Apolipoprotein Mediation in the Relationship Between Weight-Adjusted Waist Index and Cognitive Decline

Apolipoprotein Mediation in the Relationship Between Weight-Adjusted Waist Index and Cognitive Decline | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Apolipoprotein Mediation in the Relationship Between Weight-Adjusted Waist Index and Cognitive Decline Su Yue, Xu Ying, Li Xiang, Ge Yuhan, Wang Yunting, Ji Xiaowei, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7628452/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract Background: The weight-adjusted waist index (WWI) is an innovative measure of obesity. This study aimed to investigate the relationship between WWI and cognitive function in Chinese population. Methods: Using the China Health and Nutrition Survey (CHNS) database 1989-2011 dataset, cross-sectional data from 7838 participants were analyzed. The association between WWI and cognitive impairment was investigated by multiple regression analysis and subgroup analysis. In addition, restricted cubic spline (RCS) was applied to explore nonlinear relationships, and mediation analysis was carried out to assess whether Apolipoprotein influenced these relationships. Results: The research involved 7838 participants aged 55 years and older. The fully adjusted model revealed a positive and significant association between WWI and low cognitive performance [2.55(2.19,2.96)], implying that individuals with higher WWI have a higher likelihood of cognitive impairment. Restricted Cubic Spline (RCS) analyses showed a nonlinear relationship between WWI and and low cognition. Subgroup analyses and interaction tests confirmed the robustness of this positive correlation in different population settings (all P for interaction > 0.05). Mediation revealed that ApoA and ApoB may be mediators between WWI and cognitive impairment. Conclusion: A higher WWI was associated with a higher incidence of cognitive impairment. The results of this study highlight the value of the WWI in dementia prevention and managementin Chinese population. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Health sciences/Risk factors Cross-sectional study CHNS Obesity cognitive impairment Weight-adjusted waist index Apolipoprotein Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Cognitive impairment is a deterioration of one or more cognitive functions that adversely affects an individual's daily functioning and socialization [ 1 ]. Currently, cognitive health has become an important public health issue with the accelerated aging of the population. Studies have shown that approximately 15–20% of individuals aged 60 years and older experience symptoms of mild cognitive impairment (MCI) [ 2 ]. Cognitive decline is a hallmark feature of dementia. In China, 15.07 million people suffer from dementia and 38.77 million people suffer from MCI. Dementia and cognitive impairment not only impose a huge burden on individuals, but also bring a heavy medical burden and economic loss to the society [ 3 ]. The etiology of cognitive decline involves complex interactions between genetic and environmental factors and a number of physical, psychological, social and lifestyle factors as well as dietary factors [ 4 ]. Based on many failed treatment trials, the current approach focuses on early intervention to relieve or prevent progressive cognitive impairment through lifestyle and other interventions [ 5 ]. There is a growing interest in the contribution of obesity to cognitive function in the elderly. Obesity represents a novel but complex risk factor for dementia and cognitive impairment, particularly in older individuals [ 6 ]. Obesity is defined as an accumulation of excess adipose tissue or abnormal distribution, which has adverse effects on health. It is not only associated with an increased risk of cardiovascular disease but also with detrimental effects on central nervous system function and cognitive performance [ 7 ]. A substantial body of evidence indicates a correlation between obesity and MCI, as well as with hippocampal atrophy. Significant structural and functional changes are associated with obesity [ 8 ]. Excessive obesity can lead to cognitive decline and dementia [ 9 – 11 ]. Traditionally, body mass index (BMI) and waist circumference (WC) have been used as indicators of obesity. However, recent studies have challenged the accuracy of these measurements. BMI does not account for differences in muscle mass, bone density, or fat distribution, and it is influenced by age, sex, and ethnicity [ 12 , 13 ]. weight-adjusted-waist index (WWI) proposed by Park and colleagues is a novel method for assessing obesity. This index integrates changes in body composition, including muscle and adipose tissue, and is superior to BMI and WC in evaluating lean and adipose tissue mass [ 14 ]. Several studies have demonstrated that WWI has superior accuracy compared to BMI [ 15 ]. There are few studies on WWI and cognitive function, mainly focusing on the American population [ 16 ]. No previous studies have investigated the relationship between WWI and cognitive function in the Chinese population. Therefore, we conducted a cross-sectional study using data from the CHNS to explore the association between WWI and cognitive function. Apolipoprotein A and apolipoprotein B are two important components of plasma lipoproteins that play different roles in lipid metabolism and cardiovascular health. In the present study, we further explored the mediating roles of ApoA and ApoB in the above relationships by mediation analysis. 2. Methods 2.1. Study population This is an association study based on repeated measures of weight-adjusted waist circumference index (WWI) and cognitive function in a Chinese population with China Health and Nutrition Survey (CHNS). The CHNS is an ongoing, open, prospective, home-based cohort study conducted in 15 provinces and cities in China.Until now, China has seen a total of 11 CHNS surveys (1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011, 2015, and 2018) being conducted [ 17 ]. During the thirty years, the survey provinces are increasing from eight provinces in 1989 to sixteen provinces in 2018. In 2015, there were fifteen provinces, including Shandong, Liaoning, Heilongjiang, Jiangsu, Henan, Guizhou, Hunan, Hubei, Zhejiang, Yunnan, Shanxi, and Guangxi and three autonomous cities (Beijing, Shanghai, and Chongqing). A multistage, stratified, random cluster sampling design was used to ensure a probability sample. Specific individuals participated in the survey repeatedly at each round unless they were lost to follow-up [ 18 ]. Further details regarding the CHNS are described and can be accessed at the following internet website: https://www.cpc.unc.edu/projects/china (accessed on 12 July 2024), and elsewhere. Our sample consisted of 8 rounds of CHNS surveys (1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011), in which a subset of the population underwent cognitive screening tests and had waist circumference, weight, and other data measured. Figure 1 depicts the selection process for our study. Individuals with missing data on cognitive screening tests (n = 99,224), missing data on weight and waist circumference (n = 3,381) and missing data on covariates such as age, gender, and education (n = 6) were excluded from the sample population of 110,449. Finally, 7,838 participants with complete information were included in our analysis. The CHNS was approved by the ethical review committees of the Chinese Center for Disease Control and Prevention, and the Carolina Population Center at the University of North Carolina at Chapel Hill. Signed, informed consent was obtained from all participants before the survey. 2.2. Weight-adjusted-waist index Physical measurements of waist circumference and weight were taken by trained health technicians in a mobile testing center (MEC) under controlled conditions. As an exposure variable, decimal rounding of WWI results for each participant was retained to two decimal places. In our analyses, we treated WWI as a continuous variable, and subsequently, participants were grouped based on their WWI data for further analysis [ 19 ]. WWI was calculated using the following method: \(\:\:\text{W}\text{W}\text{I}(\text{c}\text{m}/\sqrt{kg}\) ) = WC/ \(\:\sqrt{Weight}\) . 2.3. Measurement of cognitive function The cognitive screening items used in the CHNS include a subset of items from the Telephone Interview for Cognitive Status-modified (TICS-M). Cognitive screening consisted of immediate and delayed recall of a list of 10 words (two recall attempts were scored as 10), counting backwards from 20 (scored as 2), and subtracting 7 consecutively (scored as 5). Cognitive functioning testing began with immediate recall of a list of 10 words. The interviewer (i.e., trained health worker) read the ten words at a rate of two seconds per word. Participants were given two minutes to memorize the ten words. A score of 1 was given for each correctly recalled word. The participant is then asked to count from 20 to 1. If the participant makes a mistake on the first attempt, a second chance is given. Two points were awarded for a correct answer on the first attempt and one point for the second attempt. After the counting test, participants were asked to subtract 7 from 100 five times in a row. Each correct subtraction was scored as 1. Finally, participants were asked to recall a list of 10 words from a prior test. Each recalled word was scored as 1[ 20 ]. In this study, we chose the first quartile of the cognitive functioning test scores as a proxy for poor cognitive functioning. Self-reported memory status was assessed by the following questions: "How good is your memory? (1) very good; (2) good; (3) OK; (4) bad; (5) very bad; (6) unknown" and "How has your memory changed in the past twelve months? (1) improved; (2) stayed the same; (3) worsened; (4) unknown". If the participant answered "bad or very bad" to the question, it was recorded as poor memory. If the answer to the question was "worse", memory loss was defined [ 21 ]. 2.4. Assessment of Covariates Potential covariates considered included socioeconomic status, lifestyle factors, and physical health, with the first two categories collected at each round of the survey using a structured questionnaire. Socioeconomic status: age, sex, ethnicity, region of residence, and education level (low: illiterate/elementary school; medium: middle school; high: high school and above). Physical health: whether they had been diagnosed with hypertension, defined as systolic blood pressure ≥ 140 mmHg and diastolic blood pressure ≥ 90 mmHg; and whether they had been diagnosed with diabetes mellitus. Lifestyle factors: smoking, alcohol consumption, and tea drinking. Specifically, smoking status was categorized as nonsmokers, quitters, and current smokers. Alcohol consumption was categorized as "yes" or "no", as was tea consumption [ 22 ]. 