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 on14-16. 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)17-19.
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 CD20. 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 quality22. 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 defined as having two or more chronic diseases.
Statistical analysis
The obesity threshold was set as follows: BMI ≥25.0kg/m2 (World Health Organization)23, WHR: 0.90 in men and 0.85 in women (Asian modified WHO criteria for metabolic syndrome) 24, WC >90 cm for men and >80 cm for women (Asian modified The National Cholesterol Education Program)24, VFA ≥100 cm2 (published literature)25-27.
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 findings are expressed as mean±standard deviation (SD). Statistical significance (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.