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W. Wichayanrat1, S. Boripuntakul1,2, P. Keawtep1, P. Worakul3, S. Sungkarat1,2


1. Department of Physical Therapy, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand; 2. A Research Group of Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand; 3. Clinical Psychology Program, Faculty of Education, Prince of Songkla University, Pattani Campus, Thailand

Corresponding Author: Somporn Sungkarat, Department of Physical Therapy, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand, 50200; E-mail:; Tel. + 66 53 949 249; Fax. +66 53 946 042

J Prev Alz Dis 2022;
Published online May 26, 2022,



BACKGROUND: Mid-life obesity has been reported to be a significant risk factor for later-life dementia and Alzheimer’s disease. Metabolic syndrome (MetS) has been suggested to have an adverse effect while cardiorespiratory fitness (CRF) has been suggested to have a protective effect on cognitive function of older adults. However, studies investigating such effects in middle-aged obese women are limited.
OBJECTIVES: To compare cognitive performances between obese and normal weight middle-aged women and examine the effects of MetS and CRF on cognitive performances when combined with obesity.
DESIGN AND PARTICIPANTS: Cross-sectional study with the data of 87 middle-aged women (58 obese and 29 normal weight, with age and education matched).
MEASUREMENTS: The non-invasive screening method for metabolic syndrome (NIM-MetS) was used to detect MetS. CRF was determined by using maximal oxygen consumption (VO2 max) and was classified as high or low (VO2 max higher or lower than 50th percentile) based on the American College of Sports Medicine’s guidelines. Neurocognitive tests including Montreal Cognitive Assessment (MoCA), digit span (DS), trail making test (TMT), hand reaction time (HRT), logical memory (LM), and semantic verbal fluency test (SVFT) were administered to all participants.
RESULTS: The obese group demonstrated significantly lower score in MoCA, DS, TMT, HRT, and LM than the normal weight group (p < 0.05). The obese with MetS subgroup (n = 28) showed significantly lower score in LM than the obese non-MetS subgroup (n = 30) (p = 0.002). Normal weight with high CRF participants (NW-high CRF; n = 28) demonstrated significantly higher score in MoCA and HRT than obese with high CRF participants (OB-high CRF; n = 24) (p < 0.05), and demonstrated better score in MoCA, DS, TMT, HRT, and LM than obese with low CRF participants (OB-low CRF; n = 24) (p < 0.05). OB-high CRF showed significantly greater score in DS, TMT and LM than OB-low CRF (p < 0.05).
CONCLUSION: Obesity shows negative impact on several cognitive functions, which memory appears to be further affected when combined with MetS in middle-aged women, whereas CRF is suggested to have benefit on certain aspects of cognitive domains. Maintaining a healthy body weight and improving CRF are beneficial for cognitive function of middle-aged women.

Key words: Cardiorespiratory fitness, cognitive performance, metabolic syndrome, middle-aged women, obesity.



