and risk of Alzheimer’s disease and dementia. Alzheimer Dis Assoc Disord. 2010; 24(3): 213–219.
28. Nouran MG, Kimiagar M, Abadi A, Mirzazadeh M, Harrison G. Peanut consumption and cardiovascular risk. Public Health Nutr. 2010 Oct;13(10):1581-6. doi: 10.1017/S1368980009992837. Epub 2009 Dec 22.
29. Guasch-Ferré M, Liu X, Malik VS, et al. Nut consumption and risk of cardiovascular disease. J Am Coll Cardiol. 2017 Nov 14;70(20):2519-2532. doi: 10.1016/j.jacc.2017.09.035.
30. Coates AM, Hill AM, Tan SY, Nuts and Cardiovascular Disease Prevention. Curr Atheroscler Rep. 2018 Aug 9;20(10):48. doi: 10.1007/s11883-018-0749-3.
31. Arab L, Ang A. A cross sectional study of the association between walnut consumption and cognitive function among adult US populations represented in NHANES. J Nutr Health Aging. 2015 Mar;19(3):284-90. doi: 10.1007/s12603-014-0569-2.
32. Cardoso BR, Apolinário D, da Silva Bandeira V, et al. Effects of Brazil nut consumption on selenium status and cognitive performance in older adults with mild cognitive impairment: a randomized controlled pilot trial. Eur J Nutr. 2016 Feb;55(1):107-16. doi: 10.1007/s00394-014-0829-2. Epub 2015 Jan 8.
33. Sala-Vila A, Valls-Pedret C, Rajaram S, et al. Effect of a 2-year diet intervention with walnuts on cognitive decline. The Walnuts And Healthy Aging (WAHA) study: a randomized controlled trial. Am J Clin Nutr. 2020 Mar 1;111(3):590-600. doi: 10.1093/ajcn/nqz328.
34. Pribis P, Bailey RN, Russell AA, et al. Effects of walnut consumption on cognitive performance in young adults. Br J Nutr. 2012 May;107(9):1393-401. doi: 10.1017/S0007114511004302. Epub 2011 Sep 19

THE INTERRELATIONSHIP BETWEEN INSULIN-LIKE GROWTH FACTOR 1, APOLIPOPROTEIN E Ε4, LIFESTYLE FACTORS, AND THE AGING BODY AND BRAIN

 

S.A. Galle1,2,*, I.K. Geraedts1,*, J.B. Deijen1,3, M.V. Milders1, M.L. Drent1,4

 

1. Department of Clinical, Neuro- & Developmental Psychology, Clinical Neuropsychology Section, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; 2. Department of Epidemiology, Erasmus Medical Centre, Rotterdam, the Netherlands; 3. Hersencentrum Mental Health Institute, Amsterdam, The Netherlands; 4. Department of Internal Medicine, Section of Endocrinology, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands * These authors contributed equally to this work

Corresponding Author: Sara A. Galle, Department of Clinical, Neuro- & Developmental Psychology, Clinical Neuropsychology Section, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-9, 1081 BT Amsterdam, The Netherlands, T: 0031205988769, E-mail: s.a.galle@vu.nl

J Prev Alz Dis 2020;4(7):265-273
Published online March 2, 2020, http://dx.doi.org/10.14283/jpad.2020.11

 


Abstract

Aging is associated with a decrease in body and brain function and with a decline in insulin-like growth factor 1 levels. The observed associations between alterations in insulin-like growth factor 1 levels and cognitive functioning and Mild Cognitive Impairment suggest that altered insulin-like growth factor 1 signaling may accompany Alzheimer’s disease or is involved in the pathogenesis of the disease. Recent animal research has suggested a possible association between insulin-like growth factor 1 levels and the Apolipoprotein E ε4 allele, a genetic predisposition to Alzheimer’s disease. It is therefore hypothesized that a reduction in insulin-like growth factor 1 signaling may moderate the vulnerability to Alzheimer’s disease of human Apolipoprotein E ε4 carriers. We address the impact of age-related decline of insulin-like growth factor 1 levels on physical and brain function in healthy aging and Alzheimer’s disease and discuss the links between insulin-like growth factor 1 and the Apolipoprotein E ε4 polymorphism. Furthermore, we discuss lifestyle interventions that may increase insulin-like growth factor 1 serum levels, including physical activity and adherence to a protein rich diet and the possible benefits to the physical fitness and cognitive functioning of the aging population.

Key words: Insulin-like growth factor, Alzheimer’s disease, ApoE-ε4 allele, physical activity, diet, aging.


 

Introduction

It is well known that the process of aging is associated with physical and mental changes. In the body, normal aging is primarily associated with a decrease in muscle mass and strength. In the brain, normal aging is mainly characterized by metabolic changes in the prefrontal cortex and associated with a decrease in brain size and synaptic plasticity (1). These changes in body and brain lead to alterations in physical, as well as cognitive functioning in elderly people, such as increased frailty and decreased cognitive performance (1, 2).
When age-related cognitive decline becomes qualitatively severe and progresses rapidly, it is likely to progress into a clinical diagnosis of dementia. The most common form of dementia is Alzheimer’s disease (AD). While there are some medications that decelerate the neuropathological progression of AD or offer some symptomatic relief, there is no cure available. In the absence of a cure for AD, research has focused on the most common risk factors and preventive strategies. Important non-modifiable risk factors for AD that have been investigated include age and genetics. Potentially modifiable factors are risk factors that are associated with lifestyle like socioeconomic factors, diet, cerebrovascular disease, and physical inactivity (3).
In the development of preventive strategies, it is important to understand the interplay between
neurobiological and lifestyle factors. One important factor that is both influenced by lifestyle factors like physical activity and diet (4, 5) and plays a role in the maintenance of physical fitness (6) and cognitive functioning (7) is insulin-like growth factor 1 (IGF-1). This review will discuss the impact of age-related decline of IGF-1 levels on physical and cognitive functioning in healthy aging and AD. In addition, we discuss the possible link between IGF-1 and ApoE-ε4. Furthermore, we explore how lifestyle interventions focusing on physical activity and diet may be useful to improve physical fitness and cognitive functioning by increasing IGF-1 serum levels.
Insulin-like growth factor 1 is a peptide growth hormone, with a structure similar to insulin, encoded by the IGF-1 gene located on chromosome 12. As part of the growth hormone (GH)/ IGF-1 axis, IGF-1 plays an essential role in growth of the body and development of the brain. IGF-1 is mainly produced in the liver, stimulated by GH, which is secreted from the anterior pituitary gland. IGF-1 can also be produced in local peripheral tissues such as muscle and bone tissue when GH binds to its Growth Hormone Receptor (GHR) (8). As IGF-1 is GH dependent and, unlike GH, circulating IGF-1 levels do not fluctuate widely over time, IGF-1 is a more reliable measure and appropriate marker for GH status (9). Therefore, this review focuses on neurobiological processes and lifestyle factors related to IGF-1.

 

IGF-1 and the aging body

Throughout the body, IGF-1 regulates the development and function of cells. It promotes cell growth and contributes to cell proliferation, stress resistance and survival in many cell types (10). IGF-1 can bind with high affinity to the IGF-1 receptor (IGF-1R), but also to the insulin receptor (11) as its structure is closely related to insulin. The IGF-1R is expressed in many distinct tissues in the body. For this reason IGF-1 can have different effects, such as the promotion of neuronal survival in the central nervous system and the facilitation of peripheral muscle regeneration (12). Because of the essential role of IGF-1 in muscle growth and the involvement of IGF-1 in many mechanisms and functions of the body, IGF-1 is an important factor for embryonic and childhood growth (13) and anabolic processes in adults (14).
Aging is associated with a decline in IGF-1 (10). The progressive decline has been termed the ‘somatopause’, which may be caused by potential alterations of the hypothalamic regulation of GH secretion, in particular an age-dependent decrease in endogenous hypothalamic GHRH output, contributing to the age-associated GH and IGF-1 decline (15). Moreover, low physical fitness and higher adiposity in older individuals also contribute to the decreased GH secretion and associated IGF-1 decline (16). Low levels of IGF-1 are associated with decreased skeletal muscle mass and function (17). Studies have shown that IGF-1 serum levels are positively associated with muscular strength and walking speed and are negatively associated with self-reported difficulty in mobility tasks (18). Systemic infusion of GH over 8 hours led to increased GH and IGF-1 concentration levels and increased muscle protein synthesis in eight healthy young adults aged 18 to 24 years (19). In addition, Rudman et al. (20) demonstrated increased lean body mass, average vertebral bone density, IGF-1 levels, and decreased body fat following GH administration over 6 months in nine healthy adults that were not observed in 12 untreated healthy adult men. Mauras et al. (14]) used recombinant human IGF-1 (rhIGF-1) treatment to increase IGF-1 plasma levels in 10 patients with Laron’s syndrome, characterized by GH receptor deficiency, and showed that increased IGF-1 plasma levels were associated with increased lean body mass and decreased fat mass. Furthermore, Dik et al. (21) demonstrated that higher IGF-1 serum levels were associated with fewer functional limitations (e.g. difficulties with climbing stairs, cutting toenails, use of public transport) in 1318 healthy participants aged 65 to 88 years. This association suggests that reduced IGF-1 levels in older people might make them more prone to these functional limitations.
The influence of IGF-1 on bone development has been demonstrated using mouse models. Bikle et al. (22) found a 24% decrease in cortical bone size and reduced femoral lengths, but increased connectivity and trabecular bone density, in IGF 1 deficient (Igf-1 -/-) mice. In addition, a study by Courtland et al. (23) used inducible liver IGF-1 deficient mice to deplete IGF-1 serum levels at varying times in mice development and demonstrated that depletion of serum IGF-1 levels at four weeks in male mice resulted in reduced trabecular and cortical bone acquisition by 16 weeks. Depletion of serum IGF-1 levels in mice of eight weeks resulted in decreased cortical bone properties at 32 weeks, whereas depletion of IGF-1 serum levels after peak bone acquisition at 16 weeks did not lead to detrimental effects on bone.
Finkenstedt et al. (24) demonstrated that 12 months of recombinant human GH (rhGH) treatment of 18 adult male and female patients, with adult onset GH deficiency, and an average age of 44 years, resulted in increased markers of bone formation and resorption and elevated IGF-1 levels compared to the untreated group. Following rhGH treatment for 12 months, markers for bone turnover, including bone formation and resorption, increased relative to baseline in those patients who were treated with rhGH. In addition, after 12 months, IGF-1 was significantly increased in all patients treated with rhGH, and bone mineral density in the lumbar and proximal spine was increased in this group, particularly in patients with low bone mass. Furthermore, one month of recombinant human GH administration in 10 healthy older men, with an average age of 68 years, led to improved balance and stair climb time as well as increased muscle IGF-1 gene expression (25). Ohlsson et al. (26) also showed that low IGF-1 serum levels in elderly men were associated with increased risk of bone fractures (e.g. hip, spine), which are partly caused by falls and are a clear marker of physical frailty. Muscle weakness, functional limitations, and age are substantial contributors to the risk of falls in elderly and these factors are all associated with a decrease in IGF-1. Hence, the age-related decrease in IGF-1 may play an important role in the increased incidence of falls in elderly.

 

IGF-1 and the aging brain

IGF-1 produced by the liver has the ability to cross the blood-brain barrier and can subsequently bind to IGF-1 receptors expressed throughout the brain. High densities of IGF-1 receptors are observed in various brain areas including the amygdala, thalamic nuclei, hippocampus, superficial and deep cortical layers, olfactory bulb, cerebellum, cerebral cortex, caudate nucleus, frontal cortex and the putamen (27). In addition, IGF-1 is also produced in brain tissues and can thereby act locally via paracrine or autocrine mechanisms. IGF-1 plays an important role in neuronal growth, the maintenance of synapses and the protection of neurons in the brain (28). Furthermore, IGF-1 has been found to enhance and maintain myelination, essential for the propagation of neuronal impulses, in the central nervous system (CNS) as well as in the peripheral nervous system.
Age-related decline of IGF-1 levels is associated with altered brain function. Sonntag et al. (29) showed age-related decreases in IGF-1 receptor density in hippocampal and cortical regions in rats. The authors found that IGF-1 mRNA levels were reduced in the cerebellum in older rats, compared to younger ones. This decline was associated with an increase in cell death (30). As IGF-1 is involved in maintaining myelination in the CNS, age-related IGF-1 decline may be associated with the breakdown of myelination which in turn may have a negative impact on cognition in humans (31). This age-related breakdown of myelin can lead to decreased signal transmission speed in neurons, essential for integration of information between highly distributed neural networks that underlie higher cognitive functions, such as executive processing (32).

 

IGF-1, cognition and MCI

Evidence thus far has supported the idea that IGF-1 plays an essential role in cognition. In healthy men and women, IGF-1 serum levels have been shown to be positively related to working memory (33), selective attention, executive function (34), verbal fluency and performance on the Mini-Mental State Examination (MMSE) (35). A recent study by Maass et al. (36) demonstrated that an increase in IGF-1 serum levels was positively associated with hippocampal volume and verbal memory recall in a population of healthy elderly. In childhood-onset GH deficient men GH substitution improved both mood and memory. These improvements were maintained during the 10 year follow-up period (37).
With respect to pathological cognitive aging, IGF-1 levels have been found to be reduced in people with MCI compared to cognitively healthy people. MCI is associated with reduced performance in various cognitive domains, including attention, executive function, processing speed, visuospatial skill and memory. Doi et al. (38) conducted a population survey in 3355 participants with an average age of 71.4 years and found that people with MCI showed decreased IGF-1 serum levels compared to cognitively healthy people. Furthermore, Calvo et al. (39) showed a positive association between IGF-1 serum levels and cognitive performance, mainly in the domains of learning and memory, in elderly people with MCI, suggesting IGF-1 may be neuroprotective in elderly people susceptible to AD. This notion is supported by the finding that the cognitive impairments in AD may be partly related to reduced IGF-1 serum levels (40).

 

IGF-1 and AD

At a neurobiological level AD is characterized by several neurotoxic effects caused by senile plaques (SPs) and neurofibrillary tangles (NFTs) that lead to synaptic dysfunction, neuronal cell death and cerebral atrophy, mainly in the hippocampus and temporal and parietal lobes. The main elements of SPs are beta-amyloid (Aβ) aggregates. These Aβ aggregates form plaques outside neurons that intervene with communication between neurons at synapses and contribute to neuronal cell death. NFTs, on the other hand, are primarily composed of hyperphosphorylated tau protein. Deviant abnormal tau proteins inside neurons (tau tangles) block the transports of essential molecules, such as nutrients in the neuron, thereby contributing to cell death. The abundance of NFTs is positively associated with the severity of AD (41). These brain alterations impede the transfer of information between synapses and cause a reduction in the number of synapses. The progression of the disease eventually leads to neuronal cell death causing a substantial shrinkage of the brain.
In 2007, Alvarez et al. (40) showed subnormal IGF-1 levels in adults diagnosed with AD. Additionally, Westwood et al. (42) showed that lower IGF-1 serum levels are associated with an increased risk of developing AD in older- and middle-aged people. This study also demonstrated that higher levels of IGF-1 are associated with greater brain volumes, even among cognitively healthy older and middle-aged people, suggesting a protective effect of IGF-1 against neurodegeneration. Recent evidence showed that IGF-1 resistance in the brain is increased in AD (43). Moloney et al. (44) demonstrated that alterations in IGF-1 receptors (IGF-1Rs) in the AD temporal cortex, including reduced expression as well as an aberrant distribution of IGF-1Rs in the neurons, contribute to impaired IGF-1R signaling in AD neurons. The deviant distribution of IGF-1Rs in neurons away from the plasma membrane suggests that IGF-1Rs are less able to respond to extracellular IGF-1 in AD, contributing to possible IGF-1R signaling resistance in neurons that degenerate (44). A decrease in IGF-1 signaling can contribute to loss of myelin function, which is thought to result in nerve fiber conduction delays found in people with AD (45). Furthermore, deficits in IGF-1 signaling have been related directly to AD pathology like increased accumulation of Aβ, phosphorylated tau, increased neuro-inflammation and apoptosis (28), suggesting that impaired IGF-1 signaling plays a role in the pathogenesis of AD. In contrast to this idea it has also been suggested that downregulation of IGF-1 signaling is a consequence of neuropathology and alterations in IGF-1 signaling could be seen as a compensatory response to attenuate the effects of aging and neurodegeneration. This idea is supported by the assocation between suppression of IGF-1 signaling and longevity in humans (46) and the observation that low IGF-1 levels predict life expectancy in exceptionally long-lived individuals (47).
In model organisms in which IGF-1 signaling was attenuated increased lifespan and a delayed process of aging has been observed (48, 49). For instance, in AD mouse models the long-term suppression of IGF-1 signaling reduced neuronal loss and increased resistance to oxidative stress and neuro-inflammation. In line with these findings, lowerd IGF-1 serum levels in transgenic mouce models, induced by a protein restriction diet, alleviated AD pathology (50).
In human observational studies, a recent meta-analysis by Ostrowski and colleagues could not confirm the hypothesized association between serum IGF-1 and AD. From 3540 studies that analyzed the relation between IGF-1 and AD, only 10 studies provided serum IGF-1 values. These 10 studies included 850 AD patients and 871 controls. From these studies 5 reported that AD subjects had higher IGF-1 levels, 2 reported no difference in IGF-1 levels and 3 reported lower IGF-1 levels in AD. The authors conclude that serum IGF-1 may be a personalized factor reflecting differential influence of genetic polymorphisms, age of onset or disease progression of AD patients (51). It is important to note that the number of included studies poses limitations to the generalizability of the results and more studies are needed to clarify the possible relationship between IGF-1 levels and AD.

 

Potential interactions of IGF-1 and ApoE-ε4 in the development of AD

The Apolipoprotein E gene, APOE, is the largest genetic risk factor associated with cognitive decline in late-onset AD (52). ApoE is involved in lipid transport in the central and peripheral nervous system, and brain injury repair. The three most common alleles of APOE (ε2, ε3, ε4) encode for the three major isoforms (ApoE-ε2, ApoE-ε3, ApoE-ε4) of the apolipoprotein E (ApoE), a protein that plays a central role in brain injury repair, lipid transport and metabolism. The ε2, ε3 and ε4 alleles have a worldwide frequency of 8.4%, 77.9% and 13.7%, respectively (53).
The strength of the effects of the different APOE genotypes on AD risk differs between ethnic groups. In the present study, we will focus on Caucasians. ApoE-ε3 is often considered the neutral allele with regard to AD risk. Compared to the ApoE-ε3, ApoE-ε4 is associated with both an increased incidence rate and an earlier onset of AD. One copy of ApoE-ε4 increases the risk of developing AD threefold, while those who are homozygous for ε4 have an approximately 13-fold increased risk (54). ApoE-ε4 carriers also have an enhanced risk for developing vascular dementia and mild cognitive impairment (MCI) (55) and studies have shown that the ApoE-ε4 allele is involved in the acceleration of cognitive decline (56). The accelerated cognitive decline observed in ApoE-ε4 carriers could be an important clinical precursor of AD. It has been shown that ApoE promotes the proteolytic breakdown of the Aβ aggregates appearing in AD, whereas the isoform ApoE-ε4 is less effective in enhancing this breakdown (57). Moreover, Kumar et al. (58) demonstrated that neurofibrillary tangle density was increased in ApoE-ε4 carriers relative to non-carriers of the allele. Hence, carrying the ApoE-ε4 allele increases the vulnerability of the brain to AD pathology.
As described earlier, IGF-1 has an opposite effect to ApoE-ε4 on N-methyl-D-aspartate receptor (NMDAR) signaling and Aβ clearance in the brain (59). With respect to NMDAR signaling, Liu et al. (60) demonstrated that the ApoE-ε4 allele enhanced an age-related decline in cognitive function in mice by decreasing NR2B subunit levels which in turn down-regulates the NMDAR pathway. Specifically, NR2B may play a role in spatial learning and long-term potentiation (61, 62). In contrast, IGF-1 has been found to positively affect the NMDARr pathway in rats by increasing NR2B subunits (62).
Impairments in Aβ clearance are a major hallmark in early as well as late AD. People carrying the ApoE-ε4 allele are more vulnerable to disturbances in Aβ clearance than people not carrying this allele (63). IGF-1 supports Aβ clearance in the healthy brain (64).
A recent study by Keeney et al. (65) was the first to report a direct association between the three isoforms of ApoE (ε2, ε3 and ε4) and IGF-1 by demonstrating deficient IGF-1 gene expression and reduced IGF-1 protein level in mice carrying the human ApoE-ε3 and ApoE-ε4, compared to mice carrying the human ApoE-ε2 allele. This association indicates that the three isoforms of ApoE affect IGF-1 signaling differently, suggesting a potential mechanism that might contribute to the differences in AD risk of ApoE isoforms (65).
Moderation of the association between IGF-1 signaling and AD by APOE genotype has previously been suggested in experimental studies. Using microarray analysis of the astrocyte transcriptome, Simpson and colleagues demonstrated that as AD pathology progresses, downregulation of gene transcription in astrocytes leads to a reduction in the expression of intra-cellular insulin and IGF signaling pathways, particularly in individuals expressing the ApoE-ε4 allele (66). Impaired IGF-1 signaling in human astrocytes is associated with a reduced ability to protect neurons from oxidative stress, which has been identified as an important factor in the promotion of tau and Aβ pathology in AD (67).
Therapeutic approaches targeting insulin resistance by increasing IGF-1, insulin, or insulin sensitivity have been promising, but do suggest differential effects in people with or without genetic susceptibility to AD. More specifically, intravenous and intranasal insulin administration in patients with AD, reduced amyloid precursor protein (APP) levels and improved memory scores only in those without the ApoE-ε4 allele (68, 69).
Previously, our group reported tentative evidence of an interaction between the ApoE-ε4 allele and IGF-1 receptor stimulating activity in an elderly cohort (59). IGF-1 receptor stimulating activity in the median and top tertiles was related with increased dementia incidence in hetero- and homozygotes of the ApoE-ε4 allele, but did not show any association with dementia risk in people without the ApoE-ε4 allele (59). The observed elevation in IGF-1 receptor stimulating activity may have marked a compensatory response to neuropathological changes associated with the ApoE-ε4 genotype. Additionally, we found that the ApoE-ε4 homozygotes, with a lifetime risk of Alzheimer’s Disease of 80% (70), have the lowest IGF-1 levels (59). Similarly, a genome-wide association study on longevity by Deelen et al. (2001) showed that the ApoE-ε4 allele was related to lower IGF-1 levels in middle-aged women. Hence, the increased risk of developing AD in ApoE-ε4 carriers might partially be attributed to alterations in IGF-1 signaling (71).