2.5 Statistical analysis This study used data from the CHNS to categorize participants into two groups, those with and without cognitive impairment, based on cognitive functioning test score results and self-reported memory status. Weighted chi-square and t-tests were used to test baseline characteristics of the study population. Continuous variables were described by weighted means and 95% confidence intervals, and categorical variables were described by weighted percentages. Before weighted regression modeling, all covariates were screened for variance inflation factor (VIF) covariates to avoid multicollinearity among variables affecting the multiple regression model. In this study, multiple logistic regression was used to analyze the relationship between WWI and cognitive status. In Model 1, the included variables were not adjusted. In Model 2, adjustments were made for gender, age, and ethnicity. All factors included in Model 3 were adjusted for gender, age, ethnicity, region of residence, education, history of hypertension, diabetes, smoking, alcohol consumption, and tea drinking. WWI was transformed into a categorical variable using quartiles, and the trend test was utilized to examine the trend of linear correlation between internal and external WWI and cognitive status. To explain the dose-response relationship (linear or nonlinear) between WWI and cognition, we used weighted multiple linear regression analyses to explore the linear relationship between WWI and cognitive status, while restricted cubic spline analysis (RCS) was used to assess the nonlinear association between WWI and cognitive status in Model 3. To further assess the predictive performance of WWI in evaluating cognitive function, ROC curve analysis was conducted for three cognitive assessments: Global cognitive function, Self-reported poor memory, and Self-reported memory decline. Three models were built for each assessment: Model 1: Included only cognitive status and WWI; Model 2: Included cognitive status, WWI, gender, and age; Model 3: Included cognitive status, WWI, gender, age, as well as additional confounding factors including nationality, education level, smoking status, tea consumption, alcohol use, hypertension, and diabetes. The AUC (Area Under the Curve) for each model was calculated to evaluate the discriminative power of WWI for each cognitive assessment. In addition, participants in the 2009 CHNS came from 216 communities in nine provinces (i.e., Heilongjiang, Liaoning, Shandong, Henan, Hubei, Hunan, Jiangsu, Guangxi, and Guizhou). In this wave, blood samples were collected and tested for the first time [ 23 ]. This study further excluded those who lacked information on blood samples (n = 4,485) from the above study population, and finally obtained participants who also had blood samples, weight, waist circumference, and cognitive status (n = 3,353), and the screening process is described in Fig. 2 . In adjusting for the main analytic model 3, mediated effects analysis was used to examine whether the correlation between WWI and cognitive status could be explained by triglycerides, HDL, LDL, ApoA, and ApoB. Model 4 in the SPSS macro program Process was used to conduct the test of mediating effects. Mediation analysis allows us to calculate how many mediation effects need to be generated. This is an ideal strategy for elucidating pathways and providing statistical evidence for mechanistic analyses. In this study, the direct effect represents the association between WWI and cognitive status; the indirect effect, i.e., the association between WWI and cognitive status, is mediated by serum markers; and the proportion mediated represents the percentage of mediated effect. Statistics were judged significant at P < 0.05. IBM SPSS Statistics 27 ( https://www.ibm.com/cn-zh/spss ) and R Studio (version 4.4.0 http://www.R-project.org ) were used for all analyses. 3. Results 3.1 Baseline characteristics of participants The characteristics of the participants according to the quartiles of the WWI are shown in Table 1 . The study involved 7,838 participants with a mean age of 56.85 ± 10.05 years, of whom 3,936 (50.2%) were male and 3,902 (49.8%) were female. The mean WWI of all participants was 10.86 ± 0.94 cm/√kg, and the values of different quartiles were as follows: quartile 1: 11.46 cm/√kg. Compared to those in the lowest quartile of WWI, those in the highest quartile of WWI quartiles were more likely to be elderly, female, Han Chinese, living in urban areas, and low educated, and they were more likely to have hypertension, diabetes, and were more likely to consume alcohol and less likely to smoke cigarettes and drink tea. Table 1 Characteristics of a sample of Chinese participants by weight-adjusted-waist index (N = 7,838) Weight-adjusted waist index (cm/√kg) p-value Q1(7.12–10.25) N = 1,989 Q2(10.25–10.81) N = 1,949 Q3(10.81–11.46) N = 1,971 Q4(11.46–15.66) N = 1,929 Age(years) 55.72 ± 9.88 56.15 ± 9.71 56.76 ± 9.84 58.82 ± 10.49 <0.001 Gender, n (%) <0.001 Male 1305(65.6%) 1133(58.1%) 951(48.3%) 546(27.7%) Female 684(34.4%) 816(41.9%) 1019(51.7%) 1383(70.2%) Nationality, n (%) <0.001 Han 1785(89.7%) 1730(88.8%) 1746(88.6%) 1691(87.7%) Miao 29(1.4%) 31(1.6%) 39(2.0%) 49(2.5%) Man 34(1.7%) 39(2.0%) 46(2.3%) 42(2.2%) Other 141(7.2%) 149(7.6%) 140(7.1%) 147(7.6%) Residence, n (%) <0.001 Rural 786(39.5%) 732(37.6%) 800(40.6%) 765(39.7%) Urban 1203(60.5%) 1217(62.4%) 1171(59.4%) 1164(60.3%) Educational levels, n (%) <0.001 Primary school and lower 1397(70.2%) 1335(69.7%) 1391(71.7%) 1531(81.2%) Middle school 284(14.3%) 281(14.7%) 271(14.0%) 186(9.6%) High school and higher 308(15.5%) 333(15.6%) 309(14.3%) 212(9.2%) Hypertension, n (%) <0.001 Yes 248(12.4%) 336(17.1%) 397(21.2%) 438(22.7%) No 1741(87.6%) 1613(82.9%) 1574(79.9%) 1491(77.3%) Diabetes, n (%) <0.001 Yes 37(1.8%) 65(3.1%) 80(4.1%) 106(5.5%) No 1952(98.2%) 1884(96.9%) 1891(95.9%) 1823(94.5%) Smoke, n (%) <0.001 Yes 889(44.6%) 747(38.3%) 619(31.4%) 427(22.1%) No 1100(55.4%) 1202(61.7%) 1352(68.6%) 1502(77.9%) Drink alcohol, n (%) <0.001 Yes 820(41.1%) 702(36.0%) 561(28.4%) 415(21.3%) No 1169(58.9%) 1247(64.0%) 1410(71.6%) 1514(78.7%) Drink tea, n (%) 0.009 Yes 893(44.8%) 885(45.5%) 886(44.9%) 812(42.1%) No 1096(55.2%) 1064(54.6%) 1085(55.2%) 1117(57.9%) Mean ± SD for continuous variables: the P value was calculated by the weighted linear regression model. (%) for categorical variables: the P value was calculated by the weighted chi-square test. 3.2 Association between WWI and cognitive function impairment Table 2 shows the correlation between WWI and cognitive impairment. WWI and cognitive impairment showed a significant positive correlation in the models unadjusted and partially adjusted for covariates. After full adjustment, these positive correlations were still present, and both were significant (P < 0.05). The probability of cognitive impairment increased with increasing WWI in both unadjusted model 1, partially adjusted covariate model 2, and fully adjusted covariate model 3. Participants in the highest WWI quartile had a 155% increase in the probability of developing cognitive impairment compared to participants in the lowest quartile [2.55(2.19,2.96)]. This was not only true for low test scores on the Cognitive Status Test, but also for subjects' self-rated poor memory and memory loss. Table 2 Association of weight-adjusted waist index with cognitive function impairment. Quartiles of WWI (cm/√kg) P for trend Quartile 1 Quartile 2 Quartile 3 Quartile 4 Global cognitive function Model 1 Reference 1.29(1.14,1.46) 1.81(1.59,2.05) 3.40(2.97,3.90) <0.001 Model 2 Reference 1.20(1.05,1.36) 1.55(1.36,1.76) 2.50(2.16,2.88) <0.001 Model 3 Reference 1.24(1.08,1.42) 1.63(1.42,1.87) 2.55(2.19,2.96) <0.001 Self-reported poor memory Model 1 Reference 1.34(1.14,1.56) 1.62(1.39,1.89) 2.45(2.09.2.86) <0.001 Model 2 Reference 1.27(1.08,1.48) 1.45(1.24,1.70) 1.96(1.67,2.31) <0.001 Model 3 Reference 1.28(1.09,1.50) 1.45(1.24,1.70) 1.88(1.59,2.23) <0.001 Self-reported memory decline Model 1 Reference 1.21(1.07,1.37) 1.38(1.22,1.56) 1.89(1.65,2.15) <0.001 Model 2 Reference 1.16(1.02,1.31) 1.26(1.11,1.44) 1.58(1.38,1.82) <0.001 Model 3 Reference 1.16(1.02,1.32) 1.24(1.09,1.41) 1.51(1.31,1.75) <0.001 *Model 1 was unadjusted; Model 2 was adjusted for gender, age, and ethnicity; Model 3 was further adjusted for educational attainment, region of residence, high blood pressure, diabetes mellitus, smoking, alcohol consumption, and tea drinking. 3.3 Dose‒response relationship analysis Restricted cubic spline (RCS) were used to analyze the relationship between WWI and cognitive function test. Based on this relationship, a nonlinear model provided a better explanation of the relationship than a linear model after adjusting for all covariates (P <0.0001) (Figure. 3). The probability of cognitive impairment was progressively higher at either WWI 10.84. 3.4 Subgroup analysis of the association between weight-adjusted-waist index with cognitive function. In order to assess the consistency of the association between WWI and cognitive function in the general population as a whole and to identify potential differences between specific populations, we conducted subgroup analyses and interaction effect analyses stratified by age, gender, ethnicity, region of residence, education, hypertension status, diabetes status, smoking, alcohol consumption, and tea drinking. As shown in Table 3 , our results indicated that WWI did not interact with age, gender, nationality, region of residence, education, hypertension status, alcohol consumption, and tea drinking (all P for interaction > 0.05). However, we observed a direct interaction of WWI with smoking and diabetes status (P for interaction ≤ 0.05), which demonstrates that smoking and diabetes status may influence the alteration of WWI, and consequently cognitive functioning as well. Table 3 Subgroup analysis of the association between weight-adjusted-waist index with cognitive function. Quartiles of WWI (cm/√kg) P trend P for interaction Quartile 1 Quartile 2 Quartile 3 Quartile 4 Gender 0.442 Male Reference 1.35(1.13,1.61) 1.91(1.59,2.31) 2.62(2.08,3.31) < 0.001 Female Reference 1.09(0.89,1.34) 1.37(1.12,1.67) 2.38(1.95,2.91) 0.315 Age 0.051 ≤ 55 Reference 1.38(1.13,1.69) 1.81(1.48,2.23) 2.64(2.097,3.33) 55 Reference 1.12(0.95,1.33) 1.54(1.30,1.83) 3.00(2.51,3.58) < 0.001 Nationality 0.913 Han Reference 1.20(1.04,1.38) 1.55(1.34,1.78) 2.53(2.16,2.96) < 0.001 Miao Reference 1.00(0.32,3.12) 2.28(0.80,6.52) 2.99(1.01,8.85) 0.344 Man Reference 0.84(0.24,2.93) 1.96(0.60,6.44) 2.70(0.70,10.43) < 0.001 Other Reference 2.04(1.26,3.31) 3.28(1.95,5.51) 3.76(2.11,6.69) 0.066 Residence 0.404 Rural Reference 1.33(1.13,1.57) 1.70(1.43,2.02) 2.41(2.00,2.92) < 0.001 Urban Reference 1.09(0.87,1.37) 1.52(1.22,1.89) 2.86(2.26,3.61) 0.021 Educational levels 0.130 Primary school and lower Reference 1.25(1.08,1.45) 1.66(1.43,1.93) 2.48(2.11,2.93) < 0.001 Middle school Reference 1.04(0.71,1.54) 1.50(1.02,2.21) 2.29(1.46,3.57) 0.234 High school and higher Reference 1.48(0.89,2.44) 2.03(1.23,3.35) 5.14(3.00,8.82) < 0.001 Hypertension 0.879 Yes Reference 1.10(0.77,1.57) 1.36(0.96,1.92) 2.46(1.72,3.52) 0.280 No Reference 1.27(1.10,1.46) 1.70(1.47,1.97) 2.61(2.21,3.07) < 0.001 Diabetes 0.013 Yes Reference 0.77(0.30,1.95) 0.57(0.23,1.10) 2.77(1.11,6.93) 0.036 No Reference 1.26(1.10,1.44) 1.71(1.49,1.96) 2.60(2.24,3.02) < 0.001 Smoke 0.039 Yes Reference 1.35(1.09,1.67) 2.09(1.66,2.62) 2.67(2.03,3.51) < 0.001 No Reference 1.18(0.99,1.40) 1.47(1.24,1.74) 2.53(2.12,3.02) < 0.001 Drink alcohol 0.699 Yes Reference 1.47(1.18,1.84) 1.84(1.45,2.34) 2.54(1.91,3.38) < 0.001 No Reference 1.14(0.97,1.35) 1.57(1.33,1.85) 2.57(2.16,3.06) < 0.001 Drink tea 0.693 Yes Reference 1.32(1.08,1.62) 1.88(1.53,2.31) 2.83(2.25,3.55) < 0.001 No Reference 1.20(1.01,1.43) 1.52(1.27,1.81) 2.46(2.03,2.98) < 0.001 3.5. ROC Analysis Results ROC analysis was conducted to evaluate the performance of WWI in predicting cognitive impairment as measured by Global cognitive function, Self-reported poor memory, and Self-reported memory decline. The AUC values for each model and assessment are presented below: Global cognitive function: For Model 1 (cognitive status and WWI), the AUC was 0.62. In Model 2 (cognitive status, WWI, gender, and age), the AUC increased to 0.64. The highest AUC of 0.73 was observed in Model 3, which included additional confounders such as nationality, education level, smoking status, tea consumption, alcohol use, hypertension, and diabetes. These results indicate that the predictive performance of WWI for global cognitive function improved as more confounding variables were accounted for(Fig. 4 A). Self-reported poor memory: In Model 1, the AUC was 0.59. The inclusion of gender and age in Model 2 resulted in a slight increase in AUC to 0.60. Further adjustments in Model 3 yielded an AUC of 0.66, suggesting that WWI's ability to predict self-reported poor memory was enhanced with the incorporation of additional covariates(Fig. 4 B). Self-reported memory decline: The AUC for Model 1 was 0.56, with a slight increase to 0.57 in Model 2. In Model 3, the AUC improved to 0.62, reflecting a moderate increase in predictive accuracy as additional confounders were included (Fig. 4 C). These findings suggest that WWI demonstrates a moderate ability to predict cognitive function and self-reported memory issues, with its predictive power improving as the model accounts for more confounding variables. 3.6 The mediation analysis of blood biomarkers on the relationship between WWI and cognitive function In addition, mediation analysis was performed to explore the mediating effects of serum markers. Table 4 shows the results of mediation analysis for five serum markers, triglycerides, LDL, HDL, ApoA and ApoB, which showed that only ApoA and ApoB had significant mediation effects (P ≤ 0.05). Figure 5 further reveals the mediating role of ApoA and ApoB in the relationship between WWI and cognitive function. The proportion mediated of ApoA and ApoB was 3.32% and 3.35%, respectively. Table 4 Mediated analysis of the association between blood biomarkers and cognitive function. OR 95%CI P value Triglyceride -0.064 (-0.128,0.001) 0.053 Low-density lipoprotein -0.024 (-0.060,0.012) 0.197 High-density lipoprotein 0.058 (-0.006,0.122) 0.078 Apolipoprotein A -0.023 (-0.034, -0.014) < 0.001 Apolipoprotein B -0.020 (-0.033, -0.008) < 0.001 4. Discussion Most previous epidemiologic studies on obesity and cognitive function have been conducted mainly in older European and American populations, with fewer analyses targeting Asian populations. Our study aimed to investigate the association between WWI and low cognitive function in a Chinese population. In this cross-sectional study, which recruited 7838 individuals aged 55 years and older, we found a positive and significant association between WWI and low cognitive performance. In the model with all adjustments, positive correlations were found between WWI and low cognitive function as indicated by global cognitive function, Self-reported poor memory, and Self-reported memory decline, implying that individuals with higher WWI have a higher likelihood of cognitive impairment. Restricted Cubic Spline (RCS) analyses showed a nonlinear relationship between WWI and and low cognition. The probability of cognitive impairment was progressively higher at either WWI 10.84. Subgroup analyses and interaction tests confirmed the robustness of this positive correlation across a variety of demographic contexts. The correlation was independent of gender, age, ethnicity, education, region of residence, hypertension, and alcohol or tea consumption. However, smoking and diabetes may influence changes in WWI, which in turn may also have an impact on cognitive function. These observations suggest that elevated WWI may be an independent risk factor for cognitive impairment, thus emphasizing the importance of WWI in the prevention and management of cognitive impairment. Obesity has become one of the important risk factors for cognitive impairment and dementia. In recent years, more and more studies have pointed out that obesity is an accelerated aging disease. The prevalence of neurodegenerative diseases such as ageing-associated cognitive impairment is significantly higher in obese populations [ 17 ]. Analysis of data based on more than 8000 Asian populations found that excessive obesity is a key metabolic risk factor for cognitive decline [ 18 ]. For every 0.27 kg increase in excess visceral fat, the degree of cognitive decline was equivalent to 0.7 years of "aging". China has the largest number of overweight and obese people in the world, and obesity has become a serious public health problem [ 19 ]. Therefore, preventing excessive obesity can help maintain or improve cognitive health and reduce the risk of future dementia in the Chinese population. BMI and WC are measures of obesity. Previous findings on the association between poor cognition and obesity-related parameters in older adults are controversial. Some studies have shown that lower BMI is associated with better cognitive function, whereas higher BMI or WC is associated with an accelerated rate of cognitive decline [ 20 , 21 ]. In contrast, some studies have found that higher BMI or WC prevents cognitive decline [ 22 , 23 ]. Therefore, traditional obesity indicators such as WC and BMI may not fully reflect the role of obesity in predicting cognitive impairment. Recent studies have shown that the WWI is effective in distinguishing between muscle mass and fat mass, leading to its wider application in a variety of medical fields, including metabolic diseases, renal diseases, and cardiovascular diseases [ 24 – 26 ]. WWI provides a more accurate measure of natural obesity than the traditional obesity index, as BMI is unable to differentiate muscle mass from fat mass. In fact, WWI has shown greater relevance and predictive power in certain diseases [ 27 , 28 ]. In Li's study, WWI correlated much more strongly with poor cognitive function than obesity markers such as BMI, WC and ABSI [ 29 ]. As a new obesity-related indicator, WWI has become a good predictive tool for cognitive dysfunction and dementia risk. The correlation between obesity and poor cognitive function can be explained by many biological mechanisms. According to several studies, the relationship between obesity and poor cognitive function may involve structural and functional changes in the cortex and subcortex. These structures are associated with cognitive domains, including working memory, verbal memory, processing speed, and intelligence [ 30 , 31 ]. In addition, obesity is a state caused by excessive accumulation of body fat. Although the pathways by which excess adipose tissue affects brain function are not fully understood, available evidence suggests that insulin resistance (IR), inflammation, and vascular dysfunction are the three main possible mechanisms by which obesity leads to cognitive impairment [ 32 ]. Cognitive deficits caused by central IR may be more due to decreased insulin receptor substrate (IRS) expression and phosphorylation, dysregulation of the PI3K/Akt pathway and the MAPK pathway, which affects neuronal cell survival and normal function, thus leading to cognitive deficits. Activation of the PI3K pathway is involved in the regulation of synaptic plasticity, memory maintenance, and nitric oxide synthesis [ 33 ]. The MAPK pathway mainly controls cell growth and differentiation, and is a key player in synaptic regulation and function [ 34 ]. Secondly, obesity increases the secretion of pro-inflammatory factors (TNF-α, IL-1β, IL-6, MCP1), which trigger localized inflammation in the brain and lead to neurodegeneration [ 35 ]. At the same time, obesity-related inflammation leads to IR, leptin resistance, and decreased secretion of lipid-transporting proteins, which increase the risk of cognitive impairment and dementia [ 36 – 38 ]. Finally, obesity also accelerates age-related atherosclerosis [ 39 ], leading to inadequate cerebral perfusion [ 40 ], impaired white matter microstructural integrity [ 41 ] and induce cognitive decline. This study has several highlights. First, this study is the first to report the relationship between WWI and cognitive function in a Chinese population based on data from the CHNS database. We adjusted for confounding factors, including age, gender, ethnicity, region of residence, education level, hypertension status, diabetes status, smoking, alcohol consumption, and tea drinking, in order to mitigate the effects of confounding factors and obtain more reliable results. The reliability and representativeness of the findings were improved. We also explored the nonlinear relationship between WWI and cognitive impairment using RCS analysis. Next, we conducted subgroup analysis to further elucidate the association between WWI and cognitive impairment in different population settings. Finally, we performed mediation analysis to clarify the mediating role of ApoA and ApoB in the association between WWI and cognitive impairment. However, there are some shortcomings in our study. Firstly, this study is a cross-sectional study exploring the association between WWI and cognitive impairment in Chinese population, a causal relationship could not be established. Therefore, prospective studies with larger sample sizes are needed to verify causality. Secondly, although the results were obtained from a dataset of CHNS database, further large-scale cohort studies may be needed in the future to demonstrate the current findings. Lastly, even after adjusting for some potential confounders, the influence of other potential confounders cannot be completely ruled out. However, the existing association between WWI and cognitive impairment is strong enough that it is unlikely to be significantly altered by unconsidered confounders. 5. Conclusion In conclusion, our study suggests that higher WWI may be associated with poorer cognitive function and may predict cognitive dysfunction in the Chinese elderly population. The association between WWI and risk of cognitive impairment showed a nonlinear pattern. ApoA and ApoB may be mediators between WWI and cognitive impairment. Focusing on the potential mediating role of ApoA and ApoB, the risk of dementia may be beneficially reduced in the Chinese population. Abbreviations WWI Weight-adjusted waist index CHNS China Health and Nutrition Survey RCS Restricted Cubic Spline ApoA Apolipoprotein A ApoB Apolipoprotein B MCI mild cognitive impairment BMI Body Mass Index WC Waist Circumference MTC mobile testing center TICS-M Telephone Interview for Cognitive Status-modified VIF variance inflation factor LDL low density lipoprotein HDL high density lipoprotein IR insulin resistance IRS insulin receptor substrate. Declarations Ethical Considerations This study was conducted in accordance with the ethical standards set forth by the National Institute for Nutrition and Health (NINH, former National Institute of Nutrition and Food Safety) at the Chinese Center for Disease Control and Prevention (CCDC). Informed consent was obtained from all participants prior to their involvement in the study. The research protocol was reviewed and approved by the relevant ethical review board, ensuring that all ethical guidelines were adhered to throughout the study. Conflict of interest The authors declare that they have no competing interests. Funding Statement This study was supported by the National Natural Science Foundation of China (No.82274218) and Innovation Project for Postgraduate Training in Jiangsu Province (KYCX25_2356). Author Contribution Su Yue and Cheng Xiaolan designed the research. Su Yue, Xu Ying, Li Xiang, Ge Yuhan, Wang Yunting, Ji Xiaowei collected, analyzed the data, and drafted the manuscript. Cheng Xiaolan drafted and revised the manuscript. All authors contributed to the article and approved the submitted version. Acknowledgements We sincerely thank all the projects (National Health and Nutrition Examination Survey) who participated in this study. Data Availability Publicly available datasets were analyzed in this study. These data can be found here: https://www.cpc.unc.edu/projects/china. References Ng, N. F., Osman, A. M., Kerlan, K. R., Doraiswamy, P. M. & Schafer, R. J. Computerized cognitive training by healthy older and younger adults: age comparisons of overall efficacy and selective effects on cognition. Front. Neurol. 