The Association between Cognitive Function and Multimorbidity in Middle-aged and Elderly Populations: A National Longitudinal Study

The Association between Cognitive Function and Multimorbidity in Middle-aged and Elderly Populations: A National Longitudinal Study | 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 Research Article The Association between Cognitive Function and Multimorbidity in Middle-aged and Elderly Populations: A National Longitudinal Study Wenwen Xiang, Yanping Li, Luyao Qiao, Quan Zhang, Jiaxing Peng, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8988548/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Multimorbidity is a major public health challenge in an aging society. There is a bidirectional relationship between cognitive function and chronic diseases, but there is still a lack of sufficient evidence regarding how cognitive function prospectively influences the occurrence and development of different multimorbidity patterns. Method: This study is based on the longitudinal data of the China Health and Retirement Longitudinal Study(CHARLS) from 2011 to 2020. A prospective cohort design was adopted, and participants aged 45 and above without multimorbidity at the baseline (n = 4,917) were included. The relationship between baseline cognitive function and the risk of multimorbidity onset was analyzed using the Cox proportional hazards model and restricted cubic spline analysis. The multimorbidity patterns were identified through latent class analysis, and then a multivariate unordered multinomial Logistic regression was used to analyze the differential effects of cognitive function and other factors on different patterns. Results: During the 9-year follow-up period, 56.7% of the participants developed new multimorbidity. After adjusting for confounding factors, higher cognitive function was an independent protective factor for the occurrence of multimorbidity (HR:0.839, 95% CI: (0.753,0.934)), and there was a non-linear relationship ( P < 0.001, P for nonlinear <0.041). When the total cognitive function score was lower than 8.5 points, the risk significantly increased. The study identified four multimorbidity patterns: "multisystem high multimorbidity group", "respiratory system disease dominant group", "mild to moderate joint-digestive system multimorbidity group", and "metabolic syndrome multimorbidity group". The protective effect of cognitive function was particularly evident in significantly reducing the risk of the "mild to moderate joint-digestive system multimorbidity pattern" (OR:0.784, 95% CI :(0.615, 0.999)). In addition, central obesity was a strong risk factor for the "metabolic syndrome multimorbidity group", while a higher level of education was a protective factor for the "respiratory system disease dominant group". Conclusions: Better cognitive function is an important protective factor for preventing the occurrence of multiple multimorbidity in the elderly population, and it has specific protective effects on certain multimorbidity patterns such as the joint-digestive system pattern. Cognitive assessment can serve as an early warning tool for the risk of multimorbidity, and future comprehensive prevention strategies should place emphasis on maintaining cognitive health. Cognitive function Multimorbidity Middle-aged and elderly Latent class analysis Prospective cohort study Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Multimorbidity refers to the situation where an individual simultaneously suffers from two or more chronic diseases or health problems. These diseases or health issues can be physical illnesses, mental and psychological disorders, or geriatric syndromes, and they are not ranked in terms of priority. They all jointly affect an individual's health status, functions, and quality of life. In the context of an increasingly aging global population, multimorbidity has become a serious public health challenge 1 – 3 . Studies have shown that the prevalence of multimorbidity significantly increases with age and is more common among the elderly population. The prevalence of multimorbidity varies between 20% and 30% across different countries 4 . The prevalence rate of multimorbidity in China is generally between 16% and 71%, and it is still on the rise in the next 20 years, exerting a huge pressure on the healthcare system 5 . In recent years, the bidirectional relationship between chronic diseases and cognitive function has gradually become a research hotspot. However, most scholars' discussions on the relationship between physical health conditions and cognitive dysfunction mostly focus on a single or specific chronic disease, and relatively few explore the influence of chronic disease multimorbidity on cognitive function. 6 Based on this, some studies have found that chronic disease multimorbidity can accelerate the decline of cognitive function. The more chronic diseases a patient has, the more severe the decline in cognitive ability will be, and the higher the risk of cognitive dysfunction will be 7–9 , this association may be regulated by multiple factors such as demographic factors (such as age and gender), drug factors, and lifestyle factors 10 – 12 . Studies have shown that compared to healthy individuals, those who suffer from chronic disease multimorbidity during middle age may have a nearly fivefold increased risk of developing cognitive impairment in the future 13 . Patients with chronic diseases who have multiple multimorbidity often need to take multiple medications, the multiple drug use and its potential interactions or cumulative effects may directly or indirectly increase the risk of cognitive decline 14 . On the other hand, unhealthy lifestyles, such as long-term smoking and excessive drinking, can exacerbate vascular damage and inflammatory responses, thereby promoting brain tissue damage and neurodegeneration, and thereby increasing the risk of cognitive dysfunction 15 , 16 . On the contrary, cognitive dysfunction itself can also be a risk factor for the occurrence and development of various chronic diseases. For instance, studies have shown that in middle-aged individuals without kidney disease at baseline, early cognitive impairment is an independent risk factor for rapid decline in kidney function and the development of new-onset chronic kidney disease 17 . Furthermore, elderly individuals with cognitive decline have a significantly higher risk of developing heart metabolic diseases, cardiovascular diseases and diabetes compared to their peers with normal cognitive function 18 . However, most existing studies are limited to cross-sectional designs or only focus on a single disease outcome, making it difficult to establish the temporal relationship and causal mechanism between cognitive function and chronic diseases. It is particularly noteworthy that chronic diseases do not occur randomly in individuals but often form a multimorbidity pattern with specific pathophysiological bases. Currently, there is still a lack of systematic prospective evidence regarding how baseline cognitive function affects the occurrence and development of different multimorbidity patterns in the future. Therefore, exploring the differential impact of cognitive function on multiple multimorbidity and their specific patterns is of great significance for revealing the underlying mechanisms and guiding early intervention. Based on the longitudinal data from the Chinese Health and Retirement Longitudinal Study (CHARLS), this study adopts a prospective cohort design. The aim is to explore the predictive role of baseline cognitive function in the occurrence of multiple multimorbidity among the elderly population in the future, analyze the association patterns between the two, identify different potential types of multimorbidity, and compare the differences in the impact of cognitive function on various multimorbidity patterns. The study aims to provide a basis for using cognitive function as an early warning indicator for chronic diseases and implementing stratified prevention strategies. 2. Materials and methods 2.1 Data sources and study population The data for this study were sourced from CHARLS, which is a nationally representative long-term follow-up study aimed at systematically collecting high-quality microdata on the health, socioeconomic status, and family characteristics of Chinese middle-aged and elderly individuals aged 45 and above. The CHARLS national baseline survey was conducted in 2011, covering 150 county-level units, 450 village-level units, approximately 10,000 households, and included a total of 17,708 middle-aged and elderly individuals 19 . This study utilized data from five rounds of follow-ups conducted in 2011 (baseline) and 2013, 2015, 2018, and 2020. All participants who completed all five rounds of the survey constituted the initial longitudinal sample pool (n = 10,927). Subsequently, participants whose age was less than 45 at baseline (n = 323) were excluded; participants with incomplete information on multimorbidity status (n = 1,530) were also excluded; then, participants with missing core cognitive function measurement data at baseline (n = 1,339) were excluded; finally, participants who already had multimorbidity at baseline (i.e., having two or more chronic diseases simultaneously) (n = 2,818) were excluded. After the above screening, a total of 4,917 participants with no multimorbidity at baseline were obtained, forming the main prospective analysis cohort for testing the association between cognitive function and the onset of multimorbidity. To control for the potential influence of reverse causality, we conducted a sensitivity analysis: We excluded individuals who had multimorbidity during the first follow-up (in 2013) from the main analysis cohort (n = 400), and finally retained 4,517 participants to form the sensitivity analysis sub-cohort ( Fig. 1 ) . This study adhered to the ethical review requirements of the CHARLS project and was approved by the Ethics Review Committee of Peking University (Approval Number: IRB 00001052–11015). All participants signed an informed consent form before the survey. 2.2 Cognitive function assessment and covariate selection This study employed the standardized cognitive function tests from the CHARLS dataset for assessment. The evaluation covered two main dimensions: mental state and situational memory ability. The mental state test consisted of three items: date cognition (answering current date information, with a maximum score of 5), calculation ability (performing 5 consecutive subtractions of 7, with a maximum score of 5), and drawing ability (copying an overlapping pentagram pattern, with a maximum score of 1). The total score of these three items was the overall mental state score (with a maximum of 11 points). The situational memory ability was assessed through an immediate word recall test, where respondents were required to recall 10 words after listening, with 1 point awarded for each correct recall, and the maximum score was 10 points. The study combined the scores of these two tests to form a comprehensive cognitive function score (with a total score range of 0–21 points), with a higher score indicating better cognitive function, the comprehensive score was included as a continuous variable in the main analysis model in this study. The detailed operational definitions and scoring criteria for the tests can be found in Supplementary Material Method S1. In this study, a series of variables that might affect the association between cognitive function and multimorbidity were selected from the baseline survey as covariates to control for potential confounding effects. These variables cover the following dimensions: sociodemographic characteristics: including age, gender, educational level, marital status, and place of residence. Lifestyle and behavioral factors: including smoking status and drinking status. Physiological and physical examination indicators: include body mass index (BMI) and waist circumference. For covariates with missing values, we adopt the following principles and steps for handling: if the proportion of missing values for a variable exceeds 20%, then it is excluded from the analysis. For variables with a missing proportion lower than 20%, we use the "mice" package in R software (version 4.4.3) for multiple chained equation imputation. 2.3 Multimorbidity In this study, "Multimorbidity" refers to the situation where an individual suffers from two or more chronic diseases simultaneously. This definition follows the standard set by the World Health Organization 20 . The diagnostic information for chronic diseases is derived from the self-reports based on questionnaires in each round of the CHARLS follow-up. The question is: "Have any doctors ever told you that you have the following diseases?" The multimorbidity status of this study was determined based on the following 13 chronic diseases: hypertension, dyslipidemia, diabetes or elevated blood sugar, malignant tumors such as cancer, chronic lung diseases, liver diseases, heart diseases, strokes, kidney diseases, gastrointestinal or digestive system diseases, emotional and mental problems, arthritis or rheumatism, and asthma. If a participant is confirmed to have any two or more of the diseases listed above at any of the follow-up visits, they will be defined as being in a state of multimorbidity at that particular time point. 