 

Physical activity and IGF-1

As mentioned earlier, IGF-1 serum levels can be influenced by lifestyle factors, such as physical activity (5). Aerobic and anaerobic exercise interventions have been shown to influence IGF-1 levels. The positive effect of aerobic exercise on IGF-1 levels has been shown in a mouse study that demonstrated upregulated mRNA levels of IGF-1 in mice after 15 days of voluntary wheel running. Protein levels of IGF-1 in the dentate gyrus had also increased (72). Replication of these results in human participants was provided by several studies that showed an increase in IGF-1 serum levels following aerobic exercise in adults (73, 74). Likewise, a study concerning the effect of anaerobic exercise on IGF-1 serum levels reported positive effects of anaerobic training on IGF-1 levels in healthy older men (75). There is, however, still much controversy concerning the association between physical exercise and IGF-1 levels. A systematic review of experimental studies on the effect of physical activity on measures of IGF-1 and cognitive functioning in healthy elderly concluded moderate intensity aerobic training and moderate and high intensity resistance training may improve circulating IGF-1 and cognition, depending on the sex of the participant and duration of the training. However, disparities in the type of exercise, protocols and samples hinder comparison of the results and the establishment of consensus (76).
Furthermore, negative associations between IGF-1 levels and physical activity, could also be explained by favorable neuromuscular anabolic adaption, which is a normal short-term adaptive response of the body to increased physical exercise (Rarick et al., 2007). It has been thought that during episodes of active muscle building IGF-1 serum levels decrease (78), but local muscle gene expression and production of IGF-1 increase (79). Longitudinal studies on exercise interventions indicate that IGF-1 serum levels may only decline temporarily and may increase after longer duration of intensive training and are maintained when training is reduced (74). The long-term effect of physical activity on IGF-1 levels may be explained by epigenetic alterations. It is known that physical activity can contribute to changes in various physiological systems by epigenetic mechanisms (80). Physical activity may induce epigenetic modifications to the IGF-1 gene, leading to sustained increased IGF-1 levels (6, 80). There is evidence showing that these types of alterations can be inherited (81). In light of epigenetics and the influence of prolonged physical activity on IGF-1 levels, the current decrease in the number of physically active people, mainly in high-income countries, is alarming.
Regular engagement in physical activity could be of special importance to those with a genetic susceptibility to AD. Several studies have indicated that the negative association between regular physical activity and cognitive decline is limited to those with one or more copies of the ApoE-ε4 allele. Schuit et al. registered engagement in physical activity in a group of elderly Dutch men and found that while risk of cognitive decline did not differ between active and inactive ApoE-ε4 non-carriers the risk was 4 times higher in inactive ApoE-ε4 carriers compared to active ApoE-ε4 carriers (82). A similar finding, indicating that inactivity is especially detrimental to cognitive abilities for ApoE-ε4 carriers, was reported in a longitudinal study in a Finnish cohort (83). Rovio et al. found a significant relationship between physical activity at midlife and risk of developing AD at a 21-year follow-up for ApoE-ε4 carriers, but not for ApoE-ε4 non-carriers. Additionally, Kivipelto et al. (84) demonstrated that physical inactivity increased the risk of AD mainly among ApoE-ε4 carriers.
Several brain-imaging studies have reported support for these findings. Deeny et al. found that in the middle-aged, sedentary ApoE-ε4 carriers exhibited lower activity levels in the temporal lobe, a region known to be vulnerable to early decline in AD, relative to active ApoE-ε4 carriers, while activity level did not distinguish between AD risk for ApoE-ε4 non carriers (85). In 2012 Head et al. demonstrated that in cognitively normal older adults those who were sedentary and ApoE-ε4 carriers showed more Aβ deposition than active ApoE-ε4 carriers, whereas this association was not present in non-carriers (86). Subsequently, Smith et al. observed that the hippocampal volume of those ApoE-ε4 carriers that displayed low levels of physical activity was on average 3% lower in comparison to non-carriers, and in comparison to ApoE-ε4 carriers who displayed high levels of physical activity (87), indicating that physical inactivity may be related to brain atrophy in ApoE-ε4 carriers. Together, these studies suggest that ApoE-ε4 carriers may be more susceptible to the negative effects of physical inactivity, and that sedentary ApoE-ε4 carriers may be at increased risk of developing AD.
In contrast, in a functional MRI study Smith et al. observed that among ApoE-ε4 carriers being engaged in higher levels of physical activity was associated with greater regional brain activation during a semantic memory task in comparison to non-carriers and ApoE-ε4 carriers who displayed lower levels of physical activity (88), suggesting that ApoE-ε4 carriers do not suffer more from inactivity than any other group but do experience more benefits from physical activity.
On the other hand, studies have shown that the interaction between physical activity and cognitive decline is restricted to ApoE-ε4 non-carriers. In a prospective study among older adults Podewils et al. found an inverse association between physical activity and risk of AD after a 5 year follow-up that was confined to ApoE-ε4 non-carriers, indicating that benefits of exercise may be confined only to ε4 non-carriers (89). A similar finding was reported after a 5 year follow-up in cognitively healthy elders (90). Fenesi et al. found a significant protective effect of physical activity regarding dementia risk in ApoE-ε4 non-carriers, and no significant effect in ApoE-ε4 carriers. One randomly controlled trial supported these two observational studies (91). Lautenschlager et al. studied the effect of an exercise intervention on cognitive functioning in a randomized trial in healthy older adults with subjective memory impairment. The researchers found a modest improvement in cognitive functioning in those treated with the intervention. In a post-hoc comparison, treatment response interacted with APOE genotype, as ApoE-ε4 non-carriers showed a significantly larger improvement compared to both carriers and non-carriers in the control condition, while no other significant differences were found (91).
One study did not find a significant interaction effect between physical activity and cognition and ApoE-ε4 carrier status (92). Luck et al. failed to find an interaction between physical activity in late life and risk of AD in an observational study after a 4.5-year follow-up in a group of healthy elderly aged 75 years and over. However, the authors did note that the interaction between ApoE-ε4 and low physical activity for AD risk verged on the border of significance.
With regard to physical fitness, it has been found that the presence of the ApoE-ε4 allele is associated with motor decline (e.g. motor performance) in older people (93) and the strength of this relationship increases with age. Further analysis showed that this association was mainly due to a greater age-related decrease in upper and lower limb muscle strength in people carrying the ApoE-ε4 allele. This study showed that ApoE-ε4 carriers are at greater risk of rapid motor decline relative to non-carriers, particularly later in life. Considering that limited physical activity is associated with motor decline, and physical activity is potentially protective against cognitive decline, physical activity is argued to be especially relevant to ApoE-ε4 carriers (86, 93).

 

Diet and IGF-1

In addition to the effect of physical activity on IGF-1 levels, diet is an important lifestyle factor affecting IGF-1 levels. Norat et al. (4) demonstrated that protein intake was positively associated with IGF-1 serum levels. This study showed that intake of milk, calcium, magnesium, phosphorus, potassium, vitamin B6, and vitamin B2 was positively related to IGF-1 serum levels and that the intake of vegetables and beta-carotene was negatively associated with IGF-1 serum levels in women. In line with this study, a study by Allen et al. (94) demonstrated that in adult women aged 20 to 70 a plant-based (vegan) diet was related to lower IGF-1 serum levels compared to women with a meat-eating or lacto-ovo-vegetarian diet. The difference in IGF-1 serum levels between the groups was mainly explained by protein intake consisting of essential amino acids. Long-term caloric restriction for a duration of 1 and 6 years was not associated with with reduced IGF-1 serum levels in healthy middle aged men and women, if protein intake is high (95). In addition, a recent study by Fontana et al. (96) showed that 2 years of caloric restriction did not affect IGF-1 serum levels in healthy non-obese young and middle-aged men and women, suggesting no sustained effects of caloric restriction on IGF-1 serum levels. Though, other studies demonstrated that short term caloric restriction for 6 days lowers IGF-1 serum levels (97), indicating that particularly short term fasting lowers IGF-1 serum levels.

 

Exercise combined with diet and IGF-1

Few studies have examined the influence of physical activity combined with a specific diet on IGF-1 levels. A negative caloric balance induced by physical exercise or caloric restriction, were both associated with equivalent decline in IGF-1 levels (98). Smith et al. (98) concluded that a decline in IGF-1 levels is mainly explained by an energy deficit, irrespective whether this deficit was induced by caloric restriction or physical exercise. A study by Rarick et al. (77) demonstrated a decline in IGF-1 serum levels after 7 days of increased physical activity in healthy men. However, the decrease in IGF-1 serum level was not moderated by fitness intensity, energy balance, or dietary protein intake. This study therefore challenges the concept of Smith et al. (98)and suggests that yet unknown mechanisms related to physical activity, such as enhanced energy flux, may affect IGF-1 levels independently.

 

IGF-1 in relation to other AD risk factors

When investigating the association between IGF-1 and Alzheimer’s disease it is important to consider the limited role of epidemiological evidence in causal inference and the possible confounding influence of a myriad of factors that are related to both AD risk and altered IGF-1 signaling. Among these potential confounders are lifestyle factors, like alcohol and nicotine consumption (99–101), and several conditions associated with alterations in insulin or IGF-1 signaling such as type 2 diabetes, obesity, cardiovascular disease, cerebral infarcts (102–107) and depression (108, 109). These cross-links between altered IGF-1 signaling and increased risk of AD highlight the importance of experimental and meta-analytic evidence, replication studies and a thorough consideration of potential confounders in the association between IGF-1 signaling and Alzheimer’s disease.

 

Conclusion and future perspectives

Although there are contradictory findings on the association between physical exercise, diet and IGF-1 it can be argued that promoting physical activity and a protein rich diet could be promising interventions that may increase IGF-1 levels, thereby increasing physical fitness and counteracting age-related neurodegeneration and AD. Further research, including experimental, epidemiological and multi-omic approaches (110), is warranted to investigate the prospective value of different biomarker profiles for future dementia risk. Findings can be applied to improve early diagnostics and to increase the efficiency of lifestyle interventions targeting IGF-1 signaling to delay or prevent the development of physical and cognitive decline, in particular for those most vulnerable for AD.

 

Highlights

– IGF-1 is associated with cognitive deficits and pathological alterations in the brain that accompany AD
– Decreased IGF-1 levels are a possible moderator of genetic vulnerability to AD
– Increasing physical activity and adherence to a protein rich diet may be useful interventions to increase IGF- serum levels, thereby increasing physical fitness and cognitive functioning

 

Funding: The authors received no financial support for the research, authorship or publication of this manuscript.

Conflict of interests: All authors declare that they have no conflict of interest.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

References

1. Peters R. Ageing and the brain. Postgrad Med J 2006;82:84–8. https://doi.org/10.1136/pgmj.2005.036665
2. Clegg A, Young J, Iliffe S, et al. Frailty in elderly people. Lancet 2013;381:752–62. https://doi.org/10.1016/S0140-6736(12)62167-9
3. Keene CD, Montine TJ. Epidemiology, pathology, and pathogenesis of Alzheimer disease. In: Post DW (ed) Up to date. UpToDate, Waltham, MA, 2015;pp 1–18
4. Norat T, Dossus L, Rinaldi S, et al. Diet, serum insulin-like growth factor-I and IGF-binding protein-3 in European women. Eur J Clin Nutr 2007;61:91–8. https://doi.org/10.1038/sj.ejcn.1602494
5. Moran S, Chen Y, Ruthie A, Nir Y. Alterations in IGF-I affect elderly: Role of physical activity. Eur Rev Aging Phys Act 2007;4:1–8. https://doi.org/10.1007/s11556-007-0022-1
6. Eliakim A, Brasel JA, Mohan S, et al. Physical fitness, endurance training, and the growth hormone-insulin-like growth factor I system in adolescent females. J Clin Endocrinol Metab 1996;81:3986–92. https://doi.org/10.1210/jcem.81.11.8923848
7. Arwert LI, Deijen JB, Drent ML. The relation between insulin-like growth factor I levels and cognition in healthy elderly: a meta-analysis. Growth Horm IGF Res 2005;15:416–22. https://doi.org/10.1016/j.ghir.2005.09.001
8. Salvatori R. Growth hormone and IGF-1. Rev Endocr Metab Disord 2004;5:15–23. https://doi.org/10.1023/B:REMD.0000016121.58762.6d
9. McMahon CD, Chai R, Radley-Crabb HG, et al. Lifelong exercise and locally produced insulin-like growth factor-1 (IGF-1) have a modest influence on reducing age-related muscle wasting in mice. Scand J Med Sci Sports 2014;24:e423-435. https://doi.org/10.1111/sms.12200
10. Ashpole NM, Sanders JE, Hodges EL, et al. Growth hormone, insulin-like growth factor-1 and the aging brain. Exp Gerontol 2015;68:76–81. https://doi.org/10.1016/j.exger.2014.10.002
11. Lesniak MA, Hill JM, Kiess W, et al. Receptors for insulin-like growth factors I and II: autoradiographic localization in rat brain and comparison to receptors for insulin. Endocrinology 1988;123:2089–99. https://doi.org/10.1210/endo-123-4-2089
12. Duan C, Ren H, Gao S. Insulin-like growth factors (IGFs), IGF receptors, and IGF-binding proteins: Roles in skeletal muscle growth and differentiation. Gen Comp Endocrinol 2010;167:344–351. https://doi.org/10.1016/j.ygcen.2010.04.009
13. Kajantie E, Fall CHD, Seppälä M, et al. Serum insulin-like growth factor (IGF)-I and IGF-binding protein-1 in elderly people: relationships with cardiovascular risk factors, body composition, size at birth, and childhood growth. J Clin Endocrinol Metab 2003;88:1059–65. https://doi.org/10.1210/jc.2002-021380
14. Mauras N, Martinez V, Rini A, Guevara-Aguirre J. Recombinant human insulin-like growth factor I has significant anabolic effects in adults with growth hormone receptor deficiency: studies on protein, glucose, and lipid metabolism. J Clin Endocrinol Metab 2000;85:3036–42. https://doi.org/10.1210/jcem.85.9.6772
15. Russell-Aulet M, Jaffe CA, Demott-Friberg R, Barkan AL. In vivo semiquantification of hypothalamic growth hormone-releasing hormone (GHRH) output in humans: Evidence for relative GHRH deficiency in aging. J Clin Endocrinol Metab 1999;84:3490–3497. https://doi.org/10.1210/jc.84.10.3490
16. Vahl N, Jørgensen JOL, Jurik AG, Christiansen JS. Abdominal adiposity and physical fitness are major determinants of the age associated decline in stimulated GH secretion in healthy adults. J Clin Endocrinol Metab 1996;81:2209–2215. https://doi.org/10.1210/jc.81.6.2209
17. Clemmons DR. Role of IGF-I in skeletal muscle mass maintenance. Trends Endocrinol Metab 2009;20:349–356. https://doi.org/10.1016/j.tem.2009.04.002
18. Cappola AR, Bandeen-Roche K, Wand GS, et al. Association of IGF-I levels with muscle strength and mobility in older women. J Clin Endocrinol Metab 2001;86:4139–46. https://doi.org/10.1210/jcem.86.9.7868
19. Fryburg DA, Barrett EJ, Louard R, al. et. Growth hormone acutely stimulates skeletal muscle but not whole-body protein synthesis in humans. Metabolism 1993;42:1223–7. https://doi.org/10.1016/0026-0495(93)90285-V
20. Rudman D, Feller AG, Nagraj HS, et al. Effects of Human Growth Hormone in Men over 60 Years Old. N Engl J Med 1990;323:1–6. https://doi.org/10.1056/NEJM199007053230101
21. Dik MG, Pluijm SM., Jonker C, et al. Insulin-like growth factor I (IGF-I) and cognitive decline in older persons. Neurobiol Aging 2003;24:573–581. https://doi.org/10.1016/S0197-4580(02)00136-7
22. Bikle D, Majumdar S, Laib A, et al . The skeletal structure of insulin-like growth factor I-deficient mice. J Bone Miner Res 2001;16:2320–9. https://doi.org/10.1359/jbmr.2001.16.12.2320
23. Courtland HW, Elis S, Wu Y, et al. Serum IGF-1 affects skeletal acquisition in a temporal and compartment-specific manner. PLoS One 2011;6:1–10. https://doi.org/10.1371/journal.pone.0014762
24. Finkenstedt G, Gasser RW, Höfle G, et al. Effects of growth hormone (GH) replacement on bone metabolism and mineral density in adult onset of GH deficiency: results of a double-blind placebo-controlled study with open follow-up. Eur J Endocrinol 1997;136:282–9
25. Brill KT, Weltman AL, Gentili A, et al. Single and Combined Effects of Growth Hormone and Testosterone Administration on Measures of Body Composition, Physical Performance, Mood, Sexual Function, Bone Turnover, and Muscle Gene Expression in Healthy Older Men. J Clin Endocrinol Metab 2002;87:5649–5657. https://doi.org/10.1210/jc.2002-020098
26. Ohlsson C, Mellström D, Carlzon D, et al. Older men with low serum IGF-1 have an increased risk of incident fractures: the MrOS Sweden study. J Bone Miner Res 2011;26:865–72. https://doi.org/10.1002/jbmr.281
27. Adem A, Jossan SS, d’Argy R, et al. Insulin-like growth factor 1 (IGF-1) receptors in the human brain: quantitative autoradiographic localization. Brain Res 1989;503:299–303
28. Bedse G, Di Domenico F, Serviddio G, Cassano T. Aberrant insulin signaling in Alzheimer’s disease: Current knowledge. Front Neurosci 2015;9:. https://doi.org/10.3389/fnins.2015.00204
29. Sonntag WE, Lynch CD, Bennett SA, et al. Alterations in insulin-like growth factor-1 gene and protein expression and type 1 insulin-like growth factor receptors in the brains of ageing rats. Neuroscience 1999;88:269–79
30. Pañeda C, Arroba AI, Frago LM, et al. Growth hormone-releasing peptide-6 inhibits cerebellar cell death in aged rats. Neuroreport 2003;14:1633–5. https://doi.org/10.1097/01.wnr.0000086252.76504.e3
31. O’Sullivan M, Jones DK, Summers PE, et al. Evidence for cortical “disconnection” as a mechanism of age-related cognitive decline. Neurology 2001;57:632–638. https://doi.org/10.1212/WNL.57.4.632
32. Bartzokis G. Age-related myelin breakdown: a developmental model of cognitive decline and Alzheimer’s disease. Neurobiol Aging 2004;25:5–18. https://doi.org/10.1016/j.neurobiolaging.2003.03.001
33. Deijen JB, Arwert LI, Drent ML. The GH/IGF-I Axis and Cognitive Changes across a 4-Year Period in Healthy Adults. ISRN Endocrinol 2011;1–6. https://doi.org/10.5402/2011/249421
34. Bellar D, Glickman EL, Juvancic-Heltzel J, Gunstad J. Serum insulin like growth factor-1 is associated with working memory, executive function and selective attention in a sample of healthy, fit older adults. Neuroscience 2011;178:133–137. https://doi.org/10.1016/j.neuroscience.2010.12.023
35. Al-Delaimy WK, von Muhlen D, Barrett-Connor E. Insulinlike growth factor-1, insulinlike growth factor binding protein-1, and cognitive function in older men and women. J Am Geriatr Soc 2009;57:1441–6. https://doi.org/10.1111/j.1532-5415.2009.02343.x
36. Maass A, Düzel S, Brigadski T, et al. Relationships of peripheral IGF-1, VEGF and BDNF levels to exercise-related changes in memory, hippocampal perfusion and volumes in older adults. Neuroimage, 2015. https://doi.org/10.1016/j.neuroimage.2015.10.084
37. Arwert LI, Deijen JB, Müller M, Drent ML. Long-term growth hormone treatment preserves GH-induced memory and mood improvements: A 10-year follow-up study in GH-deficient adult men. Horm Behav 2005;47:343–349. https://doi.org/10.1016/j.yhbeh.2004.11.015
38. Doi T, Shimada H, Makizako H, et al. Association of insulin-like growth factor-1 with mild cognitive impairment and slow gait speed. Neurobiol Aging 2015;36:942–7. https://doi.org/10.1016/j.neurobiolaging.2014.10.035
39. Calvo D, Gunstad J, Miller LA, et al. Higher serum insulin-like growth factor-1 is associated with better cognitive performance in persons with mild cognitive impairment. Psychogeriatrics 2013;13:170–4. https://doi.org/10.1111/psyg.12023
40. Alvarez A, Cacabelos R, Sanpedro C, et al. Serum TNF-alpha levels are increased and correlate negatively with free IGF-I in Alzheimer disease. Neurobiol Aging 2007;28:533–6. https://doi.org/10.1016/j.neurobiolaging.2006.02.012
41. Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 2002;297:353–6. https://doi.org/10.1126/science.1072994
42. Westwood AJ, Beiser A, Decarli C, et al. Insulin-like growth factor-1 and risk of Alzheimer dementia and brain atrophy. Neurology 2014;82:1613–9. https://doi.org/10.1212/WNL.0000000000000382
43. Talbot K, Wang H-Y, Kazi H, et al. Demonstrated brain insulin resistance in Alzheimer’s disease patients is associated with IGF-1 resistance, IRS-1 dysregulation, and cognitive decline. J Clin Invest 2012;122:1316–1338. https://doi.org/10.1172/JCI59903
44. Moloney AM, Griffin RJ, Timmons S, et al. Defects in IGF-1 receptor, insulin receptor and IRS-1/2 in Alzheimer’s disease indicate possible resistance to IGF-1 and insulin signalling. Neurobiol Aging 2010;31:224–43. https://doi.org/10.1016/j.neurobiolaging.2008.04.002
45. de la Monte SM. Contributions of brain insulin resistance and deficiency in amyloid-related neurodegeneration in Alzheimer’s disease. Drugs 2012;72:49–66. https://doi.org/10.2165/11597760-000000000-00000
46. Suh Y, Atzmon G, Cho M-O, et al. Functionally significant insulin-like growth factor I receptor mutations in centenarians. Proc Natl Acad Sci U S A 2008;105:3438–42. https://doi.org/10.1073/pnas.0705467105
47. Milman S, Atzmon G, Huffman DM, et al. Low insulin-like growth factor-1 level predicts survival in humans with exceptional longevity. Aging Cell 2014;13:769–771. https://doi.org/10.1111/acel.12213
48. Tazearslan C, Huang J, Barzilai N, Suh Y (2011. Impaired IGF1R signaling in cells expressing longevity-associated human IGF1R alleles. Aging Cell 2011;10:551–554. https://doi.org/10.1111/j.1474-9726.2011.00697.x
49. Tatar M, Kopelman A, Epstein D, et al. A mutant Drosophila insulin receptor homolog that extends life-span and impairs neuroendocrine function. Science 2001(80- ) 292:107–110. https://doi.org/10.1126/science.1057987
50. Parrella E, Maxim T, Maialetti F, et al. Protein restriction cycles reduce IGF-1 and phosphorylated tau, and improve behavioral performance in an Alzheimer’s disease mouse model. Aging Cell 2013;12:257–268. https://doi.org/10.1111/acel.12049
51. Ostrowski PP, Barszczyk A, Forstenpointner J, et al. Meta-analysis of serum insulin-like growth factor 1 in Alzheimer’s disease. PLoS One 2016;11:1–12. https://doi.org/10.1371/journal.pone.0155733
52. Rohn TT. Proteolytic cleavage of apolipoprotein E4 as the keystone for the heightened risk associated with Alzheimer’s disease. Int J Mol Sci 2013;14:14908–22. https://doi.org/10.3390/ijms140714908
53. Farrer LA. Effects of Age, Sex, and Ethnicity on the Association Between Apolipoprotein E Genotype and Alzheimer Disease. JAMA 1997;278:1349. https://doi.org/10.1001/jama.1997.03550160069041
54. Farrer LA, Cupples LA, Haines JL, et al. Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. JAMA 1997;278:1349–56. https://doi.org/10.1001/jama.1997.03550160069041
55. Liu C-C, Liu C-C, Kanekiyo T, et al. Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy. Nat Rev Neurol 2013;9:106–18. https://doi.org/10.1038/nrneurol.2012.263
56. Marioni RE, Campbell A, Scotland G, et al. Differential effects of the APOE e4 allele on different domains of cognitive ability across the life-course. Eur J Hum Genet 2015;24:919–923. https://doi.org/10.1038/ejhg.2015.210
57. Hori Y, Hashimoto T, Nomoto H, et al. Role of Apolipoprotein E in β-Amyloidogenesis. J Biol Chem 2015;290:15163–15174. https://doi.org/10.1074/jbc.M114.622209
58. Kumar NT, Liestøl K, Løberg EM, et al. Apolipoprotein E allelotype is associated with neuropathological findings in Alzheimer’s disease. Virchows Arch 2015;467:225–35. https://doi.org/10.1007/s00428-015-1772-1
59. Galle SA, Van Der Spek A, Drent ML, et al. Revisiting the role of insulin-like growth factor-I receptor stimulating activity and the apolipoprotein E in Alzheimer’s disease. Front Aging Neurosci 2019;10:1–9. https://doi.org/10.3389/fnagi.2019.00020
60. Liu D, Pan X, Zhang J, et al. APOE4 enhances age-dependent decline in cognitive function by down-regulating an NMDA receptor pathway in EFAD-Tg mice. Mol Neurodegener 2015;10:7. https://doi.org/10.1186/s13024-015-0002-2
61. Chen QS, Kagan BL, Hirakura Y, Xie CW. Impairment of hippocampal long-term potentiation by Alzheimer amyloid beta-peptides. J Neurosci Res 2000;60:65–72
62. Le Grevès M, Le Grevès P, Nyberg F. Age-related effects of IGF-1 on the NMDA-, GH- and IGF-1-receptor mRNA transcripts in the rat hippocampus. Brain Res Bull 2005;65:369–374. https://doi.org/10.1016/j.brainresbull.2005.01.012
63. Wildsmith KR, Holley M, Savage JC, et al. Evidence for impaired amyloid β clearance in Alzheimer’s disease. Alzheimers Res Ther 2013;5:33. https://doi.org/10.1186/alzrt187
64. Gasparini L, Xu H. Potential roles of insulin and IGF-1 in Alzheimer’s disease. Trends Neurosci 2003;26:404–406. https://doi.org/10.1016/S0166-2236(03)00163-2
65. Keeney JT-R, Ibrahimi S, Zhao L. Human ApoE Isoforms Differentially Modulate Glucose and Amyloid Metabolic Pathways in Female Brain: Evidence of the Mechanism of Neuroprotection by ApoE2 and Implications for Alzheimer’s Disease Prevention and Early Intervention. J Alzheimer’s Dis 2015;48:411–424. https://doi.org/10.3233/JAD-150348
66. Simpson JE, Ince PG, Shaw PJ, et al. Microarray analysis of the astrocyte transcriptome in the aging brain: Relationship to Alzheimer’s pathology and APOE genotype. Neurobiol Aging 2011;32:1795–1807. https://doi.org/10.1016/j.neurobiolaging.2011.04.013
67. Huang WJ, Zhang X, Chen WW. Role of oxidative stress in Alzheimer’s disease (review). Biomed Reports 2016;4:519–522. https://doi.org/10.3892/br.2016.630
68. Craft S, Asthana S, Schellenberg G, et al. Insulin effects on glucose metabolism, memory, and plasma amyloid precursor protein in Alzheimer’s disease differ according to apolipoprotein-E genotype. Ann N Y Acad Sci 2000;903:222–228. https://doi.org/10.1111/j.1749-6632.2000.tb06371.x
69. Reger MA, Watson GS, Frey WH, et al. Effects of intranasal insulin on cognition in memory-impaired older adults: Modulation by APOE genotype. Neurobiol Aging 2006;27:451–458. https://doi.org/10.1016/j.neurobiolaging.2005.03.016
70. van der Lee SJ, Wolters FJ, Ikram MK, et al. The effect of APOE and other common genetic variants on the onset of Alzheimer’s disease and dementia: a community-based cohort study. Lancet Neurol 2018;17:434–444. https://doi.org/10.1016/S1474-4422(18)30053-X
71. Deelen J, Beekman M, Uh HW, et al. Genome-wide association study identifies a single major locus contributing to survival into old age; the APOE locus revisited. Aging Cell 2011;10:686–698. https://doi.org/10.1111/j.1474-9726.2011.00705.x
72. Yu J-L, Ma L, Ma L, Tao Y-Z. [Voluntary wheel running enhances cell proliferation and expression levels of BDNF, IGF1 and WNT4 in dentate gyrus of adult mice]. Sheng Li Xue Bao 2014;66:559–68
73. Ardawi M-SM, Rouzi AA, Qari MH. Physical activity in relation to serum sclerostin, insulin-like growth factor-1, and bone turnover markers in healthy premenopausal women: a cross-sectional and a longitudinal study. J Clin Endocrinol Metab 2012;97:3691–9. https://doi.org/10.1210/jc.2011-3361
74. Koziris LP, Hickson RC, Chatterton RT, et al. Serum levels of total and free IGF-I and IGFBP-3 are increased and maintained in long-term training. J Appl Physiol 1999;86:1436–42
75. Amir R, Ben-Sira D, Sagiv M. Igf-I and fgf-2 responses to wingate anaerobic test in older men. J Sports Sci Med 2007;6:227–32
76. Stein AM, Silva TMV, Coelho FG de M, et al. Physical exercise, IGF-1 and cognition A systematic review of experimental studies in the elderly. Dement Neuropsychol 2018;12:114–122. https://doi.org/10.1590/1980-57642018dn12-020003
77. Rarick KR, Pikosky MA, Grediagin A, et al. Energy flux, more so than energy balance, protein intake, or fitness level, influences insulin-like growth factor-I system responses during 7 days of increased physical activity. J Appl Physiol 2007;103:1613–21. https://doi.org/10.1152/japplphysiol.00179.2007
78. Nindl BC, Headley SA, Tuckow AP, et al. IGF-I system responses during 12 weeks of resistance training in end-stage renal disease patients. Growth Horm IGF Res 220414:245–50. https://doi.org/10.1016/j.ghir.2004.01.007
79. Schmitz KH, Ahmed RL, Yee D. Effects of a 9-month strength training intervention on insulin, insulin-like growth factor (IGF)-I, IGF-binding protein (IGFBP)-1, and IGFBP-3 in 30-50-year-old women. Cancer Epidemiol Biomarkers Prev 2002;11:1597–604
80. Ntanasis-Stathopoulos J, Tzanninis JG, Philippou A, Koutsilieris M. Epigenetic regulation on gene expression induced by physical exercise. J Musculoskelet Neuronal Interact 2013;13:133–46
81. Jablonka E, Lamb MJ. The inheritance of acquired epigenetic variations. Int J Epidemiol 2915;44:1094–103. https://doi.org/10.1093/ije/dyv020
82. Schuit AJ, Feskens EJM, Launer LJ, Kromhout D. Physical activity and cognitive decline, the role of the apolipoprotein e4 allele. Med Sci Sports Exerc 2001;33:772–777
83. Rovio S, Kåreholt I, Helkala E-L, et al. Leisure-time physical activity at midlife and the risk of dementia and Alzheimer’s disease. Lancet Neurol 2005;4:705–711. https://doi.org/10.1016/S1474-4422(05)70198-8
84. Kivipelto M, Rovio S, Ngandu T, et al. Apolipoprotein E epsilon4 magnifies lifestyle risks for dementia: a population-based study. J Cell Mol Med 2008;12:2762–71. https://doi.org/10.1111/j.1582-4934.2008.00296.x
85. Deeny SP, Poeppel D, Zimmerman JB, et al. Exercise, APOE, and working memory: MEG and behavioral evidence for benefit of exercise in epsilon4 carriers. Biol Psychol 2008;78:179–187. https://doi.org/10.1016/j.biopsycho.2008.02.007
86. Head D, Bugg JM, Goate AM, et al. Exercise engagement as a moderator of the effects of APOE genotype on amyloid deposition. Arch Neurol 2012;69:636. https://doi.org/10.1001/archneurol.2011.845
87. Smith JC, Nielson K a., Woodard JL, et al. Physical activity reduces hippocampal atrophy in elders at genetic risk for Alzheimer’s disease. Front Aging Neurosci 2014;6:1–7. https://doi.org/10.3389/fnagi.2014.00061
88. Smith JC, Nielson KA, Woodard JL, et al. Interactive effects of physical activity and APOE-e4 on BOLD semantic memory activation in healthy elders. Neuroimage 2011;54:635–644. https://doi.org/10.1016/j.neuroimage.2010.07.070
89. Podewils LJ, Guallar E, Kuller LH, et al. Physical activity, APOE genotype, and dementia risk: Findings from the Cardiovascular Health Cognition Study. Am J Epidemiol 2005;161:639–651. https://doi.org/10.1093/aje/kwi092
90. Fenesi B, Fang H, Kovacevic A, et al. Physical exercise moderates the relationship of apolipoprotein E (APOE) genotype and dementia risk: a population-based study. J Alzheimer’s Dis 2017;56:297–303. https://doi.org/10.3233/JAD-160424
91. Lautenschlager NT, Cox KL, Flicker L, et al. Effect of physical activity on cognitive function in older adults at risk for Alzheimer disease: A randomized trial. JAMA – J Am Med Assoc 2008;300:1027–1037. https://doi.org/10.1001/jama.300.9.1027
92. Luck T, Riedel-Heller SG, Luppa M, et al. Apolipoprotein E epsilon 4 genotype and a physically active lifestyle in late life: analysis of gene-environment interaction for the risk of dementia and Alzheimer’s disease dementia. Psychol Med 2014;44:1319–29. https://doi.org/10.1017/S0033291713001918
93. Buchman AS, Boyle PA, Wilson RS, et al. Apolipoprotein E e4 allele is associated with more rapid motor decline in older persons. Alzheimer Dis Assoc Disord 2009;23:63–9
94. Allen NE, Appleby PN, Davey GK, et al. The associations of diet with serum insulin-like growth factor I and its main binding proteins in 292 women meat-eaters, vegetarians, and vegans. Cancer Epidemiol Biomarkers Prev 2002;11:1441–8
95. Fontana L, Weiss EP, Villareal DT, et al. Long-term effects of calorie or protein restriction on serum IGF-1 and IGFBP-3 concentration in humans. Aging Cell 2008;7:681–7
96. Fontana L, Villareal DT, Das SK, et al. Effects of 2-year calorie restriction on circulating levels of IGF-1, IGF-binding proteins and cortisol in nonobese men and women: a randomized clinical trial. Aging Cell 2016;15:22–7. https://doi.org/10.1111/acel.12400
97. Smith WJ, Underwood LE, Clemmons DR. Effects of caloric or protein restriction on insulin-like growth factor-I (IGF-I) and IGF-binding proteins in children and adults. J Clin Endocrinol Metab 1995;80:443–9. https://doi.org/10.1210/jcem.80.2.7531712
98. Smith AT, Clemmons DR, Underwood LE, et al. The effect of exercise on plasma somatomedin-C/insulinlike growth factor I concentrations. Metabolism 1987;36:533–537. https://doi.org/10.1016/0026-0495(87)90162-4
99. Durazzo TC, Mattsson N, Weiner MW. Smoking and increased Alzheimer’s disease risk: A review of potential mechanisms, 2014. https://doi.org/10.1016/j.jalz.2014.04.009
100. Tong M, Yu R, Deochand C, De La Monte SM. Differential contributions of alcohol and the nicotine-derived nitrosamine ketone (NNK) to insulin and insulin-like growth factor resistance in the adolescent rat brain. Alcohol Alcohol 2014;50:670–679. https://doi.org/10.1093/alcalc/agv101
101. De La Monte SM, Tong M, Cohen AC, et al. Insulin and insulin-like growth factor resistance in alcoholic neurodegeneration. Alcohol Clin Exp Res 2008;32:1630–1644. https://doi.org/10.1111/j.1530-0277.2008.00731.x
102. Li X, Song D, Leng SX. Link between type 2 diabetes and Alzheimer’s disease: From epidemiology to mechanism and treatment. Clin Interv Aging 2015;10:549–560. https://doi.org/10.2147/CIA.S74042
103. Talbot K, Wang H, Kazi H, et al. Demonstrated brain insulin resistance in Alzheimer’s disease patients is associated with IGF-1 resistance, IRS-1 dysregulation, and cognitive decline. J Clin Invest 2012;122:1316–1338. https://doi.org/10.1172/JCI59903
104. Steen E, Terry BM, Rivera EJ, et al. Impaired insulin and insulin-like growth factor expression and signaling mechanisms in Alzheimer’s disease–is this type 3 diabetes? J Alzheimers Dis 2005;7:63–80. https://doi.org/10.3233/JAD-2005-7107
105. Vagelatos NT, Eslick GD. Type 2 diabetes as a risk factor for Alzheimer’s disease: The confounders, interactions, and neuropathology associated with this relationship. Epidemiol Rev 2013;35:152–160. https://doi.org/10.1093/epirev/mxs012
106. Spielman LJ, Little JP, Klegeris A. Inflammation and insulin/IGF-1 resistance as the possible link between obesity and neurodegeneration. J. Neuroimmunol. 2014;273:8–21
107. Rochoy M, Rivas V, Chazard E, et al. Factors Associated with Alzheimer’s Disease: An Overview of Reviews. J Prev Alzheimer’s Dis 2019;6:121–134. https://doi.org/10.14283/jpad.2019.7
108. Paslakis G, Blum WF, Deuschle M. Intranasal insulin-like growth factor I (IGF-I) as a plausible future treatment of depression. Med Hypotheses 2012;79:222–225. https://doi.org/10.1016/j.mehy.2012.04.045
109. Mueller PL, Pritchett CE, Wiechman TN, et al. Antidepressant-like effects of insulin and IGF-1 are mediated by IGF-1 receptors in the brain. Brain Res Bull 2018;143:27–35. https://doi.org/10.1016/j.brainresbull.2018.09.017
110. Bateman RJ, Blennow K, Doody R, et al. Plasma Biomarkers of AD Emerging as Essential Tools for Drug Development: An EU/US CTAD Task Force Report. J Prev Alzheimer’s Dis 2019;6:169–173. https://doi.org/10.14283/jpad.2019.21