11 , 564317 (2020). Sanford, A. M. Mild cognitive impairment. Clin. Geriatr. Med. 33325–33337. (2017). Jia, L. et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7628452","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":545946528,"identity":"c2e0f050-a36b-40c0-8495-9a427f714665","order_by":0,"name":"Su Yue","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Su","middleName":"","lastName":"Yue","suffix":""},{"id":545946529,"identity":"b8f9bdcd-a807-438f-bc86-1c5662d94237","order_by":1,"name":"Xu Ying","email":"","orcid":"","institution":"Nanjing University of Chinese 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16:49:43","extension":"xml","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":186268,"visible":true,"origin":"","legend":"","description":"","filename":"ebe1c0c6033d4934ba9eca070772c1c11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7628452/v1/d101e0a97693e7390a941428.xml"},{"id":96847392,"identity":"c471f1a3-13f6-486b-afd7-87f0a1245a99","added_by":"auto","created_at":"2025-11-26 16:49:43","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":201091,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7628452/v1/d53c810d1211035fece3521f.html"},{"id":96847364,"identity":"c1dbcddb-1bd3-4957-a296-597b3530e4a6","added_by":"auto","created_at":"2025-11-26 16:49:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63281,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participants selection.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7628452/v1/ade7799b3068c6026bd94003.png"},{"id":96847363,"identity":"b58337a4-9d88-447d-bb96-fd12314b49dd","added_by":"auto","created_at":"2025-11-26 16:49:42","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23756,"visible":true,"origin":"","legend":"\u003cp\u003eScreening for people with both WWI and blood biomarker information.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7628452/v1/83ea2800e589f20cffe5a238.jpeg"},{"id":96847362,"identity":"e6b36fe9-4df5-4344-8bec-8948bd0f8a88","added_by":"auto","created_at":"2025-11-26 16:49:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74762,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic splines plot of WWI with cognitive function.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7628452/v1/c641e53414157bcdc0ca3580.png"},{"id":96847367,"identity":"e860a8c6-eca7-4e2a-9b17-af5b84fd17a5","added_by":"auto","created_at":"2025-11-26 16:49:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":65520,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) Curve Comparison of Three Cognitive Models.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7628452/v1/d4eabcebb5901580286bc3cd.png"},{"id":96847369,"identity":"260c695f-66b6-48b7-a52c-cbdb87b24826","added_by":"auto","created_at":"2025-11-26 16:49:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":87233,"visible":true,"origin":"","legend":"\u003cp\u003ePath diagram of the mediation analysis of Apolipoprotein A and Apolipoprotein B on the relationship between WWI and cognitive function.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7628452/v1/e9e687ac018adddae012504d.png"},{"id":96923316,"identity":"2583df32-617c-4e8e-b5f0-253397609dd4","added_by":"auto","created_at":"2025-11-27 14:21:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1765141,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7628452/v1/01bba5dc-ceb3-45c1-a14a-9d539cb9afc9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Apolipoprotein Mediation in the Relationship Between Weight-Adjusted Waist Index and Cognitive Decline","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCognitive impairment is a deterioration of one or more cognitive functions that adversely affects an individual's daily functioning and socialization [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Currently, cognitive health has become an important public health issue with the accelerated aging of the population. Studies have shown that approximately 15\u0026ndash;20% of individuals aged 60 years and older experience symptoms of mild cognitive impairment (MCI) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Cognitive decline is a hallmark feature of dementia. In China, 15.07\u0026nbsp;million people suffer from dementia and 38.77\u0026nbsp;million people suffer from MCI. Dementia and cognitive impairment not only impose a huge burden on individuals, but also bring a heavy medical burden and economic loss to the society [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The etiology of cognitive decline involves complex interactions between genetic and environmental factors and a number of physical, psychological, social and lifestyle factors as well as dietary factors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Based on many failed treatment trials, the current approach focuses on early intervention to relieve or prevent progressive cognitive impairment through lifestyle and other interventions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere is a growing interest in the contribution of obesity to cognitive function in the elderly. Obesity represents a novel but complex risk factor for dementia and cognitive impairment, particularly in older individuals [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Obesity is defined as an accumulation of excess adipose tissue or abnormal distribution, which has adverse effects on health. It is not only associated with an increased risk of cardiovascular disease but also with detrimental effects on central nervous system function and cognitive performance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A substantial body of evidence indicates a correlation between obesity and MCI, as well as with hippocampal atrophy. Significant structural and functional changes are associated with obesity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Excessive obesity can lead to cognitive decline and dementia [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Traditionally, body mass index (BMI) and waist circumference (WC) have been used as indicators of obesity. However, recent studies have challenged the accuracy of these measurements. BMI does not account for differences in muscle mass, bone density, or fat distribution, and it is influenced by age, sex, and ethnicity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. weight-adjusted-waist index (WWI) proposed by Park and colleagues is a novel method for assessing obesity. This index integrates changes in body composition, including muscle and adipose tissue, and is superior to BMI and WC in evaluating lean and adipose tissue mass [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Several studies have demonstrated that WWI has superior accuracy compared to BMI [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere are few studies on WWI and cognitive function, mainly focusing on the American population [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. No previous studies have investigated the relationship between WWI and cognitive function in the Chinese population. Therefore, we conducted a cross-sectional study using data from the CHNS to explore the association between WWI and cognitive function. Apolipoprotein A and apolipoprotein B are two important components of plasma lipoproteins that play different roles in lipid metabolism and cardiovascular health. In the present study, we further explored the mediating roles of ApoA and ApoB in the above relationships by mediation analysis.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study population\u003c/h2\u003e\u003cp\u003eThis is an association study based on repeated measures of weight-adjusted waist circumference index (WWI) and cognitive function in a Chinese population with China Health and Nutrition Survey (CHNS). The CHNS is an ongoing, open, prospective, home-based cohort study conducted in 15 provinces and cities in China.Until now, China has seen a total of 11 CHNS surveys (1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011, 2015, and 2018) being conducted [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. During the thirty years, the survey provinces are increasing from eight provinces in 1989 to sixteen provinces in 2018. In 2015, there were fifteen provinces, including Shandong, Liaoning, Heilongjiang, Jiangsu, Henan, Guizhou, Hunan, Hubei, Zhejiang, Yunnan, Shanxi, and Guangxi and three autonomous cities (Beijing, Shanghai, and Chongqing). A multistage, stratified, random cluster sampling design was used to ensure a probability sample. Specific individuals participated in the survey repeatedly at each round unless they were lost to follow-up [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Further details regarding the CHNS are described and can be accessed at the following internet website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cpc.unc.edu/projects/china\u003c/span\u003e\u003cspan address=\"https://www.cpc.unc.edu/projects/china\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed on 12 July 2024), and elsewhere.\u003c/p\u003e\u003cp\u003eOur sample consisted of 8 rounds of CHNS surveys (1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011), in which a subset of the population underwent cognitive screening tests and had waist circumference, weight, and other data measured. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the selection process for our study. Individuals with missing data on cognitive screening tests (n\u0026thinsp;=\u0026thinsp;99,224), missing data on weight and waist circumference (n\u0026thinsp;=\u0026thinsp;3,381) and missing data on covariates such as age, gender, and education (n\u0026thinsp;=\u0026thinsp;6) were excluded from the sample population of 110,449. Finally, 7,838 participants with complete information were included in our analysis. The CHNS was approved by the ethical review committees of the Chinese Center for Disease Control and Prevention, and the Carolina Population Center at the University of North Carolina at Chapel Hill. Signed, informed consent was obtained from all participants before the survey.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Weight-adjusted-waist index\u003c/h2\u003e\u003cp\u003ePhysical measurements of waist circumference and weight were taken by trained health technicians in a mobile testing center (MEC) under controlled conditions. As an exposure variable, decimal rounding of WWI results for each participant was retained to two decimal places. In our analyses, we treated WWI as a continuous variable, and subsequently, participants were grouped based on their WWI data for further analysis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. WWI was calculated using the following method:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\text{W}\\text{W}\\text{I}(\\text{c}\\text{m}/\\sqrt{kg}\\)\u003c/span\u003e\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;WC/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{Weight}\\)\u003c/span\u003e\u003c/span\u003e .\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Measurement of cognitive function\u003c/h2\u003e\u003cp\u003eThe cognitive screening items used in the CHNS include a subset of items from the Telephone Interview for Cognitive Status-modified (TICS-M). Cognitive screening consisted of immediate and delayed recall of a list of 10 words (two recall attempts were scored as 10), counting backwards from 20 (scored as 2), and subtracting 7 consecutively (scored as 5). Cognitive functioning testing began with immediate recall of a list of 10 words. The interviewer (i.e., trained health worker) read the ten words at a rate of two seconds per word. Participants were given two minutes to memorize the ten words. A score of 1 was given for each correctly recalled word. The participant is then asked to count from 20 to 1. If the participant makes a mistake on the first attempt, a second chance is given. Two points were awarded for a correct answer on the first attempt and one point for the second attempt. After the counting test, participants were asked to subtract 7 from 100 five times in a row. Each correct subtraction was scored as 1. Finally, participants were asked to recall a list of 10 words from a prior test. Each recalled word was scored as 1[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In this study, we chose the first quartile of the cognitive functioning test scores as a proxy for poor cognitive functioning.\u003c/p\u003e\u003cp\u003eSelf-reported memory status was assessed by the following questions: \"How good is your memory? (1) very good; (2) good; (3) OK; (4) bad; (5) very bad; (6) unknown\" and \"How has your memory changed in the past twelve months? (1) improved; (2) stayed the same; (3) worsened; (4) unknown\". If the participant answered \"bad or very bad\" to the question, it was recorded as poor memory. If the answer to the question was \"worse\", memory loss was defined [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Assessment of Covariates\u003c/h2\u003e\u003cp\u003ePotential covariates considered included socioeconomic status, lifestyle factors, and physical health, with the first two categories collected at each round of the survey using a structured questionnaire.\u003c/p\u003e\u003cp\u003eSocioeconomic status: age, sex, ethnicity, region of residence, and education level (low: illiterate/elementary school; medium: middle school; high: high school and above).\u003c/p\u003e\u003cp\u003ePhysical health: whether they had been diagnosed with hypertension, defined as systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg; and whether they had been diagnosed with diabetes mellitus.\u003c/p\u003e\u003cp\u003eLifestyle factors: smoking, alcohol consumption, and tea drinking. Specifically, smoking status was categorized as nonsmokers, quitters, and current smokers. Alcohol consumption was categorized as \"yes\" or \"no\", as was tea consumption [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003eThis study used data from the CHNS to categorize participants into two groups, those with and without cognitive impairment, based on cognitive functioning test score results and self-reported memory status. Weighted chi-square and t-tests were used to test baseline characteristics of the study population. Continuous variables were described by weighted means and 95% confidence intervals, and categorical variables were described by weighted percentages. Before weighted regression modeling, all covariates were screened for variance inflation factor (VIF) covariates to avoid multicollinearity among variables affecting the multiple regression model. In this study, multiple logistic regression was used to analyze the relationship between WWI and cognitive status. In Model 1, the included variables were not adjusted. In Model 2, adjustments were made for gender, age, and ethnicity. All factors included in Model 3 were adjusted for gender, age, ethnicity, region of residence, education, history of hypertension, diabetes, smoking, alcohol consumption, and tea drinking. WWI was transformed into a categorical variable using quartiles, and the trend test was utilized to examine the trend of linear correlation between internal and external WWI and cognitive status.\u003c/p\u003e\u003cp\u003eTo explain the dose-response relationship (linear or nonlinear) between WWI and cognition, we used weighted multiple linear regression analyses to explore the linear relationship between WWI and cognitive status, while restricted cubic spline analysis (RCS) was used to assess the nonlinear association between WWI and cognitive status in Model 3.\u003c/p\u003e\u003cp\u003eTo further assess the predictive performance of WWI in evaluating cognitive function, ROC curve analysis was conducted for three cognitive assessments: Global cognitive function, Self-reported poor memory, and Self-reported memory decline. Three models were built for each assessment: Model 1: Included only cognitive status and WWI; Model 2: Included cognitive status, WWI, gender, and age; Model 3: Included cognitive status, WWI, gender, age, as well as additional confounding factors including nationality, education level, smoking status, tea consumption, alcohol use, hypertension, and diabetes. The AUC (Area Under the Curve) for each model was calculated to evaluate the discriminative power of WWI for each cognitive assessment.\u003c/p\u003e\u003cp\u003e In addition, participants in the 2009 CHNS came from 216 communities in nine provinces (i.e., Heilongjiang, Liaoning, Shandong, Henan, Hubei, Hunan, Jiangsu, Guangxi, and Guizhou). In this wave, blood samples were collected and tested for the first time [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This study further excluded those who lacked information on blood samples (n\u0026thinsp;=\u0026thinsp;4,485) from the above study population, and finally obtained participants who also had blood samples, weight, waist circumference, and cognitive status (n\u0026thinsp;=\u0026thinsp;3,353), and the screening process is described in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In adjusting for the main analytic model 3, mediated effects analysis was used to examine whether the correlation between WWI and cognitive status could be explained by triglycerides, HDL, LDL, ApoA, and ApoB.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eModel 4 in the SPSS macro program Process was used to conduct the test of mediating effects. Mediation analysis allows us to calculate how many mediation effects need to be generated. This is an ideal strategy for elucidating pathways and providing statistical evidence for mechanistic analyses. In this study, the direct effect represents the association between WWI and cognitive status; the indirect effect, i.e., the association between WWI and cognitive status, is mediated by serum markers; and the proportion mediated represents the percentage of mediated effect.\u003c/p\u003e\u003cp\u003eStatistics were judged significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. IBM SPSS Statistics 27 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ibm.com/cn-zh/spss\u003c/span\u003e\u003cspan address=\"https://www.ibm.com/cn-zh/spss\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and R Studio (version 4.4.0 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used for all analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline characteristics of participants\u003c/h2\u003e\u003cp\u003eThe characteristics of the participants according to the quartiles of the WWI are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The study involved 7,838 participants with a mean age of 56.85\u0026thinsp;\u0026plusmn;\u0026thinsp;10.05 years, of whom 3,936 (50.2%) were male and 3,902 (49.8%) were female. The mean WWI of all participants was 10.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94 cm/\u0026radic;kg, and the values of different quartiles were as follows: quartile 1: \u0026lt;10.25, quartile 2: 10.25\u0026ndash;10.81, quartile 3: 10.81\u0026ndash;11.46, and quartile 4: \u0026gt;11.46 cm/\u0026radic;kg. Compared to those in the lowest quartile of WWI, those in the highest quartile of WWI quartiles were more likely to be elderly, female, Han Chinese, living in urban areas, and low educated, and they were more likely to have hypertension, diabetes, and were more likely to consume alcohol and less likely to smoke cigarettes and drink tea.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of a sample of Chinese participants by weight-adjusted-waist index (N\u0026thinsp;=\u0026thinsp;7,838)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eWeight-adjusted waist index (cm/\u0026radic;kg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1(7.12\u0026ndash;10.25)\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,989\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ2(10.25\u0026ndash;10.81)\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,949\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ3(10.81\u0026ndash;11.46)\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,971\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ4(11.46\u0026ndash;15.66)\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,929\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.72\u0026thinsp;\u0026plusmn;\u0026thinsp;9.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.15\u0026thinsp;\u0026plusmn;\u0026thinsp;9.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.76\u0026thinsp;\u0026plusmn;\u0026thinsp;9.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.82\u0026thinsp;\u0026plusmn;\u0026thinsp;10.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1305(65.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1133(58.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e951(48.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e546(27.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e684(34.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e816(41.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1019(51.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1383(70.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNationality, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1785(89.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1730(88.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1746(88.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1691(87.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiao\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29(1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31(1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39(2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49(2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34(1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39(2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46(2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42(2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141(7.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149(7.