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DOES TEA DRINKING PROMOTE HEALTH OF OLDER ADULTS: EVIDENCE FROM THE CHINA HEALTH AND NUTRITION SURVEY

 
J. Wang1, Q. Wei1, X. Wan2
 

1. School of Management, Wuhan Institute of Technology, Wuhan, China; 2. Institute of Income Distribution and Public Finance, School of Taxation and Public Finance, Zhongnan University of Economics and Law, Wuhan, China

Corresponding Authors: Xin Wan, Associate Professor, Institute of Income Distribution and Public Finance, School of Public Finance and Taxation, Zhongnan University of Economics and Law, Wuhan, China, 430073, Email: gracewanxin@zuel.edu.cn

J Prev Alz Dis 2020;
Published online November 28, 2020, http://dx.doi.org/10.14283/jpad.2020.67

 


Abstract

Objective: This study selects the health indicators of older adults to analyze the impact of tea drinking on health. Design: This is a panel data.
Setting: This study uses data from China Health and Nutrition Survey (CHNS), which covers nine provinces and ten waves, between 1997 and 2015.
Participants: a total of 706 old adults are consistently surveyed in six surveys on issues such as health and nutrition.
Measurements: Health of old adults is assessed by self-reported health (SRH), tea drinking is 0-1 dummy variable, and also analyze with the frequency of tea drinking. This study uses ordered probit model to analyze the influence of tea drinking on SRH.
Results: Findings reveal a significant negative correlation between tea drinking and SRH of older adults. It is shows that the significant positive correlation exists between the tea drinking frequency and SRH, but the quadratic term of tea frequency shows the significant negative correlation. It means drinking tea benefits older adults in terms of improved health, but excessive consumption of tea is not healthy for them. The heterogeneity analyses reveal that there are no significant geographic, tea-drinking pattern or gender differences in the conclusion that tea drinking is good for older adults’ health.
Conclusion: In this study, we find correlation between tea drinking and SRH of older adults, and tea drinking is beneficial toward the improvement of SRH, but drinking tea in excess is not good for older adults’ health.

Key words: Tea drinking, self-reported health, frequency of tea drinking, heterogeneity, older adults.


 

Introduction

The proposal of “international tea day” adopted at the 41st session of Food and Agriculture Organization of the United Nations in 2019 has a far-reaching and significant impact on the development of the global tea industry and the promotion of tea culture. In China, with the improvement of residents’ living standards, tea has been widely consumed as a daily social and common beverage. The healthy function of tea has been gradually recognized by the academic circles under the multidisciplinary cross and penetration (1). However, a detailed review of the relevant literature at home and abroad shows no consensus on the health effects of the elders of tea drinking despite the wide range of individual experimental data. The effect of tea drinking on individual health varies when different countries, certain diseases, and other research methods are involved.
Following the year 2006, China became the country with the largest total tea consumption, and the problem of ageing is also becoming more serious and the health of older adults is of great concern. So the effect of tea drinking on health of older adults in China is a meaningful and valuable topic, which complements the current research and analysis on the role of tea drinking. This study selects the health indicators of older adults to analyze the impact of tea drinking on health. The innovations of this study are as follows: First, the research samples used are representative, rich, and effective. The large-scale epidemiological investigation on the influence of tea drinking in China has not been executed, and the conclusions of the existing research on tea drinking in a small range are inconclusive. Data in this study are derived from the CHNS. The data covered cities and towns across all cardinal points of China from 1989 to 2015. CHNS database includes demographic characteristics, economy, public resources, and health indicators, which meet the needs of this research. Second, individual SRH is used as the dependent variable to study the effect of tea drinking. SRH is used as an effective and popular measure of individual health, although other undiscovered or proven effects of tea drinking can also be used. Third, the analytical method is scientific. In this study, the ordered probit model is used for analysis. Considering the possible endogenous problems between tea drinking and SRH, the robustness test is conducted by employing methods, such as lag period. This study also analyzes the heterogeneity of tea drinking effects for different gender groups to enrich the research content.

 

Data and model

Model settings

Because the dependent variable involves four kinds of discrete values, the ordered probit model should be adopted, which is widely used to deal with multiple kinds of discrete data. The empirical model is set as follows:
healthi,t=α+βteai,t +θXi,t +γϕt +ηδi+μi,t, (1)
wherehealthi,t denotes the SRH for individual i surveyed in wave t, which is measured by ordered qualitative labels, such as “very good” (the value is 1), “good” (the value is 2), “fair” (the value is 3), and “poor” (the value is 4). SRH is a commonly used health measurement index, which reflects the comprehensive condition of personal subjective and objective health and can provide supportive information for the decision makers of disease prevention to an extent (2). SRH measure is inexpensive, can easily collect data, and is synthetic by construction. teai,t is the key variable, which is a dummy indicating the tea drinking of individual i in wave t. This study also examines the effects of the frequency of tea drinking on health. tea frequencyi,t denotes the tea drinking frequency of individual i in wave t, which is measured by ordered qualitative labels, such as “almost every day” and “four to five times a week.” The values are 1−7 in order.
Xi,t represents a series of control variables, including personal basic information, chronic disease, individual habits, dietary and activity preferences, healthy exercise and nutritional diet cognition. Personal basic information is the control variable for health economics (2, 3), including age, gender, height, weight, marital status, education, medical insurance, household registration type, income and household sanitation. Medical insurance and household registration are the dummy indicating. Income is measured by personal annual income deflator. The variable of household sanitation is measured by two dummy variables of having running water and flush toilet. Chronic diseases are important factors affecting the elders’ health and harm the brain, heart, kidney, and other important organs. Having hypertension, diabetes, myocardial infarction, stroke, and fracture are five dummy variables indicating chronic diseases. Individual habits are measured by two dummy variables of smoking and drinking. Diet and activity preferences also have indirect effects on health, which are measured by six dummy variables of preferences. The cognitions of healthy exercise and diet indirectly affect health, which are dummy variables. φt is wave fixed effects, δi is individual fixed effects, and μi,t is the error term.

Data description

The data used in this study were obtained from the CHNS, which is an ongoing international collaborative project among the Carolina Population Center at the University of North Carolina at Chapel Hill, the National Institute of Nutrition and Food Safety, and the Chinese Center for Disease Control and Prevention. The survey covered nine provinces, including Liaoning, Heilongjiang, Shandong, Jiangsu, Henan, Hubei, Hunan, Guangxi and Guizhou, including the waves 1989, 1991, 1993, 1995, 1997, 2000, 2004, 2006, 2009, and 2015. When the survey was conducted, a multistage, random cluster process was used to draw the sample surveyed in each province. Approximately 4,400 households were listed in the overall tracking survey, covering 19,000 individuals. Rich personal and family microdata of older adults are 65 years of age and older provided rich and detailed data for the research. The study constructed panel data and omitted data prior to years 1997 and the year of 2015 because of data of tea drinking missing. Tea drinking was derived from the question “Do you normally drink tea?” SRH was obtained from the question “Right now, how would you describe your health compared to that of other people your age?” This study excluded individuals who reported “unknown” from the sample. The results of variable statistical characteristics are presented in Table 1.

Table 1. Descriptive statistics

 

Results and related tests

Basic regression results

Column (1) and (2) in Table 2 shows the regression results of the ordered probit model with wave fixed effect, individual fixed effect, and different control variables that are successfully added. No matter how the control variables change, the significance and sign of the estimated coefficients of dummy variables of tea drinking remain unchanged. The results indicate that the estimated coefficient of dummy variable of tea drinking is significantly negative, which means drinking tea can significantly improve SRH of older adults compared with non-tea drinkers.
Considering the possible influence of chronic diseases, individual habits, dietary and activity preferences, cognitions of healthy exercise and diet on individual health status, this study further extends the basic regression and adds the above variables into analysis respectively. The results are shown in Columns (3), (4), (5) and (6) of Table 2. All regressions in Table 2 show that regardless of how the variables are added, the estimated coefficients of dummy variables of tea drinking are significant and negative, which are correlated with the dependent variables in all regressions. Therefore, tea drinking is beneficial to SRH of older adults. The reliability of the conclusion is further verified by various robustness tests.

Table 2. Tea drinking and SRH

Note: 1. ***, **, and * indicate 1%, 5%, and 10% significance levels. 2. p-value in parentheses.

 

Robustness test

To verify the robustness of the basic conclusion, the following analysis is considered. First, change the income variable. In certain families old adults may not earn income, but they consume total family income. In many rural families, personal income cannot be separated from family income. Deaton (2003) shows that equalized family income can well measure the utilization of family resources by family members (4). Therefore, this study re-measures individual income with per capita family income deflator. The regression results are shown in Column (1) in Table 3. The beneficial effect of tea drinking on SRH of older adults remains unchanged even after changing the income variable.

Table 3. Robustness test

Note: 1. ***, **, and * indicate 1%, 5%, and 10% significance levels. 2. p-value in parentheses.

 

Second, possible endogenous effects. Considering the effect of tea drinking in the early stage on health in the later stage, a problem in which explanatory variables are related to random error terms may arise. To solve this problem, explanatory variables with a lag of two periods from existing research are analyzed. The analysis results are presented in Column (2) in Table 3. After adding the control variables, the estimated coefficient of variable of tea drinking with a lag of two periods remains significantly negative.
Third, health dynamics. In view of the dynamic status of individual health, which is the past health status, may affect the current health status. Individual SRH with a lagged period is added into the model as a new control variable for regression. The results are shown in Columns (3) in Table 3. The regression results have once again confirmed that the estimated coefficient of tea drinking is significant and negative.
Fourth, change the dependent variable. Etilé and Milcent (2010) study the health samples in France and find that people with high income likely underestimate their health status because they have high health expectations (5). People with low income overestimate their health status for the lack of relevant health information and the low level of education. They think two categories of SRH indicators (“good” and “bad”) make sense. The treatment method is as follows: SRH answers of “very good” and “good” are classified into the same category, and new variable health2 is defined as “good” whose value is 1. The rest is 0. Regression results are displayed in Columns (4) in Table 3. In the regressions, SRH with one lag period is added simultaneously. Per capita household income variable is added into the regression of Column (4). The results reveal a significant positive correlation between the dummy variables of tea drinking and binary SRH variables. This finding is consistent with the basic conclusion because the value of dependent variable is 1 when SRH is good, indicating that drinking tea can help improve SRH. All the robust regression results have further verified the beneficial effects of drinking tea on SRH of older adults.

Table 4. Results of the extended analysis

Note: 1. ***, **, and * indicate 1%, 5%, and 10% significance levels. 2. p-value in parentheses.

 

Discussion

Effect of drinking tea frequency

Tea polyphenols have beneficial effects at a certain level in the body. Heavy tea drinking by irregular steps leads to the ups and downs of components such as polyphenols, which offer far fewer healthy benefits than continuous drinking tea. Different ways of drinking tea can directly affect the curative and prevention effect of effective substances in tea, which is also one of the important reasons for the difference in the anti-cancer effect of tea drinking between eastern and western countries. On the basis of large-scale data in China, the study demonstrates whether the sustainability of drinking tea is conducive to the improvement of personal SRH of older adults.
Columns (1) and (2) in Table 4 report the effect of tea drinking frequency on the SRH of old adults. The results show that no matter how the control variables are adjusted, the significant positive correlation exists between the tea drinking frequency and SRH. But when the variables of chronic diseases are added in the model, the quadratic term of tea frequency shows the significant negative correlation with SRH. The results show that tea needs to be consumed frequently enough to reach some cumulative amount in order to contribute to the health of older adults, however, excessive consumption of tea is not healthy for older adults. The increase in tea drinking frequency indicates the stable existence of the healthy components in the body, which is beneficial for improving human body function and immunity.

Heterogeneity analysis

Locational factors

China has a vast territory, and cities in the north and south regions are divided by the Qinling Mountains-Huaihe River line. Northern regions include Liaoning, Heilongjiang, and Shandong; the rest are southern regions. The geographical location, climate characteristics, historical culture, political economy, and other aspects of the north and south are evidently different. Generally speaking, southerners are graceful and restrained. They pay attention to the process of producing tea and the utensils of making tea. Northerners are bold, unconstrained, are indifferent about the cultural attributes of drinking tea. Most northern regions do not produce tea. The most popular kinds of Chinese tea are from the South. Southerners prefer various kinds of tea, such as green, black, and white tea. Northern climate difference is large that southerners prefer black tea to warm their stomach when the weather is cold. By contrast, green tea is preferred in the summer. The bioavailability of the active components of tea and species differences in their functions may lead to different effects on people, whereas that of the active components of tea leaves may be different in the human body (1). Studies suggest that green tea is better than black tea in preventing certain diseases and improving health (6). The availability of tea polyphenols, catechins, and other components in tea drinking depends on the type of tea and how it is processed. As a result, the study analyzes the heterogeneity effect of tea drinking on SRH between north and south regions. A dummy variable, whose value of south regions are 1, and the rest are 0, is constructed. The interaction term of dummy variable and tea drinking variable is also added. The regression results suggest that interaction items are insignificant, when personal basic information, individual fixed effect and wave fixed effect are added. Therefore, no difference is observed in the effect of the different ways and types of tea drinking on SRH in the north and south of China. The most direct reason for the result is that the data do not involve the type of tea drinking.

Gender difference

It reveal that tea drinking may reduce iron absorption and increase the risk of iron deficiency, especially in vulnerable groups, for tea polyphenols combined with iron (7, 8). The present study discusses whether the conclusion of tea drinking is beneficial to SRH of older adults varies with gender in China. According to Model (1), the sample is divided into male and female groups for analysis. The regression results of the ordered probit model are presented in Columns (3) and (4) in Table 4. The analysis in Table 6 adopts per capita family income to measure income. The analysis results of the male and female groups show the estimated coefficients of the dummy variables whether tea drinking is significantly negative. Therefore, the conclusion of improving the health status of older adults by drinking tea is no significant gender differences. However, the coefficient values were greater in the male geriatric group than in the female geriatric group. This indicates that, excluding other controllable factors, tea consumption promotes the health of elderly men slightly better than that of elderly women.

 

Conclusions

Existing studies have neither used a wide range of data on tea drinking and older adults’ health in China to verify the correlation between them nor conducted an in-depth analysis. In this context, CHNS panel data, including demographic characteristics, economy, public resources, health, and other indicators, are selected in the present study. An ordered probit model is also utilized to analyze the relationship between tea drinking and older adults’ SRH. The results are as follows. First, a significant negative correlation exists between tea drinking and SRH, indicating that tea drinking is beneficial to the improvement of older adults’ SRH. In addition to the personal basic information, certain external factors can affect human health. Thus, this study adds dummy control variables of chronic diseases, individual smoking or drinking habits, diet preferences, activity preferences, cognitions of healthy exercise and diet, and performs regression. The results adding these dummy control variables respectively do not change the conclusion of tea drinking improving older adults’ SRH.
Second, the robustness test considers the following factors: change the control variables, eliminate the endogenetic effect, eliminate the possible health dynamic influence and the index of SRH is reprocessed. Different robustness test results also confirm that tea drinking is beneficial to older adults’ health. Third, the increase in the frequency of tea drinking is beneficial to the improvement of older adults’ SRH. Such an increase also indicates the stable existence of health components in the body, which are beneficial for improving the function and immunity of the human body. however, excessive consumption of tea is detrimental to the older adults’ health. Fourth, the conclusion of heterogeneity analysis reveals that no difference exists in the effect of different ways and types of tea drinking on SRH in the north and south of China, so as to gender.

 

Acknowledgement: J. Wang is supported by National Social Science Fund Youth Project of China (Project Number: 18CJY021) and X. Wan is supported by The paper is funded by the Fundamental Research Funds for the Central Universities (NO. 2722020JCG083).

Conflict of interest: The authors declared that they have no conflicts of interest to this work.

Ethical standards: The study used publicly available research data and did not violate ethical standards.