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

DIET AS A RISK FACTOR FOR COGNITIVE DECLINE IN AFRICAN AMERICANS AND CAUCASIANS WITH A PARENTAL HISTORY OF ALZHEIMER’S DISEASE: A CROSS- SECTIONAL PILOT STUDY DIETARY PATTERNS

 

A.C. Nutaitis1, S.D. Tharwani1, M.C. Serra2, F.C. Goldstein1, L. Zhao3, S.S. Sher4, D.D. Verble1, W. Wharton1

 

1. Emory University, Department of Neurology; 2. Atlanta VA Medical Center & Emory University Department of Medicine; 3. Emory University, Department of Biostatistics and Bioinformatics; 4. Emory University, Department of Internal Medicine

Corresponding Author: Whitney Wharton, PhD, Assistant Professor, Neurology, Emory University, w.wharton@emory.edu

J Prev Alz Dis 2018
Published online November 30, 2018, http://dx.doi.org/10.14283/jpad.2018.44

 


Abstract

Background: African Americans (AA) are more likely to develop Alzheimer’s disease (AD) than Caucasians (CC). Dietary modification may have the potential to reduce the risk of developing AD.
Objective: The objective of this study is to investigate the relationship between Southern and Prudent diet patterns and cognitive performance in individuals at risk for developing AD.
Design: Cross-sectional observational study.
Participants: Sixty-six cognitively normal AA and CC individuals aged 46-77 years with a parental history of AD were enrolled.
Measurements: Participants completed a Food Frequency questionnaire, cognitive function testing, which consisted of 8 neuropsychological tests, and cardiovascular risk factor assessments, including evaluation of microvascular and macrovascular function and ambulatory blood pressure monitoring.
Results:  Results revealed a relationship between the Southern diet and worse cognitive performance among AAs. AAs who consumed pies, mashed potatoes, tea, and sugar drinks showed worse cognitive performance (p<0.05) compared with CCs. In addition, gravy (p=0.06) and cooking oil/fat (p=0.06) showed negative trends with cognitive performance in AAs. In both CC and AA adults, greater adherence to a Prudent dietary pattern was associated with better cognitive outcomes. Cardiovascular results show that participants are overall healthy. AAs and CCs did not differ on any vascular measure including BP, arterial stiffness and endothelial function.
Conclusion: Research shows that dietary factors can associate with cognitive outcomes. This preliminary cross-sectional study suggests that foods characteristic of the Southern and Prudent diets may have differential effects on cognitive function in middle-aged individuals at high risk for AD. Results suggest that diet could be a non-pharmaceutical tool to reduce cognitive decline in racially diverse populations. It is possible that the increased prevalence of AD in AA could be partially reduced via diet modification.

Key words: Alzheimer’s disease, Diet, African-American, Prevention, Nutrition, Race, Cognition, Vascular.

Abbreviations:  AA: African Americans; AD: Alzheimer’s disease; CCs: Caucasians.


 

Introduction

Over five million people in the U.S. are living with Alzheimer’s disease (AD), and in the next thirty years, the prevalence will increase to over sixteen million (1). Individuals at high risk of AD include African Americans (AAs), who have a 64% higher chance of developing AD than Caucasians (CCs) (2), and individuals with a parental history of AD, who are ten times more likely to become afflicted themselves (3). In the absence of a disease-modifying treatment, it is critical that we identify modifiable risk factors to promote cognitive health and reduce AD risk. Current preventative efforts focusing on lifestyle interventions including diet, exercise, and cognitive training (4, 5). Importantly, midlife (40-65 years of age) is when the neuropathological AD related changes begin and when the impact of vascular risk factors begin to have lasting effects. Thus, middle age is the optimal time to implement an AD focused lifestyle intervention.
Research suggests that adherence to a healthy diet confers cognitive benefits in older populations (6-8). Such diets include the Prudent, Dietary Approaches to Stop Hypertension (DASH) and Mediterranean diets, characterized by fruit, vegetables, legumes, fish and olive oil. While these studies are encouraging, few studies have examined the potential influence of diet on cognition in middle-aged, ethnically diverse populations, who are at high risk for AD.
In addition to genetic contributions, the increased prevalence of AD in AAs may be a result of modifiable risk factors including dietary intake (9-12). In a study examining the association between the Mediterranean diet and cognitive decline, AA participants who had higher adherence with the Mediterranean diet had slower cognitive decline compared to participants with less Mediterranean diet adherence (13). Furthermore, current literature suggests that geographic and racial differences in cardiovascular disease risk are associated with the Southern dietary pattern (characterized by fried foods, fats, eggs, organ and processed meats and sugar-sweetened beverages) and thus it is possible that this Southern dietary pattern may contribute to cognitive decline (14). These findings stress the need for prospective studies addressing the relationships between diet and cognitive function in racially diverse populations in the U.S (15).
The goal of this study was to assess the relationship between dietary patterns, vascular function, and cognitive decline, in a middle aged, diverse cohort at high risk for AD due to a parental history of AD. We hypothesize that a higher intake of a  Southern dietary pattern and lower intake of a  Prudent (healthy) in dietary pattern increases the risk for vascular dysfunction and cognitive impairment, especially among AA, compared to CC, adults.

 

Subjects and Methods

Study Sample

Sixty-six subjects enrolled in an ongoing NIH/NIA funded study (ASCEND PI: Wharton) and with a parental history of AD took part in this cross-sectional pilot observational cohort study. Parental history was confirmed via autopsy or probable AD as defined by NINDS-ADRDA criteria and the Dementia Questionnaire (16). Subjects received vascular and cognitive assessments under the IRB approved protocol.

Demographic Information

Age, gender, level of education, income, exercise, smoking status, and depression was acquired via a self-reported survey. Exercise was reported as mean days per week of cardiovascular exercise (17).
Dietary Pattern Assessment: Diet was assessed via the Jackson Heart Study’s shortened version of the Lower Mississippi Delta Nutrition Intervention Research Initiative Food Frequency Questionnaire (FFQ) (18). The questionnaire consists of 160 items and takes 20 minutes to complete. Participants self-reported quantity and frequency of food and drink consumption on an online survey at home via a secure, individual web link. Subjects were given a $15.00 gift card for completing the survey.
Food items from the FFQ were classified into the Southern or Prudent diets in accordance Reasons for Geographic and Racial Differences in Stroke (REGARDS) study guidelines (14). Food items including fried foods, fats, eggs, organ and processed meats and sugar-sweetened beverages were classified as characteristic of the Southern diet (14). Healthy foods including fruits, vegetables, whole grains, and fish were classified as Prudent diet related items (19).

Cardiovascular Risk Factor Assessment

Vascular measures were selected based on prior research with vascular function in individuals at risk for AD (20, 21). Participants underwent a one-hour fasting assessment including microvascular vasodilatory function, using digital pulse amplitude tonometry (EndoPAT) and macrovascular vascular function (assessed by flow mediated vasodilation (FMD)). In addition, a blood pressure (BP) assessment was obtained via 24-hour ambulatory BP monitoring (Spacelabs Healthcare©). We examined 24-hour average systolic and diastolic blood pressure and nocturnal dipping patterns, all of which have been linked to cognition and AD (22).

Neuropsychological testing

Cognitive function was evaluated by a one-hour battery of eight neuropsychological tests in domains reportedly affected in early AD and susceptible to the effects of hypertension (23). The tests included: Montreal Cognitive Assessment (MOCA), Benson visuospatial memory task, Buschke Delay Memory Test, Trails A and B, Digit Span Backwards, Mental Rotation Test (MRT), and Multilingual Naming Test (MINT). These tests targeted specific AD related cognitive domains including: working memory, executive function (Trail-Making Test B) (24, 25), language (MINT) (26), verbal memory (Buschke) (27), visuospatial ability (MRT) (28) and global cognition (MOCA) (29).

Data Analysis

Researchers utilized IBM SPSS Statistics Version 22 to test for group differences between AAs and CCs in demographics, vascular risk factors, and cognitive performance. We conducted independent two-sample t-test for continuous variables and chi-square test for characteristic variables, controlling for age, gender and education. As there is not sufficient power to detect an interaction of diet and race, we examined the association between diet and cognition in each racial group separately. Correlations between cognitive performance and foods were assessed using Pearson’s r partial correlations controlling for education and age on the cognitive tests in which we found racial differences at p=0.10. Because eight cognitive tests were included in the analyses, the threshold of significance level using a false discovery rate approximation was adjusted such that a threshold p-value of 0.03 was used.

 

Results

Table 1 shows the demographic characteristics for 21 AAs and 45 CCs. Participants were middle aged (M=58.6 years), mostly female (67.6%), and highly educated (83.8% graduate or postgraduate education). While AAs and CCs did not differ on demographics including age, education, exercise, smoking status, or self-reported depression, significant racial differences were present for gender and income, such that a larger percent of AA females than CC females participated in the study, and AAs reported significantly less income compared to CCs. Participants were generally very healthy and AAs and CCs did not differ on any vascular measure including BP, arterial stiffness and endothelial function.

Table 1. Demographic Characteristics and Cardiovascular Data for African Americans and Caucasians. (AA= African American, CC=Caucasian)

Table 1. Demographic Characteristics and Cardiovascular Data for African Americans and Caucasians. (AA= African American, CC=Caucasian)

*P < 0.05; ** P < 0.01; RHI=reactive hyperemia index; AIx= augmentation index; FMD= flow mediated vasodilation

 

Table 2 shows cognitive test results by race. Results show that CCs significantly outperformed AAs on global cognition (MOCA), naming (MINT), and executive function (Trails B) tests (all p values <0.05). In addition, results revealed a trend for CCs to outperform AAs in verbal memory (Buschke Delay) (p= 0.073).
Table 3 shows Pearson’s r partial correlations between foods and cognitive performance, by race. Five of six southern foods show moderate to strong correlations with cognitive tests in AAs. In AAs, pies, mashed potatoes, and sugar drinks were correlated with cognitive performance (all p values <0.01) and trends were found with tea (p=0.04), gravy (p=0.06) and cooking oil/fat (p=0.06), such that AAs performed worse on cognitive tests with consumption of these foods. Results show that AAs were more negatively impacted than CCs by foods characteristic of the Southern diet. Conversely, CCs who consumed mashed potatoes (p=0.01) and sugar drinks (p<0.10) performed better on cognitive assessments. Foods characteristic of the Prudent diet, such as whole grain breads (p=0.04), baked fish (p=0.03), and grape juice (p<0.01), were positively associated with cognitive performance in CCs. In addition, 100% orange juice (OJ) showed a trend (p<0.10) of better performance on cognitive assessment in CC. The most pronounced relationship was seen with 100% grape juice, such that AAs consuming 100% grape juice performed significantly better on the MINT (p<0.01). Results suggest a stronger relationship between the Prudent diet and cognitive performance in CCs vs. AAs.

 

Table 2. Means and standard deviations on cognitive tests in African Americans and Caucasians. (AA= African American, CC=Caucasian)

Table 2. Means and standard deviations on cognitive tests in African Americans and Caucasians. (AA= African American, CC=Caucasian

†P<0.1; *P < 0.05

Table 3. Pearson’s r correlations between cognition and foods by race for individuals who completed Food Frequency Questionnaire. (AA= African American, CC=Caucasian; 1-6=Southern Diet, 7-10=Prudent Diet)

Table 3. Pearson’s r correlations between cognition and foods by race for individuals who completed Food Frequency Questionnaire. (AA= African American, CC=Caucasian; 1-6=Southern Diet, 7-10=Prudent Diet)

†P<0.10; *P<0.05; **P<0.01

 

Discussion

To our knowledge, this is the first study to report a relationship between diet and cognitive performance in healthy, racially diverse middle-aged adults with a parental history of AD. CCs outperformed AAs on cognitive tests of global cognition, language, and executive function. Racial differences on cognitive tests could not be explained by age, education, vascular risk factors, exercise, smoking, or depression. However, our results suggest that these differences may be partially attributed to dietary patterns specific to the Southern and Prudent diets.
A positive relationship between cognition and the Prudent (healthy) diet and a negative relationship between cognition and the Southern (less healthy) diet was observed. Similarly, Shakersain et al. recently identified a relationship between lower adherence to a Prudent diet and greater rates of cognitive decline [6]. Further, Seetharaman et al. reported that elevated diabetes risk, which is higher in AAs than CCs, is related to poorer performance on perceptual speed, verbal ability, spatial ability, and overall cognition (30).  Foods in our study characteristic of the Southern diet, such as pies, tea, and sugar drinks, were negatively associated with cognitive performance and thus it is possible that this may be a result of the higher glycemic index of these foods. Our results also align with studies showing that a diet high in gravy or butter is associated with poor cognition in older adults (31). Further, we show that racial differences in diet such that AAs reported stronger alliance with the Southern diet than CCs. This finding is not unique to our study, as previous studies show that AAs are less likely to adhere to the DASH diet compared with CCs (32). Our study highlights the need for culturally sensitive dietary interventions to combat cognitive decline in high-risk populations.
Only one Prudent item (100% grape juice) was correlated to cognitive performance in AAs, in contrast to five Prudent items (whole grain breads, mashed potatoes, baked fish, 100% grape juice and 100% OJ). The Prudent diet is nutrient dense, containing numerous nutrients with anti-inflammatory and antioxidant properties, including fiber, poly-unsaturated fatty acids, vitamins, minerals, carotenoids, and polyphenols, among others (6). Therefore, it is possible that the negative effects of elevated inflammation and oxidative stress, which is more prevalent among AAs, on cognitive health may be dampened by the effects of the Prudent diet (34, 35). The association between beverages and cognitive performance should also be noted. Individuals may be more consistent with their beverage choices, (i.e. coffee or OJ), than food choices, and thus beverages may associate more strongly with cognitive function due to a higher intake.
The need for advancements in preventative and treatment strategies in high-risk groups, including AAs is great (36). Results showed racial differences in the relationship between diet and cognitive performance. It is possible that dietary intake may be contributing to early cognitive decline in AAs, or preservation of cognitive functioning in CCs. This finding is important, as the current literature suggests that even though late-life positive dietary patterns may result in notable health improvements (19, 37), mid-life is the optimal time to incorporate these changes, before the irreversible AD cascade begins (38). Thus diet modification may hold promise as a modifiable risk factor for AD.
Strengths of this study include a comprehensive battery of neuropsychology testing and vascular measures, and a middle aged, racially diverse cohort at high risk for AD. Also the FFQ is both racially and geographically sensitive (18). Limitations of this pilot project include the small sample size and the overall health of the cohort. It is possible that diet may have a more pronounced impact in individuals with preexisting health complications. Next the FFQ does not include information regarding longitudinal food choices, and these data should be collected in future studies (39).
In summary, our results stress the need for further research investigating the potential of dietary intake as a non-pharmaceutical intervention in individuals at risk for AD. Because AAs have an increased incidence and prevalence of AD (2, 40), investigation of modifiable risk factors that target this high-risk group is essential. Specifically, nutritional education and dietary interventions designed to shift individuals, particularly AAs, from Southern diets to healthier, Prudent – like diets, may be a cost efficient way to preserve cognitive function in otherwise healthy individuals.

 

Funding: This project was funded by the National Institute of Health (NIH) and in part by the Scholarly Independent Research at Emory (SIRE) Research grant for undergraduate students. The NIH and SIRE had no role in study design, collection, analysis or interpretation of data; in the writing of the report; or in the decision to submit the article for publication.