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140(7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e147(7.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e786(39.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e732(37.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e800(40.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e765(39.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1203(60.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1217(62.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1171(59.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1164(60.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducational levels, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school and lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1397(70.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1335(69.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1391(71.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1531(81.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e284(14.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e281(14.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e271(14.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e186(9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school and higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e308(15.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e333(15.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e309(14.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e212(9.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension, n\u0026thinsp;(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e248(12.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e336(17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e397(21.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e438(22.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1741(87.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1613(82.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1574(79.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1491(77.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes, n\u0026thinsp;(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37(1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65(3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80(4.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e106(5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1952(98.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1884(96.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1891(95.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1823(94.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoke, n\u0026thinsp;(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e889(44.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e747(38.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e619(31.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e427(22.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1100(55.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1202(61.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1352(68.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1502(77.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrink alcohol, n\u0026thinsp;(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e820(41.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e702(36.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e561(28.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e415(21.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1169(58.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1247(64.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1410(71.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1514(78.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrink tea, n\u0026thinsp;(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e893(44.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e885(45.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e886(44.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e812(42.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1096(55.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1064(54.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1085(55.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1117(57.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables: the P value was calculated by the weighted linear regression model. (%) for categorical variables: the P value was calculated by the weighted chi-square test.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Association between WWI and cognitive function impairment\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the correlation between WWI and cognitive impairment. WWI and cognitive impairment showed a significant positive correlation in the models unadjusted and partially adjusted for covariates. After full adjustment, these positive correlations were still present, and both were significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The probability of cognitive impairment increased with increasing WWI in both unadjusted model 1, partially adjusted covariate model 2, and fully adjusted covariate model 3. Participants in the highest WWI quartile had a 155% increase in the probability of developing cognitive impairment compared to participants in the lowest quartile [2.55(2.19,2.96)]. This was not only true for low test scores on the Cognitive Status Test, but also for subjects' self-rated poor memory and memory loss.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of weight-adjusted waist index with cognitive function impairment.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eQuartiles of WWI (cm/\u0026radic;kg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQuartile 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQuartile 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQuartile 4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlobal cognitive function\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.29(1.14,1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.81(1.59,2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.40(2.97,3.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.20(1.05,1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.55(1.36,1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.50(2.16,2.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.24(1.08,1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.63(1.42,1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.55(2.19,2.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelf-reported poor memory\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.34(1.14,1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.62(1.39,1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.45(2.09.2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.27(1.08,1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.45(1.24,1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.96(1.67,2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.28(1.09,1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.45(1.24,1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.88(1.59,2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelf-reported memory decline\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.21(1.07,1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.38(1.22,1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.89(1.65,2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.16(1.02,1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.26(1.11,1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.58(1.38,1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.16(1.02,1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.24(1.09,1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.51(1.31,1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Model 1 was unadjusted; Model 2 was adjusted for gender, age, and ethnicity; Model 3 was further adjusted for educational attainment, region of residence, high blood pressure, diabetes mellitus, smoking, alcohol consumption, and tea drinking.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Dose‒response relationship analysis\u003c/h2\u003e\u003cp\u003eRestricted cubic spline (RCS) were used to analyze the relationship between WWI and cognitive function test. Based on this relationship, a nonlinear model provided a better explanation of the relationship than a linear model after adjusting for all covariates (P \u0026lt;0.0001) (Figure. 3). The probability of cognitive impairment was progressively higher at either WWI\u0026thinsp;\u0026lt;\u0026thinsp;9.34 or WWI\u0026thinsp;\u0026gt;\u0026thinsp;10.84.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Subgroup analysis of the association between weight-adjusted-waist index with cognitive function.\u003c/h2\u003e\u003cp\u003eIn order to assess the consistency of the association between WWI and cognitive function in the general population as a whole and to identify potential differences between specific populations, we conducted subgroup analyses and interaction effect analyses stratified by age, gender, ethnicity, region of residence, education, hypertension status, diabetes status, smoking, alcohol consumption, and tea drinking. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, our results indicated that WWI did not interact with age, gender, nationality, region of residence, education, hypertension status, alcohol consumption, and tea drinking (all P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, we observed a direct interaction of WWI with smoking and diabetes status (P for interaction\u0026thinsp;\u0026le;\u0026thinsp;0.05), which demonstrates that smoking and diabetes status may influence the alteration of WWI, and consequently cognitive functioning as well.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSubgroup analysis of the association between weight-adjusted-waist index with cognitive function.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eQuartiles of WWI (cm/\u0026radic;kg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP trend\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP for interaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartile 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQuartile 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQuartile 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQuartile 4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.35(1.13,1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.91(1.59,2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.62(2.08,3.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.09(0.89,1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.37(1.12,1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.38(1.95,2.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.38(1.13,1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.81(1.48,2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.64(2.097,3.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.12(0.95,1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.54(1.30,1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.00(2.51,3.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNationality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.913\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.20(1.04,1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.55(1.34,1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.53(2.16,2.