 

References

1. Chen L, Lee M J, Li H, Yang CS. Absorption, distribution, elimination of tea polyphenols in rats. Drug Metab Dispos 1997;25(9):1045-1050
2. Courtemanche C, Marton J, Ukert B, Yelowitz A, Zapata D. Early effects of the affordable care act on health care access, risky health behaviors, and self-assessed health. South Econ J 2018;84(3):660-691
3. Lockwood LM. Incidental bequests and the choice to self-insure late-life risks. Am Econ Rev 2018;108(9):2513-50
4. Deaton A. Health, inequality, and economic development. J Econ Lit 2003;41(1):113-158
5. Etilé F, Milcent C. Income-related reporting heterogeneity in self-assessed health: evidence from France. Health Econ 2010; 5(9):965-981
6. Pathy NB, Peeters P, Gils CV, et al. Coffee and tea intake and risk of breast cancer. Breast Cancer Res and Tre 2010;121(2):461-467
7. Zijp IM, Korver O, Tijburg LBM . Effect of Tea and Other Dietary Factors on Iron Absorption. Cri Rev in Food Sci and Nut 2000;40(5):371-398
8. Li W, Yang J, Zhu XS, Li SC, Ho PC. Correlation between tea consumption and prevalence of hypertension among Singaporean Chinese residents aged 40 years. J Hum Hypertens 2016;30: 11-17

PROSPECTIVE ASSOCIATIONS BETWEEN PLASMA AMYLOIDBETA 42/40 AND FRAILTY IN COMMUNITY-DWELLING OLDER ADULTS

 

W.-H. Lu1, K.V. Giudici1, Y. Rolland1,2, S. Guyonnet1,2, Y. Li3,4, R.J. Bateman3, P. de Souto Barreto1,2, B. Vellas1,2 for the MAPT/DSA Group*

 

1. Gerontopole of Toulouse, Institute of Ageing, Toulouse University Hospital (CHU Toulouse), Toulouse, France; 2. UPS/Inserm UMR1027, University of Toulouse III, Toulouse, France; 3. Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; 4. Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA; * Members are listed at the end of the article.

Corresponding Author: Wan-Hsuan Lu, MS, Postal address: Gérontopôle de Toulouse, Institut du Vieillissement, 37 Allée Jules Guesde, 31000 Toulouse, France, E-mail address: wanhsuanlu@gmail.com, Phone number: +33-561-145-691

J Prev Alz Dis 2021;1(8):41-47
Published online October 28, 2020, http://dx.doi.org/10.14283/jpad.2020.60

 


Abstract

BACKGROUND: Brain amyloid-beta (Aβ) plaques, a hallmark of the pathophysiology of Alzheimer’s disease, have been associated with frailty. Whether the plasma Aβ markers show similar relationship with frailty is unknown.
OBJECTIVES: To investigate the prospective associations between plasma Aβ42/40 ratio and overtime frailty in community-dwelling older adults.
METHODS: From the 5-year Multidomain Alzheimer Preventive Trial (MAPT), we included 477 adults ≥70 years with available data on plasma Aβ42/40 ratio (lower is worse). Fried frailty phenotype (robust, pre-frail and frail) was assessed at the same time-point of plasma Aβ measures and after until the end of follow-up. The outcomes of interest were the change in the frailty phenotype over time (examined by mixed-effect ordinal logistic regressions) and incident frailty (examined by Cox proportional hazard models).
RESULTS: Plasma Aβ42/40 did not show significant associations with incident frailty; however, after adjusting for Apolipoprotein E (APOE) ε4 genotype, people in the lower quartile of plasma Aβ42/40 (≤0.103) had higher risk of incident frailty (HR=2.63; 95% CI, 1.00 to 6.89), compared to those in the upper quartile (>0.123). Exploratory analysis found a significant association between the lower quartile of plasma Aβ42/40 and incident frailty among APOE ε4 non-carriers (HR=3.48; 95% CI, 1.19 to 10.16), but not among carriers. No associations between plasma Aβ42/40 and evolution of frailty were observed.
CONCLUSION: No significant associations between plasma Aβ42/40 and frailty were found when APOE ε4 status was not accounted into the model. Nevertheless, APOE ε4 non-carriers with high Aβ burden might be more susceptible to develop frailty.

Key words: Frailty, amyloid-beta, biomarker, neurodegeneration, older adults.


 

Introduction

Frailty is a common geriatric syndrome characterized by reduced physiological reserve and increased vulnerability, which leads to an increased risk of adverse health outcomes in older adults (1). Frailty was also found to be associated with cognitive decline (2), leading researchers to propose these two conditions would share similar biological pathways (3) and brain pathology.
Previous studies had shown that brain amyloid-beta (Aβ) deposition, a well-known marker of cognitive decline involved in Alzheimer’s disease (AD) pathology (4,5), was associated with frailty severity (6) and its components (7–10) over time in non-demented older adults. However, to the best of the authors’ knowledge, no investigation has examined the associations of plasma Aβ levels with frailty severity and its incidence in older people. Plasma Aβ has several advantages: it is a simple test, highly correlated to Aβ burden in the brain (11, 12), less expensive than positron emission tomography (PET) and less invasive than cerebrospinal fluid test and, then, has a potential to be used in large populations for measuring amyloid load (13).
The objective of the present study was to evaluate the prospective associations of plasma Aβ42/40 with frailty severity and incidence in community-dwelling older adults.

 

Methods

Study source

This is a secondary analysis of the Multidomain Alzheimer Preventive Trial (MAPT), whose detailed methods and main results had been described in previous publications (14, 15). In brief, the MAPT study was a multicenter, randomized controlled trial which aimed to investigate the effect of a three-year multidomain intervention, omega-3 fatty acids supplementation, or their combination, in cognitive function among community-dwelling older adults. The multidomain intervention consisted of physical activity counselling, cognitive training and nutritional advice. Participants were recruited from May 2008 to February 2011 and randomized into four groups (the three above-mentioned interventions, and a placebo control group). After the three-year period, two additional years of observational follow-up were conducted, without any intervention. The five-year follow-up ended in April 2016. The MAPT study protocol was approved by the French Ethical Committee located in Toulouse (CPP SOOM II) and was authorized by the French Health Authority. All participants signed an informed consent.

Study population

A total of 1,679 community-dwelling adults older than 70 years, with either spontaneous memory complaint, limitations in one instrumental activity of daily living or slow gait speed, were enrolled into the MAPT study. Among them, 478 subjects with prospective frailty measurements had their plasma Aβ concentrations assessed – either at the study 12-month visit, for 442 people (92.7%), or at the 24-month visit, for the rest of the sample. One subject with extremely high plasma Aβ value (>4 standard deviations (SD) above the mean value) was excluded; finally, a total of 477 participants were included in this study. Among them, 377 individuals who were robust or pre-frail (definition described in below section) at the same timepoint of plasma Aβ measurement and who had at least one repeated frailty assessment over the follow-up period were included in the investigation of frailty incidence (Supplementary Figure S1).

Main outcome measures

Frailty status was assessed at the same timepoint as for plasma Aβ, and then every one year until the end of the five-year follow-up period; frailty assessments performed before the plasma Aβ measurements were not taken into account in this study. The timepoint of plasma Aβ measures (either at 12-month or 24-month visit) was defined as the start point of follow-up (hereafter called “baseline”).
Frailty was assessed according to the Fried frailty phenotype, which is based on five components (1): (1) weakness (poor handgrip strength measured by a handheld dynamometer with sex- and body mass index (BMI)–specific cutoffs); (2) slowness (4-m usual gait speed with cutoffs established for men and women, according to height); (3) involuntary weight loss (self-reporting >4.5 kg of weight loss in the prior year); (4) exhaustion (according to two items of the Center for Epidemiologic Studies depression scale(16)); (5) low physical activity (<383 kcal/week in men and <270 kcal/week in women during the prior 2 weeks by using Minnesota Leisure Time Activity 15-item questionnaire). Frailty condition was defined as meeting three or more frailty criteria; pre-frail met 1 or 2 criteria; and robust met no criterion. Participants were identified as having incident frailty if they were initially robust or pre-frail and met frailty definition during the follow-up period.
Two main outcomes of frailty were explored in this study. We first evaluated the evolution of frailty among the overall study population (477 individuals) by using the changes in the frailty phenotype as our outcome; the median (interquartile range – IQR) follow-up time was 1408 (731) days. We further focused on 377 non-frail individuals and identified the incident frailty over the follow-up period as our second outcome; the median number of days between baseline and last frailty assessment among this subgroup was 1425 days (ranging from 286 to 1798 days).

Plasma Aβ measurement

The plasma Aβ assay methods had been described elsewhere (12). Briefly, targeted Aβ isoforms (Aβ38, Aβ40, and Aβ42) were simultaneously immunoprecipitated from 0.45 mL of plasma via a monoclonal anti-Aβ mid-domain antibody (HJ5.1, anti-Aβ13-28) conjugated to M-270 Epoxy Dynabeads (Invitrogen). Prior to immunoprecipitation, samples were spiked with a known quantity of 12C15N-Aβ38, 12C15N-Aβ40 and 12C15N-Aβ42 for use as analytical internal standards. Proteins were digested into peptides using LysN endoprotease (Pierce). Liquid chromatography-mass spectrometry was performed as previously illustrated (12). Plasma analyses were performed as targeted parallel reaction monitoring (PRM) on an Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Fisher) interfaced with an M-class nanoAcquity chromatography system (Waters). The precursor and product ion pairs utilized for analysis of Aβ species were chosen as previously illustrated (11, 17). The derived integrated peak areas were analyzed using the Skyline software package (18). The Aβ42 and Aβ40 amounts were calculated by integrated peak area ratios to known concentrations of the internal standards. The value of Aβ42/40 ratio (dividing plasma Aβ42 by Aβ40) was then calculated and their normalized values were used.
The plasma Aβ42/40 was classified based on the cut-off value from receiver operating characteristic (ROC) curve analysis; a plasma Aβ42/40 ≤0.107 with the maximum Youden’s Index was considered the best cut-off value for correlating to PET Aβ positive among MAPT participants. Subjects with plasma Aβ42/40 ≤0.107 were then defined as low plasma Aβ42/40 (Aβ42/40 >0.107 as reference group). Because there is no consensus yet on the cutoff defining plasma Aβ status in the literature, we also categorized the plasma Aβ42/40 based on the lower quartile (≤0.103) of study population, considering plasma Aβ42/40 higher than upper quartile (>0.123) as the reference group.

Confounders

Confounding variables were selected based on data availability and on the literature on frailty and plasma Aβ (6, 12, 19): age, sex, MAPT group allocation, educational level, BMI, cognitive status evaluated by the 30-item Mini-Mental State Examination (MMSE)(20) and Apolipoprotein E (APOE) ε4 genotype (carriers defined as having at least one ε4 allele). BMI and MMSE score were assessed at the same timepoint as plasma Aβ measures (either 12-month or 24-month visit).

Statistical analysis

Descriptive statistics were presented as mean and SD, median and IQR, or frequencies and percentages. Student’s t-test and Chi-square/Fisher exact test were used to compare baseline characteristics according to plasma Aβ42/40 status. We applied mixed-effect ordinal logistic regressions (with random effect on participant level and time), adjusted for all confounders mentioned above, to examine the prospective associations between plasma Aβ42/40 and evolution of the frailty phenotype; proportional odds assumption was checked. The plasma Aβ42/40 was further examined as a continuous variable, transforming from the original value multiplied with 100 for easier interpretation, and provided in Supplementary Table S2. Cox proportional hazard models with discrete time variable (ie, the clinical visits) were performed in non-frail subjects (n=377) to explore associations between plasma Aβ42/40 and incident frailty. Time-to-event was defined as the time interval between the plasma Aβ42/40 measures and the first time the participant was classified as frail; participants without the event were censored at their last frailty assessment visit. Proportional hazard assumption was tested using the Kolmogorov-type supremum test (p >0.05 was considered as non-violation of the assumption).
For mixed-effect ordinal logistic regressions and Cox regressions, we first performed an adjusted model without including APOE ε4 genotype as a confounder. Considering that the addition of APOE ε4 genotype in analyses led to a reduction in the sample size (less 42 participants (8.8%) in the mixed-effect models presented in Table 2, and 30 participants (8.0%) in the Cox models presented in Table 3), a second model with adjustment for APOE ε4 status was conducted; sensitivity analyses restricted to participants with available data of APOE ε4 status, but not including this variable in the model, are presented in Supplementary Tables S3 and S4, to explore the possibility of selection bias. If the association was significant, an interaction term between plasma Aβ42/40 and APOE ε4 genotype was introduced and the stratified results according to APOE ε4 genotype were provided (Supplementary Table S5). Statistical significance was defined as p-value <0.05. All data were analyzed by using SAS, version 9.4 (SAS Institute, Inc, Cary, NC).

 

Results

Baseline characteristics (obtained at the same time-point as Aβ measurements) of the 477 participants are presented in Table 1. The mean age of participants was 76.8 ± 4.5 years, with a majority of women (59.3%). About 33% of the study population had plasma Aβ42/40 ≤0.107 at baseline. Characteristics of the 377 participants included in incident frailty investigation were similar to the overall study population (Supplementary Table S1).

Table 1. Baseline characteristics of the study population according to plasma amyloid-β status

Values presented in number (%) for categorical variables or mean (standard deviation) for continuous variables, unless otherwise indicated; Aβ, amyloid-beta; APOE, Apolipoprotein E; CDR, Clinical Dementia Rating scale; IQR, interquartile range; MAPT, Multidomain Alzheimer Preventive Trial; MMSE, Mini-Mental State Examination (0-30, 0 is worse); *p<0.05, †p<0.01 between two groups determined by Student’s t-test or by Chi-square/Fisher exact test.

 

Results of the associations between plasma Aβ42/40 and the evolution of frailty phenotype over time are displayed in Table 2. No significant associations were found in either unadjusted models or models with adjustment for confounders. Sensitivity analysis using plasma Aβ42/40 as a continuous variable in the mixed-effect model (Supplementary Table S2) provided similar results.

Table 2. Mixed-effect ordinal logistic regressions examining associations between plasma amyloid-β 42/40 and frailty evolution over time

OR, odds ratio of increasing frailty severity over time compared to reference group; Aβ, amyloid-beta; CI, confidence interval; ref, reference group; *Random slope on time and on participants only; †Adjustments for age, sex, Multidomain Alzheimer Preventive Trial (MAPT) groups, education, body mass index, Mini-Mental State Examination (MMSE) score, time and interaction between plasma Aβ42/40 group and time; excluding participants without data of education, body mass index or MMSE score; ‡Adjustments for age, sex, MAPT groups, education, body mass index, MMSE score, APOE ε4 genotype, time and interaction between plasma Aβ42/40 group and time; excluding participants without data of education, body mass index, MMSE score or APOE ε4 genotype.

 

 

Among 377 participants who were initially robust or pre-frail, 49 (13.0%) became frail over the follow-up. In adjusted Cox models, participants with low plasma Aβ42/40 did not show a higher risk of incident frailty, compared to those with high plasma Aβ42/40 (Table 3). However, when APOE ε4 genotype was accounted into the model, participants in the lower quartile of plasma Aβ42/40 (≤0.103) had 2.6 times more risk of incident frailty compared to those in the upper quartile (>0.123) (HR=2.63; 95% CI, 1.00 to 6.89; p=0.049) (Table 3). We explored if this positive result remained without introducing APOE ε4 status in the model among the same population with available data of APOE ε4 genotype (n=343); this sensitivity analysis found that plasma Aβ42/40 was not significantly associated with incident frailty (HR=2.12; 95% CI, 0.83 to 5.45; p=0.118) (Supplementary Table S4), suggesting that there was no selection bias of the population and that APOE ε4 was playing a role in the plasma Aβ42/40-incident frailty association. We further performed the Cox analysis introducing the interaction between APOE ε4 genotype and plasma Aβ42/40. Although the interaction did not reach significance (p=0.090), a significant association between the lower quartile of plasma Aβ42/40 and incident frailty was found among APOE ε4 non-carriers (HR=3.48; 95% CI, 1.19 to 10.16), but not among carriers (Supplementary Table S5).

Table 3. Cox proportional hazard models for incident frailty over the follow-up

Aβ, amyloid-beta; CI, confidence interval; HR, hazard ratio; ref, reference group; *Adjustments for age, sex, Multidomain Alzheimer Preventive Trial (MAPT) groups, education, body mass index and Mini-Mental State Examination (MMSE) score; excluding participants without data of education, body mass index or MMSE score; †Adjustments for age, sex, MAPT groups, education, body mass index, MMSE score and APOE ε4 genotype; excluding participants without data of education, body mass index, MMSE score or APOE ε4 genotype.