Acknowledgments: All persons who have made substantial contributions to this manuscript are listed as authors. Their contributions are listed below: Alexandra C. Nutaitis, BS: Designed research, Conducted research, Analyzed data, Wrote paper, Had primary responsibility for final content . Sonum D. Tharwani: Conducted research, Wrote paper. Monica C. Serra, PhD: Provided essential reagents or materials, Analyzed data, Wrote paper. Felicia C. Goldstein, PhD: Designed research, Wrote paper. Liping Zhao, MSPH: Provided essential reagents or materials, Analyzed data, Wrote paper
Salman S. Sher, MD: Conducted research, Provided essential reagents or materials, Analyzed data, Wrote paper
Danielle D. Verble, MA: Conducted research, Wrote paper
Whitney Wharton, PhD: Designed research, Conducted research, Analyzed data, Wrote paper Had primary responsibility for final content

Sources of Support: NIH-NIA under grants: NIH-NIA 5 P50 AG025688, K01AG042498, and U01 AG016976. Independent funding for the present pilot study was obtained through Emory University’s Scholarly Inquiry Research Grant for undergraduate students (PI: Nutaitis).

Conflict of interest: No author has a conflict of interest to report.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

References

1.    Alzheimer’s Association. Alzheimer’s Disease Facts and Figures [cited 2016; Available from: https://www.alz.org/alzheimers-dementia/facts-figures.
2.    Steenland, K., et al. A Meta-Analysis of Alzheimer’s Disease Incidence and Prevalence Comparing African-Americans and Caucasians. J Alzheimers Dis 2016; 50(1):71-6.
3.    Alzheimer’s Association. African-Americans and Alzheimer’s 2016  [cited 2016; Available from: https://www.alz.org/africanamerican/.
4.    Rege, S.D., et al. Can Diet and Physical Activity Limit Alzheimer&#39;s Disease Risk? Curr Alzheimer Res 2017; 14(1):76-93.
5.    Baumgart, M., et al. Summary of the evidence on modifiable risk factors for cognitive decline and dementia: A population-based perspective. Alzheimers Dement 2015; 11(6):718-26.
6.    Shakersain, B., et al. The Nordic Prudent Diet Reduces Risk of Cognitive Decline in the Swedish Older Adults: A Population-Based Cohort Study. Nutrients 2018; 10(2).
7.    Tangney, C.C., et al. Relation of DASH- and Mediterranean-like dietary patterns to cognitive decline in older persons. Neurology 2014; 83(16):1410-6.
8.    van de Rest, O., et al. Dietary patterns, cognitive decline, and dementia: a systematic review. Adv Nutr 2015; 6(2):154-68.
9.    Chin, A.L., S. Negash, and R. Hamilton. Diversity and disparity in dementia: the impact of ethnoracial differences in Alzheimer disease. Alzheimer Dis Assoc Disord 2011; 25(3):187-95.
10.    Norton, S., et al. Potential for primary prevention of Alzheimer’s disease: an analysis of population-based data. Lancet Neurol 2014; 13(8):788-94.
11.    Biessels, G.J. Capitalising on modifiable risk factors for Alzheimer’s disease. Lancet Neurol 2014; 13(8):752-3.
12.    Sofi, F., et al. Accruing evidence on benefits of adherence to the Mediterranean diet on health: an updated systematic review and meta-analysis. Am J Clin Nutr 2010; 92(5):1189-96.
13.    Koyama, A., et al. Association between the Mediterranean diet and cognitive decline in a biracial population. J Gerontol A Biol Sci Med Sci 2015; 70(3):354-9.
14.    Shikany, J.M., et al. Southern Dietary Pattern is Associated With Hazard of Acute Coronary Heart Disease in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study. Circulation 2015; 132(9):804-14.
15.    Harmon, B.E., et al. Associations of key diet-quality indexes with mortality in the Multiethnic Cohort: the Dietary Patterns Methods Project. Am J Clin Nutr 2015; 101(3):587-97.
16.    Kawas, C., et al. A validation study of the Dementia Questionnaire. Arch Neurol 1994; 51(9):901-6.
17.    Jacobs, D.R., Jr., et al. Validity and Reliability of Short Physical Activity History: Cardia and the Minnesota Heart Health Program. J Cardiopulm Rehabil 1989; 9(11):448-459.
18.    Carithers, T.C., et al. Validity and calibration of food frequency questionnaires used with African-American adults in the Jackson Heart Study. J Am Diet Assoc 2009; 109(7):1184-1193.
19.    Shakersain, B., et al. Prudent diet may attenuate the adverse effects of Western diet on cognitive decline. Alzheimers Dement 2016; 12(2):100-109.
20.    Zhong, W., et al. Pulse wave velocity and cognitive function in older adults. Alzheimer Dis Assoc Disord 2014; 28(1):44-9.
21.    Hajjar, I., et al. Roles of Arterial Stiffness and Blood Pressure in Hypertension-Associated Cognitive Decline in Healthy Adults. Hypertension 2016; 67(1):171-5.
22.    Tarumi, T., et al. Amyloid burden and sleep blood pressure in amnestic mild cognitive impairment. Neurology 2015; 85(22):1922-9.
23.    Asthana, S., et al. Cognitive and neuroendocrine response to transdermal estrogen in postmenopausal women with Alzheimer’s disease: results of a placebo-controlled, double-blind, pilot study. Psychoneuroendocrinology 1999; 24(6):657-77.
24.    Dodrill, C.B. A neuropsychological battery for epilepsy. Epilepsia 1978; 19(6):611-23.
25.    Stroop, J. Studies of interference in serial verbal reactions. J Exp Psychol 1935; 18:643-662.
26.    Spreen, O. and E. Strauss, A compendium of neuropsychological tests. 2nd ed. 1998, New York, New York: Oxford Press.
27.    Buschke, H. Selective reminding for analysis of memory and learning. J Verb Learn Verb Behav 1973; 12:543-550.
28.    Vandenberg, S.G. and A.R. Kuse. Mental rotations, a group test of three-dimensional spatial visualization. Percept Mot Skills 1978; 47(2):599-604.
29.    Nasreddine, Z.S., et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005; 53(4):695-9.
30.    Seetharaman, S., et al. Blood glucose, diet-based glycemic load and cognitive aging among dementia-free older adults. J Gerontol A Biol Sci Med Sci 2015; 70(4):471-9.
31.    Granic, A., et al. Dietary Patterns High in Red Meat, Potato, Gravy, and Butter Are Associated with Poor Cognitive Functioning but Not with Rate of Cognitive Decline in Very Old Adults. J Nutr 2016; 146(2):265-74.
32.    Epstein, D.E., et al. Determinants and consequences of adherence to the dietary approaches to stop hypertension diet in African-American and white adults with high blood pressure: results from the ENCORE trial. J Acad Nutr Diet 2012; 112(11):1763-73.
33.    Feairheller, D.L., et al. Racial differences in oxidative stress and inflammation: in vitro and in vivo. Clin Transl Sci 2011; 4(1):32-7.
34.    Morris, A.A., et al. Differences in systemic oxidative stress based on race and the metabolic syndrome: the Morehouse and Emory Team up to Eliminate Health Disparities (META-Health) study. Metab Syndr Relat Disord 2012; 10(4):252-9.
35.    Froehlich, T.E., S.T. Bogardus, Jr., and S.K. Inouye. Dementia and race: are there differences between African Americans and Caucasians? J Am Geriatr Soc 2001; 49(4):477-84.
36.    Bardach, S.H., N.E. Schoenberg, and B.M. Howell. What Motivates Older Adults to Improve Diet and Exercise Patterns? J Community Health 2016; 41(1):22-9.
37.    Barage, S.H. and K.D. Sonawane. Amyloid cascade hypothesis: Pathogenesis and therapeutic strategies in Alzheimer’s disease. Neuropeptides 2015; 52:1-18.
38.    Montero, P., et al. Lifetime dietary change and its relation to increase in weight in Spanish women. Int J Obes Relat Metab Disord 2000; 24(1):14-9.
39.    Dilworth-Anderson, P., et al. Diagnosis and assessment of Alzheimer’s disease in diverse populations. Alzheimers Dement 2008; 4(4):305-9.

ADHERENCE TO THE MEDITERRANEAN DIET IS NOT RELATED TO BETA-AMYLOID DEPOSITION: DATA FROM THE WOMEN’S HEALTHY AGEING PROJECT

 

E. Hill1, C. Szoeke1,2, L. Dennerstein3, S. Campbell4, P. Clifton5

 

1. Institute for Health & Ageing, 215 Spring St, Victoria 3000, Melbourne, Australia; 2. Department of Medicine-Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria 3050, Australia; 3. Department of Psychiatry, University of Melbourne, Parkville, Victoria 3050, Australia; 4. Melbourne Health, Royal Melbourne Hospital, 300 Grattan St, Victoria 3050, Australia; 5. School of Pharmacy and Medical Sciences, University of South Australia, 101 Currie St, South Australia 5001, Australia

Corresponding Author: Professor Cassandra Szoeke, Department of Medicine (RMH), Level 4, Centre for Medical Research, Royal Melbourne Hospital, Royal Parade, Parkville, Victoria 3050, Australia, email: cszoeke@unimelb.edu.au, Telephone: +61 3 8344 1835, Fax: +61 3 9347 1863

J Prev Alz Dis 2018;5(2):137-141
Published online February 27, 2018, http://dx.doi.org/10.14283/jpad.2018.12

 


Abstract

Background: Research has indicated the neuroprotective potential of the Mediterranean diet. Adherence to the Mediterranean diet has shown preventative potential for Alzheimer’s disease incidence and prevalence, yet few studies have investigated the impact of Mediterranean diet adherence on the hallmark protein; beta-amyloid.
Objectives: To investigate the association between Mediterranean diet adherence and beta-amyloid deposition in a cohort of healthy older Australian women.
Design: This study was a cross-sectional investigation of participants from the longitudinal, epidemiologically sourced Women’s Healthy Ageing Project which is a follow-up of the Melbourne Women’s Midlife Health Project.
Setting: Assessments were conducted at the Centre for Medical Research, Royal Melbourne Hospital in Melbourne, Australia. F-18 Florbetaben positron emission tomography scanning was conducted at the Austin Centre for PET in Victoria, Australia.
Participants: One hundred and eleven Women’s Healthy Ageing Project participants were included in the study.
Measurements: Mediterranean diet adherence scores for all participants were calculated from the administration of a validated food frequency questionnaire constructed by the Cancer Council of Victoria. Beta-amyloid deposition was measured using positron emission tomography standardised uptake value ratios.
Results: Gamma regression analysis displayed no association between Mediterranean diet adherence and beta-amyloid deposition. This result was consistent across APOE-ε4 +/- cohorts and with the inclusion of covariates such as age, education, body mass index and cognition.
Conclusions: This study found no association between adherence to the Mediterranean diet and beta-amyloid deposition in a cohort of healthy Australian women.

Key words: Diet, Alzheimer’s disease, beta-Amyloid, deposition, Mediterranean diet.

 


 

Introduction

Over the next three decades it is estimated the global elderly (aged 65 years or older) population will become double that of children (aged 0-14 years) for the first time in history (1). Globally, it is estimated that by the year 2050, there will be between 115.4 million (2) and 130 million (3) sufferers of Alzheimer’s Disease (AD), the most common form of dementia. With no efficacious pharmacological treatment for AD currently available, research has shifted towards identification of modifiable lifestyle risk factors, such as diet. Recent research into the synergistic effects of global dietary patterns has shown that the Mediterranean Diet (MD) displays neuroprotective potential (4-7).
Dietary patterns promote synergistic, additive and interactive effects in the brain by fostering interrelated actions of multiple food components (8). The M D is believed to possess neuroprotective qualities, thereby preserving cognitive ability into later life (9-11). A growing body of epidemiological evidence supports the beneficial role of the MD, characterised by high consumption of fruits, vegetables, nuts, legumes, cereals and fish, a moderate intake of alcohol and a low intake of meat and dairy.
In cross sectional, longitudinal and review-collated evidence to date, no study has specifically investigated MD adherence and cerebral beta-amyloid (Aβ) burden. Matthews, Davies (12) investigated MD adherence, Aβ load and physical activity in a tri-factor relationship, while Merrill, Siddarth (13) assessed the relationship between AD biomarkers and MD adherence, measured using a single item Likert scale. Although the preponderance of evidence suggests a beneficial effect of greater adherence to the MD, there are inconsistencies in the literature concerning the neuroprotective nature of the MD. Greater MD adherence has been associated with lower risk for mild cognitive impairment (MCI) and AD (14), a reduced risk for AD (15)  and a reduced risk for dementia (16). In contrast, other MD studies have found no association with neuroprotective outcomes. MD adherence was not found to be protective of cognitive decline in longitudinal studies (17, 18)  and other studies found no statistically significant link between MD adherence and cognition (19, 20). In previous studies, MD adherence and beneficial cognitive outcomes were most consistently seen in pathological (dementia, MCI, AD risk) compared with healthy brain ageing. This led Knight, Bryan (10) to posit that MD may offer the most beneficial effect within the prodromal (pre-clinical early neurodegenerative symptomatology) phase of cognitive decline and to call for further research into healthy older adult populations.
Few studies have investigated the relationship between MD adherence and AD biomarkers in healthy older female populations. Women display a higher penetrance for the apolipoprotein E epsilon 4 allele (APOE-ε4) (21), are more likely to progress from MCI to AD (21) and are at increased risk of developing AD (22) compared to men. To our knowledge, no study has specifically investigated the relationship between MD adherence and cerebral Aβ burden in women. Aβ is a hallmark protein implicated in AD neuropathology, accumulating in the hippocampus, amygdala, basal forebrain and cerebral cortex, eventually leading to loss of memory and other cognitive domains (23). As MD may offer the most benefit within the prodromal phase of cognitive decline and Aβ is believed to be the initial step in the AD disease process (22), it is important to investigate this relationship in a healthy cohort. This study aimed to investigate the association between MD adherence and Aβ deposition in a healthy cohort of older Australian women. It was hypothesised that greater MD adherence would be associated with a lower level of Aβ accrual.

 

Methods

Cohort

Participants were from the 2012 follow-up of the Women’s Healthy Ageing Project (WHAP) which is an extension of the Melbourne Women’s Midlife Health Project (MWMHP). 438 Australian-born women within the Melbourne metropolitan area were identified by random digit dialling in 1991 and reinterviewed annually over 8 years, then intermittently for 5 years. Women were eligible for the cohort if they were aged 45-55 years, had menstruated in the three months prior to recruitment, and were not taking oestrogen-containing hormone replacement therapy.
In 2012, participants were contacted and invited to participate in a late-life health study. Clinical interviews were conducted on 252 participants by trained field researchers at this time. The assessment included validated measures of physical health, lifestyle, sociodemographics, cognitive function, psychological health, and pathological biomarkers. The complete methodology is published elsewhere (24). Measures specific to the current study are detailed below.

Diet

Participants completed a validated food frequency questionnaire constructed by the Cancer Council of Victoria entitled the Dietary Questionnaire for Epidemiological Studies Version 2 (DQES) (25). The DQES v2 incorporates 80 food items with frequency response options on 74 of these items. The DQES v2 covers five types of dietary intake; cereals/sweets/snacks, dairy/meat/fish, fruit, vegetables and alcoholic beverages. Data collected by the DQES v2 was used to calculate nutrient intakes, based on the Australian nutrient composition data from NUTTAB95. Adherence to a MD was calculated with a modified version of a tool devised by Sofi et al (24) using data from the literature to define the cut-points for scoring (26). The MD score is based on intake of nine dietary components: vegetables, legumes, fruit, dairy, cereals, meat and meat products, fish, alcohol and the monounsaturated fats to saturated fats (MUFA:SFA) ratio. This component was used as a proxy measure due to olive oil consumption not being included in the FFQ (25). Tertile analysis defined MUFA:SFA population specific cut-offs and 2 points were assigned to the highest MUFA:SFA ratio. Weighted medians from the literature based scoring method developed by Sofi, Macchi (26) ±2 standard deviations were used to define the three tiers. This MD score allocates each individual MD component a score of 0 points for minimal adherence, 1 point for moderate adherence or 2 points for maximal adherence, giving a total score of 0 to 18 for each participant.

Imaging

All WHAP participants in the 2012 follow-up were offered the opportunity to have cerebral imaging. Aβ accrual was measured via in vivo F-18 Florbetaben (18F-FBB) PET scanning in 2012, conducted at the Austin Health Centre for PET in Victoria, Australia (27). Participants received 250 MBq of 18F-FBB intravenously, with a 20-minute acquisition commencing 90-minutes post injection. Standardised uptake values (SUV) were calculated for all brain regions examined and standard uptake value ratios (SUVR) were generated by normalising regional SUV by the cerebellar cortex with atrophy-correction from structural magnetic resonance imaging (MRI). Neocortical SUVR, a global index of Aβ burden, is expressed as the average SUVR of the area-weighted mean. Area weighted means were calculated for each participant by averaging the frontal, superior parietal, lateral temporal, lateral occipital and anterior and posterior cingulate regions.

Covariates

Age and education in years were collected as part of the core questionnaire and were accounted for in the analyses. Total energy intake in kilojoules was calculated from the DQES v2. Physical activity was measured using the International Physical Activity Questionnaire (IPAQ) total score (28). Cardiovascular risk was measured using the Australian Absolute Cardiovascular Risk (CVR) Score (%) (29). Genetic testing was undertaken to investigate individual haplotypes at the apolipoprotein E gene locus, with presence of the APOE-ε4 allele included in the analyses. General Intelligence was included as a cognitive composite covariate and was calculated utilising a principal component factor analysis with direct oblimin rotation.

Data Analysis

IBM Statistical Package for the Social Sciences (SPSS) software was used to conduct the statistical analyses for the present research. Complete data was available for 111 participants and there were no significant differences between the included (n = 111) and excluded (n = 141) cohorts. Aβ accumulation (PET SUVR Index) displayed a highly positively skewed distribution that was not rectified by multiple square root or log transformations. Dichotomisation of PET SUVR based on standard cut-offs resulted in disproportionate groups therefore PET SUVR was treated as a continuous variable. Generalised linear models were tested and goodness of fit was determined using Akaike’s and Bayesian Information Criterion and Aβ deposition was analysed using gamma regression due to the model fit. MD adherence was entered as a predictor in model 1, age and education covariates were added in model 2 while all covariates were entered into the third model. Histograms, covariance matrices, and residuals plots were analysed to ensure that all assumptions for regression analysis were met. Power analyses were based on the continuous primary outcome (PET SUVR) and fixed sample size (n = 111). Based on generalized linear mixed model with PET SUVR as outcome and 9 predictors (MD, age, education, energy, BMI, APOE-ε4 status +/-, cognition, IPAQ, CVR), the model will have 80% power (α =0.05, two-tailed) to detect 14% minimum variance in PET SUVR levels.

 

Results

Sample characteristics

Of the 252 participants assessed in 2012, 124 underwent PET scanning and 235 had completed the DQES v2. The final sample for analyses consisted of 111 participants who had complete data, including all covariates. Demographic characteristics of the cohort stratified by MD tertiles can be found in Table 1. There were no differences in age, energy intake, PA, CVR, BMI, APOE-ε4 presence or MD adherence between the included (n = 111) and excluded (n = 141) cohorts. However, there was a significant difference in education between the included (12.9 years) and excluded (11.6 years) cohorts (p = 0.004). Participants were aged between 65-75 years (M = 69.7, S.D. = 2.5) an average BMI of 28.3 (S.D. = 5.4). Approximately one-third of participants (33%) carried the APOE-ε4 allele. MD adherence scores ranged from 2-12, with an overall average adherence score of 5.8 (S.D. = 1.9). Tertile analysis revealed those in the highest Aβ accumulation group (PET SUVR>1.11) had the highest MD (M=6.00, n = 36). The middle Aβ group (1.04<PET SUVR<1.11) had a low MD adherence (M=5.71, n = 42) similar to those in the lowest Aβ group (PET SUVR<1.04, M= 5.61, n = 33).

Table 1. Demographic Characteristics of the Cohort Included in Analysis by Tertiles of MD Adherence (n = 111)

Table 1. Demographic Characteristics of the Cohort Included in Analysis by Tertiles of MD Adherence (n = 111)

Presented are the demographic characteristic of the cohort included in analysis. If not otherwise described, data are presented as mean ± standard deviation of the mean. Intake of the 9 MD components were used to calculate MD adherence.

 

MD adherence and Aβ

Results of the regression are presented in Table 2. In the unadjusted model, MD was not a significant predictor of Aβ accumulation (β = 0.001, p = 0.917). This result was unchanged with the inclusion of age and education in the model (β = 0.002, p = 0.742) and with all covariates included (β = 0.002, p = 0.791). Participants were then stratified by presence of the APOE-ε4 allele and the results of the regression are presented in Table 3. MD was not a significant predictor of Aβ accumulation for either the APOE-ε4 positives (β = -0.009, p = 0.550) or APOE-ε4 negatives (β = 0.003, p = 0.613). For both APOE-ε4 positives and APOE-ε4 negatives, these results remained consistent after inclusion of all covariates.

Table 2. Generalised Gamma Regression Coefficient Table for MeDi adherence & Amyloid Accrual (PET SUVR Index)(n = 111)

Table 2. Generalised Gamma Regression Coefficient Table for MeDi adherence & Amyloid Accrual (PET SUVR Index)(n = 111)

†Presented are the generalised gamma regression coefficients for the MeDi score against PET SUVR Index. Model 1 was unadjusted. Model 2 was adjusted for age and education. Model 3 was adjusted for age, education, BMI, energy intake, cognitive composite (GI), physical activity (PA) and cardiovascular risk (CVR). Beta coefficients presented are standardised values for each MD point increase. Confidence intervals (CI) are stated at the 95% level. * Denotes statistical significance of the p-value (*Significant at p < 0.05, **Significant at p < 0.01).

able 3. Generalised Gamma Regression Coefficient Table Stratified by Presence/Absence of APOE-ε4 Allele for MD Adherence & Amyloid Accrual (PET SUVR Index) (n = 111)

Table 3. Generalised Gamma Regression Coefficient Table Stratified by Presence/Absence of APOE-ε4 Allele for MD Adherence & Amyloid Accrual (PET SUVR Index) (n = 111)

 

Discussion

This cross sectional study investigated the association of adherence to the MD diet with Aβ deposition in a cohort of healthy Australian women. This study is unique in its use of AD biomarkers, APOE-ε4 genotyping and a sex-specific cohort, allowing us to examine a prodromal population that is at high risk of developing AD.
In this cohort, global MD adherence was not related to cerebral Aβ protein deposition. This is in contrast to a study with similar methodology by Matthews, Davies (12), who found a relationship, but with a strong protective effect from physical exercise. Brown, Peiffer (30) also found that higher levels of physical activity were associated with a lower Aβ load; although, this association was observed to be driven solely by APOE-ε4 negative participants. As our study did not include a measure of physical activity, we suggest that our observations may have been attenuated by this confounder and thus warrant further study. Our findings are, however, in line with several studies that have found no association between MD adherence and AD markers. Cherbuin, Kumar (31) found no association between adherence to MD and clinical dementia rating  impairment or MCI incidence; however, they found high MUFA intake was predictive of MCI. As Aβ accumulation has been shown to predict transition from MCI to AD (32), it is expected that MCI participants would display a higher Aβ load and therefore be at a higher risk of transitioning to an AD neuropathology than healthy controls. The findings of Cherbuin, Kumar (31) were in contrast to previous studies showing the benefits of a higher MUFA:SFA ratio as part of a traditional MD.  A high MUFA:SFA ratio is used in some Mediterranean Diet scores to reflect the consumption of olive oil (high in MUFA), often considered the hallmark of the traditional MD (33). Oleuropein, an antioxidant constituent of olive oil has been illustrated to assist in the cleavage of amyloid precursor protein (34), the precursor to Aβ, and the oleuropein aglycone compound has been suggested to provide therapeutic potential to the AD pathology (35). The FFQ utilised in this study did not include olive oil consumption and MUFA intake in Australia is predominantly from meat (36), thus the scoring system cannot adequately assess olive oil consumption and the intake of potentially beneficial phytochemicals in olive oil.  This may explain to some extent the lack of association observed here.
This study found no association between adherence to the MD and Aβ deposition in a prodromal population of healthy Australian women in both APOE-ε4 positives and negatives. Further research regarding MD adherence in this cohort should endeavour to include olive oil given its neuroprotective nature and fundamental importance to the MD.

 

Acknowledgments: We would like to acknowledge the contribution of the participants and their supporters who have contributed their time and commitment for over 20 years to the University. A full list of all researchers contributing to the project and the membership of our Scientific Advisory Board is available online.

Funding: Funding for the Healthy Ageing Program (HAP) has been provided by the National Health and Medical Research Council (NHMRC Grants 547600, 1032350 & 1062133), Ramaciotti Foundation, Australian Healthy Ageing Organisation, the Brain Foundation, the Alzheimer’s Association (NIA320312), Australian Menopausal Society, Bayer Healthcare, Shepherd Foundation, Scobie and Claire Mackinnon Foundation, Collier Trust Fund, J.O. & J.R. Wicking Trust, Mason Foundation and the Alzheimer’s Association of Australia. Inaugural funding was provided by VicHealth and the NHMRC. The Principal Investigator of HAP (CSz) is supported by the National Health and Medical Research Council. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Conflict of interest: Prof Szoeke has provided clinical consultancy and been on scientific advisory committees for the Australian Commonwealth Scientific and Industrial Research Organisation, Alzheimer’s Australia, University of Melbourne and other relationships which are subject to confidentiality clauses. The other authors declare no conflict of interest

Ethical standards: Ethics approval was obtained from the University of Melbourne Human Research Ethics Committee (approval numbers: 931159X2, 010528 and 1339373). Informed consent was obtained from all participants prior to participation in the study.