Obesity has become a global epidemic which adversely affected public health worldwide. Obesity not only degrades overall physical health, but it is increasingly recognized as having an impact on cognitive function of individuals and increase the risk of developing dementia including Alzheimer’s disease and vascular dementia in later life (1). Previous studies have demonstrated that participants with obesity showed a significant reduced performance in working memory, executive function, processing speed, episodic memory, and verbal ability compared with normal weight participants (2, 3). Obese women are twice as likely to have dementia as normal weight women (4). The higher risk of dementia in obese women is found to be mediated by central obesity, which is related with insulin resistance, cardiovascular disease, and increased inflammatory markers (4). However, a negative relationship between obesity and cognitive performance has not been consistently demonstrated. A recent 12-year longitudinal study and a systematic review of the effects of obesity on cognitive function in adults reported that the association between obesity and cognitive decline is not always forthright and might be strongly dependent on demographic variables such as gender, age, and education (1, 5). As several studies could not adequately control for potential confounding factors, the impact of obesity on cognitive function remains inconclusive.
The mid-life period marks the start of change in cerebral white matter volume, which may possibly occur due to the biological change of oligodendrocytes that are responsible for myelination and this change was accelerated by vascular risk factors such as obesity, hypertension, and diabetes (6). The oligodendrocytes continue to differentiate until the fifth decade of life, when brain cortical thickness begins to decrease (7). Thus, mid-life is a vital period in which obese status can predict one’s cognitive functioning in the future (8). Maintaining and promoting brain health in mid-life may help reduce the risk of developing dementia in later life.
Metabolic syndrome is a well-recognized obesity-related disease which is associated with increased rates of mortality and increased risk for developing dementia (9). Most studies reported that MetS has a negative impact on multiple cognitive domains including memory, executive function, and processing speed (10, 11). A previous systematic review reported the overall prevalence of obese individuals with the presence of MetS ranged from 49% to 90%, indicating that obesity and MetS are closely related (12). As both components are risk factors for cognitive decline, recent study suggested that adding MetS could further degrade cognitive function in obese middle-aged population (9). However, to our knowledge, no study has been conducted to examine the effect of obesity with and without MetS on cognitive performance in middle-aged adults. This finding would provide an understanding on the additional contributions of MetS on cognitive performance which may lead to further investigation on the potential underlying mechanisms responsible for obesity and MetS on cognitive impairment.
Cardiovascular exercise is known to be beneficial for reducing obesity and its related comorbidities, as well as maintaining good cognitive function in older adults (13-15). A previous systematic review reported that higher cardiorespiratory fitness (CRF) has been shown to have a protective effect on cognitive function, not only in improving cognitive performance but also in enhancing brain functions and structures in older age (14). A recent magnetic resonance imaging study reported that CRF was positively associated with total brain volume and grey matter volume (15). These findings suggest that the promotion of CRF in older adults may have protective effect on brain health. Nonetheless, it is not known whether such positive effects also demonstrate in middle-aged adults. As mid-life period strongly predicts later life’s cognitive functioning and middle-aged obese women are at increased risk for developing cognitive impairment, investigating the impact of a modifiable behavioral factor such as CRF on cognition in this population would provide important information for public health.
The aims of this observational study were: 1) to compare cognitive performances between obese and normal weight middle-aged women with similar age and educational level, 2) to explore the effects of obesity with and without MetS on cognitive performances, and 3) to explore the effect of CRF on cognitive performances by comparing cognitive performances among middle-aged obese women with high and low CRF and normal weight with high CRF. We hypothesized that middle-aged obese women would have poorer cognitive performance than middle-aged normal weight women, middle-aged obese women with MetS would have further decline in cognitive performance than those without MetS, and middle-aged obese and normal weight women who have high CRF would have better cognitive performance than middle-aged obese women with low CRF.




Eighty-seven middle-aged women (58 obese and 29 normal weight with similar age and educational level) were recruited by advertising through local communities and online platforms from June 2021 to January 2022. Participants were included if they were between 40-59 years old, had body mass index (BMI) > 25 kg/m2 for obese group or BMI between 18.5-22.9 kg/m2 for normal weight group (16), had at least 6 years of education, and scored Mental State Examination T10 (MSET10) in normal range (17). Participants were excluded if they had major comorbidities or conditions which could affect testing and/or confound findings including acute or chronic medical conditions, neurological conditions, psychiatric disorders, mood disorders, depressive disorders, and visual or hearing impairments. The study protocol was approved by the Human Ethical Review Board of the primary investigator’s institution. Participants were informed about the purposes and procedures of the study before signing an informed consent.

Participants’ characteristics

The demographic data including age, education, BMI, body compositions measured by bioelectrical impedance analyzer (Tanita BC-418, Tokyo, Japan), physical activity assessed by global physical activity questionnaire (GPAQ), a validated and widely used measurement of physical activity developed by the World Health Organization (WHO) (18), 6-minute walk distance (6MWD) assessed following the American Thoracic Society guidelines (19), were collected. Maximal oxygen consumption (VO2 max) of all participants was indirectly calculated following the equation: VO2 max ( = 26.9 + 0.014 × meters walked in 6MWT – 0.38 x BMI (kg/m2), which was reported to be valid and reproducible in obese population (20).