 

Discussion

To our knowledge, this is the first work to investigate prospective associations between plasma Aβ and frailty among older adults. Neither the overtime evolution of frailty phenotypes nor incident frailty was significantly associated with plasma Aβ42/40 when APOE ε4 status was not accounted into the model. Nevertheless, once adjusting for APOE ε4 genotype, people with low plasma Aβ42/40 (as defined by the lower quartile) showed higher risk of incident frailty over the follow-up, comparing to those with high plasma Aβ42/40 (the upper quartile); this association seems to be dependent of the APOE ε4 genotype, having been found only among non-carriers in an exploratory analysis.
To the best of our knowledge, only one study had investigated the prospective associations between brain Aβ and incident frailty before (6). In that study, also performed with MAPT participants and adjusted for APOE ε4 genotype, Maltais et al. did not discover relationships between brain Aβ load and incidence of frailty (defined as frailty index (FI) ≥ 0.25) (6). Our study, which analyzed the associations between low plasma Aβ and incident frailty, differed from Maltais et al. (6) in the classifications for frailty, in the measurement of Aβ, and consequently, in the study population. Incident frailty measured by FI represents a general vulnerable status in older adults, including having depressive symptoms or uncontrolled hypertension; in contrast, the Fried frailty phenotype applied in the present work is more related to physical elements and performance. Previous studies working on physical performance had demonstrated cross-sectional and longitudinal associations between cerebral Aβ deposition and slow gait speed in older adults free of dementia (8–10). Inverse associations between physical activity level and brain Aβ had also been observed(21), although not in all studies (22). In addition, our study examined Aβ levels in blood rather than the Aβ plaques in brain. The imbalance of plasma Aβ42/40 could be detected before brain amyloidosis (12); therefore, it is plausible that plasma Aβ could be more sensitive to early preclinical impairments in cognitive performance, which further was shown to predict the elevated risk of onset of frailty (23, 24). Again, our findings must be interpreted with caution, since the significant association was only found in the analysis including APOE ε4 genotype as a confounder. Whether plasma Aβ42/40 could properly predict future frailty requires further investigation.
Complex mechanisms linking plasma Aβ and frailty might also exist, since the relationship between plasma Aβ and the progression of frailty is mediated by other covariates. Our exploratory analysis considering the interaction between APOE ε4 status and plasma Aβ provided significant association with incident frailty only among APOE ε4 non-carriers. While APOE ε4 genotype is a strong genetic risk factor of AD and ε4 positive showed increased brain Aβ deposition in both preclinical AD patients and cognitively normal individuals (25, 26), its association with frailty is controversial (27, 28). Additional studies exploring the relationship between frailty, plasma Aβ and APOE ε4 genotype, as well as the potential mechanism behind it, would shed light on this topic in the future.
The lack of associations between plasma Aβ42/40 and change in the frailty phenotype may be potentially explained by the unexpected large proportion of frail people with higher plasma Aβ42/40 at baseline. It is also possible that the change of plasma Aβ42/40 over time, rather than a single point value of plasma Aβ42/40, would be better associated with frailty progression. Alternatively, it could be that frailty is not strongly affected by the presence of amyloid plaques, but interact with this marker of Alzheimer’s disease pathology to develop further adverse outcomes including dementia (29). Further studies to explore the long-term associations between changes in plasma Aβ and frailty evolution, and their interaction effect on cognitive decline are encouraged.
This study has important strengths: it is the first to investigate the associations between the plasma Aβ marker and frailty in older adults. The plasma Aβ42/40 applied in our work was assessed by a recently improved technique, which provided a sensitive and reliable measure for predicting brain amyloidosis (11, 13). Moreover, we applied a longitudinal design and explored different kinds of frailty outcome (phenotype evolution and incidence). Nonetheless, some limitations are worth mentioning. First, as usual in longitudinal studies, some measures of frailty were missing during the follow-up period, which might have, on one hand, underestimated the time of incident frailty for cases (individuals developing the event) and, on the other hand, misclassified some individuals as non-cases (individuals without the event). In addition, this is a secondary analysis of a randomized controlled trial in which three-quarters of the population received interventions, even though interventions did not affect physical function (15) nor frailty incidence as measured by Fried frailty phenotype (30); all analyses were adjusted by allocation to intervention groups in an attempt to minimize the impact of this bias.
To conclude, our study did not demonstrate significant associations between plasma Aβ42/40 and frailty over time when APOE ε4 status is not taken into consideration. However, APOE ε4 non-carriers in the lower quartile of plasma Aβ42/40 might have an increased risk of developing frailty. Further longitudinal studies investigating the relationship between frailty, plasma Aβ and APOE ε4 genotypes should be encouraged.

 

Funding: The present work was performed in the context of the Inspire Program, a research platform supported by grants from the Region Occitanie/Pyrénées-Méditerranée (Reference number: 1901175) and the European Regional Development Fund (ERDF) (Project number: MP0022856). The plasma measures of this study was supported by institutional gift funds (R.J. Bateman, PI) and National Institute on Aging grants NIH R56AG061900 and RF1AG061900 (R.J. Bateman, PI). The MAPT study was supported by grants from the Gérontopôle of Toulouse, the French Ministry of Health (PHRC 2008, 2009), Pierre Fabre Research Institute (manufacturer of the omega-3 supplement), ExonHit Therapeutics SA, and Avid Radiopharmaceuticals Inc. The promotion of this study was supported by the University Hospital Center of Toulouse. The data sharing activity was supported by the Association Monegasque pour la Recherche sur la maladie d’Alzheimer (AMPA) and the INSERM-University of Toulouse III UMR 1027 Unit. 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: Washington University and Randall Bateman have equity ownership interest in C2N Diagnostics and receive income based on technology (blood plasma assay) licensed by Washington University to C2N Diagnostics. RJB receives income from C2N Diagnostics for serving on the scientific advisory board. Washington University, with RJB as co-inventor, has submitted the US nonprovisional patent application “Plasma Based Methods for Determining A-Beta Amyloidosis.” RJB has received honoraria as a speaker/consultant/advisory board member from Amgen, AC Immune, Eisai, Hoffman-LaRoche, and Janssen; and reimbursement of travel expenses from AC Immune, Hoffman-La Roche and Janssen.

Author contributions: WHL designed and conceptualized the research, performed the analyses, interpreted the data and drafted the manuscript. KVG, YR and SG interpreted the data and revised the draft critically for intellectual content. YL and RJB managed data of plasma amyloid-beta, interpreted the data and revised the draft critically for important intellectual content. PSB designed and conceptualized the research, interpreted the data and revised the draft critically for intellectual content. BV conceived the MAPT study, interpreted the data, and revised the draft critically for intellectual content. All authors have read and agreed with the final version to be submitted.

MAPT/DSA Group: MAPT Study Group: Principal investigator: Bruno Vellas (Toulouse); Coordination: Sophie Guyonnet; Project leader: Isabelle Carrié; CRA: Lauréane Brigitte; Investigators: Catherine Faisant, Françoise Lala, Julien Delrieu, Hélène Villars; Psychologists: Emeline Combrouze, Carole Badufle, Audrey Zueras; Methodology, statistical analysis and data management: Sandrine Andrieu, Christelle Cantet, Christophe Morin; Multidomain group: Gabor Abellan Van Kan, Charlotte Dupuy, Yves Rolland (physical and nutritional components), Céline Caillaud, Pierre-Jean Ousset (cognitive component), Françoise Lala (preventive consultation) (Toulouse). The cognitive component was designed in collaboration with Sherry Willis from the University of Seattle, and Sylvie Belleville, Brigitte Gilbert and Francine Fontaine from the University of Montreal. Co-Investigators in associated centres: Jean-François Dartigues, Isabelle Marcet, Fleur Delva, Alexandra Foubert, Sandrine Cerda (Bordeaux); Marie-Noëlle-Cuffi, Corinne Costes (Castres); Olivier Rouaud, Patrick Manckoundia, Valérie Quipourt, Sophie Marilier, Evelyne Franon (Dijon); Lawrence Bories, Marie-Laure Pader, Marie-France Basset, Bruno Lapoujade, Valérie Faure, Michael Li Yung Tong, Christine Malick-Loiseau, Evelyne Cazaban-Campistron (Foix); Françoise Desclaux, Colette Blatge (Lavaur); Thierry Dantoine, Cécile Laubarie-Mouret, Isabelle Saulnier, Jean-Pierre Clément, Marie-Agnès Picat, Laurence Bernard-Bourzeix, Stéphanie Willebois, Iléana Désormais, Noëlle Cardinaud (Limoges); Marc Bonnefoy, Pierre Livet, Pascale Rebaudet, Claire Gédéon, Catherine Burdet, Flavien Terracol (Lyon), Alain Pesce, Stéphanie Roth, Sylvie Chaillou, Sandrine Louchart (Monaco); Kristelle Sudres, Nicolas Lebrun, Nadège Barro-Belaygues (Montauban); Jacques Touchon, Karim Bennys, Audrey Gabelle, Aurélia Romano, Lynda Touati, Cécilia Marelli, Cécile Pays (Montpellier); Philippe Robert, Franck Le Duff, Claire Gervais, Sébastien Gonfrier (Nice); Yannick Gasnier and Serge Bordes, Danièle Begorre, Christian Carpuat, Khaled Khales, Jean-François Lefebvre, Samira Misbah El Idrissi, Pierre Skolil, Jean-Pierre Salles (Tarbes). MRI group: Carole Dufouil (Bordeaux), Stéphane Lehéricy, Marie Chupin, Jean-François Mangin, Ali Bouhayia (Paris); Michèle Allard (Bordeaux); Frédéric Ricolfi (Dijon); Dominique Dubois (Foix); Marie Paule Bonceour Martel (Limoges); François Cotton (Lyon); Alain Bonafé (Montpellier); Stéphane Chanalet (Nice); Françoise Hugon (Tarbes); Fabrice Bonneville, Christophe Cognard, François Chollet (Toulouse). PET scans group: Pierre Payoux, Thierry Voisin, Julien Delrieu, Sophie Peiffer, Anne Hitzel, (Toulouse); Michèle Allard (Bordeaux); Michel Zanca (Montpellier); Jacques Monteil (Limoges); Jacques Darcourt (Nice). Medico-economics group: Laurent Molinier, Hélène Derumeaux, Nadège Costa (Toulouse). Biological sample collection: Bertrand Perret, Claire Vinel, Sylvie Caspar-Bauguil (Toulouse). Safety management: Pascale Olivier-Abbal. DSA Group: Sandrine Andrieu, Christelle Cantet, Nicola Coley.

Ethical Standards: The MAPT study protocol was approved by the French Ethical Committee located in Toulouse (CPP SOOM II) and was authorized by the French Health Authority. All participants signed an informed consent.

 

SUPPLEMENTARY MATERIAL 1

SUPPLEMENTARY MATERIAL 2

 

References

1. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in Older Adults: Evidence for a Phenotype. Journals Gerontol Ser A Biol Sci Med Sci. 2001;56(3):M146–57.
2. Kiiti Borges M, Oiring de Castro Cezar N, Silva Santos Siqueira A, Yassuda M, Cesari M, Aprahamian I. The Relationship between Physical Frailty and Mild Cognitive Impairment in the Elderly: A Systematic Review. J frailty aging. 2019;8(4):192–7.
3. Sargent L, Nalls M, Starkweather A, Hobgood S, Thompson H, Amella EJ, et al. Shared biological pathways for frailty and cognitive impairment: A systematic review Ageing Res Rev. 2018;47:149-58.
4. Petersen RC, Wiste HJ, Weigand SD, Rocca WA, Roberts RO, Mielke MM, et al. Association of elevated amyloid levels with cognition and biomarkers in cognitively normal people from the community. JAMA Neurol. 2016 Jan 1;73(1):85–92.
5. Farrell ME, Kennedy KM, Rodrigue KM, Wig G, Bischof GN, Rieck JR, et al. Association of longitudinal cognitive decline with amyloid burden in middle-aged and older adults: Evidence for a dose-response relationship. JAMA Neurol. 2017 Jul 1;74(7):830–8.
6. Maltais M, De Souto Barreto P, Hooper C, Payoux P, Rolland Y, Vellas B. Association Between Brain β-Amyloid and Frailty in Older Adults. Journals Gerontol Ser A Biol Sci Med Sci. 2019;74(11):1747–52.
7. Yoon D, Lee J-Y, Shin S, Kim Y, Song W. Physical Frailty and Amyloid-β Deposits in the Brains of Older Adults with Cognitive Frailty. J Clin Med. 2018;7(7):169.
8. Del Campo N, Payoux P, Djilali A, Delrieu J, Hoogendijk EO, Rolland Y, et al. Relationship of regional brain β-amyloid to gait speed. Neurology. 2016;86(1):36–43.
9. Nadkarni NK, Perera S, Snitz BE, Mathis CA, Price J, Williamson JD, et al. Association of brain amyloid-β with slowgait in elderly individuals without dementia influence of cognition and apolipoprotein e ε4 genotype. JAMA Neurol. 2017;74(1):82–90.
10. Wennberg AMV, Lesnick TG, Schwarz CG, Savica R, Hagen CE, Roberts RO, et al. Longitudinal association between brain amyloid-beta and gait in the mayo clinic study of aging. Journals Gerontol Ser A Biol Sci Med Sci. 2018;73(9):1244–50.
11. Ovod V, Ramsey KN, Mawuenyega KG, Bollinger JG, Hicks T, Schneider T, et al. Amyloid β concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimer’s Dement. 2017 Aug 1;13(8):841–9.
12. Schindler SE, Bollinger JG, Ovod V, Mawuenyega KG, Li Y, Gordon BA, et al. High-precision plasma β-amyloid 42/40 predicts current and future brain amyloidosis. Neurology. 2019 Oct 22;93(17):e1647–59.
13. Bateman RJ, Blennow K, Doody R, Hendrix S, Lovestone S, Salloway S, 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(3):169–73.
14. Vellas B, Carrie I, Gillette-Guyonnet S, Touchon J, Dantoine T, Dartigues JF, et al. Mapt study: a multidomain approach for preventing Alzheimer’s disease: design and baseline data. J Prev Alzheimer’s Dis. 2014 Jun;1(1):13–22.
15. Andrieu S, Guyonnet S, Coley N, Cantet C, Bonnefoy M, Bordes S, et al. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. Lancet Neurol. 2017;16(5):377–89.
16. Radloff LS. The CES-D Scale: A Self-Report Depression Scale for Research in the General Population. Appl Psychol Meas. 1977;1(3):385–401.
17. Mawuenyega KG, Kasten T, Sigurdson W, Bateman RJ. Amyloid-beta isoform metabolism quantitation by stable isotope-labeled kinetics. Anal Biochem. 2013 Sep 1;440(1):56–62.
18. Pino LK, Searle BC, Bollinger JG, Nunn B, Maclean B, Maccoss MJ. The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. Mass Spectrom Rev. 2017 May;39(3):229–44.
19. Toledo JB, Vanderstichele H, Figurski M, Aisen PS, Petersen RC, Weiner MW, et al. Factors affecting Aβ plasma levels and their utility as biomarkers in ADNI. Acta Neuropathol. 2011 Oct 30;122(4):401–13.
20. 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(3):189–98.
21. Brown BM, Peiffer J, Rainey-Smith SR. Exploring the relationship between physical activity, beta-amyloid and tau: A narrative review. Ageing Res Rev. 2019;502019;50:9–18.
22. de Souto Barreto P, Andrieu S, Rolland Y. Physical Activity and β-Amyloid Brain Levels in Humans: A Systematic Review. J Prev Alzheimer’s Dis. 2015;2(1):56–63.
23. Gross AL, Xue QL, Bandeen-Roche K, Fried LP, Varadhan R, McAdams-DeMarco MA, et al. Declines and impairment in executive function predict onset of physical frailty. Journals Gerontol Ser A Biol Sci Med Sci. 2016;71(12):1624–30.
24. Gale CR, Ritchie SJ, Cooper C, Starr JM, Deary IJ. Cognitive Ability in Late Life and Onset of Physical Frailty: The Lothian Birth Cohort 1936. J Am Geriatr Soc. 2017 Jun 1;65(6):1289–95.
25. Verghese PB, Castellano JM, Holtzman DM. Apolipoprotein E in Alzheimer’s disease and other neurological disorders. Lancet Neurol. 2011;10(3):241-52.
26. Risacher SL, Kim S, Shen L, Nho K, Foroud T, Green RC, et al. The role of apolipoprotein E (APOE) genotype in early mild cognitive impairment (E-MCI). Front Aging Neurosci. 2013;5:11.
27. Mourtzi N, Ntanasi E, Yannakoulia M, Kosmidis M, Anastasiou C, Dardiotis E, et al. Apolipoprotein ε4 allele is associated with frailty syndrome: Results from the hellenic longitudinal investigation of ageing and diet study. Age Ageing. 2019 Nov 1;48(6):917–21.
28. Rockwood K, Nassar B, Mitnitski A. Apolipoprotein E-polymorphism, frailty and mortality in older adults. J Cell Mol Med. 2008 Dec;12(6B):2754–61.
29. Wallace LMK, Theou O, Godin J, Andrew MK, Bennett DA, Rockwood K. Investigation of frailty as a moderator of the relationship between neuropathology and dementia in Alzheimer’s disease: a cross-sectional analysis of data from the Rush Memory and Aging Project. Lancet Neurol. 2019 Feb 1;18(2):177–84.
30. Guerville F, de Souto Barreto P, Giudici KV, Rolland Y, Vellas B. Association of 3-Year Multidomain Intervention and Omega-3 Supplementation with Frailty Incidence. J Am Geriatr Soc.2019;67(8):1700-06.