 

References

1.     Lau FC, Shukitt-Hale B, Joseph JA. Nutritional intervention in brain aging: reducing the effects of inflammation and oxidative stress. Subcell Biochem. 2007;42:299-318.
2.    Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurol. 2011;9:819.
3.    Prince MJ, Wimo A, Guerchet MM et al. World Alzheimer Report 2015 – The Global Impact of Dementia: Alzheimer’s Disease International; 2015.
4.    Féart C, Samieri C, Allès B, Barberger-Gateau P. Potential benefits of adherence to the Mediterranean diet on cognitive health. Proc Nutr Soc. 2013;72(1):140-52.
5.    Martinez-Gonzalez MA, Salas-Salvado J, Estruch R et al. Benefits of the Mediterranean Diet: Insights From the PREDIMED Study. Prog Cardiovasc Dis. 2015(1):50-60.
6.    Feart C, Samieri C, Barberger-Gateau P. Mediterranean diet and cognitive health: an update of available knowledge. Curr Opin Clin Nutr Metab Care. 2015;18:51-62.
7.    Petersson S, Philippou E. The effects of Mediterranean Diet on cognitive function and dementia: systematic review of the evidence. Clinical Nutrition ESPEN. 2016;13:889-904.
8.    Gómez-Pinilla F. Brain foods: the effects of nutrients on brain function. Nature Reviews Neuroscience. 2008;9(7):568-78.
9.    Scarmeas N, Luchsinger JA, Schupf N et al. Physical activity, diet, and risk of Alzheimer disease. J Am Med Assoc. 2009;302(6):627-37.
10.    Knight A, Bryan J, Murphy K. Is the Mediterranean diet a feasible approach to preserving cognitive function and reducing risk of dementia for older adults in Western countries? New insights and future directions. Ageing Research Reviews. 2016;25:85-101.
11.    Gardener SL, Rainey-Smith SR, Barnes MB et al. Dietary patterns and cognitive decline in an Australian study of ageing. Mol Psychiatry. 2015;20(7):860.
12.    Matthews DC, Davies M, Murray J et al. Physical Activity, Mediterranean Diet and Biomarkers-Assessed Risk of Alzheimer’s: A Multi-Modality Brain Imaging Study. Advances in Molecular Imaging. 2014;4(4):43-57.
13.    Merrill DA, Siddarth P, Raji CA et al. Modifiable Risk Factors and Brain Positron Emission Tomography Measures of Amyloid and Tau in Nondemented Adults with Memory Complaints. Am J Geriatr Psychiatry. 2016;24(9):729-37.
14.    Gardener S, Gu Y, Rainey-Smith SR et al. Adherence to a Mediterranean diet and Alzheimer’s disease risk in an Australian population. Transl Psychiatry. 2012;2(2):164.
15.    Gu Y, Luchsinger JA, Stern Y, Scarmeas N. Mediterranean diet, inflammatory and metabolic biomarkers, and risk of Alzheimer’s disease. J Alzheimers Dis. 2010;22(2):483-92.
16.    Roberts RO, Geda YE, Cerhan JR et al. Vegetables, unsaturated fats, moderate alcohol intake, and mild cognitive impairment. Dement Geriatr Cogn Disord. 2010;29(5):413-23.
17.    Cherbuin N, Anstey KJ. The Mediterranean diet is not related to cognitive change in a large prospective investigation: the PATH Through Life study. The American Journal Of Geriatric Psychiatry: Official Journal Of The American Association For Geriatric Psychiatry. 2012;20(7):635-9.
18.    Samieri C, Okereke OI, Devore EE, Grodstein F. Long-term adherence to the Mediterranean diet is associated with overall cognitive status, but not cognitive decline, in women. J Nutr. 2013;143(4):493-9.
19.    Corley J, Starr JM, McNeill G, Deary IJ. Do dietary patterns influence cognitive function in old age? Int Psychogeriatr. 2013;25(9):1393-407.
20.    Kesse-Guyot E, Andreeva VA, Lassale C et al. Mediterranean diet and cognitive function: a French study. Am J Clin Nutr. 2013;97(2):369-76.
21.    Altmann A, Tian L, Henderson VW, Greicius MD. Sex modifies the APOE-related risk of developing Alzheimer disease. Ann Neurol. 2014;75(4):563-73.
22.    Pike KE, Ellis KA, Villemagne VL et al. Cognition and beta-amyloid in preclinical Alzheimer’s disease: Data from the AIBL study. Neuropsychologia. 2011;49:2384-90.
23.    Aderinwale OG, Ernst HW, Mousa SA. Current therapies and new strategies for the management of Alzheimer’s disease. Am J Alzheimers Dis Other Demen. 2010;25(5):414-24.
24.    Szoeke C, Coulson M, Campbell S, Dennerstein L. Cohort profile: Women’s Healthy Ageing Project (WHAP) – a longitudinal prospective study of Australian women since 1990. Women’s Midlife Health. 2016;2(1):5.
25.    Giles GG, Ireland PD. Dietary Questionnaire for Epidemiological Studies (Version 2). 1996.
26.    Sofi F, Macchi C, Abbate R et al. Mediterranean diet and health status: an updated meta-analysis and a proposal for a literature-based adherence score. Public Health Nutr. 2014;17(12):2769-82.
27.    Szoeke CEI, Robertson JS, Rowe CC et al. The Women’s Healthy Ageing Project: Fertile ground for investigation of healthy participants ‘at risk’ for dementia. Int Rev Psychiatry. 2013;25(6):726-37.
28.    Craig CL, Marshall AL, Sjostrom M et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381-95.
29.    Absolute cardiovascular disease risk. Melbourne: National Vascular Disease Prevention Alliance; 2009.
30.    Brown BM, Peiffer JJ, Taddei K et al. Physical activity and amyloid-[beta] plasma and brain levels: results from the Australian imaging, biomarkers and lifestyle study of ageing. Mol Psychiatry. 2013;18(8):875.
31.    Cherbuin N, Kumar R, Anstey K. Caloric intake, but not the Mediterranean diet, is associated with cognition and mild cognitive impairment. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. 2011;7(Sup):691.
32.    Bubu OM, Schwartz S, Umasabor-Bubu OQ. CSF p-tau, brain beta-amyloid, and hippocampal atrophy predict development of Alzheimer’s disease from MCI interactively in ADNI subjects. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. 2015;11(7):59-60.
33.    Féart C, Samieri C, Allès B, Barberger-Gateau P. Potential benefits of adherence to the Mediterranean diet on cognitive health. Proc Nutr Soc. 2013;72(1):140.
34.    Kostomoiri M, Fragkouli A, Sagnou M et al. Oleuropein, an Anti-oxidant Polyphenol Constituent of Olive Promotes [alpha]-Secretase Cleavage of the Amyloid Precursor Protein (A[beta]PP). Cell Mol Neurobiol. 2013(1):147.
35.    Casamenti F, Grossi C, Rigacci S et al. Oleuropein Aglycone: A Possible Drug against Degenerative Conditions. In Vivo Evidence of its Effectiveness against Alzheimer’s Disease. J Alzheimers Dis. 2015;45(3):679-88.
36.    Baghurst K. Red meat consumption in Australia: intakes, contributions to nutrient intake and associated dietary patterns. Eur J Cancer Prev. 1999;8(3):185-91.

MODEST OVERWEIGHT AND HEALTHY DIETARY HABITS REDUCE RISK OF DEMENTIA: A NATIONWIDE SURVEY IN TAIWAN

 

C.-Y. Lee1,*, Y. Sun1,*, H.-J. Lee2, T.-F. Chen3, P.-N. Wang4, K.-N. Lin4, L.-Y. Tang2, C.-C.Lin5, M.-J. Chiu6

 

1. Department of Neurology, En Chu Kong Hospital, New Taipei City, Taiwan  ; 2. Taiwan Alzheimer’s Disease Association, Taipei, Taiwan; 3. Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan; 4. Department of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan; 5. Department of Computer Science and Information Engineering, Chang Gung University, Tao-Yuan, Taiwan; 6. Department of Neurology, National Taiwan University Hospital, College of Medicine; Graduate Institute of Brain and Mind Sciences; Graduate Institute of Psychology; Graduate Institute of Biomedical Engineering and Bioinformatics; National Taiwan University, Taipei, Taiwan; * contribute equally to this paper.

Corresponding Author: Dr. Ming-Jang Chiu, Department of Neurology, National Taiwan University Hospital, College of Medicine; Graduate Institute of Brain and Mind Sciences; Graduate Institute of Psychology; Graduate Institute of Biomedical Engineering and Bioinformatics; National Taiwan University, Taipei, Taiwan, Tel: 886-2-23123456 ext 65339;  Fax: 886-2-23418395; E-mail: mjchiu@ntu.edu.tw

J Prev Alz Dis 2017;4(1):37-43
Published online December 13, 2016, http://dx.doi.org/10.14283/jpad.2016.123

 


Abstract

Background: Evidence of the associations of dietary habits and body mass index with dementia is inconsistent and limited in East Asian countries.
Objective: We aim to explore the associations of dietary habits and body mass index with the odds of dementia.
Design: Cross-sectional observational study.
Setting: A nationwide, population-based, door-to-door, in-person survey.
Participants: Selected by computerized random sampling from all 19 counties in Taiwan.
Measurement: Diagnosis of dementia using the criteria recommended by the National Institute on Aging-Alzheimer’s Association. Lifestyle factors, dietary habits and demographic data were compared between normal subjects and participants with dementia.
Results: A total of 10432 residents were assessed, among whom 2049 were classified as having a mild cognitive impairment (MCI), 929 were diagnosed with dementia, and 7035 were without dementia or MCI. After adjustment for age, gender, education, body mass index (BMI), dietary habits, habitual exercises and co-morbidities, including hypertension, diabetes and cerebrovascular diseases, we found inverse associations of dementia with the consumption of fish (OR 0.62, 95% CI 0.41-0.94), vegetables (OR 0.35, 95% CI 0.13-0.95), coffee (OR 0.59, 95% CI 0.35-0.97), green tea (OR 0.51, 95% CI 0.34-0.75) and other types of tea (OR 0.41, 95% CI 0.28-0.60). There was no association between dementia and fruit consumption. Compared with people who had a normal BMI (18 < BMI <= 24), older overweight people (24 < BMI <=30) had a reduced risk of dementia with an adjusted OR of 0.77 (95% CI 0.61-0.96).
Conclusions: Our study provides preliminary evidence that suggests that the consumption of fish, vegetables, tea, and coffee has potential benefits against dementia in East Asian population. Being modestly overweight (nadir risk at BMI = 25) in late life was associated with decreased odds of dementia. The benefit of fruits may be offset by their high sugar content.

Key words: Risk factor, diet, tea, coffee, body mass index.


 

Introduction

The aging population in Taiwan has grown rapidly in the past decades, with an increase in the percentage of the population over the age 65 from 6.8% in 1992 to 11.1% in 2012 (1). The size of the dementia population is expected to increase, with substantial societal impacts on health care costs and care giving.
Genetic and environmental factors could interact and modify the risk of dementia. In the absence of a currently effective disease-modifying treatment or cure, the importance of reducing the risk of dementia cannot be overemphasized. A previous report from the Alzheimer’s Association found inconsistent evidence to support that a Mediterranean diet might reduce the risk of dementia, while mid-life obesity might increase the risk of dementia (2). On the other hand, low body mass index (BMI) in late life was found to be associated with an increased risk of dementia (3). Because the evidence of the associations of dietary habits and body mass index with dementia is limited in East Asian countries (4, 5), our study aimed to investigate the associations among lifestyle factors (including drinking tea or coffee), dietary habits (including the consumption of fish, vegetables, and fruits), BMI, and dementia among people aged 65 years and older in Taiwan.

 

Methods

Study design

This nationwide, population-based, cross-sectional study was approved by the ethics committee of the National Taiwan University Hospital. This study was completed by a door-to-door, in-person survey. After obtaining written informed consent from the participants or their proxies, we performed in-person interviews to collect a brief history that focused on cognitive and functional status and administered mental tests and a structured questionnaire that recorded demographic data and lifestyle factors, including dietary habits. The interview process was performed according to an operational manual that defines all variables examined in this questionnaire. Body weight and height were also collected during the interview to calculate BMI. Overall, 10432 participants aged 65 years and older across the country were enrolled between December 2011 and March 2013. The sampling process, interviewer training, home visiting procedure, quality control, and protocol approvals were detailed in our previous report (6). All the interviews were conducted by well-trained field interviewers and underwent continuous quality control to achieve necessary quality standards. The inter-rater reliability of global CDR was substantial, as demonstrated by a kappa value of 0.671. The participation rate of this original nationwide survey was 36.5%.

Diagnostic criteria

The diagnosis for all-cause dementia was based on the core clinical criteria recommended by the National Institute on Aging-Alzheimer’s Association (NIA-AA) (7). Cognitive and functional status were determined from the participant evaluation or from a knowledgeable informant (who was a relative and a principal caregiver providing at least 10 hours of weekly direct care for the dementia patient). The informant should be capable of detecting insidious changes in behaviour personality or a decline in mentality or function at work or during activities of daily living (ADL). Objective assessments included the Clinical Dementia Rating (CDR) scale and the Taiwanese Mental State Evaluation (TMSE). Normal TMSE results were defined as a score > 24 in literate elders and > 13 in illiterate elders (8).
Functional status was assessed using the ADL scale and the instrumental activities of daily living (IADL) scale. Mild cognitive impairment(MCI) was diagnosed according to the NIA-AA criteria as a change in cognition with impairment in 1 or more cognitive domains but no evidence of impairment in social or occupational functioning as assessed by the CDR, ADL, and IADL (9).People with major depression, other mental disorders, delirium or other serious physical problems that led to cognitive or functional status decline were excluded because they did not fulfil the NIA-AA criteria for all-cause dementia.

Definition of variables of dietary habit

During an interview, lifestyle factors as well as dietary habits, including drinking tea or coffee, were recorded in detail. Information about the frequency of the habits was also recorded. These habits were developed before the onset of dementia.
Tea or coffee drinking habits were categorized into the following groups: no drinking, prior drinking habits, and frequent drinking habits (more than 3 days a week). Tea drinking habits were further divided into green tea (mostly or exclusively) and other types of tea. The frequency of specific food consumption, including fish, vegetables, and fruits, were categorized and recorded as consumed rarely (once or less than once a month), occasionally (at least twice a month), often (at least twice a week but not on a regular basis), or regularly (every day or almost every day). The BMI of each participant was measured and categorized into the following groups: underweight (BMI <= 18), normal weight (18 < BMI <= 24), overweight (24 < BMI <= 30), and obese (BMI > 30).

Statistical analyses

Categorical variables were represented by frequency or percentages, and chi square tests were used for inter-block comparisons. Means ± standard deviations and the t-test were used for continuous variables. Univariate logistic regression analyses were used to assess the associations among all the aforementioned lifestyle factors, BMI, dietary habits and dementia, and crude ORs with 95% confidence intervals (CIs) were calculated. Multiple logistic regression analyses were used to assess the aforementioned associations to obtain odds ratios (ORs) and 95% CIs adjusted by age, gender, years of education, BMI, dietary habits, habitual exercises and co-morbidities, including hypertension, diabetes and cerebrovascular diseases. All the analyses were  performed using SAS statistical software (version 9.3) with a 2-tailed statistical test.

 

Results

Among the 28600 subjects screened, 18029 were non-respondents. Of the 10571 respondents, 139 were excluded due to incomplete or erroneous data. We finally enrolled 10432 subjects, reflecting a total participation rate of 36.5%. Among the enrolled subjects, 7035 (67.4%) were without dementia or MCI, 2049 (19.6%) were classified as having a mild cognitive impairment, 929 (8.9%) fulfilled the NIA-AA core clinical criteria for all-cause dementia, and 419 (4.0%) participants were categorized in an unclassified group (this group included those participants could not receive cognitive assessment due to severe hearing impairment, poor visual acuity, aphasia, abnormal consciousness levels and so on). The demographic data for the subjects with (n = 929) and with normal cognition (n = 7035) are shown in Table 1.

Table 1. Demographic data of study participants (n=7964)

Table 1. Demographic data of study participants (n=7964)

 

Table 2 shows the associations among dietary habits, BMI and dementia from the univariate logistic regression analyses. Underweight (BMI <= 18) was associated with an increased odds of dementia, while being overweight (24 < BMI <= 30), drinking, drinking tea (both green and other teas), drinking coffee, chewing betel nuts, eating fish or vegetables (regardless of frequency), and having fruits (either every day or often) were associated with decreased odds of dementia. Obesity (BMI > 30) and vegetarianism (veganism and lacto-oval vegetarianism) were not associated with an increase or decrease in the odds of dementia.

Table 2. Crude ORs of the risk factors for dementia using univariate logistic regression analyses

Table 2. Crude ORs of the risk factors for dementia using univariate logistic regression analyses

cORs: crude odds ratios; CI: confidence interval.

 

The results of the multiple logistic regression model for the aforementioned associations after adjustment for age, gender, education years, BMI, dietary habits, habitual exercises and co-morbidities, including hypertension, diabetes and cerebrovascular diseases, are shown in Table 3. Compared with the normal-weight group (18 < BMI <= 24), the overweight group (24 < BMI <= 30) had a 0.77 (95% CI =0.61 – 0.96) decreased adjusted odds of dementia. As for dietary habits, dementia was inversely associated with eating fish (R 0.62, 95% CI 0.41-0.94), eating vegetables (OR 0.35, 95% CI 0.13-0.95), drinking coffee (OR 0.59, 95% CI 0.35-0.97), drinking green tea (OR 0.51, 95% CI 0.34-0.75) and drinking other types of tea (OR 0.41, 95% CI 0.28-0.60). There was no association between dementia and eating fruits.

Table 3. Adjusted ORs of lifestyle and diet habit risk factors for dementia using multiple logistic regression analyses

Table 3. Adjusted ORs of lifestyle and diet habit risk factors for dementia using multiple logistic regression analyses

Multiple regression adjusting age, gender, education, body mass index, dietary habit, exercise and comorbidity; CI: confidence interval; aORs (adjusted odds ratios)

 

Figure 1. The figure shows the odds of dementia (solid curve) for each body mass index (BMI) category relative to the risk of those whose BMI were > 18 and ≤ 24; the dashed curves represent the upper and lower boundaries of the 95% confidence interval. The optimal BMI for nadir odds of dementia risk is 25

Figure 1. The figure shows the odds of dementia (solid curve) for each body mass index (BMI) category relative to the risk of those whose BMI were > 18 and ≤ 24; the dashed curves represent the upper and lower boundaries of the 95% confidence interval. The optimal BMI for nadir odds of dementia risk is 25

 

Discussion

This study is the first and only large-scale, nationwide epidemiology study with a detailed random sampling plan that investigated dementia and its associated factors, including BMI, various daily lifestyle factors and dietary habits in Taiwan. All the participants underwent a detailed assessment to make a reliable diagnosis of dementia, and the criteria were based on the NIA-AA score clinical criteria, which are one of the principle diagnostic guidelines for dementia research. These demographic data were extensively collected by an in-person, face-to-face interview to customize the recording to the culture of this region and the lifestyles of the Taiwanese people.
A rapidly growing literature strongly suggests that exercise may attenuate cognitive impairment and reduce dementia risk. The results of this study showed that exercise is a protective factor of dementia. After full adjustment of variable lifestyle factors, exercise still showed a decreased odds for dementia, whether “regular exercise” or “exercise sometimes”. This study result is consistent with other cross-sectional case-controlled or prospective cohort or meta-analysis studies in other countries as well as our previous report investigating the association between lifestyle and dementia (10, 11). Physical activity should be encouraged among the elderly population.
The results of this study showed that overweight elders (with 24 < BMI <= 30) had decreased odds of dementia compared with those in the normal-weight group (18 < BMI <= 24) (3, 12, 13). Most previous studies have revealed that overweight or obesity in middle age was linked to an increased risk of dementia in old age, which could be attributed to cumulative vascular risk related to metabolic syndrome in middle age. However, when examined in old age, higher BMIs were associated with better cognition (3, 12). These results revealed a complex interplay between body weight and cognition, and this relationship could evolve over the lifetime of an individual. Another study showed that dementia-associated weight loss began years before the onset of the clinical syndrome and was accelerated by the time of diagnosis; therefore, body weight loss could merely represent a preclinical symptom of dementia (14). However, the effects of late-life overweight on dementia could also be explained by the potentially beneficial effect of certain adipokines on the aging brain. Previous studies showed that leptin, an adipokine that is primarily secreted by adipose tissue and positively correlated with BMI, reduced β-secretase activity, suggesting that it has an effect against AD pathology (13). Finally, the risk of malnutrition due to eating behaviour problems and dysphagia in advanced stages of dementia can further aggravate weight loss, resulting in a lower BMI among this diseased group. Late-life obesity (BMI > 30) did not show the same beneficial effect against dementia risk as overweight did in this study. In obesity, a decreased sensitivity to leptin occurs, resulting in an inability to detect satiety despite high-energy stores. This probably suggests that increased metabolic syndrome and vascular risk factors due to further weight gain might offset the benefit. Therefore, the effect of body weight on cognition in old age might be represented by a U-shape function relative to the risk of dementia. We plotted the odds ratios of each BMI and found that the optimal BMI for the nadir odds of dementia risk was 25 (Fig. 1).
Our study suggested that drinking coffee or tea might decrease the odds of dementia. Caffeine, the main psychoactive component of coffee and tea, may heighten alertness and arousal and improve cognitive performance. In addition to its short-term effects, recent epidemiological and experimental studies indicated that chronic administration of caffeine has beneficial effects against a number of acute and chronic neurological disorders, including stroke, Parkinson’s disease, amyotrophic lateral sclerosis, dementia, and AD (15-17). In particular, animal studies suggested that chronic caffeine consumption might inhibit Aβ production in the brains of rodents (18). In tea and coffee, other substances in addition to caffeine might also improve cognition in man; for instance, theanine, a non-dietary amino acid that crosses the BBB and is present only in tea and mushroom, showed a protective effect against oxidative stress in an animal study and could improve attention in higher doses (20-22).Epigallocatechin-3-gallate (EGCG), the main polyphenolic constituent of green tea, was also shown to reduce beta-amyloid mediated cognitive impairment in Alzheimer transgenic mice (23, 24). It is also possible that people who drink coffee or tea lead a more active social life and may occasionally drink together, which may play a protective role against dementia. Prior coffee drinking habits did not show a protective effect in our study, which may be due to an insufficient dose of caffeine or may suggest that the acute beneficial effects of caffeine on cognitive performance are far more prominent than its chronic effects that became trivial after consumption was discontinued. It is also possible that some participants could not maintain a consistent coffee drinking habit due to progressive cognitive impairment; therefore, “the prior coffee drinking habit” group could potentially contain more cognitively impaired subjects, contributing to its insignificance compared with the “no coffee drinking” group.
Taiwan is an island country with prosperous and sophisticated agriculture and fisheries, providing easy access to fish, vegetables, and fruits that are part of the Taiwanese diet. The protective effect of fish consumption is usually attributed to its high content of long-chain omega-3 polyunsaturated fatty acids (PUFA), which are a major component of neuronal membranes and have vascular and anti-inflammatory properties, explaining their protective effect against dementia (25). On the other hand, the protective effect of vegetables and fruits is usually attributed to its rich anti-oxidant content. A previous systemic review of cohort studies, similar to our study, also showed that an increased intake of vegetables but not fruits was associated with a lower risk of dementia and slower rates of cognitive decline in older age, while most studies showed beneficial effects of both vegetables and fruits (26). We postulated that the protective effect of fruits could be counteracted by its high caloric or high sugar content, which might have detrimental effects on an aging brain compared with the effects of vegetables. The consumption of fruits showed a protective effect in the univariate regression analysis but the benefit disappeared after controlling for co-morbidity and other confounding variables in the multivariate regression.
There are some limitations of this study. First, the response rate (36.5%) of this population-based study was relatively low. The low participation rate was mainly due to difficulties to get the permission entering the residences of the randomly sampled target population distributed widely in the whole country. We have examined non-respondents and participants in 2 selected city and county and found no significant differences in the distribution of age and gender. Nevertheless, there is still some possible residual selection bias that must be taken into consideration in the generalization of the results. Second, we quantified tea, coffee, vegetables, fruits, and fish consumption by frequency but not the cumulative consumption of these dietary components. Third, the questionnaire was answered by non-demented participants or a proxy. This resulted in a global impression that probably spanned several decades of life. Fourth, these lifestyle factors and dietary habits were recorded if they were developed before dementia; however, these habits or lifestyle factors may have developed during a preclinical stage or as a result of early symptoms of dementia, which might be a consequence but not a cause of dementia pathology. Controversy also exists regarding the association of BMI with dementia. The observational bias and limitations of the cross-sectional design might result in a reversal of causality; therefore, a long-term prospective cohort study in the future is required to verify these observational findings.
In summary, the report demonstrates the potential benefit of fish, vegetable, tea, and coffee consumption against dementia on Taiwanese people. Being modestly overweight (24 < BMI <= 30) in late life was associated with decreased odds of dementia.

 

Conflict of Interest: All authors claim no conflict of interest.

Ethical Standards: The study was performed according to the Declaration of Helsinky.

 

References

1.     Statistical Yearbook of Interior (2013) Available: http://sowfmoigovtw/stat/year/elisthtm.
2.    Baumgart, M., et al., Summary of the evidence on modifiable risk factors for cognitive decline and dementia: A population-based perspective. Alzheimers Dement, 2015. 11(6): p. 718-26.
3.    Fitzpatrick, A.L., et al., Midlife and late-life obesity and the risk of dementia: cardiovascular health study. Arch Neurol, 2009. 66(3): p. 336-42.
4.    Chiang, C.J., et al., Midlife risk factors for subtypes of dementia: a nested case-control study in Taiwan. Am J Geriatr Psychiatry, 2007. 15(9): p. 762-71.
5.    Wu, Y.T., et al., Nutrition and the prevalence of dementia in mainland China, Hong Kong, and Taiwan: an ecological study. J Alzheimers Dis, 2015. 44(4): p. 1099-106.
6.    Sun, Y., et al., A nationwide survey of mild cognitive impairment and dementia, including very mild dementia, in Taiwan. PLoS One, 2014. 9(6): p. e100303.
7.    McKhann, G.M., et al., The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement, 2011. 7(3): p. 263-9.
8.    Shyu, Y.I. and P.K. Yip, Factor structure and explanatory variables of the Mini-Mental State Examination (MMSE) for elderly persons in Taiwan. J Formos Med Assoc, 2001. 100(10): p. 676-83.
9.    Albert, M.S., et al., The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement, 2011. 7(3): p. 270-9.
10.    Blondell, S.J., R. Hammersley-Mather, and J.L. Veerman, Does physical activity prevent cognitive decline and dementia?: A systematic review and meta-analysis of longitudinal studies. BMC Public Health, 2014. 14: p. 510.
11.    Fan, L.Y., et al., Marital Status, Lifestyle and Dementia: A Nationwide Survey in Taiwan. PLoS One, 2015. 10(9): p. e0139154.
12.    Garcia-Ptacek, S., et al., Body mass index in dementia. Eur J Clin Nutr, 2014. 68(11): p. 1204-9.
13.    Kiliaan, A.J., I.A. Arnoldussen, and D.R. Gustafson, Adipokines: a link between obesity and dementia? Lancet Neurol, 2014. 13(9): p. 913-23.
14.    Stewart, R., et al., A 32-year prospective study of change in body weight and incident dementia: the Honolulu-Asia Aging Study. Arch Neurol, 2005. 62(1): p. 55-60.
15.    Panza, F., et al., Coffee, tea, and caffeine consumption and prevention of late-life cognitive decline and dementia: a systematic review. J Nutr Health Aging, 2015. 19(3): p. 313-28.
16.    Larsson, S.C. and N. Orsini, Coffee consumption and risk of stroke: a dose-response meta-analysis of prospective studies. Am J Epidemiol, 2011. 174(9): p. 993-1001.
17.    Liu, Q.P., et al., Habitual coffee consumption and risk of cognitive decline/dementia: A systematic review and meta-analysis of prospective cohort studies. Nutrition, 2015.
18.    Cao, C., et al., Caffeine suppresses amyloid-beta levels in plasma and brain of Alzheimer’s disease transgenic mice. J Alzheimers Dis, 2009. 17(3): p. 681-97.
19.    Prasanthi, J.R., et al., Caffeine protects against oxidative stress and Alzheimer’s disease-like pathology in rabbit hippocampus induced by cholesterol-enriched diet. Free Radic Biol Med, 2010. 49(7): p. 1212-20.
20.    Giesbrecht, T., et al., The combination of L-theanine and caffeine improves cognitive performance and increases subjective alertness. Nutr Neurosci, 2010. 13(6): p. 283-90.
21.    Sumathi, T., et al., l-Theanine alleviates the neuropathological changes induced by PCB (Aroclor 1254) via inhibiting upregulation of inflammatory cytokines and oxidative stress in rat brain. Environ Toxicol Pharmacol, 2016. 42: p. 99-117.
22.    Kahathuduwa, C.N., et al., Acute effects of theanine, caffeine and theanine-caffeine combination on attention. Nutr Neurosci, 2016.
23.    Rezai-Zadeh, K., et al., Green tea epigallocatechin-3-gallate (EGCG) reduces beta-amyloid mediated cognitive impairment and modulates tau pathology in Alzheimer transgenic mice. Brain Res, 2008. 1214: p. 177-87.
24.    Rezai-Zadeh, K., et al., Green tea epigallocatechin-3-gallate (EGCG) modulates amyloid precursor protein cleavage and reduces cerebral amyloidosis in Alzheimer transgenic mice. J Neurosci, 2005. 25(38): p. 8807-14.
25.    Barberger-Gateau, P., et al., Dietary patterns and risk of dementia: the Three-City cohort study. Neurology, 2007. 69(20): p. 1921-30.
26.    Loef, M. and H. Walach, Fruit, vegetables and prevention of cognitive decline or dementia: a systematic review of cohort studies. J Nutr Health Aging, 2012. 16(7): p. 626-30.

CEREAL INTAKE INCREASES AND DAIRY PRODUCTS DECREASE RISK OF COGNITIVE DECLINE AMONG ELDERLY FEMALE JAPANESE

R. Otsuka1, Y. Kato1, Y. Nishita1, C. Tange1, M. Nakamoto1, M. Tomida1,2, T. Imai1,3, F. Ando1,4, H. Shimokata1,5

1. Section of Longitudinal Study of Aging, National Institute for Longevity Sciences (NILS-LSA), National Center for Geriatrics and Gerontology, Aichi, Japan;

2. Research Fellow of the Japan Society for the Promotion of Science, Japan; 3. Faculty of Human Life and Science, Doshisha Women’s College of Liberal Arts, Kyoto, Japan; 4. Faculty of Health and Medical Sciences, Aichi Shukutoku University, Aichi, Japan; 5. Graduate School of Nutritional Sciences, Nagoya University of Art and Science, Aichi, Japan

Corresponding Author: Rei Otsuka, Section of NILS-LSA, National Center for Geriatrics and Gerontology, 35 Gengo, Morioka-cho, Obu, Aichi 474-8511, Japan, otsuka@ncgg.go.jp, T +81-562-46-2311 F +81-562-44-6593

J Prev Alz Dis 2014;1(3):160-167

Published online November 25, 2014, http://dx.doi.org/10.14283/jpad.2014.29


Abstract

BACKGROUND: If cognitive decline can be prevented through changes in daily diet with no medical intervention, it will be highly significant for dementia prevention.

OBJECTIVES: This longitudinal study examined the associations of different food intakes on cognitive decline among Japanese subjects.

DESIGN: Prospective cohort study.

SETTING: The National Institute for Longevity Sciences – Longitudinal Study of Aging, a community-based study.

PARTICIPANTS: Participants included 298 males and 272 females aged 60 to 81 years at baseline who participated in the follow-up study (third to seventh wave) at least one time. MEASUREMENTS: Cognitive function was assessed with the Mini-Mental State Examination (MMSE) in all study waves. Nutritional intake was assessed using a 3-day dietary record in the second wave. Cumulative data among participants with an MMSE >27 in the second wave were analyzed using a generalized estimating equation. Multivariate adjusted odds ratios (OR) and 95% confidence intervals (CI) for an MMSE score ≤27 in each study wave according to a 1 standard deviation (SD) increase of each food intake at baseline were estimated, after adjusting for age, follow-up time, MMSE score at baseline, education, body mass index, annual household income, current smoking status, energy intake, and history of diseases.

RESULTS: In men, after adjusting for age, and follow-up period, MMSE score at baseline, the adjusted OR for a decline in MMSE score was 1.20 (95% CI, 1.02-1.42; p=0.032) with a 1-SD increase in cereal intake. After adjusting for education and other confounding variables, the OR for a decrease in MMSE score did not reach statistical significance for this variable. In women, multivariate adjusted OR for MMSE decline was 1.43 (95% CI, 1.15-1.77; p=0.001) with a 1-SD increase in cereal intake and 0.80 (95% CI, 0.65-0.98; p=0.034) with a 1-SD increase in milk and dairy product intake.

CONCLUSIONS: This study indicates that a 1-SD (108 g/day) decrease in cereal intake and a 1-SD (128 g/day) increase in milk and dairy product intake may have an influence of cognitive decline in community-dwelling Japanese women aged 60 years and older. Further studies are needed in order to explore the potential causal relationship.

 

Key words: Cereal, milk and dairy products, diet, Japanese, elderly.


Introduction

Dementia, including Alzheimer’s disease, is one of the most serious geriatric diseases because it interferes with a person’s daily routines and social life. With the aging of the population, there is a growing concern that the number of dementia cases will increase in Japan (1). However, because there are no treatment strategies for dementia to date, there is a pressing need to establish prevention strategies. Many factors such as medical history, lifestyle habits, and psychological and genetic factors are thought to be associated with the onset of dementia (2). Concurrently, reports that indicate a possible link between dietary factors and cognitive function are beginning to emerge. Food consumption is vital for human life, and is an essential factor for health maintenance and health promotion throughout life. From a public health perspective, if cognitive decline can be prevented through changes in daily diet with no medical intervention, it will be highly significant for dementia prevention.

High intake of vegetables (3), fish (4), and dairy products (5) are thought to play a protective role against age-related cognitive decline or Alzheimer’s disease. However, these reports were based on studies conducted in Western countries. Japanese cuisine is based on a combination of staple foods, typically rice or noodles (6), and Japanese consume higher levels of fish, salt, and soy products compared with the Western diet, which includes high intake of meat and dairy products (7). For example, the main sources of protein among Japanese subjects in the 2008 National Health and Nutrition Survey in Japan were fish (22%), meat (18%), and beans (7%) (8). Thus, it is important to determine whether there is a specific dietary factor that would help reduce the risk of cognitive decline among Japanese. Recently, dietary patterns characterized by a high intake of soybeans, vegetables, algae, and milk and dairy products and a low intake of rice were reported to be associated with reduced risk of dementia during a median of 15 years of follow-up in the general Japanese population (9). However, that study defined dietary patterns based on a food frequency questionnaire; thus, the effect of each dietary factor and the amounts eaten on the risk of dementia were not clear. No other longitudinal studies in Japan have reported the association between dietary factors and cognitive decline.

The present longitudinal study was carried out in elderly community-dwelling Japanese subjects to clarify the effectiveness of different food intakes calculated by dietary records on cognitive decline.

Methods

Participants

Data for this survey were collected as part of the National Institute for Longevity Sciences – Longitudinal Study of Aging (NILS-LSA). In this project, the normal aging process has been assessed over time using detailed questionnaires and medical checkups, anthropometric measurements, physical fitness tests, and nutritional examinations. Participants in the NILS-LSA included randomly selected age- and sex-stratified individuals from the pool of non-institutionalized residents in the NILS neighborhood areas of Obu City and Higashiura Town in Aichi Prefecture. The first wave of the NILS-LSA was conducted from November 1997 to April 2000 and comprised 2,267 participants (1,139 men, 1,128 women; age range, 40-79 years). Details of the NILS-LSA study have been reported elsewhere (10). Subjects have been followed up every 2 years from the first wave, second wave (April 2000 – May 2002), third wave (May 2002 – May 2004), fourth wave (June 2004 – July 2006), fifth wave (July 2006 – July 2008), sixth wave (July 2008 – July 2010), and seventh wave (July 2010 – July 2012). When participants could not be followed up (e.g., they transferred to another area, dropped out for personal reasons, or died), new age- and sex-matched subjects were randomly recruited. All waves included nearly 1,200 men and 1,200 women. In this study, we selected participants who participated in the second wave (n=2,259; age range, 40-81 years) and also participated in more than one study wave from the third to seventh wave, as variables could be followed up at least one time from the second wave.

Exclusion criteria were as follows: 1) those who were <60 years in the second wave (n=1,114), because cognitive function tested by the Mini-Mental State Examination (MMSE) was assessed only among participants aged 60 or older; 2) those who had an MMSE score ≤27 in the second wave (n=414); 3) those who did not complete nutritional assessments in the second wave (n=40); and 4) those who did not complete the self-reported questionnaire (n=30). In addition, 91 men and women did not participate in more than one study wave from the third to seventh wave. Thus, a total of 570 Japanese (298 men, 272 women) who were between 60 and 81 years in the second wave of the NILS-LSA were available for analysis. Each wave was conducted for 2 years; the total length of the second through seventh waves was 10 years. However, the mean interval and participation times between the second and seventh wave for each participant was 8.1 years and 3.9 times, respectively (Table 1).

The study protocol was approved by the Committee of Ethics of Human Research of the National Center for Geriatrics and Gerontology. Written informed consent was obtained from all subjects.

Table 1. Number of subjects, follow-up time, and period in this study

*Second wave is the baseline in this study. Abbreviations: MMSE=Mini-Mental State Examination

Assessment of cognitive function

Cognitive function was assessed by the Japanese version of the MMSE through interviews with a trained psychologist or clinical psychotherapist through the second and seventh waves (11, 12). The MMSE is widely used as a brief screening test for dementia, and scores range from 0 to 30 points, with a higher score indicating better cognitive function. The MMSE includes questions on orientation of time and place, registration, attention and calculation, recall, language, and visual construction. A cut-off score of ≤23 is traditionally used to represent “suggestive cognitive impairment” (11, 12). Because only 4 to 12 individuals in each study wave had an MMSE ≤23, the number of cases was too small to analyze. However, our sample was relatively highly educated, and 58.4% of men and 39.3% of women graduated from 2- or 4-year colleges. A cut-off score ≤26 (sensitivity, 0.69; specificity, 0.91) or ≤27 (sensitivity, 0.78; specificity, 0.78) has been suggested for samples of highly educated individuals (13). Thus, we used a cut-off score of ≤27 in the main analyses. Among participants in this study with an MMSE >27 in the second wave (n=570), 134 in the third wave, 133 in the fourth wave, 137 in the fifth wave, 124 in the sixth wave, and 124 in the seventh wave had an MMSE score ≤27 and were classified as showing cognitive decline.

Nutritional assessments

Nutritional intake was assessed using a 3-day dietary record after participation in the second wave survey. The dietary record was completed over 3 continuous days (both weekdays and 1 weekend day) (14), and most subjects completed it at home and returned records within 1 month. Food was weighed separately on a scale (1-kg kitchen scales; Sekisui Jushi, Tokyo, Japan) before being cooked or portion sizes were estimated. Subjects used a disposable camera (27 shots; Fuji Film, Tokyo, Japan) to take photos of meals before and after eating. Dietitians used these photos to complete missing data and telephoned subjects to resolve any discrepancies or obtain further information when necessary. Averages for 3-day food and nutrient intakes were calculated according to the Standard Tables of Foods Composition in Japan 2010 and other sources (14).

Other measurements

Medical history of heart disease, hypertension, hyperlipidemia, and diabetes (past and current), education (≤9, 10-12, or ≥13 years of school), annual household income (11 point scale, 1; <¥1,500,000, 11; ≥¥ 20,000,000) and smoking status (yes/no) were collected using self-reported questionnaires. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. These measurements were assessed in the second wave. Follow-up time (year) was calculated by the length of time (days) that has elapsed since the day each subjects participated in the second wave.

Statistical analysis

All statistical analyses were conducted using Statistical Analysis System (SAS) software version 9.3 (SAS Institute, Cary, NC, USA). Comparisons between baseline characteristics and food intake of subjects according to the MMSE score at the follow-up study were performed by independent t-test (continuous variables) and chi- square tests (categories), respectively.

Cumulative data were analyzed using a generalized estimating equation (GEE), which takes into account the dependency of repeated observations within participants; this is an important feature that is necessary for longitudinal analyses. An additional advantage of GEE is that participants are included regardless of missing values. Thus, participants who were lost to follow-up after early wave examination were also included in the analyses. GEE models were fitted using the GENMOD procedure of SAS. The GENMOD procedure fits generalized linear models. The correlation structure was specified to be compound symmetry.

GEE analyses were performed to estimate the odds ratio (OR) and 95% confidence interval (CI) for an MMSE score ≤27 in each study wave according to a 1-SD increase of each food intake at baseline, adjusted for the following variables. The confounding variables were 1) model 1, age (year, continuous), and follow-up time (year, continuous); 2) model 2, model 1, and MMSE score at baseline (continuous); and 3) model 3, model 2, education (≤9, 10-12, ≥13 years), BMI (kg/m2), annual household income (11 point scale, continuous), current smoking status (yes or no), energy intake (kcal/day), and history of heart disease, hypertension, hyperlipidemia, and diabetes (yes or no). Probability levels less than 0.05 were considered significant.

Results

Number of subjects, follow-up time, and period in this study are shown in Table 1. About 31.3% of men (356/1137 cases) and 27.8% of women (296/1065 cases) had an MMSE score ≤27. Table 2 shows baseline characteristics and food intake of included subjects and excluded subjects by sex. Compared with included subjects, excluded subjects in the analyses had significantly lower MMSE scores, were less likely to be educated, and had lower body mass index in women.

Table 2. Baseline characteristics and food intake of subjects by sex

*Subjects excluded from the analyses included those who were older than 60 years or scored ≤27 in the second wave and those who did not participate in the follow-up survey or had any missing values. The number of excluded subjects according to the characteristics listed ranged from 249-285 in men and 253-290 in women. P value for continuous variables, independent t-test was used; for categorical variables, chi-square test or Fisher’s exact probability test was used. Abbreviations: MMSE=Mini-Mental State Examination.

Table 3 shows the longitudinal relationships between baseline food intake and OR (95% Cl) for MMSE scores ≤27 10 years later. The cumulative data were analyzed with GEE. In men, after adjusting for age, follow-up period, and MMSE score at baseline, the adjusted OR for a decline in MMSE score was 1.20 (95% CI, 1.02-1.42; p=0.032) with a 1-SD increase in cereal intake. After adjusting for education and other confounding variables (model 3), the OR for a decrease in MMSE score did not reach statistical significance for this variable. On the other hand, in men, after adjusting for age and follow-up period, the adjusted OR for a decline in MMSE score was 0.83 (95% CI, 0.69-0.99; p=0.041) with a 1-SD increase in green yellow vegetable intake. Though, after adjusting for education and other confounding variables (model 3), no significant association of each food intake with MMSE score decline was observed in men.

In women, multivariate adjusted OR (model 3) for a decline in MMSE score was 1.43 (95% CI, 1.15-1.77; p=0.001) with a 1-SD increase in cereal intake and 0.80 (95% CI, 0.65-0.98; p=0.034) with a 1-SD increase in milk and dairy product intake. The OR for a decrease in MMSE score in model 3 did not reach statistical significance for any other food variable.

Table 3. Longitudinal relationships between baseline food intake and odds ratios (95% Cl) for MMSE scores≤27 10 years later. Cumulative data were analyzed with generalized estimation equations

Model 1: Adjusted for age (year) and follow-up time (year). Model 2: Adjusted for Model 1 + MMSE score at baseline. Model 3: Adjusted for Model 2 + education (≤9, 10-12, ≥13 years), body mass index (kg/m2), household annual income (1-11 score), current smoking status (yes or no), energy intake (kcal/day), and history of heartdisease, hypertension, hyperlipidemia, and diabetes (yes or no).Abbreviations: MMSE=Mini-Mental State Examination.

Discussion

This study provides longitudinal evidence that increases in cereal intake and decreases in dairy products reduce the risk of cognitive decline in community- dwelling Japanese females aged 60 years and older. This association remained after controlling for baseline MMSE score and other variables. This is the first study to examine the association between food intake amounts calculated by dietary records and cognitive decline among Japanese subjects.

In Korean older adults, a “white rice only” dietary pattern was positively associated with the risk of cognitive impairment as assessed by the Korean version of the MMSE (15). In the general Japanese population, dietary patterns characterized by a low intake of rice and a high intake of soybeans, vegetables, algae, and milk and dairy products were reported to be associated with reduced risk of dementia (9). Although the previous study examined dietary patterns instead of specific food intake, these findings may support our results. On the other hand, in a US study, bread and cereal intake were inversely associated with cognitive impairment (16). No other Western studies focused on cereal intake and cognitive impairment. The biological mechanisms through which higher cereal intake exerts adverse effects on cognition may be due to their impact on metabolic abnormalities such as dyslipidemia or hyperglycemia. Song et al. reported that a high-carbohydrate diet was associated with dyslipidemia among adults from the Korea National Health and Nutrition Examination Survey (17). High-carbohydrate diets are also linked to hyperglycemia through an increase in blood glucose levels (18). Metabolic abnormalities, including type 2 diabetes, dyslipidemia, and obesity, are associated with declines in cognitive performance in non-demented populations (19). Higher cereal intake could be a risk factor for cognitive decline through its resulting metabolic abnormalities.

We conducted 3-day dietary records and were able to analyze the effect of each cereal intake (rice, bread, Chinese instant noodles, Japanese wheat noodles (Udon and Hiyamugi), Japanese buckwheat noodles (Soba), or Italian noodles, etc.) on the risk of cognitive decline in sub-analyses. In the multivariate adjusted GEE model, higher wheat noodle intake (1 SD: 46 g/day increase) increased the risk of cognitive decline in women (OR, 1.25; p=0.04). Rice intake did not significantly increase the risk of cognitive decline (OR, 1.18; p=0.14). Therefore, our study indicates that wheat noodles, but not rice intake, could be a risk factor for cognitive decline. In Japanese meals, wheat noodles are eaten less often than rice with other dishes, and this result might mean that easily cooked cereal foods could be a risk factor of cognitive decline.

Milk and dairy products contain nutrients such as calcium and vitamins A, B2, and B12, as well as high- quality proteins and fats. Milk and dairy products or calcium intake may be a protective factor for metabolic syndrome among Japanese (20, 21). As for cognitive function, according to a study based on data from the US National Health and Nutrition Examination Survey (NHANES), a significant positive correlation was found between the intake of dairy products in a group of elderly individuals (aged ≥60 years) and cognitive function (5). Another study of local elderly residents of the state of Alabama (United States) reported that, of the various dairy products, the more cheese that was eaten, the lower the risk for cognitive impairment (16). The above- mentioned Japanese study also found that elderly individuals who adhered to a dietary pattern high in dairy products, legumes, vegetables, and algae had a low incidence of developing dementia later in life (9). Milk and dairy products are thought to have favorable effects on cognition through reducing metabolic risk and vascular factors linked to detrimental brain changes, particularly via weight reduction (22). In this study, females with a 1-SD (128 g/day) increase in milk and dairy food intake decreased the risk of cognitive decline by 20%, indicating that a half cup of milk (100 ml) may be a protective factor against cognitive decline among Japanese.

Beans and non-green yellow vegetables did not reach statistical significance, though their intake in women was negatively associated with cognitive decline. Neuroprotective effects of phytoestrogen compounds (found in soy) (23) and anti-oxidant effects (2) in vegetables could also be a protective factor against cognitive decline.

To clarify whether cereal or milk and dairy products are more protective against cognitive decline we adjusted all food groups in a multivariate adjusted GEE model. As a result, only cereal intake in women was positively

associated with cognitive decline (OR, 1.63; 95% CI, 1.21- 2.18; p=0.001). This result means that groups with lower intake of milk and dairy products eat more cereal; that is, the intake amount of cereal affects the intake amount of other foods. Therefore, it could be recommended to reduce cereal intake and increase intake of other foods including milk and dairy products, beans, and non-green yellow vegetables to prevent cognitive decline, especially among females with high cereal intake.

In this study, after adjusting for age and follow-up time, intake of green yellow vegetables was negatively associated with cognitive decline in men (Table 3, model 1); however, the statistically significant association disappeared after adjustment for other confounding variables. This result means the effect of confounding variables rather than each food intake on cognitive decline was strong in male subjects. Male mice were reported to be more vulnerable than female mice to the impact of a high-fat diet on metabolic alterations, deficits of learning, and hippocampal synaptic plasticity (24). The precise mechanisms for these findings are not clear, although the inclusion of male participants might be more valuable than female participants for understanding the impact of daily diet on metabolic abnormalities. The other reason for sex differences in our study is that there might be confounding variables that we did not adjust for. The risk of mild cognitive impairment varied by age and sex in the Sydney Memory and Ageing Study (25). That study also reported sex differences in terms of cognitive lifestyle in the elderly, as female participants had more active current lifestyles (26).

The other reason for the lack of statistically positive correlations among male subjects might depend on the validity of the dietary assessment. We assessed nutritional intake using 3-day dietary records with photographs. Although the 3-day dietary record is one of the best ways to assess individual food intake (14), it is difficult for men to do this. More than half of the male participants in this study asked a wife, child, or daughter- in-law to record their dietary intake. Therefore, the validity of dietary records could be lower among Japanese male subjects.

Several limitations to the present study warrant consideration. First, we assessed dietary factors from one nutritional assessment at baseline. Food intake is easily changeable and affected by various factors with aging (27, 28), though we could not consider these variations during the follow-up period. Second, we used only one MMSE cut-off score of ≤27 (sensitivity and specificity, 0.78) because it has been shown to be better for detecting cognitive dysfunction compared to the value of ≤23 (sensitivity, 0.66; specificity, 0.99) among older subjects with a college education (13). On the basis of this limitation, we used the other cut-off score of 26/27 (sensitivity, 0.69; specificity, 0.91) (13) in a sub-analysis after controlling for baseline MMSE score and other variables. Significant associations were observed between cereal or bean intake and cognitive decline in women (OR of 1 SD cereal intake: 1.46, p=0.004; OR of 1 SD beans intake: 0.79, p=0.049). Milk and dairy products had no significant association with cognitive decline in women (OR of 1 SD intake: 0.79, p=0.065).