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiao\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00(0.32,3.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.28(0.80,6.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.99(1.01,8.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84(0.24,2.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.96(0.60,6.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.70(0.70,10.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.04(1.26,3.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.28(1.95,5.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.76(2.11,6.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.33(1.13,1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.70(1.43,2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.41(2.00,2.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.09(0.87,1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.52(1.22,1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.86(2.26,3.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducational levels\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school and lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.25(1.08,1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.66(1.43,1.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.48(2.11,2.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.04(0.71,1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.50(1.02,2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.29(1.46,3.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school and higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.48(0.89,2.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.03(1.23,3.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.14(3.00,8.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.10(0.77,1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.36(0.96,1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.46(1.72,3.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.27(1.10,1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.70(1.47,1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.61(2.21,3.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77(0.30,1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.57(0.23,1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.77(1.11,6.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.26(1.10,1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.71(1.49,1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.60(2.24,3.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoke\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.35(1.09,1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.09(1.66,2.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.67(2.03,3.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.18(0.99,1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.47(1.24,1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.53(2.12,3.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrink alcohol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.47(1.18,1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.84(1.45,2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.54(1.91,3.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.14(0.97,1.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.57(1.33,1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.57(2.16,3.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrink tea\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.32(1.08,1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.88(1.53,2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.83(2.25,3.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.20(1.01,1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.52(1.27,1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.46(2.03,2.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5. \u003cb\u003eROC Analysis Results\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eROC analysis was conducted to evaluate the performance of WWI in predicting cognitive impairment as measured by Global cognitive function, Self-reported poor memory, and Self-reported memory decline. The AUC values for each model and assessment are presented below: Global cognitive function: For Model 1 (cognitive status and WWI), the AUC was 0.62. In Model 2 (cognitive status, WWI, gender, and age), the AUC increased to 0.64. The highest AUC of 0.73 was observed in Model 3, which included additional confounders such as nationality, education level, smoking status, tea consumption, alcohol use, hypertension, and diabetes. These results indicate that the predictive performance of WWI for global cognitive function improved as more confounding variables were accounted for(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Self-reported poor memory: In Model 1, the AUC was 0.59. The inclusion of gender and age in Model 2 resulted in a slight increase in AUC to 0.60. Further adjustments in Model 3 yielded an AUC of 0.66, suggesting that WWI's ability to predict self-reported poor memory was enhanced with the incorporation of additional covariates(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Self-reported memory decline: The AUC for Model 1 was 0.56, with a slight increase to 0.57 in Model 2. In Model 3, the AUC improved to 0.62, reflecting a moderate increase in predictive accuracy as additional confounders were included (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese findings suggest that WWI demonstrates a moderate ability to predict cognitive function and self-reported memory issues, with its predictive power improving as the model accounts for more confounding variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.6 The mediation analysis of blood biomarkers on the relationship between WWI and cognitive function\u003c/h2\u003e\u003cp\u003eIn addition, mediation analysis was performed to explore the mediating effects of serum markers. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the results of mediation analysis for five serum markers, triglycerides, LDL, HDL, ApoA and ApoB, which showed that only ApoA and ApoB had significant mediation effects (P\u0026thinsp;\u0026le;\u0026thinsp;0.05). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e further reveals the mediating role of ApoA and ApoB in the relationship between WWI and cognitive function. The proportion mediated of ApoA and ApoB was 3.32% and 3.35%, respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediated analysis of the association between blood biomarkers and cognitive function.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglyceride\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e(-0.128,0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-density lipoprotein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e(-0.060,0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-density lipoprotein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e(-0.006,0.122)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApolipoprotein A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e(-0.034, -0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApolipoprotein B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e(-0.033, -0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMost previous epidemiologic studies on obesity and cognitive function have been conducted mainly in older European and American populations, with fewer analyses targeting Asian populations. Our study aimed to investigate the association between WWI and low cognitive function in a Chinese population. In this cross-sectional study, which recruited 7838 individuals aged 55 years and older, we found a positive and significant association between WWI and low cognitive performance. In the model with all adjustments, positive correlations were found between WWI and low cognitive function as indicated by global cognitive function, Self-reported poor memory, and Self-reported memory decline, implying that individuals with higher WWI have a higher likelihood of cognitive impairment. Restricted Cubic Spline (RCS) analyses showed a nonlinear relationship between WWI and and low cognition. The probability of cognitive impairment was progressively higher at either WWI\u0026thinsp;\u0026lt;\u0026thinsp;9.34 or WWI\u0026thinsp;\u0026gt;\u0026thinsp;10.84. Subgroup analyses and interaction tests confirmed the robustness of this positive correlation across a variety of demographic contexts. The correlation was independent of gender, age, ethnicity, education, region of residence, hypertension, and alcohol or tea consumption. However, smoking and diabetes may influence changes in WWI, which in turn may also have an impact on cognitive function. These observations suggest that elevated WWI may be an independent risk factor for cognitive impairment, thus emphasizing the importance of WWI in the prevention and management of cognitive impairment.\u003c/p\u003e\u003cp\u003eObesity has become one of the important risk factors for cognitive impairment and dementia. In recent years, more and more studies have pointed out that obesity is an accelerated aging disease. The prevalence of neurodegenerative diseases such as ageing-associated cognitive impairment is significantly higher in obese populations [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Analysis of data based on more than 8000 Asian populations found that excessive obesity is a key metabolic risk factor for cognitive decline [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For every 0.27 kg increase in excess visceral fat, the degree of cognitive decline was equivalent to 0.7 years of \"aging\". China has the largest number of overweight and obese people in the world, and obesity has become a serious public health problem [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, preventing excessive obesity can help maintain or improve cognitive health and reduce the risk of future dementia in the Chinese population. BMI and WC are measures of obesity. Previous findings on the association between poor cognition and obesity-related parameters in older adults are controversial. Some studies have shown that lower BMI is associated with better cognitive function, whereas higher BMI or WC is associated with an accelerated rate of cognitive decline [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In contrast, some studies have found that higher BMI or WC prevents cognitive decline [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, traditional obesity indicators such as WC and BMI may not fully reflect the role of obesity in predicting cognitive impairment. Recent studies have shown that the WWI is effective in distinguishing between muscle mass and fat mass, leading to its wider application in a variety of medical fields, including metabolic diseases, renal diseases, and cardiovascular diseases [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. WWI provides a more accurate measure of natural obesity than the traditional obesity index, as BMI is unable to differentiate muscle mass from fat mass. In fact, WWI has shown greater relevance and predictive power in certain diseases [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In Li's study, WWI correlated much more strongly with poor cognitive function than obesity markers such as BMI, WC and ABSI [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. As a new obesity-related indicator, WWI has become a good predictive tool for cognitive dysfunction and dementia risk.