EXPLORING FACTORS THAT CONTRIBUTE TO JOINING AND REGULARLY PRACTICING IN COGNITIVE TRAINING AMONG HEALTHY OLDER ADULTS: A ONE-YEAR FOLLOW-UP QUALITATIVE STUDY

 

P. Srisuwan1, D. Nakawiro2, S. Chansirikarnjana3, O. Kuha4, S. Kengpanich1, K. Gesakomol1

 

1. Department of Outpatient and Family Medicine, Phramongkutklao Hospital, Bangkok, Thailand; 2. Department of Psychiatry, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; 3. Department of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; 4. Institute of Geriatric Medicine, Ministry of Public Health, Nonthaburi, Thailand

Corresponding Author: Srisuwan P, Department of Outpatient and Family Medicine, Phramongkutklao Hospital, Bangkok, 10400, Thailand. Phone (662) 354-7660 ext. 93100, Fax (662) 354-9006, Email: patsri2004@yahoo.com

J Prev Alz Dis 2020;2(7):75-81
Published online March 23, 2020, http://dx.doi.org/10.14283/jpad.2020.14

 


Abstract

BACKGROUND: Cognitive interventions have the potential to enhance cognition among healthy older adults. However, little is known of the factors associated with the joining and participating of older people in group-based multicomponent cognitive training (CT).
OBJECTIVES: To explore factors that contribute to joining and regularly practicing CT over 1 year among healthy older adults.
DESIGN: A qualitative study.
SETTING: Geriatric clinic in Bangkok, Thailand.
PARTICIPANTS: 40 nondemented community-dwelling older adults
INTERVENTION: The CT of executive functions, attention, memory and visuospatial functions (TEAM-V) program was conducted over 5 sessions, with a 2-week interval between each session.
MEASUREMENTS: An inductive qualitative approach, based on semi-structure interviews with 40 healthy older adults, was employed. The interviews explored factors of joining CT at baseline, factors of regularly participating in class at 6 months and at home at 1 year. Data were coded and analyzed using and the thematic analysis approach.
RESULTS: After analyzing factors concerning joining CT, 3 core themes emerged: (1) individual characteristics with 3 subthemes of “health status”, “time arrangement”, and “financial status”; (2) individual perceptions with 2 subthemes of “perceived susceptibility to dementia” and “perceived severity of dementia” and (3) encouragement from families and friends. After analyzing factors of practicing CT in class, 3 core themes emerged: (1) program with 3 subthemes of “session”, “group facilitators” and “notification before class”; (2) accessibility with 2 subthemes of “distance” and “transportation” and (3) encouragement from families and friends. After analyzing factors of practicing CT at home, 2 core themes emerged: (1) contents of the training program and (2) encouragement from families and friends.
CONCLUSIONS: Increased awareness of holistic factors including older adults’ characteristic and perceptions, support from families and friends and accessibility should be emphasized in planning CT. Designing the content of CT that could be applied or adapted in daily living and effective program components such as a notification system could increase practicing.

Key words: Cognitive training, joining, older adults, participation, qualitative study.


 

Introduction

Dementia is the leading cause of dependence and disability in the elderly population worldwide. As the average life expectancy increases, the prevalence of dementia is expected to increase exponentially (1). Consistently high or increasing social engagement is associated with a lower risk of dementia (2,3). Furthermore, Participation in social activities such as group activities outside the family may have a bigger impact on cognitive function than social contacts with family members or non-relatives (4). Moreover, higher participation in cognitive training (CT) is associated with better cognitive function and reduced risk of cognitive decline and dementia among healthy older people (5,6) and people with dementia (7). For example, the Advanced CT for Independent and Vital Elderly (ACTIVE) study, the first large scale randomized controlled trail, demonstrated significant improvements in cognition such as memory and reasoning, which were sustained for up to ten years, and also showed benefit to instrumental activities of daily living (IADL) (8). CT is an approach using guided practice on structured tasks with the direct aim of improving or maintaining cognitive ability (9,10). This training can assume many formats. For example, it can be conducted individually or in groups, with either a single, e.g., memory, or multiple topics, e.g., memory and executive function. Moreover, the approach might consider a bottom-up training, e.g., enhancing sensory and perceptual skills to improve higher order processing, or top-down, e.g., target mechanisms of cognitive control to improve the systematic problem-solving approach (11). In our previous study, a training of executive functions, attention, memory and visuospatial functions, the TEAM-V Program was developed for group-based multicomponent CT. A top-down approach was used for CT among healthy older adults. The results showed that the TEAM-V Program helped to improve global cognitive function and memory, reduce anxious and depressive symptoms and enhance IADL among healthy older people in six months (12)..
Older people should be encouraged to participate in group activities especially CT to reduce their risk of dementia. Understanding factors that contribute to joining and participating in group-based CT are essential for planning the training. Socio-demographic and health factors were associated with the participation of elderly people in general group educational activities (13). Having an effective program and facilitators and collaboration from family members were the main factors contributing to regularly participating in CT for mild cognitive impairment (14). However, little is known of the factors associated with the joining and participating of older people in group-based multicomponent CT.
Therefore, this study aimed to explore factors contributing to joining CT in TEAM-V Programs, regularly practicing CT in classes and at home among healthy older adults over one year.

 

Methods

Study Design and Subjects

For the TEAM-V CT, using a single-blinded, controlled trial, 40 healthy older adults were randomized to the intervention group. The authors recruited participants who visited the geriatric clinic, outpatient department, Phramongkutklao Hospital, Bangkok, Thailand from April to May 2017. The enrolled participants of the TEAM-V Program were aged > 60 years and willing to participate in all five activities. The exclusion criteria were: Thai version of the Hospital Anxiety and Depression Scale (HADS) score higher than 11 on anxiety or depression (15), Thai version of the Montreal Cognitive Assessment (MoCA) score less than 26 (16), having any conditions affecting participation in program activities, e.g., balancing problems, hearing impairment as well as any psychiatric diseases and neurological problems such as stroke. The CT was conducted over five sessions, with a two-week interval between each session. Each session involved the training of different cognitive domains. The details of each session are shown in Table 1. Figure 1 illustrates the timeline of the TEAM-V Program enrollment.

Table 1. TEAM-V program CT activities

Table 1. TEAM-V program CT activities

 

The authors conducted semi-structured interviews with 40 participants in the intervention group of the TEAM-V CT. After approaching the participants, the interviewers introduced themselves. After explaining the objectives of the study, the participants were individually interviewed. All of the interviews were recorded by audiotape. Conversations were fully transcribed along with field notes and an audit trail was created immediately after each session. Three main aspects were explored: (1) factors to join CT, (2) factors to regularly practice CT in class and (3) factors to regularly practice CT at home. After collecting demographic data, the interviews explored factors of joining CT. The first question was asked at baseline, “What factors influenced your decision to join CT”. The second question was asked at 6-month follow-up, “What factors influenced your decision to regularly practice CT in classes”. Finally, the last question was asked at 1 year follow-up, “What factors influenced your decision to regularly practice CT at home”. The interview took 10 to 15 minutes each session, depending on the participant. This study was approved by the Institutional Review Board of the Royal Thai Army Medical Department Ethics Committee as instituted (IRBRTA 599/60) by the Declaration of Helsinski, and all participants were required to provide written informed consent before enrollment. The TEAM-V Program was registered under the Thai Clinical Trials Registry (TCTR20190709003).

Data Analysis

Open codes were created and analyzed using the investigator triangulation method. The codes were purely data driven. After that, the codes were discussed, modified and merged by the authors and final revised codes were developed afterward. Emerging concepts were extracted and analyzed using a thematic analysis approach.

 

Results

Participant Characteristics

All 40 healthy older adults in the intervention group of the TEAM-V Program participated in this study. Patients’ characteristics are shown in Table 2. On average, participants were young-old age (mean age 66.23±4.64 years). Most participants were female (80%), most had a bachelor’s degree (63%) and had a chronic medical condition (83%). The TEAM-V Program adherence rates were 100% including five sessions of training and at follow-up at six months and one year. The details of the TEAM-V program enrollment flow are shown in Figure 1.

Figure 1. CONSORT diagram of the TEAM-V program enrollment flow

Figure 1. CONSORT diagram of the TEAM-V program enrollment flow

 

Table 2. Participant characteristics

Table 2. Participant characteristics

a. Derived from responses to the question, “How satisfied are you with your health?” (WHO QoL)13; b. Based on the Chula ADL Index14

Thematic analysis

After analyzing final codes, three main themes in joining the program, three main themes in regular practice CT in class, and two main themes in regular practice CT at home were identified. The category, themes, subthemes and codes of the participants after the interview are shown in Table 3 and Figure 2. The quoted statements were examples of the responses by the participants.

Figure 2. Factors that contribute to joining and regularly practicing in CT

Figure 2. Factors that contribute to joining and regularly practicing in CT

Table 3. Category, themes, subthemes and codes of the participants after the interview

Table 3. Category, themes, subthemes and codes of the participants after the interview

 

Joining CT

Individual characteristics and individual perceptions were very important to join CT.

Individual characteristics

Good health status was an importance factor. Although most of participants (82.5%) reported some chronic medical conditions such as hypertension and diabetes, self-reported health statuses was high (62.5%). Moreover, most were still independent regarding ADL (95%). In addition, available time to join the program and good financial status also supported participants to join the program.
“I am so lucky, my health is good. Although I have hypertension and dyslipidemia, but the diseases do not limit my activities. I can do whatever I want. If I have stroke or disabilities, it will limit me in performing a lot of activities including joining CT class.”
“I am single and retired; I have free time to join the class”
“The class is set up in the morning. I can join the class and then come back to my home to do some housework”
“I am a retired solider, I have enough pension and have some savings, so I don’t have to worry about working after retiring. Therefore, I have free time to join the class.”
“My daughter gives some money to me every month, so I have enough money to pay the fee of transportation to the class”
Individual perceptions

Perceived susceptibility and severity of dementia were two main factors to urge participants to join the program. Perceived susceptibility to dementia from health care professionals or having dementia in their families increased their awareness of dementia. Moreover, participants who were taking care of people with dementia had experience the severity of the disease. In addition, some participants recognized the severity of dementia from media such as television or websites.
“My doctor told me that I have a risk to develop dementia because my mother and my aunt had dementia. I have taken care of my mother since the beginning of the disease until she passed away. Her memory gradually left; she repeatedly asked me the same questions; did not know how to get dressed. At the end of the disease, she did not know herself and did not know how to walk, so she had to stay in bed all the time. It is a very dangerous disease. I don’t want to get it”
“I am 70 years old, and forget something sometimes that’s normal, but my nurse who visits me at home told me aging increases the risk of dementia, I am elderly, so I think I have some risk of dementia although I don’t have family with dementia”.
“Dementia is very scary, I saw the news of an elderly woman with dementia who didn’t know herself, wandered far away from home and didn’t know how to come back”

Regularly practicing CT in class

Actually, the CT program was complicated because the program had to be designed to train many specific cognitive domains incorporating various activities. However, designing of the program in sessions with small groups, interactive activities and practicing step by step would help participants to understand the training. Moreover, friendly, warm, enthusiastic group facilitators could support participants during class. In addition, before class, the program used notification by phone call from one of the group facilitators to remind participants to join the session. Participants who lived near the class or accessed convenient transportation to the class tended to regularly practice CT in class.
“At the beginning in the large class, I hesitated to speak or answer questions, but after participating in a lot of interactive activities in a small group, I was not hesitant at all due to the friendlier environment. The small group drew my attention and was also more interesting than large group.”
“Before training, I thought brain training was very complex; maybe I couldn’t follow all the steps of training, but step by step training from easy to difficult techniques helped me to understand”
“Group facilitators were always enthusiastic to answer my questions throughout the session. They were very friendly, so I dared to ask or answer questions.”
“Most of facilitators helped me to understand the sessions, explained the objectives and summarized the session. If they did not explain, I would not have understood.”
“I am a housewife, so I have a lot of activities at home; sometime I nearly forgot to join class, but fortunately phone calls from the program always reminded me”
“My home is near class, the distance between my home and the class is only 3 bus stops. It is easy for me to participate in the class.”
“My son’s workplace is near class, he drops me to join the class in the morning and picks me up in the afternoon after class. I often have right knee pain. If I have to go by bus by myself, I cannot participate in the class for sure”.
“Although my home is far from class, one of the bus lines begins near my home and goes to the hospital. Therefore, I can sit on the bus. It is not difficult to me to go to the hospital.”

Regularly practiceing CT at home

Most participants said the benefits in everyday life were necessary. Therefore, participants were interested to practicing techniques that they could adapt in daily life to improve their cognition.
“I felt at first brain training techniques were going to very difficult. However, after learning in class, I realized it was quite fun and I could adapt to my daily life. My favorite technique of improving short-term memory is memorizing by various techniques such as mnemonics and mind map. I practice every morning when I try to summarize news and write it in my notebook.”

Encouragement from family members and friends

Encouragement from family members and friends not only stimulated participants to join class but also urged participants to regularly attend and practice CT including in class and at home
“My wife wanted to join the program, and she encouraged me to join the program too. After classes, the program has some homework; she always encouraged me to do homeworks. Morover, after finishing all five sessions, she still encouraged me to practice as much as possible such as when we go to shopping, I have to remember the shopping list by the various techniues”
“My close friend decided to join the program, so I wanted to join too.”
“My daugther always asked me what techniques I have learned in the class; she encourage me to practice everyday. Forexample, when she comes back home, I have to summerize the news using various techniques that I have learned in the program.”
“My friend, I just know her from the program because we are in the same small group. She always supports me to practice in the class and also at home. I enjoy practicing with her in the class. She calls me at home to discuss about homework and also notify me to practice after class.”

 

Discussion

This study comprised a qualitative research that explored the factors promoting practicing in CT among healthy older adults using a holistic perspective. The duration of the study involved a one-year follow-up with five sessions in group-based, multicomponent, cognitive stimulation involved in training different cognitive domains every two weeks and all participants attended all of the five sessions in the training. No lost follow-up occurred at six months and one year. Therefore, the participation rate of the program was very high (100%).
In response to our research question, the authors found that there were multifactorial compositions promoting joining or practicing in CT Not only clinical factors such as health status but also psychological such as participants’ perceptions and social factors such as financial status were influenced to participants decisions. Individual characteristics such as having good health and individual perceptions such as concerning about susceptibility of dementia were influenced participants to join CT. The results can be explained by the health belief model relates a sociopsychologic theory of decision making to individual health-related behavior (19). To illustrate, individual perceptions of susceptibility and severity of dementia which are people believes about health problems impact the engagement. The perceptions of high benefits from the program and low barriers to attend the program such as easy accessibility, friend and family support promote the adherence of the training. Self-efficacy including good health status and free time also gets into high engagement too. This was similar to the results of a cross-sectional mixed methods analysis involving 1,028 participants in rual Sri Lanka by Marsh and colleagues revealed that there were several barriers of social participation in older adults such as having poor health, living alone and time constrain (20). This study revealed that most participants had a chronic medical condition such as hypertension. This was similar to the results of a cross-sectional analysis involving 3,034 participants in Brazil by Dias and colleagues revealed that the morbidities in the elderly who participated in education activities were hypertension and diabetes mellitus. Among non-participants, spinal cord problems and vision problems prevailed (13). People with physical limitations are more likely to have activities of daily living problems (21). In Fibra study in Brazil by Pinto and colleague revealed that not only physical health problems such as vision impairment and low cognitive status but also mental problems such as depressive were symptoms were related to low social participation in older adults (22). Therefore, design of CT program should pay attention to older adults with physical limitations and mental problems.
The present findings suggest that appropriate program and good accessibility to CT class were importance factors to regular attend and practice CT in class. CT is very complex and can take many formats7. If the trainings are not appropriate such as too complicate, difficult to understand or not interesting, participants do not want to attend all of the classes. Therefore, in this study found that appropriate sessions such as having small group and interactive activities, friendly group facilitators and notification before class helped participants attended and practiced CT. This was similar to the results of factors that contribute to regular participation and practice in CT among older adults with minor neurocognitive disorder (14). Accessibility such as transportation was also an importance factor in this study. This was similar to the results of National Health and Aging Trends Study involving 7,197 participants in the USA revealed that poor transportation was a barrier to social participation 12.2-18.2% in homebound older adults (23). Moreover, older adults with multicomorbidity who used of public transport or of their own car had higher levels of participation in social activities (24).
In the present study, contents of the training program could apply or adapt in daily living was an important factor to regular practice CT at home. Recent study about CT in Parkinson’s disease found that CT using real-life activities could foster relevance and meaning to the participants’ life which, in turn, enhance motivation, engagement in the intervention and follow-through (25). Encouragements from families and friends were main factors to support participants to join CT and regular practice CT in class and at home. This was similar to the results of Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) Study involving 631 participants in Finland revealed that being married or cohabiting was independently associated with the greater probability of starting computer-based CT (26). Compare with study of adherence to diabetes therapy in the US. found that practical and emotional support received by both family and friends had a positive influence on global measures of disease management in patients with diabetes. Adherance was 27% higher when patients had practical available to them (27). Moreover, study of barriers to social participation among lonely older adults in United Kingdom revealed not only illness/disability and lack of a supportive community but also loss of friends and families were importance barriers too (28). Neighborhood factors were not mentioned in this study. However, in ACTIVE study in the USA revealed that neighborhood factors do impact cognitive outcomes albeit in a subtle way (i.e., mostly through practice-related effect) (29).
This study was limited by several factors. First, the data was collected in Pramongkutklao hospital, tertiary care hospital, in the Bangkok city of Thailand. For implementation in larger scale, such as primary care or rural area, the results may different from these research outcomes due to educational, economical, and social factors. Second, we found factors that maybe effect on the training program. Nevertheless, we did not explored further outcomes because those were not the main objective of this study. Third, this study was designed base on single blind method, as same as other interviewed quantitative study, the bias might be occurred due to mutual relationship. Moreover, interviewing passages were set in Thai language and were translated into English by native speaker before analyzing process. Therefore, translation bias may occur due to mistranslation. Finally, the participants were selected and enrolled with voluntary procedure. The factors from non-participating patients might be miss and lost to be analyzed.