2.4 Data analysis This study is based on the longitudinal data from CHARLS 2011–2020. It adopts a prospective cohort design and includes 4,917 participants aged 45 and above who had no multimorbidity at the baseline and completed five follow-ups. First, the baseline characteristics of the study population are described: continuous variables are expressed as the median (interquartile range), and categorical variables are expressed as frequencies (percentages). Comparisons between groups are conducted using non-parametric Mann-Whitney U test and chi-square test respectively 21 . To investigate the influence of cognitive function on the occurrence time of multimorbidity, we employed the Cox proportional hazards regression model, using four cognitive function indicators as exposure variables: the continuous score of total cognitive function (ranging from 0 to 21 points), the continuous score of mental status, the continuous score of memory function, and the three-quartile grouping of total cognitive function (divided into low, medium, and high quartiles based on the overall distribution, with cut-off points at 11 points and 14 points). We constructed four progressively adjusted models and used the Schoenfeld residual test to verify the proportional hazards assumption 22 . At the same time, we used restricted cubic splines (RCS) to examine the dose-response relationship between cognitive function indicators and the risk of multimorbidity, as well as to test for nonlinear associations. To assess the potential impact of the reverse causal relationship and the robustness of the results, we conducted a sensitivity analysis. We excluded participants who developed multimorbidity within the first two years after the baseline (wave = 2), and calculated the relative percentage change in the risk ratio (HR) between the original model and the sensitivity analysis model (the formula is | HR_original - HR_sensitivity | / HR_original × 100%). A relative change of less than 5% was considered highly stable. In terms of the identification and analysis of multimorbidity patterns, we first employed Latent Class Analysis (LCA) to model the prevalence of 13 chronic diseases, in order to identify the underlying, unobserved category structures present in the data. Model selection was based on the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), entropy, and the Vuong-Lo-Mendell-Rubin Likelihood Ratio Test (VLMR), and the optimal number of categories was determined through a comprehensive consideration of clinical interpretability 23 , 24 . Furthermore, we employed association rule analysis to explore the frequent co-occurrence of binary and ternary combinations among chronic diseases, in order to reveal common disease association patterns. Based on the co-morbidity patterns identified by LCA, we conducted a multivariate unordered multinomial Logistic regression analysis to examine the independent effects of cognitive function and other covariates (including gender, age, BMI group, smoking, drinking, place of residence, marital status, educational level, and waist circumference group) on different co-morbidity patterns, and calculated the odds ratio (OR) and its 95% confidence interval. All statistical analyses were conducted using the R software (version 4.4.3), and all tests were two-tailed. A P value less than 0.05 was considered statistically significant. 3. Results 3.1 Baseline characterization Table 1 presents the socio-demographic and clinical characteristics of the 4,917 participants included in this study who had no multimorbidity at the baseline. During the follow-up period, 2,788 participants (56.7%) developed new multimorbidity, while the remaining 2,129 participants (43.3%) remained disease-free. Compared with the participants without multimorbidity, those with multimorbidity were older (median age: 57 years vs. 55 years, P < 0.001), and had lower cognitive function scores (median total cognitive score: 12.5 points vs. 13 points, P < 0.001; among which, memory score: 3.5 points vs. 4 points, P = 0.004). Furthermore, the participants in the multimorbidity group had higher BMI and waist circumference indices. In terms of sociodemographic and behavioral factors, the proportion of participants with a primary school education or lower in the multimorbidity group was higher ( P = 0.007). There were no significant differences in the distribution of residence (urban or rural), gender, and marital status between the two groups. Table 1 Baseline analysis of the participants involved in the study(n = 4917) Characteristics No multimorbidity Multimorbidity P N = 2129 N = 2788 Age, years 55 (49, 62) 57 (51, 63) < 0.001 Residence, n (%) Urban community 747 (35.09%) 983 (35.26%) 0.925 Rural village 1382 (64.91%) 1805 (64.74%) Gender, n (%) Male 1137 (53.41%) 1414 (50.72%) 0.068 Female 992 (46.59%) 1374 (49.28%) Education level, n (%) Primary school below 763 (35.84%) 1117 (40.06%) 0.007 Primary school 490 (23.02%) 649 (23.28%) Middle school 574 (26.96%) 673 (24.14%) Highschool and above 302 (14.19%) 349 (12.52%) Marital status, n (%) Married 1951 (91.64%) 2553 (91.57%) 0.973 Divorced/Widowed/ Never married 178 (8.36%) 235 (8.43%) Smoking status, n (%) No 1349 (63.36%) 1856 (66.57%) 0.021 Yes 780 (36.64%) 932 (33.43%) Drinking statu, n (%) No 1297 (60.92%) 1792 (64.28%) 0.017 Yes 832 (39.08%) 996 (35.72%) BMI, kg/m² 22.64 (20.57, 25.01) 23.29 (20.9, 26.03) < 0.001 Waist, cm 82.4 (76.2, 89) 84.6 (78, 92) < 0.001 Memeory scores 4 (2.5, 5) 3.5 (2.5, 4.5) 0.004 Executive scores 9 (7, 11) 9 (7, 10) < 0.001 Total cognition scores 13 (10, 15) 12.5 (10, 14.5) < 0.001 Note. BMI=Body Mass Index Data are presented as median (interquartile range) for continuous variables and number (percentage) for categorical variables. P-values were calculated using the Mann-Whitney U test for continuous variables and Chi-square test for categorical variables. 3.2 Association between cognitive function and risk of multimorbidity The P-value of the proportional hazards assumption test (Schoenfeld test) was greater than 0.05, which met the application prerequisites of the Cox proportional hazards model (Table S1 ) . As shown in Table 2 , multivariate Cox regression analysis indicated that baseline cognitive function was an independent predictor of multimorbidity onset. In unadjusted model 1, for every 1-point increase in the total cognitive function score, the risk of multimorbidity occurrence decreased by 2.6%. After sequentially adjusting for sociodemographic characteristics (model 2), lifestyle (model 3), and physiological indicators (model 4), this protective association remained robust. In the fully adjusted Model 4, for every 1-point increase in the total cognitive score, the risk decreased significantly by 2.2% (HR = 0.978, 95% CI: 0.966–0.991). The analysis of the total cognitive function grouped into tertiles showed that compared with the low score group (≤ 10 points), the multimorbidity risk in the high score group (≥ 14 points) was significantly reduced by 16.1% (HR = 0.839, 95% CI: 0.753–0.934), while the risk in the medium score group (10–14 points) showed a decreasing trend but did not reach statistical significance. The analysis of different indicators of cognitive function revealed distinct patterns: the mental status score showed a significant linear negative correlation with the risk of multimorbidity. However, the memory function score did not show a significant association in the adjusted linear model. Table 2 The relationship between cognitive function and multimorbidity. Variable Model 1 Model 2 Model 3 Model 4 HR(95%CI) P HR(95%CI) P HR(95%CI) P HR(95%CI) P Total cognition scores 0.974(0.964,0.984) < 0.001 0.981(0.968,0.993) 0.002 0.981(0.969,0.993) 0.002 0.978(0.966,0.991) < 0.001 Executive scores 0.965(0.952,0.979) < 0.001 0.974(0.957,0.990) 0.002 0.974(0.958,0.991) 0.002 0.970(0.954,0.987) < 0.001 Memeory scores 0.966(0.945,0.988) 0.003 0.981(0.958,1.005) 0.118 0.981(0.958,1.005) 0.114 0.980(0.957,1.004) 0.097 Total cognition tertile <Q1 1.000(Reference) 1.000(Reference) 1.000(Reference) 1.000(Reference) Q1-Q2 0.919(0.843,1.003) 0.059 0.953(0.868,1.045) 0.304 0.955(0.870,1.047) 0.324 0.939(0.856,1.031) 0.185 ≥Q2 0.805(0.734,0.884) < 0.001 0.854(0.767,0.950) 0.004 0.855(0.768,0.952) 0.004 0.839(0.753,0.934) 0.001 Note. HR: Hazard Ratio, CI: Confidence Interval, BMI=Body Mass Index, Q1 = 11, Q2 = 14 Model 1:Crude Model: Unadjusted. Model 2:Adjusted: Age, Gender, Education level, Marital status, Residence. Model 3:Adjusted: Age, Gender, Education level, Marital status, Residence, Smoking status, Drinking status. Model 4:Adjusted: Age, Gender, Education level, Marital status, Residence, Smoking status, Drinking status, BMI, Waist To deeply explore the potentially complex dose-response relationship, we conducted a restricted cubic spline analysis (Restricted Cubic Spline, RCS). In the fully adjusted model, there was a significant nonlinear association between total cognitive function and the risk of multimorbidity ( P -nonlinear = 0.041), and the overall effect was extremely significant ( P -overall < 0.001). Four statistical inflection points were identified: 8.51, 12.51, 12.63 and 19.59 points. Based on the clinical warning principle of the primary inflection point, we determined the first inflection point of 8.51 points as the critical clinical cutoff value. This inflection point marks the transition from the high plateau phase of the risk curve to the decline phase, indicating that when the total cognitive function score is below this threshold, the co-morbidity risk significantly and sharply increases ( Fig. 2 A ) . It is worth noting that in the unadjusted rough model, the nonlinear relationship was not significant ( P -nonlinear = 0.098), indicating that fully controlling for confounding factors is crucial for revealing the true nonlinear pattern. The relationship between mental status and the risk of multimorbidity is mainly linear, and the non-linear test was not significant ( P -nonlinear = 0.089) ( Fig. 2 C ) . On the contrary, the memory function showed a significant nonlinear correlation (adjusted model P -nonlinear = 0.037), and identified four statistical inflection points: 2.78, 3.75, 5.28, and 7.84. To obtain a more robust estimate that is less sensitive to model fluctuations, we calculated the median of these inflection points (4.51 points), and based on the convenience of clinical application, recommended 4.5 points as the screening cutoff value for memory function. Scores lower than 4.5 were identified as individuals at high risk of multimorbidity. Similar to total cognitive function, the nonlinear relationship of memory function was fully revealed only after adjusting for covariates ( Fig. 2 B ) . 3.3 Multimorbidity patterns and latent class analysis This study identified 5 binary disease combinations that met the preset thresholds (support > 10%, confidence > 40%) through association rule analysis (Table S2) , and 3 ternary disease combinations that met the thresholds (support > 5%, confidence > 40%) (Table S3) . The data analysis shows that the most common binary disease combination is hypertension and arthritis, with a support degree of 17.23%, and it exhibits a significant bidirectional correlation. The most common triad disease combination is hypertension, dyslipidemia and arthritis, with a support degree of 6.47%. Figure 3 illustrates the trend of prevalence changes for 13 chronic diseases over 5 follow-up periods. In general, the prevalence of most chronic diseases shows an upward trend with increasing age, among which the prevalence of arthritis, diabetes and digestive system diseases increases the most significantly. In contrast, the prevalence of cancer, stroke, kidney diseases, etc. remains at a relatively low level and is relatively stable. Subsequently, this study employed latent class analysis (LCA) to identify the intrinsic heterogeneity patterns of multimorbidity. By comparing the fitting indicators of models 1 to 8 (Table S4) , the 4 models demonstrated the lowest Bayesian Information Criterion (BIC = 48,390.013) and the highest entropy value (Entropy = 0.664), and the probability of belonging to all potential categories was greater than 5%. Although the likelihood ratio test (LRT) showed that the addition of up to 8 classes could significantly improve the fit (P < 0.05), models with 5 classes or more began to show potential categories with an assignment probability less than 5% (2.4% of the categories existed in the 5-class model), suggesting that there might be overfitting of the model. Taking into account the robustness of statistical indicators and the principle of clinical interpretability of categories, this study ultimately selected four models as the classification scheme for multimorbidity ( Fig. 4 ) . Latent class analysis revealed four distinct patterns of multimorbidity development (Table S5) . The first category was the "multi-system high multimorbidity group", accounting for 5.2% of the study population. Its characteristic was a high incidence of multiple systemic diseases, with particularly prominent pulmonary diseases (77.25%), arthritis (77.25%), and hypertension (67.84%). The second category was the "respiratory system disease dominant group", accounting for 9.9%, with a very high prevalence of pulmonary diseases (93.21%) as the core feature. The third category is the "mild to moderate joint-digestive system multimorbidity group", which is the largest subgroup (68.3%). The disease burden is relatively light, mainly concentrated on arthritis (40.47%) and digestive system diseases (26.62%). The fourth category is the "metabolic syndrome multimorbidity group", accounting for 16.6%, showing a typical aggregation of cardiovascular and metabolic diseases, including dyslipidemia (93.28%), hypertension (79.34%), and diabetes (40.83%). 3.4 Description of chronic diseases The analysis based on Table S6 shows that there is a significant correlation between the baseline cognitive function level and the number of chronic diseases that occurred during the follow-up period ( P < 0.001). As the cognitive function score declines, the risk of an individual developing multiple chronic diseases shows a gradually increasing trend. From the perspective of the three-quartile classification of cognitive function, in the low-cognitive-level group (< Q1), 38.8% to 41.1% of the individuals had two or more chronic diseases, and 41.1% of them had five or more chronic diseases, indicating a close correlation between low cognitive function and severe multimorbidity. In contrast, in the high-cognitive-level group (≥ Q2), only 26.7% to 30.8% of the individuals had two or more chronic diseases, and only 24.9% had five or more chronic diseases, showing a trend that individuals with better cognitive function have a relatively lighter burden of chronic diseases. It is worth noting that all three dimensions of cognitive function (total cognitive score, mental status, and memory) showed a dose-response relationship with the number of chronic diseases. Although the median mental status score was 9 in each group, the distribution variation significantly increased with the increase in the number of chronic diseases (χ² = 28.3, P < 0.001). Especially in the group with ≥ 4 chronic diseases, the lower limit of the interquartile range of the mental status score decreased from 7 to 6, suggesting that people with poorer mental status are more likely to accumulate multiple chronic diseases. The memory function score also decreased from 4 points in the healthy group to 3.5 points in the multimorbidity group ( P = 0.002), while the total cognitive function score dropped from 13 points to 12.5 points ( P < 0.001), further confirming the close connection between cognitive impairment and the accumulation of chronic diseases. 3.5 The differential impact of cognitive function on the patterns of multimorbidity: Based on multivariate unordered Logistic regression analysis To identify the independent risk factors for different multimorbidity patterns and establish a clear health reference benchmark, this study made methodological adjustments based on the four multimorbidity patterns identified through latent class analysis (LCA). The initial LCA model did not automatically separate out independent health patterns. Based on the cumulative number of chronic diseases of each individual, we separated the participants who were completely healthy (with 0 chronic diseases) at the end of the follow-up from the third category (low-burden joint-digestive pattern) of the original LCA. Finally, we constructed an analysis framework consisting of five categories: complete health pattern, mild joint-digestive disease pattern, respiratory system-joint disease pattern, vascular metabolic disease pattern, and complex systemic disease pattern. Based on this five-category classification system, we employed a multi-factor unordered multi-class Logistic regression model, with the completely healthy pattern as the reference, to systematically evaluate the differential impacts of demographic characteristics, lifestyle, anthropometric indicators, and cognitive functions on each multimorbidity pattern ( Table 3 ) . The analysis results show that different multimorbidity patterns have distinct risk factor profiles. Age is the strongest predictor of multimorbidity occurrence, especially for severe multimorbidity patterns. Compared with the 45–54 age group, the risk of all four multimorbidity patterns significantly increased in the 65–74 age group. Among them, the risk of suffering from complex systemic disease patterns increased most sharply (OR = 3.359, P < 0.001). After adjusting for multiple confounding factors such as age, gender, education, BMI, and waist circumference, a high baseline cognitive function (total cognitive score ≥ Q2) was significantly associated with a 22% reduced risk of developing the mild joint-digestive disease pattern. Central obesity (measured by waist circumference) had an extremely strong dose-response relationship with the vascular metabolic disease pattern (for those with waist circumference ≥ 90.4 cm, OR = 2.914, P < 0.001). High educational level is a protective factor for the respiratory system - arthritis pattern (OR = 0.549, P = 0.007) and the mild joint - digestive disease pattern (OR = 0.665, P = 0.008); female gender is associated with a lower risk of the respiratory system - arthritis pattern (OR = 0.711, P = 0.049). Table 3 Multivariate multiclass logistic regression analysis of the impact of cognitive function on different multimorbidity patterns Variable Class 1 Class 2 Class 3 Class 4 OR(95%CI) P OR(95%CI) P OR(95%CI) P OR(95%CI) P Age Group, years 45–54 1(reference) — 1(reference) — 1(reference) — 1(reference) — 55–64 1.659 (1.160, 2.373) 0.006 1.304 (0.988, 1.720) 0.061 1.113 (0.915, 1.353) 0.285 1.513 (1.187, 1.929) < 0.001 65–74 3.359 (2.140, 5.270) < 0.001 1.662 (1.132, 2.442) 0.010 1.658 (1.242, 2.214) < 0.001 2.583 (1.838, 3.629) < 0.001 ≥75 1.572 (0.706, 3.500) 0.269 1.213 (0.651, 2.258) 0.543 1.158 (0.722, 1.858) 0.543 1.082 (0.575, 2.035) 0.807 Gender, n (%) Male 1(reference) — 1(reference) — 1(reference) — 1(reference) — Female 1.371 (0.898, 2.092) 0.143 0.711 (0.506, 0.999) 0.049 1.025 (0.802, 1.310) 0.843 1.148 (0.853, 1.546) 0.362 Residence, n (%) Urban community 1(reference) — 1(reference) — 1(reference) — 1(reference) — Rural village 1.267 (0.920, 1.747) 0.148 1.248 (0.964, 1.616) 0.092 1.184 (0.986, 1.422) 0.071 1.041 (0.834, 1.300) 0.721 Education level, n (%) Primary school below 1(reference) — 1(reference) — 1(reference) — 1(reference) — Primary school 0.876 (0.581, 1.321) 0.528 0.988 (0.711, 1.373) 0.943 1.048 (0.818, 1.343) 0.709 1.083 (0.799, 1.468) 0.608 Middle school 0.714 (0.455, 1.118) 0.141 0.710 (0.500, 1.009) 0.056 0.880 (0.684, 1.132) 0.321 1.091 (0.801, 1.486) 0.582 Highschool and above 0.96 (0.57, 1.60) 0.871 0.549 (0.354, 0.850) 0.007 0.665 (0.493, 0.898) 0.008 0.997 (0.692, 1.438) 0.988 Marital status, n (%) Divorced/Widowed/ Never married 1(reference) — 1(reference) — 1(reference) — 1(reference) — Married 1.146 (0.688, 1.911) 0.600 1.064 (0.698, 1.623) 0.772 1.333 (0.972, 1.828) 0.075 1.067 (0.726, 1.567) 0.743 Smoking status, n (%) No 1(reference) — 1(reference) — 1(reference) — 1(reference) — Yes 1.045 (0.706, 1.545) 0.826 0.908 (0.670, 1.229) 0.532 0.810 (0.646, 1.015) 0.067 0.693 (0.526, 0.915) 0.010 Drinking status, n (%) No 1(reference) — 1(reference) — 1(reference) — 1(reference) — Yes 1.070 (0.756, 1.514) 0.702 1.195 (0.907, 1.574) 0.205 0.968 (0.791, 1.185) 0.752 1.253 (0.977, 1.608) 0.076 BMI Group, kg/m² <18.5 1(reference) — 1(reference) — 1(reference) — 1(reference) — 18.5–23.9 0.752 (0.443, 1.278) 0.292 0.577 (0.383, 0.869) 0.009 1.036 (0.745, 1.442) 0.832 0.932 (0.595, 1.460) 0.758 24-27.9 1.287 (0.690, 2.401) 0.428 0.537 (0.325, 0.889) 0.016 1.224 (0.832, 1.801) 0.304 1.520 (0.924, 2.500) 0.099 ≥28 1.220 (0.579, 2.570) 0.602 0.767 (0.419, 1.405) 0.391 1.310 (0.825, 2.080) 0.253 2.057 (1.167, 3.625) 0.013 Waist, cm <77.2 1(reference) — 1(reference) — 1(reference) — 1(reference) — 77.2–83.7 0.785 (0.522, 1.180) 0.245 1.031 (0.751, 1.415) 0.852 0.931 (0.739, 1.173) 0.546 1.167 (0.843, 1.615) 0.353 83.8–90.3 0.754 (0.480, 1.183) 0.219 1.043 (0.732, 1.485) 0.817 1.028 (0.797, 1.325) 0.833 1.987 (1.428, 2.764) < 0.001 ≥90.4 1.111 (0.673, 1.833) 0.681 1.191 (0.781, 1.817) 0.416 1.301 (0.959, 1.764) 0.091 2.914 (2.006, 4.234) < 0.001 Total cognition tertile <Q1 1(reference) — 1(reference) — 1(reference) — 1(reference) — Q1-Q2 0.919 (0.637, 1.327) 0.653 0.801 (0.592, 1.083) 0.149 0.915 (0.732, 1.143) 0.434 1.009 (0.767, 1.327) 0.951 ≥Q2 0.693 (0.453, 1.061) 0.092 0.759 (0.543, 1.060) 0.106 0.784 (0.615, 0.999) 0.049 0.872 (0.647, 1.176) 0.369 Note. HR: Hazard Ratio, CI: Confidence Interval, BMI=Body Mass Index, Q1 = 11, Q2 = 14. Reference group: The healthy pattern (N = 618). Class 1: Complex systemic disease pattern; Class 2: Respiratory system - arthritis pattern; Class 3: Mild joint-digestive disorder pattern; Class 4: Vascular metabolic disease pattern 3.6 Sensitivity analysis The sensitivity analysis revealed that, after excluding individuals with early onset, the association pattern between cognitive function and the risk of multimorbidity remained consistent with the original analysis (Table S7) . In the unadjusted model, for every 1-point increase in the total cognitive function score, the risk of multimorbidity decreased by 2.8%. In the fully adjusted model (Model 4), this association remained significant. The mental status score also demonstrated a similar protective effect (Model 4: HR = 0.970, P = 0.001), while the memory function score did not show a significant association in the adjusted model. The analysis based on the tertiles of the total cognitive function score indicated that compared with the low cognitive group, the high cognitive group (≥ Q2) showed a 16.8% reduction in the risk of multimorbidity in the fully adjusted model, and the medium cognitive group (Q1-Q2) showed a 9.9% reduction in the risk. By comparing the results of the original Cox analysis with those of the sensitivity analysis, it was found that the effect sizes obtained from the two analyses were extremely similar (Table S8) . For the total cognitive function score, the mental status score, and the memory function score, the percentage change in effect size (CP) ranged from 0% to 0.2%. For the cognitive function tertile groups, the percentage change in effect size for the medium-level group (Q1-Q2) and the high-level group (≥ Q2) was 0.8% − 4.7% and 0.8% − 2.0% respectively. All changes were less than 5%, and the effect direction was exactly the same as that of the original analysis. This result indicates that excluding cases of early multimorbidity has a negligible impact on the association between cognitive function and the risk of multimorbidity. The effect size from the original analysis remains stable, and the possibility of a reverse causal relationship is low. 4. Discussion This study, based on national longitudinal data, found that better cognitive function among the elderly population is an independent protective factor for the occurrence of multiple multimorbidity in the future, and there is a non-linear relationship between the two. The study identified four potential categories of multimorbidity and found that the protective effect on cognitive function was particularly evident in reducing the risk of "mild to moderate joint-gastrointestinal multimorbidity patterns". These results have deepened our understanding of the relationship between cognition and physical health, providing a basis for multimorbidity risk stratification and precise prevention based on cognitive assessment. This study has confirmed that even after controlling for a series of strong confounding factors such as age, education, lifestyle, and metabolic indicators, better cognitive function remains a powerful protective factor against the occurrence of multiple multimorbidity in the future. Previous studies have revealed the central role of cognitive function in the occurrence and development of chronic diseases. Large-scale prospective studies consistently show that a healthy lifestyle and better cognitive function are both closely related to a reduced risk of all-cause mortality, while cognitive decline weakens an individual's ability to maintain healthy behaviors, adhere to treatment, and identify health risks, thereby increasing the risk of death. This emphasizes the importance of maintaining cognitive health while promoting healthy lifestyles 25 , 26 . Further research has revealed that cognitive activities can buffer cardiovascular risks in specific community settings, while cognitive impairment itself is associated with a progressive increase in the risks of stroke, heart failure and cardiovascular death 27 , 28 . More importantly, a systematic review on the co-occurrence of chronic diseases and cognitive impairments clearly indicates that moderate to severe cognitive impairment significantly increases the clinical burden of patients with various chronic diseases, leading to an increased risk of death, prolonged hospital stays, and accelerated decline in physical function 29 . The above studies collectively point to a core conclusion: Cognitive decline is not only an important risk factor for chronic diseases, but also a key reason for the deterioration of co-morbidity management and outcomes. Our research also found that a high level of good cognitive function is an independent protective factor for the occurrence of multiple multimorbidity in the future, providing direct prospective evidence for the existence of the aforementioned vicious cycle. It confirms that good cognitive function is itself a key protective factor against the accumulation of chronic diseases, especially specific multimorbidity patterns (such as joint-digestive system patterns). Cognitive function may influence the risk of multimorbidity through various potential mechanisms. On one hand, individuals with better cognitive function usually have higher health literacy and better self-management skills, which enables them to perform better in maintaining healthy behaviors, early detection of chronic diseases, treatment compliance, and doctor-patient communication, effectively delaying or preventing the occurrence of diseases 30 . On the other hand, higher cognitive function indicates better brain health and neural reserve in individuals. The underlying common biological basis, such as lower systemic inflammation levels 31 , more stable hypothalamic-pituitary-adrenal axis function 32 and less oxidative stress damage 33 , may simultaneously provide protection against cognitive decline and the occurrence and development of various chronic diseases. Furthermore, our research has revealed a non-linear relationship between baseline cognitive function and future risk of multimorbidity, and has identified specific risk thresholds (with a total cognitive function score below 8.5) that have significant clinical warning significance. Furthermore, the study found that the influence patterns of different core dimensions of cognitive function on the risk of multimorbidity exhibited significant heterogeneity: the mental condition centered on executive function demonstrated a robust linear protective effect, and its mechanism might be related to optimizing individuals' comprehensive health decision-making and self-management behaviors. The memory function exhibits a distinct non-linear threshold effect. We speculate that damage to this function will only have a substantial impact on specific health management aspects such as medication compliance when it reaches a certain critical point (with a score lower than 4.5), thereby causing a sharp increase in risks. Furthermore, the core triad multimorbidity pattern identified in this study - hypertension, dyslipidemia and arthritis - is highly consistent with previous research. This may suggest that metabolic disorders and chronic inflammation have a profound mutual influence rather than occurring independently 34 . It reminds us that in clinical treatment, when managing any one of these diseases, we should consciously conduct screening and intervention for the other two diseases. Multivariate unordered multi-class Logistic regression revealed the unique risk factor profiles of each model. The study found that age was the strongest and most common predictor of multimorbidity, which is consistent with the consensus that the burden of multimorbidity has increased in the context of global aging 35 . It is worth noting that age has the greatest impact on the risk of complex systemic disease patterns, suggesting that as people age, the accumulation of diseases may not increase linearly but rather there is an accelerating effect or a tendency to evolve towards a specific complex pattern. The protective effect of cognitive function was confirmed in this study. After adjusting for various factors, higher cognitive levels were associated with a lower risk of "mild joint-digestive disorders". This indicates that good cognitive function is not only an indicator of brain health, but may also reflect the body's ability to cope with diseases and maintain overall health. Previous genome-wide association studies have shown that bone density and brain imaging phenotypes share a genetic basis. The SLC39A8 gene is simultaneously associated with osteoarthritis inflammation and brain synaptic plasticity, suggesting that there may be a common genetic background for osteoarthritis and cognitive function 36 . A proteomics study has shown that inflammatory mediators such as HAVCR1, GDF15 and IL-6 play a bridging role between joint diseases and cognitive decline. Better cognitive function may reflect lower systemic inflammatory levels, thereby reducing inflammation-driven multisystem multimorbidity 37 . Furthermore, the "microbiota-gut-brain axis" is also involved. Animal experiments have shown that the damage to the intestinal barrier associated with aging can increase inflammatory signals in the blood, thereby activating neuroimmune responses in the brain and impairing cognition. Better cognitive function may imply a more stable intestinal environment and better communication between the gut and the brain 38 . On the other hand, those with better cognitive abilities usually have higher health literacy and self-management skills, and are better able to adhere to healthy lifestyles and medical advice. Together with biology, they can delay the occurrence of various diseases. Our research has revealed that central obesity (measured by waist circumference) has an extremely strong dose-response relationship with vascular metabolic disease patterns. This finding confirms the central role of visceral fat accumulation in the occurrence of such diseases. A study conducted on the elderly population in China indicates that abdominal obesity is an independent and significant risk factor for cardiovascular metabolic multimorbidity. Its predictive power even exceeds that of simple dyslipidemia, and when both conditions coexist, the risk is compounded 39 . The data from the National Health and Nutrition Examination Survey of the United States shows that among the diabetic population, there is a clear U-shaped or J-shaped correlation between waist circumference and all-cause mortality 40 . Another study also confirmed that the waist circumference index after weight correction was linearly and positively correlated with the mortality risk of patients with metabolic-related fatty liver disease 41 . These pieces of evidence collectively indicate that central obesity is a more crucial metabolic risk factor than overall overweight. The educational level was found to be a protective factor for the respiratory system-arthritis pattern and the mild joint-digestive disease pattern. This result is consistent with the profound influence of socioeconomic status on health outcomes revealed by previous studies. A prospective cohort study conducted among the Austrian population has confirmed that low educational level is an independent risk factor for rheumatoid arthritis (RA), while higher education can significantly reduce the risk of the disease 42 . The Mendelian randomization study provided genetic evidence for the negative association between educational level and the risk of respiratory diseases, supporting the potential causal role of this relationship 43 . A better educational background may imply a more favorable career environment and a stronger ability to utilize medical services, which is conducive to the early management and control of chronic inflammatory diseases such as arthritis. This study is based on representative longitudinal data from China, adopts a prospective design, and effectively controls the interference of reverse causality through sensitivity analysis. By comprehensively applying statistical methods such as latent class analysis and restricted cubic splines, it systematically reveals the nonlinear association and specific risk thresholds between cognitive function and multiple multimorbidity patterns. However, the research still has certain limitations: the information on chronic diseases mainly relies on self-reports, which may have certain measurement errors; the assessment of cognitive function fails to cover all core dimensions; the research population is limited to the Chinese population, and the research conclusions should be cautiously generalized. 5. Conclusion This study has confirmed that better cognitive function is a key protective factor for preventing the occurrence of multiple multimorbidity in the elderly population. Central obesity and lower educational level have been identified as important risk factors for vascular metabolic diseases and specific patterns such as respiratory system-arthritis. These findings suggest that maintaining good cognitive function has a positive effect in reducing the overall risk of multimorbidity. Future prevention and treatment practices should place emphasis on the maintenance of cognitive health, combined with the control of central obesity and the improvement of health literacy, in order to effectively break the vicious cycle between cognitive decline and the accumulation of chronic diseases, and promote the process of healthy aging. Declarations Authorship contribution statement W.X., Y.L., L.Q., and Q.Z. contributed to software, validation, and visualization. W.X., Q.Z., J.P., and Z.T. contributed to conceptualization. W.X. and Q.Z. performed data curation. W.X. and Z.T. wrote the original draft. W.X. handled review and editing. Q.X., M.W., K.L., X.H., and Z.T. performed formal analysis. Z.T. acquired funding. All authors reviewed the manuscript. Ethics approval This study adhered to the ethical review requirements of the CHARLS project and was approved by the Ethics Review Committee of Peking University (Approval Number: IRB 00001052-11015). Clinical trial number Not applicable. Funding This research was funded by the Science and Technology Fund Project of the Health Commission of Jiangxi Province (Project Number: 202410228). Declaration of competing interest The authors have no conflicts of interest to disclose. Acknowledgments The authors thank the CHARLS research team for establishing and maintaining the cohort, and all participants for contributing their time and information. This study was made possible by their efforts. This research was funded by the Science and Technology Fund Project of the Health Commission of Jiangxi Province (Project Number: 202410228) Data availability The data for this study are sourced from the China Health and Retirement Longitudinal Study (CHARLS), which is an openly accessible resource available to researchers worldwide. The data access address is: https://charls.pku.edu.cn/. References Le Reste JY, Nabbe P, Manceau B, et al. The european general practice research network presents a comprehensive definition of multimorbidity in family medicine and long term care, following a systematic review of relevant literature. J Am Med Dir Assoc . 2013;14(5):319-325. doi:10.1016/j.jamda.2013.01.001 PLOS Medicine Editors. Multimorbidity: Addressing the next global pandemic. PLoS Med . 2023;20(4):e1004229. doi:10.1371/journal.pmed.1004229 Nicholson K, Makovski TT, Griffith LE, Raina P, Stranges S, van den Akker M. 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Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Yanping","middleName":"","lastName":"Li","suffix":""},{"id":618831503,"identity":"e6d943ac-0929-414d-a5b1-8f71177a269a","order_by":2,"name":"Luyao Qiao","email":"","orcid":"","institution":"The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Luyao","middleName":"","lastName":"Qiao","suffix":""},{"id":618831504,"identity":"3b3c3b93-8e49-4335-bccd-ebba97292238","order_by":3,"name":"Quan Zhang","email":"","orcid":"","institution":"The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Quan","middleName":"","lastName":"Zhang","suffix":""},{"id":618831505,"identity":"9aa85cd3-43cd-43fb-ac43-ff4e952c18f3","order_by":4,"name":"Jiaxing Peng","email":"","orcid":"","institution":"The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Jiaxing","middleName":"","lastName":"Peng","suffix":""},{"id":618831506,"identity":"36fd15ce-6fa4-4110-8e59-d7fa179bdc39","order_by":5,"name":"Qing Xie","email":"","orcid":"","institution":"The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Xie","suffix":""},{"id":618831507,"identity":"51ea79cc-18ec-4945-afe7-6f3d45c2a46d","order_by":6,"name":"Mengqian Wu","email":"","orcid":"","institution":"The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Mengqian","middleName":"","lastName":"Wu","suffix":""},{"id":618831508,"identity":"d71de0e4-66fa-4d04-83f0-e267929141d3","order_by":7,"name":"Kangwei Liao","email":"","orcid":"","institution":"The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Kangwei","middleName":"","lastName":"Liao","suffix":""},{"id":618831509,"identity":"14975554-4d80-4548-af83-531c0f554e9e","order_by":8,"name":"Xiaojuan Huang","email":"","orcid":"","institution":"The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojuan","middleName":"","lastName":"Huang","suffix":""},{"id":618831510,"identity":"0db7a135-f0d6-4e3d-8d56-a5d9f505fa2b","order_by":9,"name":"Zhenyu Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYPACNgZ+ZubDD4hSywPTItnOlmZAihYGBoPzPAoSRGmxZz97+OXPNr5848M8DAYMNTbRhG3hyUuzkGxjs9x2mPfAA4ZjabkNhB2WY2Zg2MZmYHaYL8GAseEwEVr435gZJAK1GDfzGEgQp0Uix/jBQaAWA2aitdx4Y8bYcI7NQOIwMJATiPELe3+O8ccfZccM+PsPH37wocaGsBYgYJNgZDsGYSYQoRwEmD8w/KkhUu0oGAWjYBSMSAAAVpo5IQzU7UwAAAAASUVORK5CYII=","orcid":"","institution":"The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Zhenyu","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2026-02-27 13:25:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8988548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8988548/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106399646,"identity":"0a972516-9bb8-47f8-a037-53cbf13ec7be","added_by":"auto","created_at":"2026-04-08 08:31:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1103482,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8988548/v1/8e94e3ff1625dfc59b232619.png"},{"id":106399647,"identity":"937f3d2e-3ae0-49f1-8ce5-abf92f08af40","added_by":"auto","created_at":"2026-04-08 08:31:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6014530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted cubic spline analysis of the association between cognitive function and multimorbidity risk.\u003c/strong\u003e\u003cbr\u003e\n Note. (A) Total cognitive function score.(B) Memory function score.(C) Mental status score.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8988548/v1/14443051cfacd77077038f35.png"},{"id":106404618,"identity":"837236a1-66d3-47a7-8d48-30fa503a58cb","added_by":"auto","created_at":"2026-04-08 09:16:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1305956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends in prevalence of 13 chronic diseases across five waves of follow-up.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote. \u003cstrong\u003eHibpe\u003c/strong\u003e=hypertension, \u003cstrong\u003eDiabe\u003c/strong\u003e=Diabetes or elevated blood sugar (including impaired glucose tolerance and elevated fasting blood sugar), \u003cstrong\u003eCancre\u003c/strong\u003e=Malignant tumors such as cancer (excluding mild skin cancer), \u003cstrong\u003eLunge\u003c/strong\u003e=Chronic lung diseases such as chronic bronchitis or emphysema, pulmonary heart disease (excluding tumors or cancers), \u003cstrong\u003eHearte\u003c/strong\u003e=Heart disease (such as myocardial infarction, coronary heart disease, angina, congestive heart failure, and other heart diseases), \u003cstrong\u003eStroke\u003c/strong\u003e=stroke, \u003cstrong\u003ePsyche\u003c/strong\u003e = Psychiatric disorders, \u003cstrong\u003eArthre\u003c/strong\u003e=Arthritis or rheumatism, \u003cstrong\u003eDyslipe\u003c/strong\u003e=Dyslipidemia (high or low lipids), \u003cstrong\u003eLivere\u003c/strong\u003e=Liver disease (other than fatty liver, tumor or cancer), \u003cstrong\u003eKidneye\u003c/strong\u003e=Kidney disease (excluding tumors or cancers), \u003cstrong\u003eDigeste\u003c/strong\u003e=Diseases of the stomach or digestive system (excluding tumors or cancers), \u003cstrong\u003eAsthmae\u003c/strong\u003e=Asthma (non-lung disease).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8988548/v1/c908f69b60573e5400a8c691.png"},{"id":106399650,"identity":"23b8556c-1a1e-4cc5-82c3-90e7e6e5f8eb","added_by":"auto","created_at":"2026-04-08 08:31:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2056947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFour-class multimorbidity patterns identified by latent class analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote. Class 1: Complex systemic disease pattern; Class 2: Respiratory system - arthritis pattern; Class 3: Mild joint-digestive disorder pattern; Class 4: Vascular metabolic disease pattern\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHibpe\u003c/strong\u003e=hypertension, \u003cstrong\u003eDiabe\u003c/strong\u003e=Diabetes or elevated blood sugar (including impaired glucose tolerance and elevated fasting blood sugar), \u003cstrong\u003eCancre\u003c/strong\u003e=Malignant tumors such as cancer (excluding mild skin cancer), \u003cstrong\u003eLunge\u003c/strong\u003e=Chronic lung diseases such as chronic bronchitis or emphysema, pulmonary heart disease (excluding tumors or cancers), \u003cstrong\u003eHearte\u003c/strong\u003e=Heart disease (such as myocardial infarction, coronary heart disease, angina, congestive heart failure, and other heart diseases), \u003cstrong\u003eStroke\u003c/strong\u003e=stroke, \u003cstrong\u003ePsyche\u003c/strong\u003e = Psychiatric disorders, \u003cstrong\u003eArthre\u003c/strong\u003e=Arthritis or rheumatism, \u003cstrong\u003eDyslipe\u003c/strong\u003e=Dyslipidemia (high or low lipids), \u003cstrong\u003eLivere\u003c/strong\u003e=Liver disease (other than fatty liver, tumor or cancer), \u003cstrong\u003eKidneye\u003c/strong\u003e=Kidney disease (excluding tumors or cancers), \u003cstrong\u003eDigeste\u003c/strong\u003e=Diseases of the stomach or digestive system (excluding tumors or cancers), \u003cstrong\u003eAsthmae\u003c/strong\u003e=Asthma (non-lung disease).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8988548/v1/828c30a368fa5adbefb98995.png"},{"id":106959884,"identity":"199fc3b7-4867-45e9-97fe-814e409ad23b","added_by":"auto","created_at":"2026-04-15 09:16:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10874581,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8988548/v1/ffbd7ba4-ee86-47ec-81e2-a04c86f2d27b.pdf"},{"id":106399649,"identity":"019e2645-5461-4cce-af3c-4f90ce286ce0","added_by":"auto","created_at":"2026-04-08 08:31:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35077,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8988548/v1/d29e9ae0b5e13096fbb1530b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association between Cognitive Function and Multimorbidity in Middle-aged and Elderly Populations: A National Longitudinal Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMultimorbidity refers to the situation where an individual simultaneously suffers from two or more chronic diseases or health problems. These diseases or health issues can be physical illnesses, mental and psychological disorders, or geriatric syndromes, and they are not ranked in terms of priority. They all jointly affect an individual's health status, functions, and quality of life. In the context of an increasingly aging global population, multimorbidity has become a serious public health challenge\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Studies have shown that the prevalence of multimorbidity significantly increases with age and is more common among the elderly population. The prevalence of multimorbidity varies between 20% and 30% across different countries\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The prevalence rate of multimorbidity in China is generally between 16% and 71%, and it is still on the rise in the next 20 years, exerting a huge pressure on the healthcare system\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn recent years, the bidirectional relationship between chronic diseases and cognitive function has gradually become a research hotspot. However, most scholars' discussions on the relationship between physical health conditions and cognitive dysfunction mostly focus on a single or specific chronic disease, and relatively few explore the influence of chronic disease multimorbidity on cognitive function.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Based on this, some studies have found that chronic disease multimorbidity can accelerate the decline of cognitive function. The more chronic diseases a patient has, the more severe the decline in cognitive ability will be, and the higher the risk of cognitive dysfunction will be\u003csup\u003e7\u0026ndash;9\u003c/sup\u003e, this association may be regulated by multiple factors such as demographic factors (such as age and gender), drug factors, and lifestyle factors\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Studies have shown that compared to healthy individuals, those who suffer from chronic disease multimorbidity during middle age may have a nearly fivefold increased risk of developing cognitive impairment in the future\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Patients with chronic diseases who have multiple multimorbidity often need to take multiple medications, the multiple drug use and its potential interactions or cumulative effects may directly or indirectly increase the risk of cognitive decline\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. On the other hand, unhealthy lifestyles, such as long-term smoking and excessive drinking, can exacerbate vascular damage and inflammatory responses, thereby promoting brain tissue damage and neurodegeneration, and thereby increasing the risk of cognitive dysfunction\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOn the contrary, cognitive dysfunction itself can also be a risk factor for the occurrence and development of various chronic diseases. For instance, studies have shown that in middle-aged individuals without kidney disease at baseline, early cognitive impairment is an independent risk factor for rapid decline in kidney function and the development of new-onset chronic kidney disease\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Furthermore, elderly individuals with cognitive decline have a significantly higher risk of developing heart metabolic diseases, cardiovascular diseases and diabetes compared to their peers with normal cognitive function\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, most existing studies are limited to cross-sectional designs or only focus on a single disease outcome, making it difficult to establish the temporal relationship and causal mechanism between cognitive function and chronic diseases. It is particularly noteworthy that chronic diseases do not occur randomly in individuals but often form a multimorbidity pattern with specific pathophysiological bases. Currently, there is still a lack of systematic prospective evidence regarding how baseline cognitive function affects the occurrence and development of different multimorbidity patterns in the future. Therefore, exploring the differential impact of cognitive function on multiple multimorbidity and their specific patterns is of great significance for revealing the underlying mechanisms and guiding early intervention.\u003c/p\u003e \u003cp\u003eBased on the longitudinal data from the Chinese Health and Retirement Longitudinal Study (CHARLS), this study adopts a prospective cohort design. The aim is to explore the predictive role of baseline cognitive function in the occurrence of multiple multimorbidity among the elderly population in the future, analyze the association patterns between the two, identify different potential types of multimorbidity, and compare the differences in the impact of cognitive function on various multimorbidity patterns. The study aims to provide a basis for using cognitive function as an early warning indicator for chronic diseases and implementing stratified prevention strategies.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data sources and study population\u003c/h2\u003e \u003cp\u003eThe data for this study were sourced from CHARLS, which is a nationally representative long-term follow-up study aimed at systematically collecting high-quality microdata on the health, socioeconomic status, and family characteristics of Chinese middle-aged and elderly individuals aged 45 and above. The CHARLS national baseline survey was conducted in 2011, covering 150 county-level units, 450 village-level units, approximately 10,000 households, and included a total of 17,708 middle-aged and elderly individuals\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This study utilized data from five rounds of follow-ups conducted in 2011 (baseline) and 2013, 2015, 2018, and 2020. All participants who completed all five rounds of the survey constituted the initial longitudinal sample pool (n\u0026thinsp;=\u0026thinsp;10,927).\u003c/p\u003e \u003cp\u003eSubsequently, participants whose age was less than 45 at baseline (n\u0026thinsp;=\u0026thinsp;323) were excluded; participants with incomplete information on multimorbidity status (n\u0026thinsp;=\u0026thinsp;1,530) were also excluded; then, participants with missing core cognitive function measurement data at baseline (n\u0026thinsp;=\u0026thinsp;1,339) were excluded; finally, participants who already had multimorbidity at baseline (i.e., having two or more chronic diseases simultaneously) (n\u0026thinsp;=\u0026thinsp;2,818) were excluded. After the above screening, a total of 4,917 participants with no multimorbidity at baseline were obtained, forming the main prospective analysis cohort for testing the association between cognitive function and the onset of multimorbidity. To control for the potential influence of reverse causality, we conducted a sensitivity analysis: We excluded individuals who had multimorbidity during the first follow-up (in 2013) from the main analysis cohort (n\u0026thinsp;=\u0026thinsp;400), and finally retained 4,517 participants to form the sensitivity analysis sub-cohort \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. This study adhered to the ethical review requirements of the CHARLS project and was approved by the Ethics Review Committee of Peking University (Approval Number: IRB 00001052\u0026ndash;11015). All participants signed an informed consent form before the survey.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Cognitive function assessment and covariate selection\u003c/h2\u003e \u003cp\u003eThis study employed the standardized cognitive function tests from the CHARLS dataset for assessment. The evaluation covered two main dimensions: mental state and situational memory ability. The mental state test consisted of three items: date cognition (answering current date information, with a maximum score of 5), calculation ability (performing 5 consecutive subtractions of 7, with a maximum score of 5), and drawing ability (copying an overlapping pentagram pattern, with a maximum score of 1). The total score of these three items was the overall mental state score (with a maximum of 11 points). The situational memory ability was assessed through an immediate word recall test, where respondents were required to recall 10 words after listening, with 1 point awarded for each correct recall, and the maximum score was 10 points. The study combined the scores of these two tests to form a comprehensive cognitive function score (with a total score range of 0\u0026ndash;21 points), with a higher score indicating better cognitive function, the comprehensive score was included as a continuous variable in the main analysis model in this study. The detailed operational definitions and scoring criteria for the tests can be found in Supplementary Material Method S1. In this study, a series of variables that might affect the association between cognitive function and multimorbidity were selected from the baseline survey as covariates to control for potential confounding effects. These variables cover the following dimensions: sociodemographic characteristics: including age, gender, educational level, marital status, and place of residence. Lifestyle and behavioral factors: including smoking status and drinking status. Physiological and physical examination indicators: include body mass index (BMI) and waist circumference. For covariates with missing values, we adopt the following principles and steps for handling: if the proportion of missing values for a variable exceeds 20%, then it is excluded from the analysis. For variables with a missing proportion lower than 20%, we use the \"mice\" package in R software (version 4.4.3) for multiple chained equation imputation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Multimorbidity\u003c/h2\u003e \u003cp\u003eIn this study, \"Multimorbidity\" refers to the situation where an individual suffers from two or more chronic diseases simultaneously. This definition follows the standard set by the World Health Organization\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The diagnostic information for chronic diseases is derived from the self-reports based on questionnaires in each round of the CHARLS follow-up. The question is: \"Have any doctors ever told you that you have the following diseases?\" The multimorbidity status of this study was determined based on the following 13 chronic diseases: hypertension, dyslipidemia, diabetes or elevated blood sugar, malignant tumors such as cancer, chronic lung diseases, liver diseases, heart diseases, strokes, kidney diseases, gastrointestinal or digestive system diseases, emotional and mental problems, arthritis or rheumatism, and asthma. If a participant is confirmed to have any two or more of the diseases listed above at any of the follow-up visits, they will be defined as being in a state of multimorbidity at that particular time point.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cp\u003eThis study is based on the longitudinal data from CHARLS 2011\u0026ndash;2020. It adopts a prospective cohort design and includes 4,917 participants aged 45 and above who had no multimorbidity at the baseline and completed five follow-ups. First, the baseline characteristics of the study population are described: continuous variables are expressed as the median (interquartile range), and categorical variables are expressed as frequencies (percentages). Comparisons between groups are conducted using non-parametric Mann-Whitney U test and chi-square test respectively\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. To investigate the influence of cognitive function on the occurrence time of multimorbidity, we employed the Cox proportional hazards regression model, using four cognitive function indicators as exposure variables: the continuous score of total cognitive function (ranging from 0 to 21 points), the continuous score of mental status, the continuous score of memory function, and the three-quartile grouping of total cognitive function (divided into low, medium, and high quartiles based on the overall distribution, with cut-off points at 11 points and 14 points). We constructed four progressively adjusted models and used the Schoenfeld residual test to verify the proportional hazards assumption\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt the same time, we used restricted cubic splines (RCS) to examine the dose-response relationship between cognitive function indicators and the risk of multimorbidity, as well as to test for nonlinear associations. To assess the potential impact of the reverse causal relationship and the robustness of the results, we conducted a sensitivity analysis. We excluded participants who developed multimorbidity within the first two years after the baseline (wave\u0026thinsp;=\u0026thinsp;2), and calculated the relative percentage change in the risk ratio (HR) between the original model and the sensitivity analysis model (the formula is | HR_original - HR_sensitivity | / HR_original \u0026times; 100%). A relative change of less than 5% was considered highly stable. In terms of the identification and analysis of multimorbidity patterns, we first employed Latent Class Analysis (LCA) to model the prevalence of 13 chronic diseases, in order to identify the underlying, unobserved category structures present in the data. Model selection was based on the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), entropy, and the Vuong-Lo-Mendell-Rubin Likelihood Ratio Test (VLMR), and the optimal number of categories was determined through a comprehensive consideration of clinical interpretability\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, we employed association rule analysis to explore the frequent co-occurrence of binary and ternary combinations among chronic diseases, in order to reveal common disease association patterns. Based on the co-morbidity patterns identified by LCA, we conducted a multivariate unordered multinomial Logistic regression analysis to examine the independent effects of cognitive function and other covariates (including gender, age, BMI group, smoking, drinking, place of residence, marital status, educational level, and waist circumference group) on different co-morbidity patterns, and calculated the odds ratio (OR) and its 95% confidence interval. All statistical analyses were conducted using the R software (version 4.4.3), and all tests were two-tailed. A P value less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characterization\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the socio-demographic and clinical characteristics of the 4,917 participants included in this study who had no multimorbidity at the baseline. During the follow-up period, 2,788 participants (56.7%) developed new multimorbidity, while the remaining 2,129 participants (43.3%) remained disease-free. Compared with the participants without multimorbidity, those with multimorbidity were older (median age: 57 years vs. 55 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and had lower cognitive function scores (median total cognitive score: 12.5 points vs. 13 points, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; among which, memory score: 3.5 points vs. 4 points, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). Furthermore, the participants in the multimorbidity group had higher BMI and waist circumference indices. In terms of sociodemographic and behavioral factors, the proportion of participants with a primary school education or lower in the multimorbidity group was higher (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). There were no significant differences in the distribution of residence (urban or rural), gender, and marital status between the two groups.\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\u003eBaseline analysis of the participants involved in the study(n\u0026thinsp;=\u0026thinsp;4917)\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo multimorbidity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultimorbidity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2129\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2788\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (49, 62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (51, 63)\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\u003eResidence, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e747 (35.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e983 (35.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural village\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1382 (64.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1805 (64.74%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\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 \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\u003e1137 (53.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1414 (50.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.068\u003c/p\u003e \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\u003e992 (46.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1374 (49.28%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e763 (35.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1117 (40.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e490 (23.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e649 (23.28%)\u003c/p\u003e \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\u003e574 (26.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e673 (24.14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighschool and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e302 (14.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e349 (12.52%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1951 (91.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2553 (91.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/Widowed/ Never married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (8.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235 (8.43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\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 \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\u003e1349 (63.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1856 (66.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.021\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\u003e780 (36.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e932 (33.43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking statu, n (%)\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 \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\u003e1297 (60.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1792 (64.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.017\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\u003e832 (39.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e996 (35.72%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.64 (20.57, 25.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.29 (20.9, 26.03)\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\u003eWaist, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.4 (76.2, 89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.6 (78, 92)\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\u003eMemeory scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.5, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5 (2.5, 4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExecutive scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (7, 11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (7, 10)\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\u003eTotal cognition scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (10, 15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5 (10, 14.5)\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 \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote. BMI=Body Mass Index\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are presented as median (interquartile range) for continuous variables and number (percentage) for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eP-values were calculated using the Mann-Whitney U test for continuous variables and Chi-square test for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association between cognitive function and risk of multimorbidity\u003c/h2\u003e \u003cp\u003eThe P-value of the proportional hazards assumption test (Schoenfeld test) was greater than 0.05, which met the application prerequisites of the Cox proportional hazards model \u003cb\u003e(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, multivariate Cox regression analysis indicated that baseline cognitive function was an independent predictor of multimorbidity onset. In unadjusted model 1, for every 1-point increase in the total cognitive function score, the risk of multimorbidity occurrence decreased by 2.6%. After sequentially adjusting for sociodemographic characteristics (model 2), lifestyle (model 3), and physiological indicators (model 4), this protective association remained robust. In the fully adjusted Model 4, for every 1-point increase in the total cognitive score, the risk decreased significantly by 2.2% (HR\u0026thinsp;=\u0026thinsp;0.978, 95% CI: 0.966\u0026ndash;0.991). The analysis of the total cognitive function grouped into tertiles showed that compared with the low score group (\u0026le;\u0026thinsp;10 points), the multimorbidity risk in the high score group (\u0026ge;\u0026thinsp;14 points) was significantly reduced by 16.1% (HR\u0026thinsp;=\u0026thinsp;0.839, 95% CI: 0.753\u0026ndash;0.934), while the risk in the medium score group (10\u0026ndash;14 points) showed a decreasing trend but did not reach statistical significance. The analysis of different indicators of cognitive function revealed distinct patterns: the mental status score showed a significant linear negative correlation with the risk of multimorbidity. However, the memory function score did not show a significant association in the adjusted linear model.\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\u003eThe relationship between cognitive function and multimorbidity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cognition scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.974(0.964,0.984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.981(0.968,0.993)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.981(0.969,0.993)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.978(0.966,0.991)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\u003eExecutive scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.965(0.952,0.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.974(0.957,0.990)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.974(0.958,0.991)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.970(0.954,0.987)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\u003eMemeory scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.966(0.945,0.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.981(0.958,1.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.981(0.958,1.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.980(0.957,1.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cognition tertile\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1-Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.919(0.843,1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.953(0.868,1.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.955(0.870,1.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.939(0.856,1.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.805(0.734,0.884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.854(0.767,0.950)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.855(0.768,0.952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.839(0.753,0.934)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote. HR: Hazard Ratio, CI: Confidence Interval, BMI=Body Mass Index, Q1\u0026thinsp;=\u0026thinsp;11, Q2\u0026thinsp;=\u0026thinsp;14\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 1:Crude Model: Unadjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 2:Adjusted: Age, Gender, Education level, Marital status, Residence.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 3:Adjusted: Age, Gender, Education level, Marital status, Residence, Smoking status, Drinking status.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 4:Adjusted: Age, Gender, Education level, Marital status, Residence, Smoking status, Drinking status, BMI, Waist\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo deeply explore the potentially complex dose-response relationship, we conducted a restricted cubic spline analysis (Restricted Cubic Spline, RCS). In the fully adjusted model, there was a significant nonlinear association between total cognitive function and the risk of multimorbidity (\u003cem\u003eP\u003c/em\u003e -nonlinear\u0026thinsp;=\u0026thinsp;0.041), and the overall effect was extremely significant (\u003cem\u003eP\u003c/em\u003e -overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Four statistical inflection points were identified: 8.51, 12.51, 12.63 and 19.59 points. Based on the clinical warning principle of the primary inflection point, we determined the first inflection point of 8.51 points as the critical clinical cutoff value. This inflection point marks the transition from the high plateau phase of the risk curve to the decline phase, indicating that when the total cognitive function score is below this threshold, the co-morbidity risk significantly and sharply increases \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. It is worth noting that in the unadjusted rough model, the nonlinear relationship was not significant (\u003cem\u003eP\u003c/em\u003e -nonlinear\u0026thinsp;=\u0026thinsp;0.098), indicating that fully controlling for confounding factors is crucial for revealing the true nonlinear pattern. The relationship between mental status and the risk of multimorbidity is mainly linear, and the non-linear test was not significant (\u003cem\u003eP\u003c/em\u003e -nonlinear\u0026thinsp;=\u0026thinsp;0.089) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. On the contrary, the memory function showed a significant nonlinear correlation (adjusted model \u003cem\u003eP\u003c/em\u003e -nonlinear\u0026thinsp;=\u0026thinsp;0.037), and identified four statistical inflection points: 2.78, 3.75, 5.28, and 7.84. To obtain a more robust estimate that is less sensitive to model fluctuations, we calculated the median of these inflection points (4.51 points), and based on the convenience of clinical application, recommended 4.5 points as the screening cutoff value for memory function. Scores lower than 4.5 were identified as individuals at high risk of multimorbidity. Similar to total cognitive function, the nonlinear relationship of memory function was fully revealed only after adjusting for covariates \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Multimorbidity patterns and latent class analysis\u003c/h2\u003e \u003cp\u003eThis study identified 5 binary disease combinations that met the preset thresholds (support\u0026thinsp;\u0026gt;\u0026thinsp;10%, confidence\u0026thinsp;\u0026gt;\u0026thinsp;40%) through association rule analysis \u003cb\u003e(Table S2)\u003c/b\u003e, and 3 ternary disease combinations that met the thresholds (support\u0026thinsp;\u0026gt;\u0026thinsp;5%, confidence\u0026thinsp;\u0026gt;\u0026thinsp;40%) \u003cb\u003e(Table S3)\u003c/b\u003e. The data analysis shows that the most common binary disease combination is hypertension and arthritis, with a support degree of 17.23%, and it exhibits a significant bidirectional correlation. The most common triad disease combination is hypertension, dyslipidemia and arthritis, with a support degree of 6.47%. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the trend of prevalence changes for 13 chronic diseases over 5 follow-up periods. In general, the prevalence of most chronic diseases shows an upward trend with increasing age, among which the prevalence of arthritis, diabetes and digestive system diseases increases the most significantly. In contrast, the prevalence of cancer, stroke, kidney diseases, etc. remains at a relatively low level and is relatively stable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, this study employed latent class analysis (LCA) to identify the intrinsic heterogeneity patterns of multimorbidity. By comparing the fitting indicators of models 1 to 8 \u003cb\u003e(Table S4)\u003c/b\u003e, the 4 models demonstrated the lowest Bayesian Information Criterion (BIC\u0026thinsp;=\u0026thinsp;48,390.013) and the highest entropy value (Entropy\u0026thinsp;=\u0026thinsp;0.664), and the probability of belonging to all potential categories was greater than 5%. Although the likelihood ratio test (LRT) showed that the addition of up to 8 classes could significantly improve the fit (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), models with 5 classes or more began to show potential categories with an assignment probability less than 5% (2.4% of the categories existed in the 5-class model), suggesting that there might be overfitting of the model. Taking into account the robustness of statistical indicators and the principle of clinical interpretability of categories, this study ultimately selected four models as the classification scheme for multimorbidity \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Latent class analysis revealed four distinct patterns of multimorbidity development \u003cb\u003e(Table S5)\u003c/b\u003e. The first category was the \"multi-system high multimorbidity group\", accounting for 5.2% of the study population. Its characteristic was a high incidence of multiple systemic diseases, with particularly prominent pulmonary diseases (77.25%), arthritis (77.25%), and hypertension (67.84%). The second category was the \"respiratory system disease dominant group\", accounting for 9.9%, with a very high prevalence of pulmonary diseases (93.21%) as the core feature. The third category is the \"mild to moderate joint-digestive system multimorbidity group\", which is the largest subgroup (68.3%). The disease burden is relatively light, mainly concentrated on arthritis (40.47%) and digestive system diseases (26.62%). The fourth category is the \"metabolic syndrome multimorbidity group\", accounting for 16.6%, showing a typical aggregation of cardiovascular and metabolic diseases, including dyslipidemia (93.28%), hypertension (79.34%), and diabetes (40.83%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Description of chronic diseases\u003c/h2\u003e \u003cp\u003eThe analysis based on \u003cb\u003eTable S6\u003c/b\u003e shows that there is a significant correlation between the baseline cognitive function level and the number of chronic diseases that occurred during the follow-up period (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As the cognitive function score declines, the risk of an individual developing multiple chronic diseases shows a gradually increasing trend.\u003c/p\u003e \u003cp\u003eFrom the perspective of the three-quartile classification of cognitive function, in the low-cognitive-level group (\u0026lt;\u0026thinsp;Q1), 38.8% to 41.1% of the individuals had two or more chronic diseases, and 41.1% of them had five or more chronic diseases, indicating a close correlation between low cognitive function and severe multimorbidity. In contrast, in the high-cognitive-level group (\u0026ge;\u0026thinsp;Q2), only 26.7% to 30.8% of the individuals had two or more chronic diseases, and only 24.9% had five or more chronic diseases, showing a trend that individuals with better cognitive function have a relatively lighter burden of chronic diseases. It is worth noting that all three dimensions of cognitive function (total cognitive score, mental status, and memory) showed a dose-response relationship with the number of chronic diseases. Although the median mental status score was 9 in each group, the distribution variation significantly increased with the increase in the number of chronic diseases (χ\u0026sup2; = 28.3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Especially in the group with \u0026ge;\u0026thinsp;4 chronic diseases, the lower limit of the interquartile range of the mental status score decreased from 7 to 6, suggesting that people with poorer mental status are more likely to accumulate multiple chronic diseases. The memory function score also decreased from 4 points in the healthy group to 3.5 points in the multimorbidity group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), while the total cognitive function score dropped from 13 points to 12.5 points (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), further confirming the close connection between cognitive impairment and the accumulation of chronic diseases.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.5 The differential impact of cognitive function on the patterns of multimorbidity: Based on multivariate unordered Logistic regression analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo identify the independent risk factors for different multimorbidity patterns and establish a clear health reference benchmark, this study made methodological adjustments based on the four multimorbidity patterns identified through latent class analysis (LCA). The initial LCA model did not automatically separate out independent health patterns. Based on the cumulative number of chronic diseases of each individual, we separated the participants who were completely healthy (with 0 chronic diseases) at the end of the follow-up from the third category (low-burden joint-digestive pattern) of the original LCA. Finally, we constructed an analysis framework consisting of five categories: complete health pattern, mild joint-digestive disease pattern, respiratory system-joint disease pattern, vascular metabolic disease pattern, and complex systemic disease pattern.\u003c/p\u003e \u003cp\u003eBased on this five-category classification system, we employed a multi-factor unordered multi-class Logistic regression model, with the completely healthy pattern as the reference, to systematically evaluate the differential impacts of demographic characteristics, lifestyle, anthropometric indicators, and cognitive functions on each multimorbidity pattern \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The analysis results show that different multimorbidity patterns have distinct risk factor profiles. Age is the strongest predictor of multimorbidity occurrence, especially for severe multimorbidity patterns. Compared with the 45\u0026ndash;54 age group, the risk of all four multimorbidity patterns significantly increased in the 65\u0026ndash;74 age group. Among them, the risk of suffering from complex systemic disease patterns increased most sharply (OR\u0026thinsp;=\u0026thinsp;3.359, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for multiple confounding factors such as age, gender, education, BMI, and waist circumference, a high baseline cognitive function (total cognitive score\u0026thinsp;\u0026ge;\u0026thinsp;Q2) was significantly associated with a 22% reduced risk of developing the mild joint-digestive disease pattern. Central obesity (measured by waist circumference) had an extremely strong dose-response relationship with the vascular metabolic disease pattern (for those with waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;90.4 cm, OR\u0026thinsp;=\u0026thinsp;2.914, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). High educational level is a protective factor for the respiratory system - arthritis pattern (OR\u0026thinsp;=\u0026thinsp;0.549, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and the mild joint - digestive disease pattern (OR\u0026thinsp;=\u0026thinsp;0.665, P\u0026thinsp;=\u0026thinsp;0.008); female gender is associated with a lower risk of the respiratory system - arthritis pattern (OR\u0026thinsp;=\u0026thinsp;0.711, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049).\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\u003eMultivariate multiclass logistic regression analysis of the impact of cognitive function on different multimorbidity patterns\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eClass 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eClass 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eClass 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eClass 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group, years\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.659 (1.160, 2.373)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.304 (0.988, 1.720)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.113 (0.915, 1.353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.513 (1.187, 1.929)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003e65\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.359 (2.140, 5.270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.662 (1.132, 2.442)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.658 (1.242, 2.214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.583 (1.838, 3.629)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003e\u0026ge;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.572 (0.706, 3.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.213 (0.651, 2.258)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.158 (0.722, 1.858)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.082 (0.575, 2.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \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\u003e1.371 (0.898, 2.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.711 (0.506, 0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.025 (0.802, 1.310)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.148 (0.853, 1.546)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence, n (%)\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural village\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.267 (0.920, 1.747)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.248 (0.964, 1.616)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.184 (0.986, 1.422)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.041 (0.834, 1.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, n (%)\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.876 (0.581, 1.321)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.988 (0.711, 1.373)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.048 (0.818, 1.343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.083 (0.799, 1.468)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.608\u003c/p\u003e \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\u003e0.714 (0.455, 1.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.710 (0.500, 1.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.880 (0.684, 1.132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.091 (0.801, 1.486)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighschool and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.57, 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.549 (0.354, 0.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.665 (0.493, 0.898)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.997 (0.692, 1.438)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, n (%)\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/Widowed/ Never married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.146 (0.688, 1.911)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.064 (0.698, 1.623)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.333 (0.972, 1.828)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.067 (0.726, 1.567)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\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\u003e1.045 (0.706, 1.545)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.908 (0.670, 1.229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.810 (0.646, 1.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.693 (0.526, 0.915)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status, n (%)\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\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\u003e1.070 (0.756, 1.514)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.195 (0.907, 1.574)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.968 (0.791, 1.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.253 (0.977, 1.608)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI Group, kg/m\u0026sup2;\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.752 (0.443, 1.278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.577 (0.383, 0.869)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.036 (0.745, 1.442)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.932 (0.595, 1.460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24-27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.287 (0.690, 2.401)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.537 (0.325, 0.889)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.224 (0.832, 1.801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.520 (0.924, 2.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.220 (0.579, 2.570)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.767 (0.419, 1.405)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.310 (0.825, 2.080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.057 (1.167, 3.625)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist, cm\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;77.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e77.2\u0026ndash;83.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.785 (0.522, 1.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.031 (0.751, 1.415)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.931 (0.739, 1.173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.167 (0.843, 1.615)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e83.8\u0026ndash;90.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.754 (0.480, 1.183)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.043 (0.732, 1.485)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.028 (0.797, 1.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.987 (1.428, 2.764)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003e\u0026ge;90.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.111 (0.673, 1.833)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.191 (0.781, 1.817)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.301 (0.959, 1.764)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.914 (2.006, 4.234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003eTotal cognition tertile\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1-Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.919 (0.637, 1.327)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.801 (0.592, 1.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.915 (0.732, 1.143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.009 (0.767, 1.327)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.693 (0.453, 1.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.759 (0.543, 1.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.784 (0.615, 0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.872 (0.647, 1.176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote. HR: Hazard Ratio, CI: Confidence Interval, BMI=Body Mass Index, Q1\u0026thinsp;=\u0026thinsp;11, Q2\u0026thinsp;=\u0026thinsp;14.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eReference group: The healthy pattern (N\u0026thinsp;=\u0026thinsp;618).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eClass 1: Complex systemic disease pattern; Class 2: Respiratory system - arthritis pattern; Class 3: Mild joint-digestive disorder pattern; Class 4: Vascular metabolic disease pattern\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eThe sensitivity analysis revealed that, after excluding individuals with early onset, the association pattern between cognitive function and the risk of multimorbidity remained consistent with the original analysis \u003cb\u003e(Table S7)\u003c/b\u003e. In the unadjusted model, for every 1-point increase in the total cognitive function score, the risk of multimorbidity decreased by 2.8%. In the fully adjusted model (Model 4), this association remained significant. The mental status score also demonstrated a similar protective effect (Model 4: HR\u0026thinsp;=\u0026thinsp;0.970, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), while the memory function score did not show a significant association in the adjusted model. The analysis based on the tertiles of the total cognitive function score indicated that compared with the low cognitive group, the high cognitive group (\u0026ge;\u0026thinsp;Q2) showed a 16.8% reduction in the risk of multimorbidity in the fully adjusted model, and the medium cognitive group (Q1-Q2) showed a 9.9% reduction in the risk.\u003c/p\u003e \u003cp\u003eBy comparing the results of the original Cox analysis with those of the sensitivity analysis, it was found that the effect sizes obtained from the two analyses were extremely similar \u003cb\u003e(Table S8)\u003c/b\u003e. For the total cognitive function score, the mental status score, and the memory function score, the percentage change in effect size (CP) ranged from 0% to 0.2%. For the cognitive function tertile groups, the percentage change in effect size for the medium-level group (Q1-Q2) and the high-level group (\u0026ge;\u0026thinsp;Q2) was 0.8% \u0026minus;\u0026thinsp;4.7% and 0.8% \u0026minus;\u0026thinsp;2.0% respectively. All changes were less than 5%, and the effect direction was exactly the same as that of the original analysis. This result indicates that excluding cases of early multimorbidity has a negligible impact on the association between cognitive function and the risk of multimorbidity. The effect size from the original analysis remains stable, and the possibility of a reverse causal relationship is low.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study, based on national longitudinal data, found that better cognitive function among the elderly population is an independent protective factor for the occurrence of multiple multimorbidity in the future, and there is a non-linear relationship between the two. The study identified four potential categories of multimorbidity and found that the protective effect on cognitive function was particularly evident in reducing the risk of \"mild to moderate joint-gastrointestinal multimorbidity patterns\". These results have deepened our understanding of the relationship between cognition and physical health, providing a basis for multimorbidity risk stratification and precise prevention based on cognitive assessment.\u003c/p\u003e \u003cp\u003eThis study has confirmed that even after controlling for a series of strong confounding factors such as age, education, lifestyle, and metabolic indicators, better cognitive function remains a powerful protective factor against the occurrence of multiple multimorbidity in the future. Previous studies have revealed the central role of cognitive function in the occurrence and development of chronic diseases. Large-scale prospective studies consistently show that a healthy lifestyle and better cognitive function are both closely related to a reduced risk of all-cause mortality, while cognitive decline weakens an individual's ability to maintain healthy behaviors, adhere to treatment, and identify health risks, thereby increasing the risk of death. This emphasizes the importance of maintaining cognitive health while promoting healthy lifestyles\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Further research has revealed that cognitive activities can buffer cardiovascular risks in specific community settings, while cognitive impairment itself is associated with a progressive increase in the risks of stroke, heart failure and cardiovascular death\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. More importantly, a systematic review on the co-occurrence of chronic diseases and cognitive impairments clearly indicates that moderate to severe cognitive impairment significantly increases the clinical burden of patients with various chronic diseases, leading to an increased risk of death, prolonged hospital stays, and accelerated decline in physical function\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The above studies collectively point to a core conclusion: Cognitive decline is not only an important risk factor for chronic diseases, but also a key reason for the deterioration of co-morbidity management and outcomes. Our research also found that a high level of good cognitive function is an independent protective factor for the occurrence of multiple multimorbidity in the future, providing direct prospective evidence for the existence of the aforementioned vicious cycle. It confirms that good cognitive function is itself a key protective factor against the accumulation of chronic diseases, especially specific multimorbidity patterns (such as joint-digestive system patterns). Cognitive function may influence the risk of multimorbidity through various potential mechanisms. On one hand, individuals with better cognitive function usually have higher health literacy and better self-management skills, which enables them to perform better in maintaining healthy behaviors, early detection of chronic diseases, treatment compliance, and doctor-patient communication, effectively delaying or preventing the occurrence of diseases\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. On the other hand, higher cognitive function indicates better brain health and neural reserve in individuals. The underlying common biological basis, such as lower systemic inflammation levels\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, more stable hypothalamic-pituitary-adrenal axis function \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and less oxidative stress damage \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, may simultaneously provide protection against cognitive decline and the occurrence and development of various chronic diseases.\u003c/p\u003e \u003cp\u003eFurthermore, our research has revealed a non-linear relationship between baseline cognitive function and future risk of multimorbidity, and has identified specific risk thresholds (with a total cognitive function score below 8.5) that have significant clinical warning significance. Furthermore, the study found that the influence patterns of different core dimensions of cognitive function on the risk of multimorbidity exhibited significant heterogeneity: the mental condition centered on executive function demonstrated a robust linear protective effect, and its mechanism might be related to optimizing individuals' comprehensive health decision-making and self-management behaviors. The memory function exhibits a distinct non-linear threshold effect. We speculate that damage to this function will only have a substantial impact on specific health management aspects such as medication compliance when it reaches a certain critical point (with a score lower than 4.5), thereby causing a sharp increase in risks.\u003c/p\u003e \u003cp\u003eFurthermore, the core triad multimorbidity pattern identified in this study - hypertension, dyslipidemia and arthritis - is highly consistent with previous research. This may suggest that metabolic disorders and chronic inflammation have a profound mutual influence rather than occurring independently\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. It reminds us that in clinical treatment, when managing any one of these diseases, we should consciously conduct screening and intervention for the other two diseases. Multivariate unordered multi-class Logistic regression revealed the unique risk factor profiles of each model. The study found that age was the strongest and most common predictor of multimorbidity, which is consistent with the consensus that the burden of multimorbidity has increased in the context of global aging\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. It is worth noting that age has the greatest impact on the risk of complex systemic disease patterns, suggesting that as people age, the accumulation of diseases may not increase linearly but rather there is an accelerating effect or a tendency to evolve towards a specific complex pattern. The protective effect of cognitive function was confirmed in this study. After adjusting for various factors, higher cognitive levels were associated with a lower risk of \"mild joint-digestive disorders\". This indicates that good cognitive function is not only an indicator of brain health, but may also reflect the body's ability to cope with diseases and maintain overall health. Previous genome-wide association studies have shown that bone density and brain imaging phenotypes share a genetic basis. The SLC39A8 gene is simultaneously associated with osteoarthritis inflammation and brain synaptic plasticity, suggesting that there may be a common genetic background for osteoarthritis and cognitive function\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. A proteomics study has shown that inflammatory mediators such as HAVCR1, GDF15 and IL-6 play a bridging role between joint diseases and cognitive decline. Better cognitive function may reflect lower systemic inflammatory levels, thereby reducing inflammation-driven multisystem multimorbidity \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Furthermore, the \"microbiota-gut-brain axis\" is also involved. Animal experiments have shown that the damage to the intestinal barrier associated with aging can increase inflammatory signals in the blood, thereby activating neuroimmune responses in the brain and impairing cognition. Better cognitive function may imply a more stable intestinal environment and better communication between the gut and the brain\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. On the other hand, those with better cognitive abilities usually have higher health literacy and self-management skills, and are better able to adhere to healthy lifestyles and medical advice. Together with biology, they can delay the occurrence of various diseases.\u003c/p\u003e \u003cp\u003eOur research has revealed that central obesity (measured by waist circumference) has an extremely strong dose-response relationship with vascular metabolic disease patterns. This finding confirms the central role of visceral fat accumulation in the occurrence of such diseases. A study conducted on the elderly population in China indicates that abdominal obesity is an independent and significant risk factor for cardiovascular metabolic multimorbidity. Its predictive power even exceeds that of simple dyslipidemia, and when both conditions coexist, the risk is compounded\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The data from the National Health and Nutrition Examination Survey of the United States shows that among the diabetic population, there is a clear U-shaped or J-shaped correlation between waist circumference and all-cause mortality\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Another study also confirmed that the waist circumference index after weight correction was linearly and positively correlated with the mortality risk of patients with metabolic-related fatty liver disease\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. These pieces of evidence collectively indicate that central obesity is a more crucial metabolic risk factor than overall overweight. The educational level was found to be a protective factor for the respiratory system-arthritis pattern and the mild joint-digestive disease pattern. This result is consistent with the profound influence of socioeconomic status on health outcomes revealed by previous studies. A prospective cohort study conducted among the Austrian population has confirmed that low educational level is an independent risk factor for rheumatoid arthritis (RA), while higher education can significantly reduce the risk of the disease\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The Mendelian randomization study provided genetic evidence for the negative association between educational level and the risk of respiratory diseases, supporting the potential causal role of this relationship\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. A better educational background may imply a more favorable career environment and a stronger ability to utilize medical services, which is conducive to the early management and control of chronic inflammatory diseases such as arthritis.\u003c/p\u003e \u003cp\u003eThis study is based on representative longitudinal data from China, adopts a prospective design, and effectively controls the interference of reverse causality through sensitivity analysis. By comprehensively applying statistical methods such as latent class analysis and restricted cubic splines, it systematically reveals the nonlinear association and specific risk thresholds between cognitive function and multiple multimorbidity patterns. However, the research still has certain limitations: the information on chronic diseases mainly relies on self-reports, which may have certain measurement errors; the assessment of cognitive function fails to cover all core dimensions; the research population is limited to the Chinese population, and the research conclusions should be cautiously generalized.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study has confirmed that better cognitive function is a key protective factor for preventing the occurrence of multiple multimorbidity in the elderly population. Central obesity and lower educational level have been identified as important risk factors for vascular metabolic diseases and specific patterns such as respiratory system-arthritis. These findings suggest that maintaining good cognitive function has a positive effect in reducing the overall risk of multimorbidity. Future prevention and treatment practices should place emphasis on the maintenance of cognitive health, combined with the control of central obesity and the improvement of health literacy, in order to effectively break the vicious cycle between cognitive decline and the accumulation of chronic diseases, and promote the process of healthy aging.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.X., Y.L., L.Q., and Q.Z. contributed to software, validation, and visualization. W.X., Q.Z., J.P., and Z.T. contributed to conceptualization. W.X. and Q.Z. performed data curation. W.X. and Z.T. wrote the original draft. W.X. handled review and editing. