Associations of Body Mass Index, Visceral Fat Area, Waist Circumference, and Waist-To-Hip Ratio with Cognitive Function in Western China: Results from A WCHAT Study

Objectives This study examined the relationship between cognitive performance and obesity parameters, such as body mass index (BMI), visceral fat area (VFA), waist circumference (WC), and waist-to-hip ratio (WHR) in western China. A cross-sectional study Methods 3914 participants, aged >50 years, were recruited in this study. Anthropometrics measurements, life-style factors, chronic disease comorbidities, and sleep qualities were recorded for each participant. Among the anthropometrics, BMP, WC, and WHR were assessed using standard procedures, while VHA was calculated using bioelectrical impedance analysis. Cognitive performance was estimated using the Short Portable Mental Status Questionnaire (SPMSQ). Finally, relationships between cognitive abilities and BMI, VFA, WC, and WHR were evaluated using univariate and multivariate regression analyses.


Background
Obesity, or excessive body fat, is one of the leading disease conditions in the world today. Several studies have demonstrated a positive association between obesity and dementia or cognitive decline (CD). However, the impact of obesity markers, such as body mass index (BMI), visceral fat area (VFA), waist circumference (WC) and waist-to-hip ratio (WHR), in patients without dementia remains unclear. Among the obesity markers, BMI is considered the most common risk factor for dementia. However, the evidences for this are controversial. Several studies have suggested that a higher middle age BMI can signi cantly increase the risk for dementia 1,2 . Conversely, in another study, BMI was discovered to have a U-shaped relationship with dementia. Therefore, in patients < 76 year old, a high BMI increased dementia risk, whereas, in patients > 76 year old, high BMI reduced dementia risk 3 . Moreover, several longitudinal studies demonstrated that in adulthood, excessive body fat augmented risk of CD later in life [4][5][6] .
The evidences concerning the relationship between VFA and cognitive abilities are also con icting. In one study, increased visceral adiposity was associated with poor cognitive performance in subjects < 70 year old 7 . Likewise, in another study, excessive VFA was shown to worse cognitive abilities in young adults 8 . Conversely, a recent study demonstrated that a high VHA is closely correlated with a low risk of non-amnestic mild cognitive impairement (MCI) in women, suggesting visceral fat may partially protect against CD 9 . Alternately, in a study of type 2 diabetes (T2D) patients, it was proposed that the impaired endothelium-independent vascular smooth muscle reactivity may mediate the relationship between elevated visceral adiposity and reduced cognitive performance 10 .
In terms of WHR, researchers discovered an association with cognitive impairment, which may be a result of neurodegenerative, vascular, or metabolic processes that modulate brain structures 11 . Likewise, a longitudinal study demonstrated that both WHR and WC can predict a nearly two-fold decrease in cognitive performance in T2D participants within 2 yrs 12 . Conversely, in another report, WHR was shown to be related to slower cognitive decline in elderly patients, indicating cognitive protection associated with higher WHR in the elderly 13 .
In this study, we collected cross-sectional data from the West-China Health and Aging Trend (WCHAT) study, involving a multi-ethnic cohort study in western China, to assess the relationship between adiposity parameterss (such as, BMI, VFA, WHR, and WC) and CD.

Methods
This cross-sectional study collected data from the WCHAT study, which examined the relationship between geriatrics syndromes and lifestyle of people in western China, such as diet, alcohol consumption, and smoking, and so on [14][15][16] .
The WCHAT study received approval from the Ethical Review Committee (reference: 2017-445) in the West China Hospital, Sichuan University, Chengdu, China. 4 independent interviewers were involved in data assembly from 4 western China provinces, namely, Yunnan, Guizhou, Sichuan, and Xinjiang.

Study participants
The study participants, aged >50 yr old, were selected by the local government and provided verbal and written consent to their participation in the study. The WCHAT study, which provided us the data for this study, included 7,536 participants aged ≥50 yr old from over 18 ethnic populations among 4 western China provinces; Sichuan, Yunnan, Guizhou, and Xinjiang. Among them, 4,500 participants with bioimpedance data, measured using Inbody770, were selected for the study. Among these participants, those with stroke (62), mental illness (5), participants missing cognitive function assessment (239), and participants belonging to a small ethnic group (280) were excluded from the study, resulting in a total of 3,914 participants included in this study.

Demographic, anthropometric, and life styles data collection
The baseline demographic information included age, gender, marital status, educational level, ethnic background, occupation, fertility condition, and hearing ability. The lifestyle variables included tea-drinking, alcohol drinking, smoking, and sleep quality. The anthropometric data included height, weight, BMI, WC, WHR, and VFA. WHR was measured twice by volunteers and the average data was used in the analysis. To calculate WC, measurement was taken at the midpoint between the iliac crest and lower rib at the end of a normal expiration. The hip circumference was measured at the maximum circumference over the buttocks below the iliac crest. WHR was calculated as the ratio of WC to hip circumference. Weight measurement was performed using a digital scale and height was calculated using a stadiometer. The BMI was calculated as the weight over height squared. VFA was assessed using bioimpedance analysis with the help of Inbody 770 (BioSpace, Seoul, Korea) [17][18][19] .
Assessment of cognitive decline, depression, sleep quality, and chronic diseases.
Cognitive performance was assessed according to the questions on the Short Portable Mental Status Questionnaire (SPMSQ). A high SPMSQ score denoted poor cognitive ability. In particular, a score of 0-2 represented good cognitive performance, a score of 3-4 indicated MCI, a score of 5-7 meant moderate cognitive impairment, and lastly, a score of 8-10 indicated severe cognitive impairement or CD 20 . In addition, depression was evaluated using a 15 question Geriatric Depression Scale (GDS-15) 21 with yes/no answers. The GDS-15 is a universally used questionnaire for the detection of depression. Sleep quality was evaluated using the widely used sleep assessment tool, Pittsburgh Sleep Quality Index (PSQI), where scores >5 were indicative of poor self-reported sleep quality 22 . Lastly, a self-reported medical history of chronic diseases was taken from each participant. Chronic diseases included hypertension, osteoarticular disease, lung disease, diabetes mellitus, and so on and comorbidities was de ned as having two or more chronic diseases.
The normality of variables was assessed using R version 3.6.1. The participants were assigned to one of two groups: complete cognitive function (CCF) or CD. Our ndings are expressed as mean±standard deviation (SD). Statistical signi cance (p< 0.05, two sided) was assessed with student's t-test (comparing 2 different groups) and the count data is represented as a percentage, using the χ2 test.
As this study explored the associations between cognitive performance and obesity variables like BMI, VFA, WC, and WHR, we established both univariate and multivariate regression models with BMI, VFA, WC, and WHR as the independent variables and cognitive performance as the dependent variable. The multivariate models were next adjusted according to BMI, VFA, WC, and WHR to produce fully adjusted cognitive results.

Results
Characteristics of the study participants 3,914 participants, aged ≥ 50 yrs were included in this study. The incidence of CD among the selected participants was 13.29%. A breakdown of their age groups showed that < 60, 60-69, 70-79 and ≥ 80 had a CD prevalence of 10.23%, 11.79%, 20.65%, and 33.03%, respectively, suggesting an increase in CD with age. Among the different ethnic populations, CD was distributed as follows: Han, Zang, and Qiang exhibited 9.52%, 17.66%, and 15.25% of CD respectively. A summary of data, including the clinical and sociodemographic, anthropometric, sleeping quality, lifestyle factors, depression, and chronic diseases comorbidity are provided in Table 1. Based on our analysis, participants with CD were, on average, older females who were missing a signi cant other, were of low educational background, worked as laborer, drank less tea and alcohol, smoked less, had multiple children, had impaired hearing, high GDS-15 score, high WC, and low WHR, as compared to participants without CD (Table 1). We also demonstrated that sleep quality and chronic disease comorbidities had a negative effect on CD. However, VFA and BMI were not signi cantly altered between the CCF and the CD groups (Table 1).

Discussion
We performed a large cohort cross-sectional study to investigate the relationship between obesity and CD. Among the 3,914 participants examined in this paper, 13.29% exhibited CD, which increased with age. This is consistent with other studies that reported a close link between aging and CD. In a recent meta-analysis study (n = 80 studies), the prevalence of CD ranged between 5.1% and 41% with a median of 19.0% 28  We also discovered that WHR is positively associated with CD among the elderly population. So, a high WHR was related to a slower CD among ≥ 70 yrs olds. This nding, along with our elderly BMI data, suggests that adiposity may have a protective effect on cognitive function. In fact, there are a few reports on possible mechanisms of this adipose-cognition relationship 13 . One study, for instance, proposed that estrogen connects excess adipose tissue to improved cognition in the elderly. Elders with more fat cells often have high estrogen levels, due to the peripheral androgen conversion to estrogens, and estrogen is bene cial for cognitive function 31 . In another study, leptin, which is elevated in obese individuals, was suggested to play a role due to its neuprotective nature 32 . Indeed, in the Framingham Study, increased leptin levels were shown to be associated with decreased dementia 33 . Interestingly, we observed a strong negative association between WHR and CD risk in participants with normal BMI in the fully adjusted model. This indicated that WHR might protect against CD in people with normal BMI. Our WHR ndings do not corroborate others ndings 34 where they showed a positive correlation between WHR and CD risk among elderly with BMI > 25.3 kg/m 2 . Further investigation into this relationship is necessary to understand the complicated nature of WHR regulating CD.
Based on our results, WC was signi cantly associated with CD in males. But, once we adjusted for age, the relationship was no longer signi cant. This may be because age attenuated this relationship. Higher WC is normally indicative of central obesity, which is a known risk factor for CD. One study demonstrated that excessive fat mass, measured by WC and adjusted for BMI, was closely related to increased risk of CD 35 . However, in this study, we did not observe any signi cant association between WC and CD under any BMI groups. There is more work to be done before a consensus is reached regarding the relationship between WC and CD.
Lastly, our analysis did not show any signi cant correlation between VFA and CD in the adjusted and unadjusted models. It may be due to our use of the BIA method, instead of CT or MRI which is far more accurate. There are limited studies on the relationship between VFA and CD. One study demonstrated that high VFA quartiles in women were associated with a low risk of non-amnestic MCI 9 . Similarly, another study revealed an inverse relationship between VFA and delayed memory and language scores in T2D 36 . Further longitudinal studies are warranted for a better understanding of the relationship between VFA and CD.

Limitations
This was a large-population study. As such, caution must be taken in adjusting for essential confounders that might be associated with the study, such as dietary intake. Moreover, this study was primarily observational. A causal relationship between obesity and CD must be explored in future investigations. Lastly, those who volunteered to participate in this study were relatively young and healthy, which may have introduced bias in our results.

Conclusions
Our ndings suggest that a high BMI increases the probability of CD in middle aged men. Alternately, increased WHR increased CD risk in elderly women and in individuals with normal BMI. Lastly, our study has not reached a general consensus regarding the relationship between CD and WC or VFA and further investigation is necessary in this regard.