 

Conclusion

To summarize, Increase awareness of holistic factors including older adults’ characteristic such as health, time and financial status and perceptions such as concerning about susceptibility and severity of dementia, support from families and friends and accessibility should be emphasized in planning CT. The current study adds new knowledge to designing the content of CT that could apply or adapt in daily living and effective program such as interesting sessions, appropriate group facilitators and notification system can increase practicing CT in class and at home.

 

Acknowledgement: The authors would like to express our gratitude to the participants and PMK aging team in Geriatric Clinic, Phramongkutklao Hospital. We also would like to thank the Institute of Geriatric Medicine for allowing the research team to be a part of the larger study and conducted project activities in the Central region of Thailand. We also thank Ms. Worarachanee Imjaijitt for statistical analysis.

Funding: The research project was partially supported by The Thai Health Promotion Foundation. 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 disclosure: There is no conflict of interest.

Ethical standards: The TEAM-V study was approved by the Institutional Review Board of the Royal Thai Army Medical Department Ethics Committee as instituted by the Declaration of Helsinski.

 

References

1. Martin M, Clare L, Altgassen AM , Cameron MH, Zehnder F. Cognitive-1. Satizabal C, Beiser AS, Seshadri S. Incidence of Dementia over Three Decades in the Framingham Heart Study. N Engl J Med 2016;375(1):93-4.
2. Zhou Z, Wang P, Fang Y. Social engagement and Its change are associated with dementia risk among Chinese older adults: A longitudinal study. Sci Rep 2018;8(1):1551. doi: 10.1038/s41598-017-17879-w.
3. Chertkow H. An action plan to face the challenge of dementia: INTERNATIONAL STATEMENT ON DEMENTIA from IAP for health. J Prev Alzheimers Dis 2018;5(3):207-212.
4. Glei DA, Landau DA, Goldman N, Chuang YL, Rodríguez G, Weinstein M. Participating in social activities helps preserve cognitive function: an analysis of a longitudinal, population-based study of the elderly. Int J Epidemiol 2005;34(4):864-71.
5. Giuli C, Fattoretti P, Gagliardi C, Mocchegiani E, Venarucci D, Balietti M, et al. My Mind Project: the effects of cognitive training for elderly-the study protocol of a prospective randomized intervention study. Aging Clin Exp Res 2017;29(3):353-60. doi: 10.1007/s40520-016-0570-1.
6. Y Lee. Primary prevention of dementia: The future of population-based multidomain lifestyte interventions. J Prev Alz Dis 2018;5(1):5-7.
7. King JB, Jones KG, Goldberg E, Rollins M, MacNamee K, Moffit C, et al. Increased Functional Connectivity After Listening to Favored Music in Adults With Alzheimer Dementia. J Prev Alzheimers Dis 2019;6(1):56-62.
8. Rebok GW, Ball K, Guey LT, Jones RN, Kim HY, King JW, et al. Ten-year effects of the advanced cognitive training for independent and vital elderly cognitive training trial on cognition and everyday functioning in older adults. J Am Geriatr Soc. 2014; 62(1):16-24. https://doi.org/ 10.1111/jgs.12607
9. Bahar-Fuchs A, Martyr A, Goh AM, Sabates J, Clare L. Cognitive training for people with mild to moderate dementia. Cochrane Database Syst Rev 2019;3:CD013069. doi: 10.1002/14651858.CD013069.pub2.
10. Anstey KJ, Peters R. Oversimplification of dementia risk reduction messaging is a threat to knowledge translation in dementia prevention research. J Prev Alz Dis 2018;5(1):2-4.
11. Lenze EJ, Bowie CR. Cognitive training for older adults: what works?. J Am Geriatr Soc. 2018; 66(4):645-7. https://doi.org/10.1111/jgs.15230
12. Srisuwan P, Nakawiro D, Chansirikarnjana S, Kuha O,Janbumrung S, Thammachot T. Effects of group-based multicomponent cognitive training on cognition, mood and instrumental activities of daily living among older people living in the community. IJMBS 2019;3(2):102-13. https://doi.org/10.32553/ijmbs.v3i2.107
13. Dias FA, Tavares DM. Factors associated with the participation of elderly people in group educational activities. Rev Gaúcha Enferm 2013;34(2):70-7.
14. Srisuwan P, Nakawiro D, Chansirikarnjana S. Exploring factors that contribute to regular participation and practice in cognitive stimulation training for mild cognitive impairment: A qualitative study. JARH 2017;1(4):1-10. DOI: 10.14302/issn.2474-7785.jarh-16-1348.
15. Nilchaikovit T, Lortrakul M, Phisansuthideth U. Development of Thai version of Hospital Anxiety and Depression Scale in cancer patients. J Psychiatr Assoc Thailand. 1996; 41(1):18-30. http://www.psychiatry.or.th/JOURNAL/vol411.html#hads
16. Julayanont P, Tangwongchai S, Hemrungrojn S, Tunvirachaisakul C, Panthumchinda K, Hongsawat J, et al. The montreal cognitive assessment-basic; as creening tool for mild cognitive impairment in illiterate and low-educated elderly adults. J Am Geriatr Soc. 2015; 63(12):2550-4. https://doi.org/10.1111/jgs.13820
17. Hongthong D, Somrongthong R, Ward P. Factors influencing the Quality of Life (Qol) among Thai older people in a rural area of Thailand. Iran J Public Health 2015;44(4):479-85.
18. Jitapunkul S, Kamolratanakul P, Ebrahim S. The meaning of activities of daily living in a Thai elderly population; development of a new index. Age Ageing 1994; 23(2):332-36.
19. Harrison JA, Mullen PD, Green LW. A meta-analysis of studies of the Health Belief Model with adults. Health Educ Res 1992;7(1):107-16.
20. Marsh C, Agius P.A, Jayakody G, Shajehan R, Abeywickrema C, Durrant K, et al. Factors associated with social participation amongst elders in rural Sri Lanka: A cross-sectional mixed methods analysis. BMC Public Health 2018;18:636.
21. Houtum LV, Rijken M, Groenewegen P. Do everyday problems of people with chronic illness interfere with their disease management?. BMC Public Health 2015;15:1000.
22. Pinto JM, Neri AL. Factors related to low social participation in older adults: fining from the Fibra study, Brazil. Cad. saúde colet 2017;25(3):286-293.
23. Szanton SL, Roberts L, Leff R, Walker JL, Seplaki CL, Soones T, et al. Home but still engaged: participation in social activities among the homebound. Qual Life Res 2016;25:1913-20.
24. Galenkamp H, Gagliardi C, Principi A, Golinowska S, Moreira A, Schmidt AE, et al. Predictors of social leisure activities in older Europeans with and without multicomorbidiy. Eur J Ageing 2016;13:129-43.
25. Foster ER, Spence D, Toglia J. Feasibility of a cognitive strategy training intervention for people with Parkinson disease. Disabil Rehavil 2018;40(10):1127-34.
26. Turunen M, Hokkanen L, Bäckman L, Stingkdotter-Neely A, Hänninen T, Paajanen T, et al. Computer-based cognitive training for older adults: Determinants of adherence. PLoS One 2019;14(7):e0219541. doi: 10.1371/journal.pone.0219541. eCollection 2019.
27. Miller TA, DiMatteo MR. Importance of family/social support and impact on adherence to diabetes therapy. Diabetes Metab Syndr Obes 2013;6:421-6.
28. Goll JC, Charlesworth G, Scior K, Stott J. Barriers to social participation among lonely older adults: the influence of social fears and identity. PLoS One 2015;10(2):e0116664.
29. Meyer OL, Sisco SM, Harvey D, Zahodne LB, Glymour MM, Manly JJ, et al. Neighborhood predictors of cognitive training outcomes and trajectories in ACTIVE. Res Aging 2017;39(3):422-467.

EFFECTS OF A GROUP-BASED 8-WEEK MULTICOMPONENT COGNITIVE TRAINING ON COGNITION, MOOD AND ACTIVITIES OF DAILY LIVING AMONG HEALTHY OLDER ADULTS: A ONE-YEAR FOLLOW-UP OF A RANDOMIZED CONTROLLED TRIAL

 

P. Srisuwan¹, D. Nakawiro², S. Chansirikarnjana³, O. Kuha4, P. Chaikongthong5, T.Suwannagoot5

 

1. Department of Outpatient and Family Medicine, Phramongkutklao Hospital, Bangkok, Thailand; 2. Department of Psychiatry, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; 3. Department of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; 4. Institute of Geriatric Medicine, Ministry of Public Health, Nonthaburi, Thailand; 5. Phramongkutklao Geriatric Clinic, Phramongkutklao Hospital, Bangkok, Thailand

Corresponding Author: Patsri Srisuwan, Department of Outpatient and Family Medicine, Phramongkutklao Hospital, Bangkok, 10400, Thailand, Phone (662) 354-7660 ext. 93100, Fax (662) 354-9006, Email: patsri2004@yahoo.com

J Prev Alz Dis 2019;
Published online October 10, 2019, http://dx.doi.org/10.14283/jpad.2019.42

 


Abstract

BACKGROUND: Cognitive interventions have the potential to enhance cognition among healthy older adults. However, little attention has been paid to the effect of cognitive training (CT) on mood and activities of daily living (ADL).
OBJECTIVES: To assess the effectiveness of a multicomponent CT using a training program of executive functions, attention, memory and visuospatial functions (TEAM-V Program) on cognition, mood and instrumental ADL.
DESIGN: A randomized, single-blinded, treatment-as-usual controlled trial.
SETTING: Geriatric clinic in Bangkok, Thailand.
PARTICIPANTS: 77 nondemented community-dwelling older adults (mean age 65.7±4.3 years).
INTERVENTION: The CT (TEAM-V) program or the treatment-as-usual controlled group. The TEAM-V intervention was conducted over 5 sessions, with a 2-week interval between each session. Of 77 participants randomized (n=40 the TEAM-V program; n=37 the control group).
MEASUREMENTS: The Thai version of Montreal Cognitive Assessment (MoCA), The  Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-cog), Thai version of Hospital Anxiety and Depression Scale (HADS) and The Chula ADL were used to assess at baseline, 6 months and 1 year.
RESULTS: Compared with the control arm, the TEAM-V Program was associated with reducing anxiety (P = 0.004). Compared with the baseline, participants receiving the TEAM-V Program were associated with significantly improved general cognition (MoCA, P < 0.001), immediate recall (word recall task, P = 0.01), retrieval and retention of memory process (word recognition task, P = 0.01), attention (number cancellation part A, P < 0.001) and executive function (maze test, P = 0.02) at 1 year. No training effects on depression (P = 0.097) and IADL (P = 0.27) were detected.
CONCLUSIONS: The TEAM-V Program was effective in reducing anxiety. Even though, the program did not significantly improve cognition, depression and ADL compared with the control group, global cognition, memory, attention and executive function improved in the intervention group compared with baseline. Further studies incorporating a larger sample size, longitudinal follow-up and higher-intensity CT should be conducted.

Key words: Activities of daily livings, cognitive training, mood, older adults.


 

Introduction

Cognitive interventions such as group activities and problem solving, reading and computer-based training have the potential to enhance cognition among healthy older adults (1,2) and thus constitute promising approaches to prevent cognitive aging or even to delay the onset of cognitive impairment or dementia (3). Cognitive training (CT) uses guided practice on a set of tasks related to memory, executive function, attention or other brain functions. The goals of training are to improve or maintain ability in specific cognitive domains (4). This training can take many formats. For example, it can be conducted individually or in group, with either a single ,e.g., memory, or multiple, e.g., memory and executive function topics. Moreover, the approach might consider a bottom-up training, e.g., enhancing sensory and perceptual skills to improve higher order processing, or top-down, e.g., target mechanisms of cognitive control to improve the systematic problem-solving approach (5). CT not only improve cognition but also improve instrumental activities of daily living (IADL). For example, the Advanced CT for Independent and Vital Elderly (ACTIVE) study, the first large scale randomized controlled trail, demonstrated significant improvements in cognition such as memory and reasoning, which were sustained for up to 10 years, and also showed benefit to IADL(6).
Although several studies proved the beneficial effects of CT among healthy older adults concerning cognition (7), little attention has been paid to the effect of CT on mood, despite depression and anxiety being known to negatively impact cognition and related to early cognitive decline (8). Interestingly, CT among people at risk of dementia such as depression and/or minor neurocognitive disorder showed improvements in cognitive and mood functions (9). In addition, recent study has revealed CT among older people with minor neurocognitive disorders could protect against a decline in mental abilities such as depression and anxiety (10). However, some CT programs used advanced or expensive technology. Furthermore, trainings for healthy older adults were mostly computer-based (11) that were difficult to apply to some locations such as rural areas in Thailand. Moreover, no consensus exists regarding which specific CT is most appropriate such as multidomain versus unidomian; group versus individual settings and the most effective number of sessions.
In Thailand, studies pertaining to CT are conducted mostly among older adults with major neurocognitive disorder (12) and for minor neurocognitive disorder (13-16). In our previous study, a training of executive functions, attention, memory and visuospatial functions (TEAM-V Program) was developed for group-based multicomponent CT. A top-down approach was employed for CT among healthy older adults. The results showed that the intervention group exhibited significantly improvement from baseline regarding general cognitive function, immediate recall, retrieval and retention of memory process. Moreover, anxiety and depression scores decreased, and IADL scores increased in the intervention group. However, these improvements were not statistically significant when the results were compared with participants in the control group at 6-month follow-up (17). Therefore, longitudinal follow-ups could be extended to strengthen the study findings.
The aim of the present study was to assess the effectiveness of a group-based 8-week multicomponent CT by using the TEAM-V Program concerning cognition, mood and IADL among healthy older adults over 1 year. The hypothesis was individuals who received the program would demonstrate improved cognitive function, mood and IADL outcomes or delaying their impairment compared with those who not receiving the program at 6-months follow-up and durable to 1 year.

 

Methods

Eligibility criteria and recruitment methods

This experimental study is a part of the CT Program in Healthy Older People Project, conducted by the research team from The Institute of Geriatrics Medicine, Department of Psychiatry and Geriatrics Medicine Unit at Ramathibodi Hospital and Outpatients and Family Medicine Department at Phramongkutklao Hospital, Bangkok, Thailand. In this project 217 participants were enrolled from across the four region of the country. Research sites in each region were led by medical professionals such as geriatrics nurses, who received training during a 4-day workshop, held in February 2017, by the main research team. The workshop covered both clinical and instrumental assessment of cognitive function and method of delivering the CT program.
Participants in this study were from central region of Thailand; we recruited 98 participants who visited geriatric clinic, Outpatient department, Phramongkutklao Hospital, Bangkok, Thailand between April-May 2017. The enrolled participants were age > 60 years and willing to participate in all 5 activities. The exclusion criteria were: Thai version of Hospital Anxiety and Depression Scale (HADS) higher than 11 on anxiety or depression (18), Thai version of Montreal Cognitive Assessment (MoCA) less than 26 (19), had any conditions affecting participation in program activites, e.g. balancing problems, hearing impairment as well as any psychiatric diseases and neurological problems such as stroke. This study was approved by the Institutional Review Board of the Royal Thai Army Medical Department Ethics Committee as instituted (IRBRTA 599/60) by the Declaration of Helsinski, and all participants were required to provide written informed consent before enrollment. The study was registered under Thai Clinical Trials Registry (TCTR20190709003).