Metabolic syndrome assessment

The non-invasive screening method for early detection of metabolic syndrome (NIM-MetS) was used in this study. This method has shown high sensitivity and specificity with the reference test (NCEP ATP III). The cut-off values for both genders and all different age groups were waist-to-hip ratio over 0.56 and blood pressure over 128/80 mmHg. Further details on NIM-MetS are described elsewhere (21). All participants with obesity were screened for MetS and those with and without MetS were defined as MetS obese group and non-MetS obese group, respectively.

Cardiorespiratory fitness classification

Cardiorespiratory fitness (CRF) was classified by using VO2 max as high CRF (VO2 max higher than 50th percentile) or low CRF (VO2 max lower than 50th percentile) based on the American College of Sports Medicine’s guidelines on CRF classifications by age and sex (22). Participants with normal weight and obesity were classified as high CRF group and low CRF group.

Outcome measurements

Global- and domain-specific cognitive performance including attention, executive function, processing speed, episodic memory, and language were assessed. Montreal Cognitive Assessment (MoCA) is a global cognitive test which includes questions assessing visuospatial ability, executive function, naming, memory, attention, language, delayed recall, and orientation. A higher score of MoCA indicates better global cognitive function. Digit span (DS) forward test was used to assess attention. Participants were asked to listen carefully to a series of random numbers and correctly repeat it in forward order. The participants were given a second chance if the first attempt was failed and if the participants also failed in the second attempt, the test was stopped, and the maximal digit number of the test was scored (23). Trail making test (TMT) was used to evaluate executive function which consisted of 2 parts, A and B. For part A, participants were instructed to draw lines sequentially to connect 25 encircled numbers distributed on a sheet of paper as quickly and correctly as possible. For part B, the instructions were similar as part A except the participants must alternate between numbers and letters. The score on each part was the amount of time required to correctly complete the task, the difference duration of time to complete part B and A (B-A) was used to reflect executive function (24). Processing speed was tested by hand reaction time test (HRT) in which hand-held electronic timer was used. The participants were asked to sit on a chair and place their dominant index finger on the right button of a modified mouse. The participants were then asked to press the button as quickly as possible in response to the red light stimulus. An average time in seconds over 10 trials was recorded (25). Logical memory test (LM) in delayed recall was used to assess episodic memory. Two short narrative stories were read aloud, the participants were asked to listen and remember as many details of both stories as possible. After a delay of 30 minutes, participants were asked to repeat each story as closely as possible to the original story. The corrected details told by the participants were scored (26). Semantic verbal fluency test (SVFT) was used to evaluated language skill. The participants were asked to generate as many animal names as possible in the duration of 60 seconds (27). The outcome measures were assessed in random orders and five minutes break (or as needed) was provided between each test.

Statistical analysis

Statistical analyses were performed by using SPSS software (version 23.0, IBM Corporation, Chicago, IL, USA) and the significance level was set at p < 0.05. Shapiro-Wilk test was used to test for the normality of the data. Independent t-test was used to compare participants’ characteristics between obese and normal weight group. The differences of cognitive outcome measures between 1) the obese and normal weight groups, 2) the obese with and without metabolic syndrome groups, and 3) among normal weight group with high CRF and obese group with high and low CRF were compared by using the analysis of covariance (ANCOVA). The ANCOVA was performed using GPAQ and comorbidities including hypertension, diabetes mellitus type 2 and dyslipidemia as covariates.



Participants’ characteristics

Descriptive statistics of participants’ characteristics are illustrated in Table 1. There were no significant differences in age, education and GPAQ between groups (p > 0.05). The obese group showed significantly higher BMI, body fat percentage, and total fat mass than the normal weight group, whereas the normal weight group demonstrated greater lean body mass, 6MWD, and VO2 max (p < 0.001).

Table 1. Participants’ characteristics

Notes: Data are presented as mean ± SD; Abbreviations: 6MWD = 6-minute walk distance; BMI = body mass index; BW = body weight; GPAQ = global physical activity questionnaire; MET = metabolic equivalent; VO2 max = maximal oxygen consumption


Cognitive performances between normal weight and obese group

The mean ± SD of cognitive outcome measures between the normal weight and obese group are shown in Table 2. ANCOVA revealed a significant difference in MoCA, DS, TMT, HRT, and LM, whereby the obese group demonstrated lower score in those tests than the normal weight group (p < 0.05). No significant difference was found in SVFT score between the two groups (p > 0.05).

Table 2. Comparisons of cognitive outcome measures between the normal weight and obese group

Notes: Data are presented as mean ± SD; Abbreviations: MoCA = Montreal Cognitive Assessment


Cognitive performances between obese group with and without MetS

All participants with obesity (n = 58) were screened for MetS and were divided into non-MetS obese group (n = 30) and MetS obese group (n = 28). Participants in both groups showed no difference in age and education (p > 0.05). Comparisons of cognitive performances between the non-MetS obese and MetS obese groups are displayed in Table 3. The MetS obese group showed significantly lower score in LM than the non-MetS obese group (p = 0.002). There was no significant difference in MoCA, DS, TMT, HRT, and SVFT (p > 0.05).

Table 3. Comparisons of cognitive outcome measures between obese with and without MetS group

Notes: Data are presented as mean ± SD; Abbreviations: MoCA = Montreal Cognitive Assessment


Cognitive performances among NW-high CRF, OB-high CRF, and OB-low CRF

All participants with normal weight and obesity were classified as high CRF and low CRF group as follows; normal weight with high CRF (NW-high CRF) (n = 28), obese with high CRF (OB-high CRF) (n = 32), and obese with low CRF (OB-low CRF) (n = 26). Nonetheless, after matching for age and education, 24-matched pairs of obese with high and low CRF were included in the statistical analysis. ANCOVA revealed significant group differences in MoCA, DS, TMT, HRT, and LM scores. Post hoc comparisons showed that the NW-high CRF group demonstrated significantly higher score in MoCA and HRT than the OB-high CRF group, and demonstrated better score in MoCA, DS, TMT, HRT, and LM than the OB-low CRF group (p < 0.05). Further, the OB-high CRF group had significantly greater score in DS, TMT and LM than the OB-low CRF group (p < 0.05) (Table 4).

Table 4. Comparisons of cognitive outcome measures among normal weight with high CRF and obese with high and low CRF

Notes: Data are presented as mean ± SD; Post-hoc analysis: a = high CRF normal weight vs. high CRF obese; b = high CRF normal weight vs. low CRF obese; c = high CRF obese vs. low CRF obese; Abbreviations: MoCA = Montreal Cognitive Assessment