The main strengths of the present study are as follows: 1) the longitudinal design of our analyses lends strength to our inferences; the inclusion of the same individuals who were followed over 10 years (mean: 8.1 years) provides evidence of a causal association between food intake and cognitive decline; 2) the use of an older sample of randomly selected age- and sex-stratified non- institutionalized individuals from the community; the results are therefore applicable to non-institutionalized Japanese elderly; and 3) the use of the intake amount of each food assessed by 3-day dietary records with photographs helps determine how the amount of a specific food decreases/increases the risk of cognitive decline in community-dwelling Japanese.

Conclusions

In conclusion, the findings of this study indicate a 1-SD (108 g/day) decrease in cereal intake and a 1-SD (128 g/day) increase in milk and dairy products may have an influence of cognitive decline in community-dwelling Japanese women aged 60 years and older. Further studies are needed in order to explore the potential causal relationship.

 

Acknowledgements: We wish to express our sincere appreciation to the study participants and our colleagues in the NILS-LSA for completing the survey for this study. This work was supported in part by grants from the Japanese Ministry of Education, Culture, Sports, Science and Technology (22790584 to R.O.), Research Funding for Longevity Sciences from the National Center for Geriatrics and Gerontology, Japan (25-22 to R.O.), and research funding from the Japan Dairy Association (J-milk to R.O.)

Financial disclosure: All authors declare no conflict of interest.

Ethical standards: The study protocol was approved by the Committee of Ethics of Human Research of the National Center for Geriatrics and Gerontology(No.369-2) in 2010. Written informed consent was obtained from all subjects.

 

References

 

  1. Ikejima C, Hisanaga A, Meguro K, et al. Multicentre population-based dementia prevalence survey in Japan: a preliminary report. Psychogeriatr 2012;12:120–123.
  2. Otaegui-Arrazola A, Amiano P, Elbusto A, Urdaneta E, Martinez-Lage P. Diet, cognition, and Alzheimer’s disease: food for thought. Eur J Nutr 2014;53:1–23.
  3. Loef M, Walach H. Fruit, vegetables and prevention of cognitive decline or dementia: a systematic review of cohort studies. J Nutr Health Aging 2012;16:626–630.
  4. Morris MC, Evans DA, Bienias JL, et al. Consumption of fish and n-3 fatty acids and risk of incident Alzheimer disease. Arch Neurol 2003;60:940–946.
  5. Park KM, Fulgoni VL III. The association between dairy product consumption and cognitive function in the National Health and Nutrition Examination Survey. Br J Nutr 2013;109:1135–1142.
  6. Nakamura Y, Ueshima H, Okamura T, et al. A Japanese diet and 19-year mortality: national integrated project for prospective observation of non- communicable diseases and its trends in the aged, 1980. Br J Nutr 2009;101: 1696–1705.
  7. Nanri A, Shimazu T, Takachi R, et al. Dietary patterns and type 2 diabetes in Japanese men and women: the Japan Public Health Center-based Prospective Study. Eur J Clin Nutr 2013;67:18–24.
  8. Ministry of Health, Labor and Welfare of Japan. National Health and Nutrition Survey in Japan 2008. 2013. Daiichi Publishing, Tokyo, p 323
  9. Ozawa M, Ninomiya T, Ohara T, et al. Dietary patterns and risk of dementia in an elderly Japanese population: the Hisayama Study. Am J Clin Nutr 2013;97:1076–1082.
  10. Shimokata H, Ando F, Niino N. A new comprehensive study on aging–the National Institute for Longevity Sciences, Longitudinal Study of Aging (NILS-LSA). J Epidemiol 2000;10(1 Suppl):S1–S9.
  11. Folstein MF, Folstein SE, McHugh PR. « Mini-mental state ». A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189–198.
  12. Mori E, Mitani Y, Yamadori A. Usefulness of a Japanese version of the Mini- Mental State Test in neurological patients. Jpn J Neuropsychol 1985;1:82-90.
  13. O’Bryant SE, Humphreys JD, Smith GE, et al. Detecting dementia with the mini-mental state examination in highly educated individuals. Arch Neurol 2008;65:963–967.
  14. Imai T, Sakai S, Mori K, Ando F, Niino N, Shimokata H. Nutritional assessments of 3-day dietary records in National Institute for Longevity Sciences–Longitudinal Study of Aging (NILS-LSA). J Epidemiol 2000;10(1 Suppl): S70–76.
  15. Kim J, Yu A, Choi BY, et al. Dietary patterns and cognitive function in Korean older adults. Eur J Nutr (Accepted for publication) 2014, doi: 10.1007/s00394- 014-0713-0
  16. Rahman A, Sawyer Baker P, Allman RM, Zamrini E. Dietary factors and cognitive impairment in community-dwelling elderly. J Nutr Health Aging 2007;11:49–54.
  17. Song SJ, Lee JE, Paik HY, Park MS, Song YJ. Dietary patterns based on carbohydrate nutrition are associated with the risk for diabetes and dyslipidemia. Nutr Res Pract 2012;6:349–356.
  18. Garg A, Grundy SM, Koffler M. Effect of high carbohydrate intake on hyperglycemia, islet function, and plasma lipoproteins in NIDDM. Diabetes Care1992;15:1572–1580.
  19. van den Berg E, Kloppenborg RP, Kessels RP, Kappelle LJ, Biessels GJ . Type 2 diabetes mellitus, hypertension, dyslipidemia and obesity: A systematic comparison of their impact on cognition. Biochim Biophys Acta 2009;1792:470–481.
  20. Uenishi K, Tanaka S, Ishida H, et al. Milk, dairy products and metabolic syndrome: A cross-sectional study of Japanese. J Jpn Soc Nutr Food Sci 2010;63:151-159. (in Japanese with English abstract).
  21. Otsuka R, Imai T, Kato Y, Ando F, Shimokata H . Relationship between number of metabolic syndrome components and dietary factors in middle- aged and elderly Japanese subjects. Hypertens Res 2010;33:548–554.
  22. Crichton GE, Bryan J, Murphy KJ, Buckley J. Review of dairy consumption and cognitive performance in adults: findings and methodological issues. Dement Geriatr Cogn Disord 2010;30:352–361.
  23. Soni M, Rahardjo TB, Soekardi R, et al. Phytoestrogens and cognitive function: a review. Maturitas 2014;77: 209–220.
  24. Hwang LL, Wang CH, Li TL, et al. Sex differences in high-fat diet-induced obesity, metabolic alterations and learning, and synaptic plasticity deficits in mice. Obesity (Silver Spring), 2010;18: 463–469.
  25. Sachdev PS, Lipnicki DM, Crawford J, et al. Risk profiles for mild cognitive impairment vary by age and sex: the Sydney Memory and Ageing study. Am J Geriatr Psychiatry 2012;20: 854–865.
  26. Valenzuela MJ, Leon I, Suo C, et al. Cognitive lifestyle in older persons: the population-based Sydney Memory and Ageing Study. J Alzheimers Dis 2013;36:87–97.
  27. Wakimoto P, Block G. Dietary intake, dietary patterns, and changes with age: an epidemiological perspective. J Gerontol A Biol Sci Med Sci 2001;56 (suppl 2):65–80.
  28. Zhu K, Devine A, Suleska A, et al. Adequacy and change in nutrient and food intakes with aging in a seven-year cohort study in elderly women. J Nutr Health Aging 2010;14:723–729.

MEDITERRANEAN DIET AND MAGNETIC RESONANCE IMAGING-ASSESSED BRAIN ATROPHY IN COGNITIVELY NORMAL INDIVIDUALS AT RISK FOR ALZHEIMER’S DISEASE

L. Mosconi, J. Murray, W.H. Tsui, Y. Li, M. Davies, S. Williams, E. Pirraglia, N. Spector, R.S. Osorio, L. Glodzik, P. McHugh, M.J. de Leon

New York University School of Medicine, New York, NY 10016

Corresponding Author: Dr. Lisa Mosconi, Department of Psychiatry, NYU School of Medicine, 145 East 32nd St, 2nd Floor, New York NY, 10016. Tel: (212) 263-3255, Fax: (212) 263-3270 Email: lisa.mosconi@nyumc.org

J Prev Alz Dis 2014;1(1):23-32

Published online November 14, 2014, http://dx.doi.org/10.14283/jpad.2014.17


Abstract

OBJECTIVES: Epidemiological evidence linking diet, one of the most important modifiable environmental factors, and risk of Alzheimer’s disease (AD) is rapidly increasing. Several studies have shown that higher adherence to a Mediterranean diet (MeDi) is associated with reduced risk of AD. This study examines the associations between high vs. lower adherence to a MeDi and structural MRI-based brain atrophy in key regions for AD in cognitively normal (NL) individuals with and without risk factors for AD.

DESIGN: Cross-sectional study. SETTING: Manhattan (broader area).

PARTICIPANTS: Fifty-two NL individuals (age 54+12 y, 70% women) with complete dietary information and cross-sectional, 3D T1-weighted MRI scans were examined.

MEASUREMENTS: Subjects were dichotomized into those showing higher vs. lower adherences to the MeDi using published protocols. Estimates of cortical thickness for entorhinal cortex (EC), inferior parietal lobe, middle temporal gyrus, orbitofrontal cortex (OFC) and posterior cingulate cortex (PCC) were obtained by use of automated segmentation tools (FreeSurfer). Multivariate general linear models and linear regressions assessed the associations of MeDi with MRI measures.

RESULTS: Of the 52 participants, 20 (39%) showed higher MeDi adherence (MeDi+) and 32 (61%) showed lower adherence (MeDi-). Groups were comparable for clinical, neuropsychological measures, presence of a family history of AD (FH), and frequency of Apolipoprotein E (APOE) ε4 genotype. With and without controlling for age and total intracranial volume, MeDi+ subjects showed greater thickness of AD-vulnerable ROIs as compared to MeDi- subjects (Wilk’s Lambda p=0.026). Group differences were most pronounced in OFC (p=0.001), EC (p=0.03) and PCC (p=0.04) of the left hemisphere. Adjusting for gender, education, FH, APOE status, BMI, insulin resistance scores and presence of hypertension did not attenuate the relationship.

CONCLUSION: NL individuals showing lower adherence to the MeDi had cortical thinning in the same brain regions as clinical AD patients compared to those showing higher adherence. These data indicate that the MeDi may have a protective effect against tissue loss, and suggest that dietary interventions may play a role in the prevention of AD.

 

Key words: Alzheimer’s disease, diet, Mediterranean diet, MRI, early detection, brain imaging.


Introduction

Epidemiological evidence linking diet, one of the most important modifiable environmental factors, and risk of Alzheimer’s disease (AD), the most common cause of dementia, is rapidly increasing. Given the current lack of disease-modifying treatments, as well as increasing awareness that symptoms develop over many years or even decades, there has been growing interest in identifying effective strategies for prevention (1, 2). Delaying symptoms onset by as little as one year could potentially lower AD prevalence by more than 9 million cases over the next 40 years (1).

Several studies have provided evidence for dietary patterns that are protective against AD (3-8). Among possible dietary patterns (DPs), there is consensus that higher adherence to a Mediterranean diet (MeDi) is associated with reduced risk of AD (3, 4, 8-12). While regional differences may subsist, the MeDi is characterized by high intake of plant foods (i.e., fruits, nuts, legumes, and cereals); moderate consumption of dairy products, fish, poultry; with olive oil as the primary source of monounsaturated fats; low to moderate intake of wine, low intake of red meat and poultry, and very low intake of processed foods (12). This diet is known to be one of the healthiest dietary patterns in the world, and it has been associated with reduced risk of cardiovascular disease, cancer, and overall mortality rates (10, 13-15).

While there is growing interest in implementing dietary recommendations prior to the onset of symptoms of AD, the overall picture remains equivocal as clinical trials failed to show consistent relationships between the hypothesized protective nutrients and clinical outcome (16). These studies would greatly benefit from biological markers of disease as surrogate endpoints of clinical change (16), especially during the recently re- conceptualized preclinical stages of AD (2). In vivo biomarkers are needed to clarify how diet promotes healthy brain aging, and can therefore be protective against AD.

Pathologically, AD is characterized by presence of amyloid-beta (Aβ) plaques, neurofibrillary tangles and neuronal loss in selectively vulnerable brain regions (17). Neuronal loss in AD originates in the medial temporal lobes during the normal stages of cognition and spreads to cortical regions, especially posterior cingulate and parieto-temporal cortices, along with clinical progression (18). These changes can be visualized in vivo by means of Magnetic Resonance Imaging (MRI). Several studies have shown that brain atrophy can be detected on MRI several years prior to dementia onset and correlates with AD progression (17, 19-21).

MRI studies have shown that higher adherence to the MeDi is associated with reduced cerebrovascular disease burden (i.e., white matter lesions) in the elderly (22, 23). However, to the best of our knowledge, there are no MRI studies that examined the MeDi in relation to brain atrophy in cognitively normal (NL) individuals with and without risk factors of AD. Here, we investigated whether structural MRI-based measures of cortical thickness (i.e., brain atrophy) in key AD-regions differ among young to late middle-aged NL individuals as a function of higher vs. lower adherence to the MeDi.

 

Methods

Participants

Among a larger pool of clinically and cognitively normal (NL) individuals participating in longitudinal brain MRI imaging studies at NYU School of Medicine, this study included a sub-set of 52 NL participants who completed clinical, laboratory, MRI exams and dietary questionnaires within 4 months of each other between 2013-2014. Subjects were derived from multiple community sources, including individuals interested in research participation, family members and caregivers of impaired patients. Informed consent was obtained from all subjects for participation in this NYU institutional review board-approved study.

All subjects underwent a thorough physical examination and a detailed medical history was recorded. Individuals with medical conditions or history of conditions that may affect brain structure or function, i.e. stroke, diabetes, head trauma, any neurodegenerative diseases, depression, hydrocephalus, intracranial mass, and infarcts on MRI, and those taking psychoactive medications were excluded. Subjects were 25-72 y of age, with education>12 y, Clinical Dementia Rating (CDR)=0, Global Deterioration Scale (GDS)<2, Mini Mental State Examination (MMSE)>28, Hamilton depression scale<16, Modified Hachinski Ischemia Scale<4 and normal cognitive test performance for age and education (24).

None of the participants were diabetics, smokers, or met criteria for obesity as defined by a Body-Mass index (BMI)>30 kg/m2. While all subjects were normoglycemic young adults, the Homeostasis Model Assessment (HOMA) (25) for insulin sensitivity was calculated, as there is evidence for an association between increased insulin resistance (IR) and reduced brain volumes in AD- regions (26). Presence of hypertension (HTN) was determined based on current antihypertensive treatment or blood pressure assessments performed in a sitting position after 5 min rest (systolic blood pressure≥ 140 mmHg or diastolic blood pressure≥ 90 mmHg) (27, 28). Subjects were divided into 2 groups based on presence (HTN+) or absence of HTN (HTN-). A family history (FH) of late-onset AD that included at least one 1st degree relative whose AD onset was after age 60 was elicited using standardized questionnaires (24). DNA was obtained from venous blood samples to determine APOE genotypes using standard polymerase chain reaction (PCR) techniques (29, 30).

Dietary intake of nutrients

Dietary data regarding average food consumption over the prior year were obtained using the 61-item version of Harvard/Willett’s semi-quantitative food frequency questionnaire (SFFQ) (31). The SFFQ has been used and validated for the determination of nutrient intake in the elderly as well as in young adults, yielding high reliability (31-35). The 61 food items were categorized into 30 food groups based on similarities in food and nutrient composition, and intake (grams per day) of each food group was then calculated by summing the intakes of member food items. The daily intake of nutrients was computed by multiplying the consumption frequency of each portion of every food by the nutrient content of the specified portion( 31).

Published methods were followed for the construction of the MeDi (3, 4, 8-12). Briefly, we first regressed caloric intake (in kilocalories) and calculated the derived residuals of daily gram intake for each of the following seven food categories: dairy, meat, fruits, vegetables, legumes, cereals, and fish. The median value was determined for each caloric intake-residual food category. Categories were divided into beneficial (fruits, vegetables, legumes, cereals and fish) or detrimental (meat and dairy products). A value of 0 or 1 was assigned to each subject based on their scores on each of the seven above categories, using sex-specific medians as cut-offs, following standardized scoring procedures. Specifically, (a) subjects whose consumption of beneficial components was below the median were assigned a value of 0, while those whose consumption was at or above the median were assigned a value of 1, for each of the 5 categories. (b) Subjects whose consumption of detrimental components was at or above the median were assigned a value of 0, while those whose consumption was below the median were assigned a value of 1, for each of the 2 categories. (c) For fat intake (8th food category), we used the ratio of daily consumption of monounsaturated to saturated fats (in grams) (12) using sex-specific median cutoffs for assignment of values of 0 for low monounsaturated/saturated fats ratio (reflecting higher intake of saturated vs. monounsaturated fats) and 1 for high monounsaturated/saturated fats ratio (reflecting higher intake of monounsaturated vs. saturated fats). (d) For alcohol intake (9th food category), alcohol consumption was dichotomized into mild to moderate alcohol consumption (>0 drinks per week but <2 drinks per day in the previous year) and no (0 g/day) or more than moderate (>2 drinks per day) consumption (3, 4, 8- 12). Subjects showing mild to moderate consumption were assigned a value of 1, other subjects a value of 0, as a moderate amount of alcohol consumption with meals is a characteristic component of the MeDi. The MeDi score was generated for each participant as the sum of the scores in the food categories (range 0-9), with a greater score indicating higher adherence to the MeDi. The MeDi score was analyzed as a dichotomous variable (<5: low vs. >5: high adherence) and as a continuous variable to facilitate comparison with other studies of the MeDi score.

Data Acquisition and Preparation

All subjects received a diagnostic and a research MRI study on a 1.5 T GE Signa imager (General Electric, Milwaukee, USA). The diagnostic study was performed using contiguous 3 mm axial T2-weighted images. The research scan was a 124 slice T1-weighted Fast-Gradient- Echo acquired in a sagittal orientation as 1.2 mm thick sections (field of view=25 cm, NEX=1, matrix=256×128, repetition time= 35 ms, echo time= 9 ms and flip angle=60 0, no interslice gaps). Clinical scans were used to rule out MRI evidence of hydrocephalus, intracranial mass, strokes, subcortical gray matter lacunes, non- specific white matter disease, and to identify focal white matter hyperintensities.

Volumetric segmentation, cortical surface reconstruction and parcellation of the research scans were performed using a data analysis pipeline based on the FreeSurfer software package (20, 36-38). The automated whole-brain segmentation procedure for volumetric measures of the different brain structures uses a probabilistic atlas and applies a Bayesian classification rule to assign a neuroanatomic label to each voxel (38). A label is automatically assigned to each voxel in the MRI volume based on probabilistic information automatically estimated from a manually labeled atlas. The atlas consists of a manually derived training set created by the Center for Morphometric Analysis (Massachusetts General Hospital, Harvard Medical School) from 40 individuals across the adult age range. The classification technique uses a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with aging, and which has been shown to be comparable in accuracy to manual labeling (38). Automated volumetric segmentation required only qualitative review to ensure that there was no technical failure of the application. The cortical surface was then reconstructed to measure thickness at each surface location, or vertex, to allow visualization of group differences at each vertex. Cortical thickness was obtained by reconstructing representations of the gray/white matter boundary and the cortical surface, and the distance between these surfaces at each point across the cortical mantle was calculated (20, 36-38). This method uses both intensity and continuity information from the entire 3D MR volume in segmentation and deformation procedures to construct representations of cortical thickness. The maps were created using spatial intensity gradients across tissue classes and are therefore not simply reliant on absolute signal intensity. The maps are not restricted to the voxel resolution of the original data and are thus capable of detecting sub-millimeter differences between groups (37). Maps were smoothed using a circularly symmetric Gaussian kernel across the surface with a full width at half maximum of 15 mm and averaged across participants using a non-rigid high- dimensional spherical averaging method to align cortical folding patterns (39). This procedure provides accurate matching of morphologically homologous cortical locations among participants on the basis of each individual’s anatomy while minimizing metric distortion, resulting in a measure of cortical thickness for each person at each point on the reconstructed surface. The surface was parceled into distinct regions of interest (ROIs). The cortical-surface model was manually reviewed and edited for accuracy. Minimal editing was performed according to standard, objective rules, including correction of errors in removal of non-brain areas and inclusion of white-matter areas of hypointensity adjacent to the cortical ribbon. Qualitative review and editing were performed, with blinding to the diagnostic status, by an expert neuroanatomist with more than 10 years of experience (Y.L.).

Thickness measures were calculated for 5 a priori selected ROIs which are known to show early atrophic changes in AD, including: entorhinal cortex, orbito- frontal cortex, inferior parietal lobule, inferior and middle temporal cortex and posterior cingulate cortex (17, 19-21). These ROIs were sampled separately for each hemisphere (Figure 1). The total intracranial volume (TIV) was used as the reference to account for possible differences in brain size.

Figure 1. Three-dimensional representations of the 5 ROIs examined in the current study (only right hemisphere is shown). All of the ROIs are visible in the lateral (top) and medial (bottom) views of the gray matter surface

 

Statistics

SPSS v.21 (SPPS Inc., 2013) was used for data analysis. Differences in clinical and demographical measures between groups were examined with χ2 tests and the general linear model (GLM), as appropriate. All regression models were tested for violations of the model assumptions. All dependent variables were normally distributed.

Multivariate GLMs with follow-up univariate post-hoc comparisons performed using F statistics were used to test for differences in ROI thickness between groups. Multivariate GLMs were used to test for main effects of MedDi group, with brain structure (5×2 levels) as the within-subjects factor (i.e., dependent variables), and MedDi group as the between-subjects factor (i.e., independent variable). The multivariate GLM is a statistical test procedure for comparing multivariate (population) means of two or more dependent variables of several groups. Unlike univariate analysis, it uses the variance-covariance between variables in testing the statistical significance of the mean differences, and in testing for interactions among the dependent variables. Age, gender, education, TIV, FH status (positive vs. negative FH), APOE genotype (APOE ε 4 carriers, APOE4+ vs. non carriers, APOE4-) [Model 1], BMI, HOMA-IR and HTN group [Model 2] were examined as confounders using two regression models so as to avoid over-fitting. Only covariates showing significant effects were retained in the models. Confounding variables which showed significant effects on the association between MRI measures and MeDi group were examined for interaction effects in adjusted models that included a 2-way interaction term. For example, to test main effects of MeDi group and MeDi × APOE interactions, GLMs were used, with brain structure (10 levels) as the within- subjects factor, and MeDi and APOE as between-subjects factors. Only significant interaction terms were retained in the model.

Results were considered significant at p<0.05. The multivariate approach controls for the multiple chances to find group differences, and it does so without assuming independence of the dependent variables, yielding corrected p values. We first examined all ROIs together (left and right hemisphere, i.e. 10 levels), and then each hemisphere separately (i.e., 5 levels). For the latter analysis, multivariate results were considered significant at a Bonferroni corrected p=0.05/2=0.025.

Linear regressions were used to evaluate the associations between MRI measures, neuropsychological measures (dependent variables), MeDi scores (independent variable), and the same confounds as above. MRI measures were regressed by age and TIV to generate age- and TIV-adjusted residuals. Neuropsychological measures were regressed by age and education to generate age- and education-adjusted residuals. Results were considered significant at p<0.05.

Results

Subjects

Subjects’ characteristics are found in Table 1. Of the 52 subjects, 20 (39%) showed higher adherence to a Mediterranean diet (MeDi+) and 32 (61%) showed lower adherence (MeDi-). There were no differences between MeDi groups for clinical, demographical and neuropsychological measures, frequency of APOE4 genotype and presence of a FH of AD. The MeDi- group showed a trend towards a higher frequency of HTN+ subjects than the MeDi+ group (p=0.06, Table 1).

Table 1. Demographic and clinical characteristics by MeDi group

Values are mean (SD) unless otherwise specified; Abbreviations: MeDi = Mediterranean diet, lower (MeDi-) vs. higher (MeDi+) adherence 

MeDi group differences on MRI

All regions of interest. A multivariate GLM with 10 ROI measures (i.e., dependent variables), MeDi group (i.e., independent variable), age, gender, education, APOE, FH and TIV (i.e., covariates) showed significant effects of MeDi group on MRI measures (Wilk’s Lambda p=0.015). In this fully corrected model, MeDi+ subjects had overall greater thickness of AD-vulnerable ROIs as compared to MeDi- subjects. Post-hoc analysis for each structure is presented in Table 2. On post-hoc examination, group differences were most pronounced in orbitofrontal cortex (OFC, 9%, p=0.004), entorhinal cortex (EC, 6%, p=0.028) and posterior cingulate cortex (PCC, 4%, p=0.05) of the left hemisphere, and there was a non- significant linear trend for PCC of the right hemisphere (3%, p=0.11).

Table 2. Regional MRI thickness measures by MeDi group

Lower than MeDi+, *p<0.05, **p<0.01 on post-hoc univariate GLM analysis; Abbreviations: EC = entorhinal cortex, IPL = MeDi = inferior parietal lobule, Mediterranean diet, lower (MeDi-) vs. higher (MeDi+) adherence, MTG = middle temporal gyrus, n.a. = not applicable, OFC = orbitofrontal cortex, PCC = posterior cingulate cortex, TIV = total intracranial volume

Gender, education and FH were not significantly associated with MRI measures and did not show interactions with MeDi group. Removing these variables from the model left results unchanged (Wilk’s Lambda p=0.026), with group differences being most pronounced in left OFC (8%, p=0.001), EC (7%, p=0.034) and PCC (4%, p=0.041), and with a trend for right PCC (4%, p=0.10).