Study design

This was a single-blinded randomized controlled trial. Figure 1 illustrates the timeline of the study enrollment. Our reporting in the manuscript adheres to the CONSORT 2010 guidelines (20). After informed consent, participants were randomly allocated to either: intervention group; or control group on 1:1 basic using simple randomization methods. Randomization was carried out by a Clinical Trials Manager who was blinded to patient status throughout the study. Independent teams conducted the administration of the cognitive measure and the training session. All subjects were given an explanation about the protocol of their allocated group. Those who refuse to fully attend all required activities were excluded from the study. Recruitment was continuously done until the number of participants reached 40 for each group. The cognitive functions, moods, IADL were assessed by a neuropsychologist at baseline, 6 months and 1 year. The neuropsychologist was blinded to patient status throughout the study.

Description of the intervention

The control group received standard clinical care from their usual health-care professionals. The intervention group received CT using the TEAM-V Program that was a multidomain CT program consisted of training of executive function, attention, memory and visuospatial function. The training was held from May to July 2017, 5 sessions, with a 2-week interval between each session and 120 minutes per session. Each session involved training of different domains of cognition. Participants were encouraged to practice their homework during the intervention period. The details of each session are shown in Table 1. After the intervention, participants were encouraged to continue practice CT as much as possible.

Table 1. The TEAM-V Program cognitive training activities

Table 1. The TEAM-V Program cognitive training activities

 

Neuropsychological testing for baseline assessment and outcome measures

The primary outcome was change of cognitive function of participants. To identify changes of global cognitive function, we applied the Thai version of  MoCA to assess various domains of cognition including attention, executive function, memory, language, visuospatial skills, conceptualization, calculation and orientation. To identify changes of different domains of cognitive function, we applied main sub-tests of Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-cog) (21,22) (Table 2).
– Word recall task was administered to measure immediate recall memory. The participants were given 3 trials to remember a list of 10 words in block letter on white cards. Scoring was 1 point for each word if the participant did not remember it. Average total learning over 3 trials was examined.
– Constructional praxis was used to assess visuospatial function. The participants were asked to copy a cube on a piece of paper. Scoring was 1 point for each error including not 3-dimensional, the front face in the incorrect orientation, internal lines drawn incorrectly between corners and opposite sides of face not parallel or not equal size.
– Number cancellation part A to C was considered to be a general measure of attention. The participants were asked to cross off as many target as possible in 45 seconds. Scoring for part A was total of correct numbers crossed off, for part B was total of incorrect numbers crossed off and for part C was number of times reminded of task.
– Delayed recall was administered to measure delayed recall memory. The participants were asked to recall as many words as possible from the 10 words presented during the Word recall task.
– Maze test was used to assess executive function. The participants were asked to find the route from the start to the exit of the 7 mazes in the paper. Times of completion were records.
– Word recognition task was considered to be a general measure of retrieval and retention of memory process. The participants were given 3 trials to remember a list of 12 words in block letter on white cards. Then, the participants were given another set of words, some of the words were on the cards, but some of the words were not on the cards. Scoring was 1 point for each word if the participant did not remember it. Average total learning over 3 trials was examined.

Secondary outcomes were:
1.     Thai version of HADS comprised an anxiety and depression assessment tool. It consists of 14 question; 7 questions for anxiety and 7 questions for depression. Each item had been answered by the patient on a four point (0-3) response category. Score rage from 0-21 for anxiety and 0-21 for depression. A score of 11 or more are generally considered to have anxiety or depression (18).
2.     The Chula ADL Index is the Thai version and was used in this study under the term IADL, which assess the ability to perform complex tasks such as shopping and house keeping. The IADL indicates the ability to exist in the community independently, including the ability to perform daily tasks. Score rage from 0-9 (23).

Table 2. Alzheimer’s Disease Assessment Scale-Cognitive subscale for assessing cognitive function

Table 2. Alzheimer’s Disease Assessment Scale-Cognitive subscale for assessing cognitive function

Abbreviations: ADAS-cog, Alzheimer’s Disease Assessment Scale-Cognitive subscale

 

Statistical analyses

Statistic analyses were performed using Statistical Package for the Social Sciences version 23.0 for Window (SPSS, Chicago, IL, USA).  Unless otherwise stated, all values were presented as the mean + SD. Demographic comparisons between the intervention and control groups used the Fisher’s exact test, Independent t-test and Mann-Whitney U test. Participants in the intervention group who attended less than 80% of the sessions (4 sessions) were not included in the analysis. The intervention effects from baseline, 6 months follow-up and 1 year follow-up were evaluated using repeated measures of ANOVA. A value of P < 0.05 was considered statistically significant.

 

Results

Demographic analysis and baseline characteristic

A detail of flow chart of the present study is shown in Figure 1, of the 98 participants referred into the trial, 80 participants met eligibility criteria and completed baseline assessment. 77 (96.2%) participants performed the 6-month follow-up and 76 (95%) participants completed the 1-year follow-up. Adverse events were not reported from either group.
Patients’ characteristics at baseline for each group are shown in Table 3. The mean age of participants was 65.7 years old. Most participants were woman (80% in the intervention group vs. 73 % in the control group), most had a bachelor’s degree (63% VS 59%), had a chronic medical condition (83% vs 89%), regular exercise (95% vs 79%), and having leisure activities (85% vs 92%). There were no significant intergroup differences between age, gender, marital status, level of education, existing medical conditions, lifestyle such as exercise, leisure activities and participation in social activities, body mass index and the scores of MoCA, HADS and IADL on baseline.

Figure 1. CONSORT diagram of study enrollment flow

Figure 1. CONSORT diagram of study enrollment flow

Table 3. Participant characteristics at baseline

Table 3. Participant characteristics at baseline

Notes: a. Chi-square test, b. Fisher’s exact test, c. Independent t-test, d. Mann-Whitney U test. Abbreviations: MoCA, the Thai version of Montreal Cognitive Assessment; HADS, the Thai version of Hospital Anxiety and Depression Scale; IADL, instrumental activities of daily living; SD, standard deviation; NA, not applicable.

 

Effects of intervention on cognitive function and IADL

Table 4,5 illustrates baseline, 6-month and 1-year follow-up data of neuropsychological tests and IADL for the intervention and control groups. No significant differences were found in neuropsychological and IADL assessment results between two groups at 1 year. However, at 1-year follow-up, the intervention group showed significantly improvement from baseline in general cognitive function (MoCA, P < 0.001), immediate recall (word recall task, P = 0.01), retrieval and retention of memory process (word recognition task, P = 0.01), attention (number cancellation part A, P < 0.001) and executive function (maze test, P = 0.02). No training effects on IADL (P = 0.27) were detected at 1 year.

Table 4. Results of neuropsychological test and IADL scores at baseline, 6-month and 1-year follow-up

Table 4. Results of neuropsychological test and IADL scores at baseline, 6-month and 1-year follow-up

Notes: a. One-way repeated measure ANOVA, b. Two-way repeated measure ANOVA. *P<0.05. Abbreviations: MoCA, the Thai version of Montreal Cognitive Assessment; IADL, instrumental activities of daily living; Chula ADL index, the Chula activities of daily living index; SD, standard deviation; NA, not applicable.

Table 5. Mean differences in neuropsychological test and IADL scores between intervention group and control group at 6-month and 1-year follow-up

Table 5. Mean differences in neuropsychological test and IADL scores between intervention group and control group at 6-month and 1-year follow-up

Notes: a. Independent t-test, bMann-Whitney U test. Abbreviations: MoCA, the Thai version of Montreal Cognitive Assessment; IADL, instrumental activities of daily living; SD, standard deviation; NA, not applicable.

 

Effects of intervention on mood

Figure 2 illustrates baseline and follow-up data of HADS for the intervention and control groups. At 1-year follow-up, the intervention group showed significantly greater improvement than the control group in anxiety (Thai HADS: anxiety score, P = 0.004) but not in depression (Thai HADS: depression score, P = 0.097);

Figure 2. HADS score at the three assessment points (baseline, 6-month and 1-year follow-up) on anxiety (a) and depression (b). At 1-year follow-up, the intervention group showed significantly greater improvement than the control group in anxiety but not in depression

Figure 2. HADS score at the three assessment points (baseline, 6-month and 1-year follow-up) on anxiety (a) and depression (b). At 1-year follow-up, the intervention group showed significantly greater improvement than the control group in anxiety but not in depression

Note: a. Mann-Whitney U test, b. Two-way repeated measure ANOVA. *P<0.05. Abbreviations: HADS, the Thai version of Hospital Anxiety and Depression Scale.

 

Discussion

The aim of this study was to compare the effectiveness of a group-based 8-week multicomponent CT using the TEAM-V Program with treatment-as-usual control among healthy older adults. The participants in the intervention group showed significantly improvement in anxiety compared with the control group. Moreover, the intervention group also showed significant improvement from baseline in general cognitive function, memory, attention and executive function at 1-year follow-up.
Regarding the improvement of general cognitive function, in the intervention group received CT using the TEAM-V Program that was a multidomain CT program consisted of training of executive function, attention, memory and visuospatial function, the beneficial effects of parallel training in difference domains of a multidomain cognitive program could have additive effects versus a cognitive program focusing on a single restricted area (24). Moreover, this may be explained by the fact that CT enhances resting state neural activity and connectivity (25), increasing the blood supply to these regions via neurovascular coupling (26). However, the MoCA score in the intervention group showed significant change but the change of the raw scores was less than 2 points (mean change was 1.1 points) that did not reach clinical significance. Memory problems are a common concern for general older adults. Therefore, the TEAM-V Program provided 2 sessions for memory training, but other domains received 1 session per domain. In the intervention group, our findings demonstrated significant improvements in immediate recall memory, retrieval and retention of memory process at both 6-month and 1-year follow-ups. However, no improvements were found for delayed recall, possibly because delayed recall needs more rehearsal (27) and refreshing (28) than other types of memoires. Compared with the baseline in the intervention group, executive function and attention were improved at 1-year follow-up, but not at 6-month follow-up. Quite possibly the duration of training and complexity of task in the session may have be insufficient to show improvement at 6-month follow-up. For example, a CT of executive function using a breakfast cooking task by Wang and colleagues had 5 weeks with one session each week. The training session lasted for about 1 hour. Participants had to switch, update and plan to control the cooking of several foods and concurrently performed a table setting. The cooking training task significantly improved participants’ executive function (29).  Compared with the TEAM-V Program, the executive function training had only 1 session of planning and creating sandwiches with limited resources. Furthermore, a CT of attention using the online training by Wennberg and colleagues had 6 weeks with 1 session weekly. The training session lasted for about 1 hour. Participants had to train in the ATTENTION WORKOUT online program consisting of 5 attention training tasks (30). Compared with the TEAM-V Program, attention training had only 1 session consisting of 3 tasks of attention training.
Despite the assessment, executive function and attention did not show improvement at 6-month follow-up, but the assessment tended to improve at 1-year follow-up. Quite possibly each session of the TEAM-V Program involved homework and suggestions of practice techniques to perform in the participants’ daily life such as identifying internal and external distractors in the session of training in attention. Therefore, when participants continued practicing these techniques, their scores may have improved, and also greater familiarity from taking multiple tests might have helped to increase their scores due to practice effects (31). In addition, visuospatial functions were not improved in the intervention group because visuospatial processing involves a very dynamic and complex brain network such as interconnection with dorsal (spatial localization: where is it?) and ventral (identification of a stimulus: what is it?) pathways (32). Therefore, the visuospatial training in the TEAM-V Program provided only 1 session, and only a simple homework assignment such as drawing a map from home to hospital and may have been insufficient to improve visuospatial function. Training with various methods such as navigation task, visuomotor training and visuoconstruction procedures would further improve the function (33). Overall, the TEAM-V Program involved a 2-week interval between each session, 5 sessions over 8 weeks. Comparing with a meta-analysis by Chiu and colleagues suggested that the CT showed better effectiveness with weekly training sessions >3 times weekly, totaling training weeks >8 weeks, and total training session >24 sessions among healthy older adults (7). Therefore, weekly training sessions and total training sessions may have been insufficient to show significant improvement when comparing the intervention group with the control group.
Participants in the intervention group showed greater improvement in anxiety (P = 0.004) and depression (P = 0.097) compared with the control group at 1-year follow-up. However, the change of the raw HADS: anxiety scores was less than 1 point (mean change was 0.1 point) that did not reach clinical significance. The results of the current study concurred with related studies showing that CTs improved mood among healthy older adults (34), at risk of dementia (9) and people with Alzheimer’s disease (35). Accordingly the China Health and Retirement Longitudinal Survey (CHARLS) study revealed social engagement not only significantly improved self-rated health but also reduced mental distress (36). The effect of reducing anxiety could have been from social engagement. Interaction with others during group activities in the TEAM-V Program could have helped participants to improve their self-esteem and to enjoy activities that helped them to achieve a better mental health.
In the present study, no statistic significant changes were found in IADL scores in both intervention and control groups. This was similar to the results of a randomized controlled trail study involving 2,832 participants by Ball and colleagues revealing that no training effects on everyday functioning were detected at 2 years (37). Quite possibly successful completion of ADL depends upon having the cognitive skills, e.g., memory,  executive function, necessary to accomplish the tasks (38). However, because of minimal functional decline across healthy older adults, longer follow-up is likely required to observe training effects on ADL. For example, the ACTIVE study, an experimental study among 2,832 healthy older adults with 10-years follow-up found that in 3 intervention groups of CT by memory training, reasoning training or speed training was reportedly less difficulty in IADL compared with the no-contact control group (6). Moreover, CT may improve IADL among people with dementia more than among healthy older adults. An experimental study of CT conducted among healthy older adults, healthy older adults with minor neurocognitive disorders and Alzheimer’s disease also found IADL significant improved in the intervention group of Alzheimer’s disease, but not among those with minor neurocognitive disorders and healthy older adults (39).
To our knowledge, the present study is the first to report multicomponent CT among healthy older adults with 1-year follow-up in Thailand. The TEAM-V Program consisted of many simple activities that are easy to apply in remote areas in Thailand or in other countries. For example, the program could easily train volunteers to conduct stimulating cognition activities for healthy older adults in elderly clubs. In addition, although some older adults would not be familiar with high technology such as computer or online program, they could still join the program. The program not only improves cognition functions such as memory, attention and executive functions but also improves mood especially anxiety. In addition, the participants in the intervention group remained enrolled in the study throughout the study 1-year follow-up; and no loss to follow-up was observed. It would be possible that participants adhere with the study because participants felt the program was useful and practical to apply in their daily life. However, the program still needed to be tested comparing other group activities and other settings as well as to be corrected for some limitations. Then, the program can also serve as a model to improve the mental health of healthy older adults.
However, some limitations were involved in the present study. First, participants were recruited from a hospital in the central region in Thailand. Therefore, the results may be unable to generalize to other regions of Thailand or other countries. It should also be noted that in both groups, the percentage of females was greater than that of males. Moreover, the use of treatment-as-usual control limited the interpretation of the training effects found. An improved design incorporating active control may better clarify the nature of the training effects found. Quite possibly that the improvements in cognition and mood could have been attributed to nonspecific effects and from factors such as other interventions that may have formed part of a patient’s routine management or CT undertaken by participants of their own accords. Participants were not evaluated cognitive function right after the end of the 8-week intervention due to avoiding practice effects (31). Another limitation of this study is that the authors did not check how long and to what extend the participants conducted their homework. For future studies, a homework-diary should be implemented to control for intensity and total time spent on the cognitive homework. In addition, sample size, longitudinal follow-up (at least 7-14 years especially to assess the improvement of IADL (37)) and higher intensity CT (often 150 minutes per weekly (38)) could be extended in future studies to strengthen conclusions. Moreover, future research should include holistic assessments including possible confounding factors that might interfere with cognitive function such as hospitalization and medication, and lifestyle factors such as physical exercise (40), social engagement (41) and sleep hygiene (42), brain imagery techniques and biomarkers to identify the specific aspects related to cognitive improvement in CT.

 

Conclusion

To summarize, the current randomized controlled trial provided evidence that the TEAM-V Program was effective in reducing anxiety. Although the program did not show significantly improve cognition, depression and IADL compared with the control group, global cognition, memory, attention and executive function in the intervention group tended to improve compared with baseline. Further studies incorporating a larger sample size, longitudinal follow-up and higher-intensity CT should be conducted. Interestingly, the program could be easily implemented across a variety of settings, and may even enable healthy older adults to continue engagement in inexpensive and practical CT.

 

Acknowledgement: The authors would like to express our gratitude to the participants and PMK aging team in Geriatric Clinic, Phramongkutklao Hospital. We also would like to thank the Institute of Geriatric Medicine for allowing the research team to be a part of the larger study and conducted project activities in the Central region of Thailand. We also thank Ms. Worarachanee Imjaijitt for statistical analysis.

Funding: The research project was partially supported by The Thai Health Promotion Foundation. 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 disclosure: There is no conflict of interest.

Ethical standards:The TEAM-V study was approved by the Institutional Review Board of the Royal Thai Army Medical Department Ethics Committee as instituted by the Declaration of Helsinski.

 

SUPPLEMENTAL MATERIAL

 

References

1.     Martin M, Clare L, Altgassen AM , Cameron MH, Zehnder F. Cognitive-based interventions for healthy older people and people with mild cognitive impairment. Cochrane Database of Systematic Reviews. 2011, Issue 1. Art. No.: CD006220. https://doi.org/10.1002/14651858.CD006220.pub2
2.    Li Ting, Yao Ye, Cheng Y, Xu B, Cao Xinyi, Waxman D, et al. Cognitive training can reduce the rate of cognitive aging; a neuroimaging cohort study. BMC Geriatrics 2016;16:12.
3.    Smith GE. Healthy cognitive aging and dementia prevention. Am Psychol 2016; 71(4):268-75.
4.    Bahar-Fuchs A, Clare L, Woods B.  Cognitive training and cognitive rehabilitation for persons with mild to moderate dementia of the Alzheimer’s or vascular type: a review.  Alzheimers Res Ther 2013; 5(4):35.
5.    Lenze EJ, Bowie CR. Cognitive training for older adults: what works?. J Am Geriatr Soc 2018; 66(4):645-7.
6.    Rebok GW, Ball K, Guey LT, Jones RN, Kim HY, King JW, et al. Ten-year effects of the advanced cognitive training for independent and vital elderly cognitive training trial on cognition and everyday functioning in older adults. J Am Geriatr Soc 2014; 62(1):16-24.
7.    Chiu HL, Chu H, Tsai JC, Liu D, Chen YR, Yang HL, et al. The effect of cognitive-based training for the healthy older people: a meta-analysis of randomized controlled trials. PLoS One 2017; 12(5);e0176742.
8.    Mah L, Szabuniewicz C, Fiocco AJ. Can anxiety damage the brain?. Curr Opin Psychiatry 2016; 29:56–63.
9.    Diamond K, Mowszowski L, Cockayne N, Norrie L, Paradise M, Hermens DF, et al. Randomized controlled trial of a healthy brain ageing cognitive training program: effects on memory, mood, and sleep. J Alzheimers Dis 2015; 44(4):1181-91.
10.    Belleville S, Hudon C, Bier N, Brodeur C, Gilbert B, Grenier S. et al. MEMO+: Efficacy, Durability and Effect of Cognitive Training and Psychosocial Intervention in Individuals with Mild Cognitive Impairment. J Am Geriatr Soc 2018; 66(4):655-63.
11.    Butler M, McCreedy E, Nelson VA, Desai P, Ratner E, Fink HA, et al. Does Cognitive Training Prevent Cognitive Decline?: A Systematic Review. Ann Intern Med 2018; 168(1):63-8.
12.    Chaiwong P, Rattakorn P, Mumkhetvit P. Effect of cognitive training program on cognitive abilities and quality of life in elderly with suspected dementia. Bull Chiang Mai Assoc Med Sci 2015; 48(3): 182-91.
13.    Chaikham A, Putthinoi S, Lersilp S, Bunpun A, Chakpitak N. Cognitive training program for Thai older people with mild cognitive impairment. Procedia Environmental Sciences 2016; 36:42-5.
14.    Nakawiro D, Chansirikarnjana S, Srisuwan P, Aebthaisong O, Sudsakorn P, Vidhyachak C, et al. Group-based training of executive function, attention, memory and visuospatial function (Team-V) in patient with mild neurocognitive disorder. J Psychiatr Assoc Thailand 2017; 62(4):337-48.
15.    Sukontapol C, Kemsen S, Chansirikarn S, Nakawiro D, Kuha O, Taemeeyapradit U. The effectiveness of a cogntivie training program in people with mild cognitive impairment: A study in urban community. Asian J Psychiatr 2018; 35:18-23.
16.    Jirayucharoensak S1,2, Israsena P1, Pan-Ngum S2, Hemrungrojn S3, Maes M3. A game-based neurofeedback training system to enhance cognitive performance in healthy elderly subjects and in patients with amnestic mild cognitive impairment. Clin Interv Aging 2019; 14:347-60.
17.    Srisuwan P, Nakawiro D, Chansirikarnjana S, Kuha O,Janbumrung S, Thammachot T. Effects of group-based multicomponent cognitive training on cognition, mood and instrumental activities of daily living among older people living in the community. Int J Med Biomed Sci 2019; 3(2):102-13.
18.    Nilchaikovit T, Lortrakul M, Phisansuthideth U. Development of Thai version of Hospital Anxiety and Depression Scale in cancer patients. J Psychiatr Assoc Thailand 1996; 41(1):18-30.
19.    Julayanont P, Tangwongchai S, Hemrungrojn S, Tunvirachaisakul C, Panthumchinda K, Hongsawat J, et al. The montreal cognitive assessment-basic; as creening tool for mild cognitive impairment in illiterate and low-educated elderly adults. J Am Geriatr Soc 2015; 63(12):2550-4.
20.    Moher D, Hopewell S, Schulz KF, Montori V, Gøtzsche PC, Devereaux PJ, et al. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ 2010; 340:c869.
21.    Rosen WG, Mohs RC, Davis KL. A new rating scale for Alzheimer’s disease. Am J Psychiatry 1984; 141:1356-64.
22.    Mohs RC, Knopman D, Petersen RC, Ferris SH, Ernesto C, Grundman M, et al. Development of cognitive instruments for use in clinical trials of antidementia drugs: additions to Alzheimer’s Disease assessment scale that broaden its scope. Alzheimer Dis Assoc Disord 1997; 11(Suppl 2):S13-21.
23.    Jitapunkul S, Kamolratanakul P, Ebrahim S. The meaning of activities of daily living in a Thai elderly population; development of a new index. Age Ageing 1994; 23(2):332-6.
24.    Kim H, Yang Y, Oh J, Oh S, Choi H, Kim KH, et al. Effectiveness of a community-based multidomain cognitive intervention program in patients with Alzheimer’s disease. Geriatr Gerontol Int 2016;16:191-199.
25.    ten Brinke LF, Davis JC, Barha CK, Liu-Ambrose T. Effects of computerized cognitive training on neuroimaging outcomes in older adults: a systematic review. BMC Geriatrics 2017; 17:139.
26.    Chapman SB, Aslan S, Spence JS, Hart JJ, Bartz EK, Didehbani N, et al. Neural mechnisms of brain plasticity with complex cognitive training in healthy seniors. Cerebral Cortex 2015; 25:396-405.
27.    Wixted JT. Conditions and consequences of maintenance rehearsal. J Exp Psychol Learn Mem Cogn 1991;17(5):963-73.
28.    Camos V, Portrat S. The impact of cognitive load on delayed recall. Psychon Bull Rev 2015; 22(4):1029-34.
29.    Wang M, Chang C, Su S. What’s cooking?- Cognitive training of executive function in the elderly. Front Psychol 2011; 2:228.
30.    Wennberg A, Kueider A, Spira A, Adams G, Rager R, Rebok G. Online Attention Training for Older Adults. Int J Cogn Technol 2014; 19(2):13-21.
31.    Cooley SA, Heaps JM, Bolzenius JD, Salminen LE, Baker LM, Scott SE, et al. Longitudinal change in performance on the Montreal Cognitive Assessment in older adults. Clin Neuropsychol 2015;29(6):824-835.
32.    Trés ES, Brucki SM. Visuospatial processing: A review from basic to current concepts. Dement Neuropsychol 2014; 8(2): 175–81.
33.    Tippett WJ, Rizkalla MN. Brain training: rationale, methods, and pilot data for a specific visuomotor/visuospatial activity program to change progressive cognitive decline. Brain Behav 2014; 4(2):171–9.
34.    Smith M, Jones MP, Dotson MM, Wolinsky FD. Computerized cognitive training to improve mood in senior living settings: design of a randomized controlled trial. Open Access Journal of Clinical Trials 2018; 10:29-41.
35.    Kim HJ, Yang Y, Oh JG, Oh S, Choi H, Kim KH, et al. Effectiveness of a community-based multidomain cognitive intervention program in patients with Alzheimer’s disease. Geriatr Gerontol Int 2016;16(2):191-9.
36.    Liu J, Rozelle S, Xu Q, Yu N, Zhou T. Social Engagement and Elderly Health in China: Evidence from the China Health and Retirement Longitudinal Survey (CHARLS).  Int. J. Environ. Res. Public Health 2019; 16(2):278.
37.    Ball K, Berch DB, Helmers KF, Jobe JB, Leveck MD, Marsiske M, et al. Effects of cognitive training interventions with older adults; a randomized controlled trial JAMA 2002; 288(18):2271-81.
38.    Jones RN. Cognitive training improves cognitive performances, but what else?. J Am Geriatr Soc 2018; 66:648-9.
39.    Giuli C, Fattoretti P, Gagliardi C, Mocchegiani E, Venarucci D, Balietti M, et al. My Mind Project: the effects of cognitive training for elderly-the study protocol of a prospective randomized intervention study. Aging Clin Exp Res 2017; 29(3):353-360.
40.    Sanders LMJ, Hortobágyi T, la Bastide-van Gemert S, van der Zee EA, van Heuvelen MJG. Dose-response relationship between exercise and cognitive function in older adults with and without cognitive impairment: A systematic review and meta-analysis. PLoS One 2019; 14(1):e0210036.
41.    Evans IEM, Llewellyn DJ, Matthews FE, Woods RT, Brayne C, Clare L, et al. Social isolation, cognitive reserve, and cognition in healthy older people. PLoS One 2018; 13(8):e0201008.
42.    Baumgart M, Snyder HM, Carrillo MC, Fazio S, Kim H, Johns H. Summary of the evidence on modifiable risk factors for cognitive decline and dementia: a population-based perspective. Alzheimer’s & Dementia 2015; 11:718-26.

IMPACT OF DIETARY FACTORS AND INFLAMMATION ON COGNITION AMONG OLDER ADULTS

 

E.P. Handing1, B.J. Small1, S.L. Reynolds1, N.B. Kumar2

 

1. School of Aging Studies, University of South Florida, USA; 2. Moffitt Cancer Center, University of South Florida, USA

Corresponding Author: Elizabeth Handing, School of Aging Studies, University of South Florida, Tampa, FL 33612, USA. Email: handing@mail.usf.edu

J Prev Alz Dis 2015;2(4):220-226
Published online January 14, 2015, http://dx.doi.org/10.14283/jpad.2015.50


Abstract

OBJECTIVE: This study examined the influence of age, nutrition (as measured through food diaries and serum/plasma biomarkers) and inflammatory markers on cognitive performance in adults 60 years of age and older.

DESIGN: A cross-sectional population based study, data from the National Health and Nutrition Examination Survey (NHANES; 2001-2002 wave).

PARTICIPANTS: This study included 1,048 adults who had valid dietary data, blood biomarkers, were 60 years or older, completed the cognitive test, and had complete demographic information.

METHOD: A series of regression models were used to examine the relationship between cognitive function as measured by the Digit Symbol Substitution Task (DSST), dietary factors/biomarkers and inflammation. Mediation analyses were then utilized to examine whether individual nutrients accounted for the relationships between age and DSST performance.

RESULTS: Dietary fat intake, serum vitamin E, serum folate, serum iron, plasma homocysteine, and serum vitamin D were significantly associated with better DSST performance. Elevated fibrinogen and C-reactive protein, were significantly associated with poorer cognitive function, but did not remain statistically significant after controlling for age, gender, education, ethnicity, income, and total calorie intake. Serum vitamin D and plasma homocysteine accounted for a portion of age-related variance in DSST. Specifically, higher levels of vitamin D were related to better DSST performance, while higher homocysteine resulted in poorer cognitive performance.

CONCLUSION: Diet and nutrition are important modifiable factors that can influence health outcomes and may be beneficial to remediate age-related declines in cognition. Adequate nutrition may provide a primary preventive approach to healthy aging and maintenance of cognitive functioning in older adults.

 

Key words: Nutrition, cognition, older adults.


 

Introduction 

As the aging population is steadily on the rise, it is estimated that the number of adults in the United States 65 and older will reach 88.5 million by 2050 (1). The majority of older adults experience some form of cognitive decline (2, 3) and this has fueled research toward interventions aimed at ways for older adults to maintain and preserve their cognitive abilities. One potential intervention is through nutrition and dietary modification (2). A growing area of research in the aging field suggests that dietary components (antioxidant nutrients, fish, dietary fats, and B-vitamins) may play a role in the risk of age-related cognitive decline (4-6). This study will investigate the contribution of nutritional factors as well as inflammatory markers to cognitive function in older adults.

The relationship between diet and cognitive decline has mainly been investigated using a single nutrient approach (7, 8). For example, Morris et al (9) found that higher Vitamin E intake (from food alone and supplementation) was related to less cognitive decline in older adults across four cognitive tasks.  Llewellyn et al. (8) examined Vitamin D  (as measured by levels of serum 25-hydroxyvitamin D) and risk of cognitive decline finding that Vitamin D deficiency was related to an increase in cognitive decline over 6 years. Recently, vitamin D deficiency has also been linked to increased risk of dementia and Alzheimer’s disease (10). Although these studies provide insight into the link between diet and cognition, they are limited by viewing nutrients in isolation rather than considering nutrients from diet and have yet to include inflammation as a potential mediator in this pathway.

In these analyses, data from the National Health and Nutrition Examination Survey (NHANES) 2001-2002 wave were used to examine dietary macronutrients (fat, protein, and carbohydrates), and select blood serum/plasma biomarkers (vitamin B12, vitamin D, vitamin E, folate, iron, and homocysteine) (6). The purpose of this study is to examine the association between age, nutrition, cognitive performance (as measured by the Digit Symbol Substitution Task), and inflammatory markers in adults 60 and older.

Additional studies suggest an association between the pathogenesis of cognitive decline and inflammatory markers including C-reactive protein (CRP), ferritin, and fibrinogen (11, 12). The release of C- reactive protein (CRP) and other inflammatory markers may contribute to increased cognitive decline via the inflammatory pathway (13). Previous literature suggests that CRP levels are associated with mild cognitive impairment, a prodromal stage of Alzheimer’s Disease (14). Other biomarkers such as fibrinogen and ferritin have been related to cognitive decline (15), however few studies have analyzed the association between these inflammatory markers, cognitive function, and dietary factors.

This study examined how nutritional factors measured through diet and serum/plasma along with inflammatory markers may be associated with cognitive decline and investigates potential mediators to cognitive performance.

Method

Participants

This project used data from the National Health and Nutrition Examination Survey (NHANES) waves 2001-2002. NHANES conducts an extensive nutritional survey combining in-person interviews, questionnaires, and physical examinations on individuals varying from children to older adults. NHANES over-samples persons 60 and older, African Americans, and Hispanics to include a diverse sample population. Recruitment was performed using a stratified, multistage probability sample of non-institutionalized individuals living in the United States (16). Participation in the study required an in-home visit to administer questionnaires and cognitive testing, as well as a visit to a mobile examination center (MEC) for a comprehensive health examination. Our target population was adults 60 and older with valid dietary information and health measures.

Measures

Demographic variables 

Information on age, gender, ethnicity, education, and income was collected from a self-reported questionnaire. Age was coded as a continuous variable, gender was categorical, ethnicity was categorized as Non-Hispanic White, Non-Hispanic Black, Mexican American, other Hispanic, and other race, education was categorized as less than high school, high school, some college, or college graduate or above, and income was categorized as earning less than $14,999, earning $15,000-$49,000 or earning more than $50,000.

Cognitive assessment

Cognitive functioning was measured by the Digit Symbol Substitution Task (DSST) from a version of WAIS-IV. Participants were instructed to draw symbols corresponding to a number key, and the score is the number of correct symbols drawn within a period of 120 seconds. One point is given for each correctly drawn symbol completed within the time limit. The range in our sample was from 6-100 points, the average was 44.8 points.

Dietary analysis

Each participant completed a 24-hour dietary recall which was completed at the in-person interview. Participants were asked to describe their previous day’s consumption of foods and beverages using detailed diagrams and pictures for accurate portion size and ingredients. The second dietary recall was collected by telephone and was scheduled 3 to 10 days later. Dietary information from both days was averaged for total nutrient values. NHANES 2001-2002 nutrient intakes were calculated using USDA’s Food and Nutrient Database for Dietary Studies (FNDDS). The following dietary nutrients were included as predictors in the analyses: total fat (gm), protein (gm), and carbohydrates (gm).

Serum samples

Approximately 6 tablespoons of blood was drawn via venipuncture by a certified medical professional during the MEC visit. In the current study, the following serum/plasma samples were and used for analyses: serum vitamin B12 (pg/mL), serum folate (ng/mL), plasma homocysteine (μmol/L), serum iron (μg /dL), vitamin E serum (μg/mL), and vitamin D serum (ng/ mL).

Inflammatory markers

CRP (mg/dL), fibrinogen (mg/dL), and ferritin (ng/mL) were collected through the blood draw as part of the NHANES medical examination. Collection techniques and details can be found in the NHANES Laboratory/Medical Technologists Procedures Manual (17).

Statistical Analysis

The analytic strategy consisted of regression models to examine the predictive value of age, nutrients, and inflammation to cognitive performance among older adults. First, a series of univariate linear regression models were used to examine individual dietary and serum biomarkers and inflammatory markers on DSST, independent of demographic characteristics. Second, a multiple regression model was conducted based upon significant predictors from the previous models. Finally, mediation analyses were used to examine the effects of dietary/ serum biomarkers and age on DSST performance. It is known that areas of cognition decline with age, however it is not known how diet may influence cognition. By examining age, nutrient status, and cognitive function in a mediation model, we are better able to examine these continuous variables in relation to each other.  Mediation provides calculations of direct effects for the model X ->Y (where X represents age and Y represents DSST performance) as well as indirect effects X-> Z -> Y where Z represents nutrients as a mediator between age and DSST performance. The bootstrapping method (with bias correction and 5,000 iterations) was used to evaluate the direct and indirect mediating paths (18). All analyses were analyzed using SAS software (SAS Institute, Cary, NC) Version 9.3.

Results

A flowchart with the number of participants included in analyses is indicated in Figure 1. Participants were not included in analyses if they had missing dietary information (n=1,408), missing cognitive testing (n=8,361), were outliers by 3 standard deviations in cognitive testing (ie., score of <5; n=15), were outliers by 3 standard deviations in calorie intake (ie., <500 kcal/day or >5,000 kcal/day; n= 21), or missing demographic information (n=188). Our final analytic sample was 1,048 older adults. Table 1 displays the basic demographic characteristics of the sample. The mean age was approximately 71 years of age and mean calorie consumption was almost 1,800 calories per day.

Table 1. Sample demographic characteristics

 Note: NHANES= National Health And Nutrition Examination Survey

Table 2 presents the results of the univariate regression models examining demographic variables, nutrients, and inflammatory markers as predictors for cognitive functioning. Statistically significant demographic factors included, age, gender, ethnicity, education, income, and total calorie intake. Being older, male, non-Hispanic black, having a high school education or less, having less than $15,000 income, and a low calorie intake was related to worse DSST performance. All dietary macronutrients and serum biomarkers were significant predictors of DSST performance. All of the nutrients and biomarkers were positively related to DSST performance, except for homocysteine, which was related to worse cognitive performance. For the inflammatory markers, higher values of fibrinogen and CRP were related to worse DSST performance. Table 3 depicts the multivariate analyses of significant nutrient markers to DSST performance controlling for age, gender, education, ethnicity, income, and total calorie intake. Positive associations were found with higher intake of dietary fat, serum vitamin E, serum folate, serum iron, and serum vitamin D being related to better DSST performance, while a negative association was found for plasma homocysteine meaning higher values were related to lower DSST scores. There were no significant results for inflammatory factors.

Table 2. Univariate results for predictor variables with DSST performance as the outcome, n=1048

Note:  Vit= Vitamin, CRP= C-Reactive Protein, BMI= Body Mass Index; *=significance at the .05 alpha level; a. Estimates are based upon the covariates entered together on the first step; b. Estimates are based upon variables entered singly after controlling for covariates.

 

Table 3. Regression model of nutrients and inflammation with DSST as outcome controlling for age, sex, education, ethnicity, income, and total calorie intake. An asterisk indicates significance at the .05 alpha level

The final set of analyses examined the potential for the statistically significant nutrient markers to mediate age-related differences in DSST performance (Figure 2). The results indicated that serum vitamin D and plasma homocysteine acted as full mediators of age-related differences in performance (indirect effect and 95% CI in brackets): plasma homocysteine (-.036, CI [-.071 to -.01]), serum vitamin D (.017, CI [.004 to .038]). Homocysteine levels increased with age, but higher levels were associated with poorer performance. Vitamin D level increased with age and resulted in better DSST performance. Several nutrients acted as partial mediators (indirect effect and 95% CI in brackets): dietary fat (.001, CI [-.006 to .012]), serum vitamin E (.002, CI [-.008 to .017]), serum folate (.017, CI [-.008 to .047]) and serum iron (-.001 CI [-.015 to .013]. In the case of iron, older age was associated with lower values but not statistically significant, however higher iron values resulted in better cognitive performance. Vitamin E, and folate increased with age, but higher levels were not significantly related to DSST performance.

Figure 1. Flowchart of participants from the National Health And Nutrition Examination Survey (NHANES) 2001-2002

Figure 2. Multiple mediation with nutrient intake as mediators controlling for gender, education, ethnicity, income, and total calorie intake. The multiple mediation model of X -> Z -> Y (where X represents age, Z represents the nutrient, Y represents the Digit Symbol Substitution Task (DSST) performance. The bootstrapping method with bias corrected confidence estimates (based upon 5,000 iterations) was used to test the mediation hypothesis (18). Note: Vit= vitamin, hmcy= homocysteine, an asterisk indicates significance at the .05 alpha level.

 

Discussion

Findings from the univariate and multiple regression models suggest that several nutrients were associated with performance on the DSST including dietary fat, serum values for vitamin E, folate, iron, plasma homocysteine, and vitamin D. Mediation analyses further examined these relationships and found vitamin D and homocysteine acted as significant mediators between age and cognitive performance.

Mediation for biomarkers and cognitive performance

Recently Littlejohns and colleagues (10) reported that low serum vitamin D levels (< 25nmol/L) resulted in a two-fold increase in risk for dementia and Alzheimer’s Disease. Additional studies have found evidence suggesting a relationship between insufficient serum vitamin D and cognitive decline (19, 20). Our results indicate that higher serum vitamin D was related to better cognitive performance on the DSST.

A study by Bowman and colleagues (21) examining multiple biomarkers and cognitive function found that a dietary nutrient biomarker pattern high in antioxidant vitamins B, C, D, and E, was related to better executive function, attention, visuospatial function, and global cognition. Our findings of vitamin D and E as important nutrients for brain health and function are supported.

Additionally, plasma homocysteine was found to negatively mediate the relationship between age and cognitive performance. With age, homocysteine increased while higher values negatively affected DSST scores. Elevated plasma homocysteine concentrations have been consistently associated with both cognitive impairment and dementia and found to negatively mediate the relationship between age and cognitive performance (22). Research from the Framingham study found an association between higher homocysteine levels (>14 µmol per liter) and nearly a two-fold increase in risk of Alzheimer’s disease (23). Homocysteine is a sulfur-containing amino acid generated through the demethylation of the essential amino acid methionine. It is largely catabolized by trans-sulfuration to cysteine, but it may also be remethylated to methionine. Deficiencies in the homocysteine re-methylation cofactors cobalamin (B12) and folate, as well as the trans-sulfuration cofactor vitamin B6, are commonly seen in the elderly population, with a resultant increase in homocysteine with advancing age (24). Increasing number of studies have demonstrated that high red cell folate levels were associated with worse long-term episodic memory, total episodic memory, and global cognition (25-27). In a large population-based sample of elderly people, the association between high homocysteinemia and decreased cognition was only seen in participants with low folate levels (26). Similarly, Blasko et al (27) reported higher levels of homocysteine predictive of  moderate/severe global brain atrophy at five years while folate demonstrated a protective ability to reduce conversion to dementia in moderately cognitively impaired patients.  Based on these studies, hyperhomocysteinemia continues to be consistently associated with an increased risk of cognitive impairment in the elderly with more recent studies suggesting that folate levels may also influence the course of cognitive decline.

These observations may have significant implications for future interventions with nutritional cofactors for proper functioning of the methionine cycle which may ultimately improve methylation and protect the brain from damage.

Interpretation of partial mediators

Partial mediation was found for serum vitamin E and folate, which positively increased with age. Dietary fat intake and serum iron were not related to age, but were significantly related to better cognitive performance. Fat intake can be beneficial for cognition, but this interpretation should be evaluated with care. Presently, omega 3 fatty acids via dietary intake (i.e. higher amounts of fish) have been associated with a reduced risk for dementia (28). In a study by Morris et al. (5) older adults who consumed 1 or more fish meals per week compared with those with less than weekly consumption showed a reduced rate of decline by 10% to 13% as indicated by the Mini Mental Status Exam score. Conversely, high intake of saturated fat (found in cheese, red meat, and whole milk) has been related to an increased risk of dementia with the greatest effect for vascular type dementia (Relative Risk Ratio =2.9, 95% CI (0.6-13.8) p =0.01) (29). In our study, we further investigated the type of fat and found no significant effect for saturated fat, monounsaturated fat, or polyunsaturated fat on cognitive performance (data not shown). Therefore, our finding that dietary fat intake was associated with cognitive performance should be interpreted within a holistic dietary pattern, rather than individual fat values.

In our study, serum iron was predictive of better cognitive performance. The study population had an average of 86.36 µg/dL (normal range 60-170 µg/dL) meaning most older adults in our sample were receiving adequate iron from their diet and/or supplements. This finding is provocative in light of the association of the role of iron and other metals like copper in inducing oxidative stress and should be more thoroughly examined with additional studies, in particular its association with homocysteine. The involvement of homocysteine involving iron dysregulation and oxidative stress designated as the ferric cycle has also been implicated in AD (30).

Strengths & Limitations

Strengths of this study include the detailed dietary record information on over 1,000 older adults. NHANES over-recruits older adults with a focus on dietary assessment and health status of Americans. It is important to note that our results are from a cross sectional sample and longitudinal data would strengthen the results.

Our study is limited by containing a single assessment of cognition which may not capture the specific changes that occur with age. We were unable to examine the potential for reverse causation (i.e. lower cognition impacting dietary choices) which could be addressed in a longitudinal model.  Previous studies show that processing speed changes with age and this may be of interest given our aim was to examine cognitive performance in older adults. Our study also used self-reported dietary records, which are susceptible to over/under estimation.

Conclusion

In summary, age accounts for some of the decline in DSST performance, but a poorer performance may be exacerbated by an unhealthy diet. Initially, inflammatory markers showed an association with poorer cognitive performance, however after controlling for age, sex, education, ethnicity, income, and total calorie intake, results were no longer significant which limited the potential for examining them as mediators. From our mediation results from nutrients, we found that serum vitamin D served as a full mediator and was related to better cognitive performance. A significant negative meditation of plasma homocysteine was related to worse cognitive performance.

Future studies are warranted to provide longitudinal analysis of the effects of age, dietary, and inflammatory factors on cognitive functioning. Multiple cognitive tests should be included to examine whether nutrients affect certain parts of the brain more than others, which could have significant clinical public health implications such as revising diet recommendations for older adults. Dietary factors and nutrient values may play a pivotal role in brain health among older adults and can be translated into lifestyle modifications to promote healthy aging and remediate cognitive decline.

Conflict of interest: Authors have no conflicts of interest.

Ethical standards:  Institutional Review Board (IRB) approval and documented consent was obtained from all participants.

Funding: NHANES is funded by the U.S. government, National Center for Health Statistics, Centers for Disease Control and Prevention (CDC). The sponsors had no role in the design and conduct of study; in the collection, analysis, and interpretation of the data; in the preparation of the manuscript; or in the review and approval of the manuscript.

References

1. Vincent GK, Velkoff VA. The next four decades: The older population in the United States: 2010 to 2050.2010. Population estimates and projections. Washington, D.C., U.S. Dept. of Commerce, Economics and Statistics Administration, U.S. Census Bureau: p 14

2. Deary IJ, Corley J, Gow AJ et al. Age-associated cognitive decline. Brit Med Bull. 2009; 92:1 135-152

3. Salthouse T. What and when of cognitive aging. Curr Dir Psychol  Sci. 2004; 13:4 140-144

4. Spencer J. Flavonoids and brain health: Multiple effects underpinned by common mechanisms. Genes Nutr. 2009; 4: 243-250

5. Morris MC, Evans DA, Tangney CC, Bienias JL, Wilson RS. Fish consumption and cognitive decline with age in a large community study. Arch Neurol. 2005; 62:12 1849-1853

6. Gu Y, Scarmeas N. Dietary patterns in Alzheimer’s disease and cognitive aging. Curr Alzheimer Res. 2011; 8:5 510-519

7. Kang JH, Cook N, Manson J, Buring JE, Grodstein F. A randomized trial of vitamin E supplementation and cognitive function in women. Arch Intern Med. 2006; 166:22 2462-2468

8. Llewellyn DJ, Lang IA, Langa KM et al. Vitamin D and risk of cognitive decline in elderly persons. Arch Intern Med. 2010; 170:13 1135-1141

9. Morris MC, Evans DA, Bienias JL, Tangney CC, Wilson RS. Vitamin E and cognitive decline in older persons. Arch  Neurol. 2002; 59:7 1125-1132

10. Littlejohns TJ, Henley WE, Lang IA et al. Vitamin D and the risk of dementia and Alzheimer disease. Neurology. 2014; 83 1-10

11. Luciano M, Marioni RE, Gow AJ, Starr JM, Deary IJ. Reverse Causation in the Association Between C-Reactive Protein and Fibrinogen Levels and Cognitive Abilities in an Aging Sample. Psychosom Med. 2009; 71:4 404-409

12. Schiepers OJG, van Boxtel MPJ, de Groot RHM et al. Serum Iron Parameters, HFE C282Y Genotype, and Cognitive Performance in Older Adults: Results From the FACIT Study. J Gerontol. A Biol Sci Med Mci. 2010; 65:12 1312-1321

13. Teunissen CE, van Boxtel MP, Bosma H et al. Inflammation markers in relation to cognition in a healthy aging population. J Neuroimmun. 2003; 134:1-2 142-150

14. Schmidt R, Schmidt H, Curb JD et al. Early inflammation and dementia: a 25-year follow-up of the Honolulu-Asia Aging Study. Ann  Neurol. 2002; 52:2 168-174

15. Marioni RE, Stewart MC, Murray GD et al. Peripheral levels of fibrinogen, C-reactive protein, and plasma viscosity predict future cognitive decline in individuals without dementia. Psychosom Med. 2009; 71:8 901-906

16. National Health and Nutrition Examination Survey: Survey Operations.  Available from: http://www.cdc.gov/nchs/nhanes.htm

17. National Health and Nutrition Examination Survey Laboratory/Medical Technologists Procedures Manual (2002); Available from: http://wwwn.cdc.gov/nchs/nhanes/search/nhanes01_02.aspx

18. Preacher KJ, Hayes AF. SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behav Res Methods Instrum  Compt. 2004; 36:4 717-731

19. Annweiler C, Montero-Odasso M, Llewellyn DJ et al. Meta-analysis of memory and executive dysfunctions in relation to vitamin D. J Alzheimers Dis. 2013; 37:1 147-171

20. Balion C, Griffith LE, Strifler L et al. Vitamin D, cognition, and dementia: a systematic review and meta-analysis. Neurology. 2012; 79:13 1397-1405

21. Bowman GL, Silbert LC, Howieson D et al. Nutrient biomarker patterns, cognitive function, and MRI measures of brain aging. Neurology. 2012; 78:4 241-249

22. Quadri P, Fragiacomo C, Pezzati R et al. Homocysteine, folate, and vitamin B-12 in mild cognitive impairment, Alzheimer disease, and vascular dementia. Am J Clin Nutr. 2004; 80:1 114-122

23. Seshadri S, Beiser A, Selhub J et al. Plasma homocysteine as a risk factor for dementia and Alzheimer’s disease. New Eng J Med. 2002; 346:7 476-483

24. Miller AL. The methionine-homocysteine cycle and its effects on cognitive diseases. Altern Med Rev. 2003; 8:1 7-19

25. Faux NG, Ellis KA, Porter L et al. Homocysteine, vitamin B12, and folic acid levels in Alzheimer’s disease, mild cognitive impairment, and healthy elderly: baseline characteristics in subjects of the Australian Imaging Biomarker Lifestyle study. J Alzheimers Dis. 2011; 27:4 909-922

26. Vidal JS, Dufouil C, Ducros V, Tzourio C. Homocysteine, folate and cognition in a large community-based sample of elderly people–the 3C Dijon Study. Neuroepidemiology. 2008; 30:4 207-214

27. Blasko I, Hinterberger M, Kemmler G et al. Conversion from mild cognitive impairment to dementia: influence of folic acid and vitamin B12 use in the VITA cohort. J Nutr Health Aging. 2012; 16:8 687-694

28. Barberger-Gateau P, Letenneur L, Deschamps V et al. Fish, meat, and risk of dementia: cohort study. BMJ. 2002; 325:7370 932-933

29. Kalmijn S, Launer LJ, Ott A et al. Dietary fat intake and the risk of incident dementia in the Rotterdam Study. Ann  Neurol. 1997; 42:5 776-782

30. Dwyer BE, Takeda A, Zhu X, Perry G, Smith MA. Ferric cycle activity and Alzheimer disease. Curr Neurovasc Res. 2005; 2:3 261-267