The present study compared cognitive performances between middle-aged women with normal weight and obesity and further evaluated the effects of MetS and CRF on their cognitive performances. The main finding of this study was that middle-aged obese women demonstrated poorer performances in global cognitive function, attention, executive function, processing speed, and episodic memory than their normal weight peers. The results confirm prior research which found that mid-life obesity may adversely affect cognitive performance (7, 28-29). Dye et al. (28) and Gunstad et al. (29) reported that obesity is associated with impaired cognitive performance in middle-aged adults, and it also found to be linked with an increased risk of dementia later in life. Growing evidence indicates a number of potential mechanisms that might contribute to the effect of obesity on cognitive performance, e.g., systemic inflammation, leptin elevation, increment of brain oxidative stress, and insulin signaling pathway impairment (30-32). Obesity is related with chronic low-grade systemic inflammation and adipocyte hypertrophy, leading to an increase of circulating levels of proinflammatory cytokines such as interleukin-6, tumor necrosis factor-α and associated hormones such as leptin (30). The elevated level of proinflammatory cytokines also activated microglia and increased brain oxidative stress which may contribute to cerebral atrophy, neurodegeneration, synaptic function impairment and also brain volume reduction (33). Nonetheless, the definite mechanisms or mediators underlying the association between obesity and cognitive decline remain to be established. Moreover, middle-aged obese women demonstrated lower lean body mass than their normal weight peers. Previous studies revealed that lower lean mass has been reported to be associated with cognitive impairment such as Alzheimer’s disease (34, 35). Although the underlying mechanism is not yet clarified, it is hypothesized that both components share common mechanisms, such as hormonal imbalance and systemic inflammation (34).
Accumulating evidence has indicated that MetS is associated with cognitive impairment and decreased performances in multiple domains, such as executive function, memory, semantic verbal fluency, and processing speed in older adults (10, 11, 36). However, research on the combined effect of MetS and obesity on cognitive performance is currently limited. Therefore, this study examined the cognitive performance between metabolically healthy obese participants and obese participants with MetS. The results demonstrated that obese participants with the presence of MetS showed worse performance in the area of episodic memory than those without MetS. With limited studies on this regard, the present findings could not be compared to those of other studies. Kim et al. (37) found that obese participants with MetS showed greater insulin resistance than those without MetS. Insulin resistance has found to impair the insulin receptor activity in the brain and lessen brain insulin level. Brain insulin plays an important role in the facilitation of learning and memory therefore, the reduction of insulin leads to learning and memory impairments (38). It may be possible that both components have a synergistic influence on cognitive decline, but it is complicated to separate their individual contributions (39). Further studies that investigate the mechanisms underlying the effect of obesity combined with MetS on cognition would provide more insight into this matter.
Accumulating evidence has revealed that improving CRF enhances not only neurocognitive functions but also brain structures in young to older individuals (40, 41). Talukdar et al. (41) demonstrated that several regions within frontal, temporal, parietal, and cerebellar areas which play important roles in executive function, attention, learning, and memory are associated with CRF. This could be due to the increment of cerebral blood flow, vascularization, promotion of neurotrophic factor, and reduction of energy metabolic indices and inflammatory cytokines (42-44). Exercise appears to encourage the formation of new capillaries, along with the improvement of resting cerebral blood flow (42). Moreover, regular exercise has an anti-inflammatory effect, which can lead to a decrease in the production and release of pro-inflammatory cytokines (43). The brain-derived neurotrophic factor (BDNF) produced after exercise also plays an important role in promoting neuronal survival and synaptic integrity (44). The present study found the protective effect of CRF on certain cognitive performances in participants with obesity which is consistent with Boidin et al., (45) who reported that higher-fit obese participants showed better cognitive performances compared with lower-fit obese participants. Nonetheless, our findings demonstrated that obese participants with high CRF had poorer performances than normal weight participants with high CRF in several cognitive domains. This finding is in contrast with Boidin et al., (45) who further reported that higher-fit obese participants performed similarly in every cognitive domain compared with their normal weight participants. This could be because their normal weight participants had low CRF (lowest among all groups), while our normal weight participants had high level of CRF. If this is the case, these findings altogether would imply that a high level of CRF is necessitated for cognitive performance even in normal weight participants. Taken together, results from the present study suggest that mid-life women should maintain normal body weight as well as attain high level of CRF, in order to prevent cognitive decline.
The strength of this study includes the control of potential confounding factors for cognitive performance including gender, age, education as well as physical activity and comorbidities of the participants between both groups. Previous systematic reviews indicated that one of the methodological limitations that lead to an inconclusive finding on the effect of obesity on cognitive performance is the lack of control for these factors. Moreover, this study further performed sub-analysis to explore the effects of MetS and CRF on cognitive function in participants with obesity. However, there are some limitations in this study. First, the sample size in this study is relatively small. Second, the menstrual status of the participants was not determined. Thus, it is not known whether the two groups had similar hormonal level which might influences the cognitive outcomes. Nevertheless, as the obese and normal weight groups were age matched, their menstrual status might not be much difference. Further study with larger sample size and additional control for hormonal level would yield more solid results. Third, without biomarker assessment, the potential mechanisms underlying the effect of obesity, MetS, and CRF on cognitive function remained unknown. Finally, since this study was an observational study, it was unable to identify causation, yet we believe that our findings would raise public awareness and encourage further investigation on this aspect.



Middle-aged obese women demonstrated poorer cognitive performances in global cognitive function and specific cognitive domains including attention, executive function, processing speed, and memory compared with their normal weight peers. Middle-aged obese women with MetS showed worse memory performance than those without MetS. Middle-aged obese women with high CRF outperformed those with low CRF in the cognitive domains of attention, executive function, and memory. However, they demonstrated lower cognitive performance in global cognitive function and processing speed than middle-aged normal weight women with high CRF. Thus, weight management and exercise that enhances CRF should be promoted in middle-aged women.