While APOE was borderline associated with MRI measures (p=0.12), there was a significant interaction between MeDi and APOE group (Wilk’s Lambda p=0.013). Post-hoc examination showed that the interaction was driven by the fact that APOE4- subjects showing higher MeDi adherence had the greatest ROI thickness of all other subgroups (Figure 2).

Figure 2. Mediterranean diet and APOE genotype interactions on regional MRI thickness

Abbreviations: MeDi = Mediterranean diet group (MeDi- = lower adherence vs MeDi+ = higher adherence), APOE4 = Apolipoprotein E ε4 allele (APOE4- = non carriers, APOE4+ = carriers). MRI measures are age and total intracranial volume- adjusted residuals 

A multivariate GLM with 10 ROI measures (i.e., dependent variables), MeDi group (i.e., independent variable), age, TIV, BMI, HOMA-IR, and HTN group (i.e., covariates) left results substantially unchanged (Wilk’s Lambda p=0.029), with MeDi+ subjects showing overall greater ROI thickness than MeDi- subjects, with most pronounced group differences in left OFC (p=0.004) and EC (p=0.028). BMI, HOMA-IR and HTN were not significantly associated with MRI measures and did not show interactions with MeDi group. Removing these variables resulted in the same results as with Model 1 (age and TIV-adjusted data).

Left hemisphere. A multivariate GLM with 5 ROI measures, MeDi group, and covariates confirmed results from the entire ROI data set (Wilk’s Lambda p=0.003; Table 2). Gender, education, FH, BMI, HOMA-IR and HTN were not significantly associated with MRI measures of the left hemisphere. Removing these variables from the model left results unchanged (Wilk’s Lambda p=0.002), with more pronounced group differences in OFC (p=0.002), EC (p=0.02) and PCC (p=0.04). The interaction between MeDi and APOE group remained significant (Wilk’s Lambda p=0.02; Figure 2).

Right hemisphere. A multivariate GLM with 5 ROI measures, MeDi group, and covariates showed no significant effects of MeDi group on MRI measures (Wilk’s Lambda p=0.56, n.s., Table 2). None of the covariates, except for TIV, were significantly associated with MRI measures of the right hemisphere. Results remained unchanged after removing non-significant confounds from the model (Wilk’s Lambda p=0.55, n.s.).

Correlations between MeDi scores and MRI measures

MeDi scores were significantly associated with OFC, EC and PCC of the left hemisphere, with and without correcting for covariates (Figure 3). For every unit increase in MeDi scores, thickness of OFC increased by β=0.51 units (R2=0.28, p<0.001), EC by β=0.25 units (R2=0.07, p=0.05), and PCC by β=0.28 units (R2=0.08, p=0.04; Figure 3). Given the semi-categorical nature of the MeDi scores, non-parametric tests were used to confirm these associations (Spearman’s rho: OFC σ=0.47, p<0.001, EC σ=0.26, p=0.03, PCC σ=0.29, p=0.02).

Figure 3. Associations between Mediterranean diet scores and regional MRI thickness

MRI measures are age and total intracranial volume-adjusted residuals

Correlations between MeDi scores, clinical and cognitive measures

Controlling for age, higher MeDi scores were associated with a smaller hip-to-waist ratio (β=-0.25, p=0.03) and were borderline associated with lower plasma insulin and triglycerides levels (β=-0.18 and β=- 0.13, p<0.09). There were no significant associations between MeDi score and neuropsychological measures, with or without controlling for covariates. The MRI scans of two representative cases showing higher vs. lower adherence to the MeDi are shown in Figure 4.

Figure 4. MRI scans of two representative NL cases showing higher vs. lower adherence to the MeDi

Participants were 52 and 50 year old, respectively, with MMSE>28, education>12 y, normal cognitive test performance by age and education. The MeDi+ subject shows no ventricular enlargement or hippocampal atrophy by age. The MeDi- subject shows mild ventricular enlargement, hippocampal and temporal cortex atrophy by age (arrows)

Discussion

Among young to late middle aged NL individuals, lower adherence to a MeDiet was associated with structural MRI-based cortical thinning (i.e., atrophy) in key AD-regions as compared to a higher adherence. These effects were restricted to brain areas of the left hemisphere, and were most pronounced in OFC, EC and PCC. These results were independent of possible risk factors for LOAD such as age, gender, education, APOE genotype, FH, as well as of BMI, insulin resistance and hypertension.

Prospective studies have provided evidence for a favorable relation of a MeDi-type diet with slower cognitive decline, reduced risk of progression from mild cognitive impairment (MCI) to AD, lower risk of AD, and reduced mortality in AD patients (3, 4, 8-12). These effects were independent of physical activity (40) and were not mediated by vascular comorbidity (10).

Various nutrients have been associated with the MeDi pattern, including B-complex vitamins, antioxidants, vitamin D, and polyunsaturated fatty acids (PUFA), which are all known to have neuro-protective effects ranging from anti-oxidant, anti-inflammatory and Aβ anti-oligomerization properties, to vasculo-protective and synaptic plasticity-enhancing effects, and to modulation of vascular endothelial factor expression, angiogenin, and advanced glycation end products (41-47). Conversely, higher intake of saturated fats is known to have negative effects on cardiovascular function (5, 7).

Our findings of increased cortical thinning in NL showing lower adherence to the MeDi are consistent with epidemiological findings, and provide a possible pathophysiological substrate to the clinical data. Moreover, while all our subjects had lab values within normal limits and MeDi groups were comparable for clinical measures, lower MeDi scores were associated with larger hip-to-waist ratios and, to a lesser extent, with higher plasma insulin and triglycerides levels, which lends further support to prior observations of less favorable medical profiles.

MeDi effects on MRI biomarkers were significant in the left, but not in the right hemisphere, and were most pronounced in OFC, EC and PCC. Previous MRI studies have shown that atrophic changes in these AD-vulnerable regions, especially of the left hemisphere, are associated with increased risk for developing memory impairments and dementia (48-51). Given the known relationship between brain atrophy and onset of clinical symptoms in AD (17), our data suggests that the pathological AD process leading to neuronal loss may be influenced by modifiable lifestyle practices, such as a healthy diet, during the normal stages of cognition. Additionally, a novel association between MeDi and APOE status was observed, as APOE4- showing higher adherence to the MeDi diet had the largest ROI thickness of all other subgroups. The APOE ε4 genotype is a well-established risk factor for late-onset AD and has been associated with increased brain atrophy in NL elderly (52). To our knowledge, there are no prior investigations of interactive effects of APOE status and MeDi diet on MRI biomarkers in NL individuals. Within the MeDi+ group, APOE4- showed greater cortical thickness than APOE4+, whereas no APOE group differences were observed within the MeDi- group. These findings suggest that diet may have greater impact on APOE4-, as APOE4+ subjects seem to develop brain atrophy regardless of diet, while APOE4- may put themselves at greater risk for AD- related brain changes by not following a healthy diet. These findings are consistent with previous reports of more beneficial effects of physical activity for APOE4- than APOE4+ individuals (53), although results are not always consistent (54, 55). More studies with larger samples and longitudinal follow-ups are warranted to replicate our preliminary research studies and to specifically examine the effects of APOE status on dietary patterns in AD, and whether the relationship varies with age and disease.

The biological mechanisms for the observed associations between MeDi and cortical thickness remain to be clarified. In the adjusted models, the association between MeDi and MRI features was essentially unchanged by including age, gender, education, presence of family history, APOE status, BMI, insulin resistance scores and presence of hypertension as possible confounds. These data suggest that MeDi is a protective factor independent of traditional AD risk factors. Other studies are needed to assess whether the observed association would change depending on additional factors such as vascular structure/function or markers of inflammation (56).

Most, if not all participants reported stability of their dietary patterns over the past 2-5 years. Examination of our records showed that approximately 90% of the surveyed participants have been living the lifestyle reported in the surveys for 5 years or more, with a very conscientious focus on their diet and food choices.

Approximately 8% of those surveyed reported their nutritional intake to be a lifestyle span of about 2-5 years. Only 1 participant in the MeDi- group reported their nutritional behavior starting within the last 1-2 years. Overall, our MeDi+ cohort included people for whom the MeDi was their normal dietary pattern, and most of the MeDi+ participants reported following the MeDi since childhood. Previous longitudinal studies of the MeDi with repeated dietary assessments over up to 13 years, demonstrated that adherence to the MeDi is remarkably stable over time, especially in healthy individuals (10, 57, 58). However, while we consider it more likely that the MeDi adherence reported reflects our population’s longstanding dietary habits, because of the synchronous timing of dietary and MRI assessments and the cross- sectional nature of our study, we cannot exclude that adherence the MeDi may be a more recent lifestyle choice in our cohort. Since this is the first study demonstrating an association between the MeDi and MRI biomarkers of AD in a relatively young NL population, future studies are needed to replicate our preliminary research findings, to test whether cortical thickness changes only after long- term exposure to certain ingredients of MeDi (e.g., vitamin B, antioxidants, etc.) or whether short-term exposure is sufficient to preserve brain volumes, and whether adherence to the MeDi from a very young age would be particularly beneficial to healthy brain aging. These biomarker findings are valuable for future research studies as well as possible randomized clinical trials in which participants are assigned to a standard low-fat diet vs. MeDi, with change in ROI thickness being a primary endpoint or outcome measure.

MeDi scores were not associated with neuropsychological measures, most likely because our subjects were cognitively normal and all high-school graduates, which resulted in a “ceiling-effect”. As such, present cross-sectional findings do not offer information on risk of future AD in our NL cohort. Longitudinal studies with larger samples are warranted to determine whether reduced brain thickness in MeDi- vs. MeDi+ subjects is predictive of cognitive decline, and whether the relationship between MeDi, AD-biomarkers and cognitive performance varies with age and disease. Our preliminary results provide the rationale for performing a larger, longitudinal study to assess how diet, biomarkers and risk factors of AD modulate AD-risk years in advance of possible clinical symptoms. We caution that present results were found in small numbers of carefully screened subjects under controlled clinical conditions. Most participants belonged to middle-class; none were smokers, diabetics, met criteria for obesity or had significant cardiovascular disease. Replication of these preliminary findings in community-based populations with more diversified socio-economic and medical status, as well as with other biomarkers of AD, particularly of AD pathology (i.e., amyloid beta and neurofibrillary tangles), is warranted and clinical application is not justified.

 

Conclusions

Our biomarker findings provide biological evidence in support of epidemiological studies showing that the MeDi diet may be protective against AD. In our study, lower adherence to the MeDi was associated with increased atrophy of key brain regions for AD among NL individuals, which provides support for further exploration of dietary behavior as a possible AD prevention strategy.

 

Acknowledgments: This study was supported by NIH/NIA grants AG035137, AG13616 and P30AG008051.

Competing interests: Disclosures: Dr. Mosconi has a patent on a technology that was licensed to Abiant Inc. by NYU and, as such, has a financial interest in this license agreement and hold stock and stock options on the company. Dr. Mosconi has received compensation for consulting services from Abiant Inc. Mr. Murray reports no disclosures. Dr. Tsui has a patent on a technology that was licensed to Abiant Inc. by NYU and, as such, has a financial interest in this license agreement and hold stock and stock options on the company. Dr. Li has received compensation for consulting services from Abiant Inc. Ms. Davies reports no disclosures. Ms. Williams reports no disclosures. Dr. Pirraglia reports no disclosures. Dr. Osorio reports no disclosures. Dr. Glodzik has received honoraria from the French Alzheimer Foundation and was PI on an investigator initiated clinical trial supported by Forrest Labs. Dr. McHugh was PI on an investigator initiated clinical trial supported by Bayer Healthcare Pharmaceuticals. Dr. de Leon has a patent on a technology that was licensed to Abiant Inc. by NYU and, as such, has a financial interest in this license agreement and hold stock and stock options on the company. Dr. de Leon has received compensation for consulting services from Abiant Inc., has received honoraria from the French Alzheimer Foundation, and was PI on an investigator initiated clinical trial supported by Neuroptix.

Contributions: Dr. Mosconi – study concept and design, acquisition of data, analysis and interpretation, critical revision of the manuscript for important intellectual content, study supervision. Mr. Murray – acquisition of data, analysis and interpretation, critical revision of the manuscript for important intellectual content. Dr. Tsui – acquisition of data, analysis and interpretation, critical revision of the manuscript for important intellectual content. Dr. Li – acquisition of data, analysis and interpretation, critical revision of the manuscript for important intellectual content. Ms. Davies – acquisition of data, critical revision of the manuscript for important intellectual content. Ms. Williams – acquisition of data, analysis and interpretation, study supervision. Dr. Pirraglia – analysis and interpretation, critical revision of the manuscript for important intellectual content. Dr. Osorio – acquisition of data, analysis and interpretation, critical revision of the manuscript for important intellectual content. Dr. Glodzik – study concept and design, analysis and interpretation, critical revision of the manuscript for important intellectual content. Dr. McHugh – study concept and design, acquisition of data, critical revision of the manuscript for important intellectual content. Dr. de Leon – study concept and design, analysis and interpretation, critical revision of the manuscript for important intellectual content. Statistical Analyses were done by Lisa Mosconi and Elizabeth Pirraglia

Study funding: This study was supported by NIH/NIA grants AG035137, AG13616 and P30AG008051.

 

References

  1. Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurol 2011;10, 819-828.
  2. Sperling RA, Karlawish J, Johnson KA. Preclinical Alzheimer disease-the challenges ahead. Nat Rev Neurol 2013;9, 54-58.

  3. Gu Y, Nieves JW, Stern Y, Luchsinger JA, Scarmeas N. Food combination and Alzheimer disease risk: a protective diet. Arch Neurol 2010;67, 699-706.

  4. Gu Y, Scarmeas N. Dietary patterns in Alzheimer’s disease and cognitive aging. Curr Alzheimer Res 2011;8, 510-519.
  5. Kalmijn S, Launer LJ, Ott A, Witteman JC, Hofman A, Breteler MM. Dietary fat intake and the risk of incident dementia in the Rotterdam Study. Ann Neurol 1997;42, 776-782.
  6. Kesse-Guyot E, Andreeva VA, Ducros V, Jeandel C, Julia C, Hercberg S, Galan P. Carotenoid-rich dietary patterns during midlife and subsequent cognitive function. Br J Nutr, 2013;1-9.
  7. Morris MC, Evans DA, Bienias JL, Tangney CC, Bennett DA, Aggarwal N, Schneider J, Wilson RS. Dietary fats and the risk of incident Alzheimer disease. Arch Neurol 2003;60, 194-200.
  8. Feart C, Samieri C, Rondeau V, Amieva H, Portet F, Dartigues JF, Scarmeas N, Barberger-Gateau P. Adherence to a Mediterranean diet, cognitive decline, and risk of dementia. JAMA 2009;302, 638-648.
  9. Kesse-Guyot E, Andreeva VA, Lassale C, Ferry M, Jeandel C, Hercberg S, Galan P. Mediterranean diet and cognitive function: a French study. Am J Clin Nutr 2013;97, 369-376.
  10. Scarmeas N, Stern Y, Mayeux R, Luchsinger JA. Mediterranean diet, Alzheimer disease, and vascular mediation. Arch Neurol 2006;63, 1709-1717.
  11. Scarmeas N, Stern Y, Mayeux R, Manly JJ, Schupf N, Luchsinger JA. Mediterranean diet and mild cognitive impairment. Arch Neurol 2009;66, 216-225.
  12. Trichopoulou A, Costacou T, Bamia C, Trichopoulos D. Adherence to a Mediterranean diet and survival in a Greek population. N Engl J Med 2003;348, 2599-2608.
  13. Scarmeas N, Luchsinger JA, Mayeux R, Stern Y. Mediterranean diet and Alzheimer disease mortality. Neurology 2007;69, 1084-1093.
  14. Sofi F, Cesari F, Abbate R, Gensini GF, Casini A. Adherence to Mediterranean diet and health status: meta-analysis. BMJ 2008;337, a1344.
  15. Estruch R, Ros E, Martinez-Gonzalez MA. Mediterranean diet for primary prevention of cardiovascular disease. N Engl J Med 2013;369, 676-677.
  16. Morris MC, Tangney CC. A potential design flaw of randomized trials of vitamin supplements. JAMA 2011;305, 1348-1349.
  17. Jack CR, Jr., Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, Petersen RC, Trojanowski JQ. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 2010;9, 119-128.
  18. Morrison JH, Hof PR. Life and death of neurons in the aging brain. Science 1997;278, 412-419.
  19. Fox NC, Warrington EK, Freeborough PA, Hartikainen P, Kennedy AM, Stevens JM, Rossor MN. Presymptomatic hippocampal atrophy in Alzheimer’s disease. A longitudinal MRI study. Brain 119 ( Pt 6), 1996;2001- 2007.
  20. Holland D, Brewer JB, Hagler DJ, Fennema-Notestine C, Dale AM. Subregional neuroanatomical change as a biomarker for Alzheimer’s disease. Proc Natl Acad Sci U S A 2009;106, 20954-20959.
  21. Jack CR, Jr., Vemuri P, Wiste HJ, Weigand SD, Lesnick TG, Lowe V, Kantarci K, Bernstein MA, Senjem ML, Gunter JL, Boeve BF, Trojanowski JQ, Shaw LM, Aisen PS, Weiner MW, Petersen RC, Knopman DS. Shapes of the Trajectories of 5 Major Biomarkers of Alzheimer Disease. Arch Neurol 2012.
  22. Scarmeas N, Luchsinger JA, Stern Y, Gu Y, He J, DeCarli C, Brown T, Brickman AM. Mediterranean diet and magnetic resonance imaging-assessed cerebrovascular disease. Ann Neurol 2011;69, 257-268.
  23. Gardener H, Scarmeas N, Gu Y, Boden-Albala B, Elkind MS, Sacco RL, DeCarli C, Wright CB. Mediterranean diet and white matter hyperintensity volume in the Northern Manhattan Study. Arch Neurol 2012;69, 251-256.
  24. Mosconi L, Brys M, Switalski R, Mistur R, Glodzik L, Pirraglia E, Tsui W, De Santi S, de Leon MJ. Maternal family history of Alzheimer’s disease predisposes to reduced brain glucose metabolism. Proc Natl Acad Sci USA 2007;104, 19067-19072.
  25. Lann D, LeRoith D. Insulin resistance as the underlying cause for the metabolic syndrome. Med Clin North Am 2007;91, 1063-1077, viii.
  26. Convit A, Wolf OT, Tarshish C, de Leon MJ. Reduced glucose tolerance is associated with poor memory performance and hippocampal atrophy among normal elderly. Proc Natl Acad Sci U S A 2003;100, 2019-2022.
  27. Glodzik L, Mosconi L, Tsui W, de Santi S, Zinkowski R, Pirraglia E, Rich KE, McHugh P, Li Y, Williams S, Ali F, Zetterberg H, Blennow K, Mehta P, de Leon MJ. Alzheimer’s disease markers, hypertension, and gray matter damage in normal elderly. Neurobiol Aging, 2011.
  28. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL, Jr., Jones DW, Materson BJ, Oparil S, Wright JT, Jr., Roccella EJ. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA 2003;289, 2560-2572.
  29. de Leon MJ, Convit A, Wolf OT, Tarshish CY, DeSanti S, Rusinek H, Tsui W, Kandil E, Scherer AJ, Roche A, Imossi A, Thorn E, Bobinski M, Caraos C, Lesbre P, Schlyer D, Poirier J, Reisberg B, Fowler J. Prediction of cognitive decline in normal elderly subjects with 2-[(18)F]fluoro-2-deoxy-D- glucose/poitron-emission tomography (FDG/PET). Proc Natl Acad Sci U S A 2001;98, 10966-10971.
  30. Saunders AM, Strittmatter WJ, Schmechel D, George-Hyslop PH, Pericak- Vance MA, Joo SH, Rosi BL, Gusella JF, Crapper-MacLachlan DR, Alberts MJ, et al. Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer’s disease. Neurology 1993;43, 1467-1472.
  31. Willett WC, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J, Hennekens CH, Speizer FE. Reproducibility and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol 1985;122, 51-65.
  32. Willett WC. Accuracy of food-frequency questionnaires. Am J Clin Nutr 2000;72, 1234-1236.
  33. Willett WC, Hu FB. The food frequency questionnaire. Cancer Epidemiol Biomarkers Prev 2007;16, 182-183.
  34. Willett WC, Reynolds RD, Cottrell-Hoehner S, Sampson L, Browne ML. Validation of a semi-quantitative food frequency questionnaire: comparison with a 1-year diet record. J Am Diet Assoc 1987;87, 43-47.
  35. Jacques PF, Sulsky SI, Sadowski JA, Phillips JC, Rush D, Willett WC Comparison of micronutrient intake measured by a dietary questionnaire and biochemical indicators of micronutrient status. Am J Clin Nutr 1993;57, 182-189.
  36. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 1999;9, 179-194.
  37. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 2000;97, 11050- 11055.
  38. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33, 341-355.
  39. Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 1999;9, 195- 207.
  40. Scarmeas N, Luchsinger JA, Schupf N, Brickman AM, Cosentino S, Tang MX, Stern Y. Physical activity, diet, and risk of Alzheimer disease. JAMA 2009;302, 627-637.
  41. de Oliveira BF, Veloso CA, Nogueira-Machado JA, de Moraes EN, Santos RR, Cintra MT, Chaves MM. Ascorbic acid, alpha-tocopherol, and beta-carotene reduce oxidative stress and proinflammatory cytokines in mononuclear cells of Alzheimer’s disease patients. Nutr Neurosci, 2012.
  42. Durga J, van Boxtel MP, Schouten EG, Kok FJ, Jolles J, Katan MB, Verhoef P. Effect of 3-year folic acid supplementation on cognitive function in older adults in the FACIT trial: a randomised, double blind, controlled trial. Lancet 2007;369, 208-216.
  43. Johnson EJ. The role of carotenoids in human health. Nutr Clin Care 2002;5, 56-65.
  44. Luchsinger JA, Tang MX, Miller J, Green R, Mayeux R. Relation of higher folate intake to lower risk of Alzheimer disease in the elderly. Arch Neurol 2007;64, 86-92.
  45. Morris MC, Evans DA, Bienias JL, Tangney CC, Hebert LE, Scherr PA, Schneider JA. Dietary folate and vitamin B12 intake and cognitive decline among community-dwelling older persons. Arch Neurol 2005;62, 641-645.
  46. Takasaki J, Ono K, Yoshiike Y, Hirohata M, Ikeda T, Morinaga A, Takashima A, Yamada M. Vitamin A has anti-oligomerization effects on amyloid-beta in vitro. J Alzheimers Dis 2011;27, 271-280.
  47. Balion C, Griffith LE, Strifler L, Henderson M, Patterson C, Heckman G, Llewellyn DJ, Raina P. Vitamin D, cognition, and dementia: a systematic review and meta-analysis. Neurology 2012;79, 1397-1405.
  48. Chetelat G, Landeau B, Eustache F, Mezenge F, Viader F, de la Sayette V, Desgranges B, Baron JC. Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study. Neuroimage 22005;7, 934-946.
  49. Galton CJ, Patterson K, Xuereb JH, Hodges JR. Atypical and typical presentations of Alzheimer’s disease: a clinical, neuropsychological, neuroimaging and pathological study of 13 cases. Brain 123 Pt 2000;3, 484-498.
  50. Risacher SL, Shen L, West JD, Kim S, McDonald BC, Beckett LA, Harvey DJ, Jack CR, Jr., Weiner MW, Saykin AJ. Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort. Neurobiol Aging 2010;31, 1401-1418.
  51. Querbes O, Aubry F, Pariente J, Lotterie JA, Demonet JF, Duret V, Puel M, Berry I, Fort JC, Celsis P. Early diagnosis of Alzheimer’s disease using cortical thickness: impact of cognitive reserve. Brain 2009;132, 2036-2047.
  52. Reiman EM, Uecker A, Caselli RJ, Lewis S, Bandy D, de Leon MJ, De Santi S, Convit A, Osborne D, Weaver A, Thibodeau SN. Hippocampal volumes in cognitively normal persons at genetic risk for Alzheimer’s disease. Ann Neurol 1998;44, 288-291.
  53. Podewils LJ, Guallar E, Kuller LH, Fried LP, Lopez OL, Carlson M, Lyketsos CG. Physical activity, APOE genotype, and dementia risk: findings from theCardiovascular Health Cognition Study. Am J Epidemiol 2005;161, 639-651.
  54. Liang KY, Mintun MA, Fagan AM, Goate AM, Bugg JM, Holtzman DM, Morris JC, Head D. Exercise and Alzheimer’s disease biomarkers in cognitively normal older adults. Ann Neurol 2010;68, 311-318.
  55. Head D, Bugg JM, Goate AM, Fagan AM, Mintun MA, Benzinger T, Holtzman DM, Morris JC. Exercise Engagement as a Moderator of the Effects of APOE Genotype on Amyloid Deposition. Arch Neurol 2012;69, 636-643.
  56. Gu Y, Luchsinger JA, Stern Y, Scarmeas N. Mediterranean diet, inflammatory and metabolic biomarkers, and risk of Alzheimer’s disease. J Alzheimers Dis 2010;22, 483-492.
  57. Luchsinger JA, Tang MX, Shea S, Mayeux R. Caloric intake and the risk of Alzheimer disease. Arch Neurol 2002;59, 1258-1263.

  58. Luchsinger JA, Tang MX, Siddiqui M, Shea S, Mayeux R. Alcohol intake and risk of dementia. J Am Geriatr Soc 2004;52, 540-546.