\u003c/p\u003e\u003cp\u003eThe correlation between obesity and poor cognitive function can be explained by many biological mechanisms. According to several studies, the relationship between obesity and poor cognitive function may involve structural and functional changes in the cortex and subcortex. These structures are associated with cognitive domains, including working memory, verbal memory, processing speed, and intelligence [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, obesity is a state caused by excessive accumulation of body fat. Although the pathways by which excess adipose tissue affects brain function are not fully understood, available evidence suggests that insulin resistance (IR), inflammation, and vascular dysfunction are the three main possible mechanisms by which obesity leads to cognitive impairment [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Cognitive deficits caused by central IR may be more due to decreased insulin receptor substrate (IRS) expression and phosphorylation, dysregulation of the PI3K/Akt pathway and the MAPK pathway, which affects neuronal cell survival and normal function, thus leading to cognitive deficits. Activation of the PI3K pathway is involved in the regulation of synaptic plasticity, memory maintenance, and nitric oxide synthesis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The MAPK pathway mainly controls cell growth and differentiation, and is a key player in synaptic regulation and function [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Secondly, obesity increases the secretion of pro-inflammatory factors (TNF-α, IL-1β, IL-6, MCP1), which trigger localized inflammation in the brain and lead to neurodegeneration [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. At the same time, obesity-related inflammation leads to IR, leptin resistance, and decreased secretion of lipid-transporting proteins, which increase the risk of cognitive impairment and dementia [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Finally, obesity also accelerates age-related atherosclerosis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], leading to inadequate cerebral perfusion [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], impaired white matter microstructural integrity [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and induce cognitive decline.\u003c/p\u003e\u003cp\u003eThis study has several highlights. First, this study is the first to report the relationship between WWI and cognitive function in a Chinese population based on data from the CHNS database. We adjusted for confounding factors, including age, gender, ethnicity, region of residence, education level, hypertension status, diabetes status, smoking, alcohol consumption, and tea drinking, in order to mitigate the effects of confounding factors and obtain more reliable results. The reliability and representativeness of the findings were improved. We also explored the nonlinear relationship between WWI and cognitive impairment using RCS analysis. Next, we conducted subgroup analysis to further elucidate the association between WWI and cognitive impairment in different population settings. Finally, we performed mediation analysis to clarify the mediating role of ApoA and ApoB in the association between WWI and cognitive impairment. However, there are some shortcomings in our study. Firstly, this study is a cross-sectional study exploring the association between WWI and cognitive impairment in Chinese population, a causal relationship could not be established. Therefore, prospective studies with larger sample sizes are needed to verify causality. Secondly, although the results were obtained from a dataset of CHNS database, further large-scale cohort studies may be needed in the future to demonstrate the current findings. Lastly, even after adjusting for some potential confounders, the influence of other potential confounders cannot be completely ruled out. However, the existing association between WWI and cognitive impairment is strong enough that it is unlikely to be significantly altered by unconsidered confounders.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our study suggests that higher WWI may be associated with poorer cognitive function and may predict cognitive dysfunction in the Chinese elderly population. The association between WWI and risk of cognitive impairment showed a nonlinear pattern. ApoA and ApoB may be mediators between WWI and cognitive impairment. Focusing on the potential mediating role of ApoA and ApoB, the risk of dementia may be beneficially reduced in the Chinese population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWWI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWeight-adjusted waist index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCHNS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChina Health and Nutrition Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRestricted Cubic Spline\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eApoA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eApolipoprotein A\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eApoB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eApolipoprotein B\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emild cognitive impairment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWaist Circumference\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMTC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emobile testing center\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTICS-M\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTelephone Interview for Cognitive Status-modified\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVIF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003evariance inflation factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elow density lipoprotein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHDL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehigh density lipoprotein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einsulin resistance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIRS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einsulin receptor substrate.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthical Considerations\u003c/h2\u003e\u003cp\u003e This study was conducted in accordance with the ethical standards set forth by the National Institute for Nutrition and Health (NINH, former National Institute of Nutrition and Food Safety) at the Chinese Center for Disease Control and Prevention (CCDC). Informed consent was obtained from all participants prior to their involvement in the study. The research protocol was reviewed and approved by the relevant ethical review board, ensuring that all ethical guidelines were adhered to throughout the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (No.82274218) and Innovation Project for Postgraduate Training in Jiangsu Province (KYCX25_2356).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSu Yue and Cheng Xiaolan designed the research. Su Yue, Xu Ying, Li Xiang, Ge Yuhan, Wang Yunting, Ji Xiaowei collected, analyzed the data, and drafted the manuscript. Cheng Xiaolan drafted and revised the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003e We sincerely thank all the projects (National Health and Nutrition Examination Survey) who participated in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ePublicly available datasets were analyzed in this study. These data can be found here: https://www.cpc.unc.edu/projects/china.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNg, N. F., Osman, A. M., Kerlan, K. R., Doraiswamy, P. M. \u0026amp; Schafer, R. J. Computerized cognitive training by healthy older and younger adults: age comparisons of overall efficacy and selective effects on cognition. \u003cem\u003eFront. Neurol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 564317 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSanford, A. M. Mild cognitive impairment. \u003cem\u003eClin. 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Rev.\u003c/em\u003e \u003cb\u003e70\u003c/b\u003e, 101397 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cross-sectional study, CHNS, Obesity, cognitive impairment, Weight-adjusted waist index, Apolipoprotein","lastPublishedDoi":"10.21203/rs.3.rs-7628452/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7628452/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe weight-adjusted waist index (WWI) is an innovative measure of obesity. This study aimed to investigate the relationship between WWI and cognitive function in Chinese population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eUsing the China Health and Nutrition Survey (CHNS) database 1989-2011 dataset, cross-sectional data from 7838 participants were analyzed. The association between WWI and cognitive impairment was investigated by multiple regression analysis and subgroup analysis. In addition, restricted cubic spline (RCS) was applied to explore nonlinear relationships, and mediation analysis was carried out to assess whether Apolipoprotein influenced these relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe research involved 7838 participants aged 55 years and older. The fully adjusted model revealed a positive and significant association between WWI and low cognitive performance [2.55(2.19,2.96)], implying that individuals with higher WWI have a higher likelihood of cognitive impairment. Restricted Cubic Spline (RCS) analyses showed a nonlinear relationship between WWI and and low cognition. Subgroup analyses and interaction tests confirmed the robustness of this positive correlation in different population settings (all P for interaction \u0026gt; 0.05). Mediation revealed that ApoA and ApoB may be mediators between WWI and cognitive impairment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eA higher WWI was associated with a higher incidence of cognitive impairment. The results of this study highlight the value of the WWI in dementia prevention and managementin Chinese population.\u003c/p\u003e","manuscriptTitle":"Apolipoprotein Mediation in the Relationship Between Weight-Adjusted Waist Index and Cognitive Decline","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 16:49:38","doi":"10.21203/rs.3.rs-7628452/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-11-17T01:47:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-17T01:44:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-25T12:10:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-25T05:14:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-25T04:22:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"472347be-9eae-416f-af4d-370c28a376fa","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":58077098,"name":"Health sciences/Diseases"},{"id":58077099,"name":"Health sciences/Health care"},{"id":58077100,"name":"Health sciences/Medical research"},{"id":58077101,"name":"Health sciences/Neurology"},{"id":58077102,"name":"Biological sciences/Neuroscience"},{"id":58077103,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-07T10:53:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 16:49:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7628452","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7628452","identity":"rs-7628452","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}