Acknowledgements: WW gratefully acknowledges the master’s degree program in Movement and Exercise Sciences in Faculty of Associated Medical Sciences, Chiang Mai University, under the CMU Presidential Scholarship.

Funding: This study was supported by The CMU Presidential Scholarship (WW), Chiang Mai University, Thailand, The research fund for graduate study, Faculty of Associated Medical Sciences, Chiang Mai University, and The Thailand Science and Research Innovation (TSRI): Fundamental Fund (FF65/019, SS).

Author’s contributions: Design and conduct of the study, WW, SB, PK, SS; data collection, data analysis, and interpretation of data, WW, SB, PK, PW, SS; manuscript preparation, WW; manuscript editing, SS; supervision, SB, SS. All authors have read and approved the final version of the manuscript.

Ethical standards: The study protocol has been approved by the Human Ethical Review Board of the Faculty of Associated Medical Sciences, Chiang Mai University (approval number: AMSEC-64EX-026).

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



1. Prickett C, Brennan L, Stolwyk R. Examining the relationship between obesity and cognitive function: a systematic literature review. Obes Res Clin Pract 2015;9:93-113. doi:10.1016/j.orcp.2014.05.001
2. Fagundo AB, de la Torre R, Jiménez-Murcia S, Agüera Z, Granero R, Tárrega S, et al. Executive functions profile in extreme eating/weight conditions: from anorexia nervosa to obesity. PLoS One 2012;7:1-9. doi:10.1371/journal.pone.0043382
3. Loprinzi PD, Frith E. Obesity and episodic memory function. J Physiol Sci 2018;68: 321-331. doi:10.1007/s12576-018-0612-x
4. Whitmer RA, Gunderson EP, Barrett-Connor E, Quesenberry CP, Yaffe K. Obesity in middle age and future risk of dementia: a 27 year longitudinal population based study. Bmj 2005;330:1360-1364. doi:10.1136/bmj.38446.466238.E0
5. Deckers K, Van Boxtel MPJ, Verhey FRJ, Köhler S. Obesity and cognitive decline in adults: Effect of methodological choices and confounding by age in a longitudinal study. J Nutr Health Aging 2017;21:546-553. doi:10.1007/s12603-016-0757-3
6. Ronan L, Alexander-Bloch AF, Wagstyl K, Farooqi S, Brayne C, Tyler LK, et al. Obesity associated with increased brain age from midlife. Neurobiol Aging 2016;47:63-70. doi:10.1016/j.neurobiolaging.2016.07.010
7. Bartzokis G. Age-related myelin breakdown: a developmental model of cognitive decline and Alzheimer’s disease. Neurobiol Aging 2004;25:5-18. doi:10.1016/j.neuro biolaging.2003.03.001
8. Hassing LB, Dahl AK, Pedersen NL, Johansson B. Overweight in midlife is related to lower cognitive function 30 years later: a prospective study with longitudinal assessments. Dement Geriatr Cogn Disord 2010;29:543-552. doi:10.1159/000314874
9. Haase Alasantro L, Hicks TH, Green-Krogmann E, Murphy C. Metabolic syndrome and cognitive performance across the adult lifespan. PLoS One 2021;16. doi:10.1371/ journal.pone.0249348
10. Komulainen P, Lakka TA, Kivipelto M, et al. Metabolic syndrome and cognitive function: a population-based follow-up study in elderly women. Dement Geriatr Cogn Disord 2007;23:29-34. doi:10.1159/000096636
11. Hassenstab JJ, Sweat V, Bruehl H, Convit A. Metabolic syndrome is associated with learning and recall impairment in middle age. Dement Geriatr Cogn Disord 2010;29: 356-362. doi:10.1159/000296071
12. Rey-López JP, de Rezende LF, Pastor-Valero M, Tess BH. The prevalence of metabolically healthy obesity: a systematic review and critical evaluation of the definitions used. Obes Rev 2014;15:781-90. doi:10.1111/obr.12198
13. Daimiel L, Martínez-González MA, Corella D, Salas-Salvadó J, Schröder H, Vioque J, et al. Physical fitness and physical activity association with cognitive function and quality of life: baseline cross-sectional analysis of the PREDIMED-Plus trial. Sci Rep 2020;10:3472. doi:10.1038/s41598-020-59458-6
14. Angevaren M, Aufdemkampe G, Verhaar HJ, Aleman A, Vanhees L. Physical activity and enhanced fitness to improve cognitive function in older people without known cognitive impairment. Cochrane Database Syst Rev 2008;3:1-72. doi:10.1002/146518 58.CD005381.pub3
15. Wittfeld K, Jochem C, Dörr M, et al. Cardiorespiratory fitness and gray matter volume in the temporal, frontal, and cerebellar regions in the general population. Mayo Clin Proc 2020;95:44-56. doi:10.1016/j.mayocp.2019.05. 030
16. Pan WH, Yeh WT. How to define obesity? Evidence-based multiple action points for public awareness, screening, and treatment: an extension of Asian-Pacific recommendations. Asia Pac J Clin Nutr 2008;17:370-374. doi:10.6133/APJCN.2008. 17.3.02
17. Banjongrewadee M, Wongpakaran N, Wongpakaran T, Pipanmekaporn T, Punjasawadwong Y, Mueankwan S. Role of perceived stress in postoperative delirium: an investigation among elderly patients. Aging Ment Health 2020;24:148-154. doi: 10.1080/13607863.2018.1523881
18. Bull FC, Maslin TS, Armstrong T. Global physical activity questionnaire (GPAQ): nine country reliability and validity study. J Phys Act Health 2009;6:790-804. doi:10.1123/jpah.6.6.790
19. Brooks D, Solway S, Gibbons WJ. ATS statement on six-minute walk test. Am J Respir Crit Care Med 2003;167: doi:10.1164/ajrccm.167.9.950 doi:10.1164/ajrccm.167.9.950
20. Vanhelst J, Fardy PS, Salleron J, Béghin L. The six-minute walk test in obese youth: reproducibility, validity, and prediction equation to assess aerobic power. Disabil Rehabil 2013;35:479-482. doi:10.3109/09638288.2012.699581
21. Romero-Saldaña M, Tauler P, Vaquero-Abellán M, et al. Validation of a non-invasive method for the early detection of metabolic syndrome: a diagnostic accuracy test in a working population. BMJ Open 2018;8:1-11. doi:10.1136/bmjopen-2017-020476
22. American College of Sports Medicine. ACSM’s guidelines for exercise testing and prescription, 10th edn. 2018. Wolters Kluwer, Philadelphia
23. Choi HJ, Lee DY, Seo EH, et al. A normative study of the digit span in an educationally diverse elderly population. Psychiatry Investig 2014; 11:39-43. doi:10.4306/pi.2014. 11.1.39
24. Tombaugh TN. Trail Making Test A and B: normative data stratified by age and education. Arch Clin Neuropsychol 2004;19:203-214. doi:10.1016/s0887-6177(03) 00039-8
25. Lord SR, Menz HB, Tiedemann A. A physiological profile approach to falls risk assessment and prevention. Phys Ther 2003;83:237-252. doi:10.1093/ptj/83.3.237
26. Ahn YD, Yi D, Joung H, et al. Normative data for the logical memory subtest of the wechsler memory scale-IV in middle-aged and elderly korean people. Psychiatry Investig 2019;16:793-799. doi:10.30773/pi.2019.0061
27. Lopes M, Brucki SMD, Giampaoli V, Mansur LL. Semantic verbal fluency test in dementia: preliminary retrospective analysis. Dement Neuropsychol 2009;3:315-320. doi:10.1590/s1980-57642009dn30400009
28. Dye L, Boyle NB, Champ C, Lawton C. The relationship between obesity and cognitive health and decline. Proc Nutr Soc 2017;76:443-454. doi:10.1017/s0029665117002014
29. Gunstad J, Paul RH, Cohen RA, Tate DF, Spitznagel MB, Gordon E. Elevated body mass index is associated with executive dysfunction in otherwise healthy adults. Compr Psychiatry 2007;48:57-61. doi:10.1016/j.comppsych.2006.05.001
30. Nguyen JC, Killcross AS, Jenkins TA. Obesity and cognitive decline: role of inflammation and vascular changes. Front Neurosci 2014;8:375. doi:10.3389/fnins. 2014.00375
31. Tan BL, Norhaizan ME. Effect of high-fat diets on oxidative stress, cellular inflammatory response and cognitive function. Nutrients 2019;11. doi:10.3390/nu 11112579
32. Feng J, Lu S, Ou B, et al. The role of JNk signaling pathway in obesity-driven insulin resistance. Diabetes Metab Syndr Obes 2020;13:1399-1406. doi:10.2147/dmso.S23 6127
33. Bischof GN, Park DC. Obesity and aging: consequences for cognition, brain structure, and brain function. Psychosom Med 2015;77:697-709. doi:10.1097/psy.000000000 0000212
34. Burns JM, Johnson DK, Watts A, Swerdlow RH, Brooks WM. Reduced lean mass in early Alzheimer disease and its association with brain atrophy. Arch Neurol 2010;67:428-33. doi:10.1001/archneurol.2010.38
35. Wirth R, Smoliner C, Sieber CC, Volkert D. Cognitive function is associated with body composition and nutritional risk of geriatric patients. J Nutr Health Aging 2011;15:706-10. doi:10.1007/s12603-011-0089-2
36. Guicciardi M, Crisafulli A, Doneddu A, Fadda D, Lecis R. Effects of metabolic syndrome on cognitive performance of adults during exercise. Front Psychol 2019;10:1845. doi:10.3389/fpsyg.2019.01845
37. Kim YM, Kim S, Kim SH, Won YJ. Clinical and body compositional factors associated with metabolic syndrome in obese koreans: A cross-sectional study. Metab Syndr Relat Disord 2018;16:290-298. doi:10.1089/met.2017.0174
38. Zhao WQ, Alkon DL. Role of insulin and insulin receptor in learning and memory. Mol Cell Endocrinol 2001;177:125-34. doi:10.1016/s0303-7207(01)00455-5
39. Bahchevanov KM, Dzhambov AM, Chompalov KA, Massaldjieva RI, Atanassova PA, Mitkov MD. Contribution of components of metabolic syndrome to cognitive performance in middle-aged adults. Arch Clin Neuropsychol 2021;36:498-506. doi:10. 1093/arclin/acaa081
40. Flodin P, Jonasson LS, Riklund K, Nyberg L, Boraxbekk CJ. Does aerobic exercise influence intrinsic brain activity? An aerobic exercise intervention among healthy old adults. Front Aging Neurosci 2017;9:267. doi:10.3389/fnagi.2017.00267
41. Talukdar T, Nikolaidis A, Zwilling CE, et al. Aerobic fitness explains individual differences in the functional brain connectome of healthy young adults. Cereb Cortex 2018;28:3600-3609. doi:10.1093/cercor/bhx232
42. Chapman SB, Aslan S, Spence JS, et al. Shorter term aerobic exercise improves brain, cognition, and cardiovascular fitness in aging. Front Aging Neurosci 2013;5:75. doi:10.3389/fnagi.2013.00075
43. Gleeson M, Bishop NC, Stensel DJ, Lindley MR, Mastana SS, Nimmo MA. the anti-inflammatory effects of exercise: mechanisms and implications for the prevention and treatment of disease. Nat Rev Immunol 2011;11:607-615. doi:10.1038/nri3041
44. Wang R, Holsinger RMD. Exercise-induced brain-derived neurotrophic factor expression: Therapeutic implications for Alzheimer’s dementia. Ageing Res Rev 2018;48:109-121. doi:10.1016/j.arr.2018.10.002
45. Boidin M, Handfield N, Ribeiro PAB, et al. Obese but fit: the benefits of fitness on cognition in obese older adults. Can J Cardiol 2020;36:1747-1753. doi:10.1016/ j.cjca.2020.01.005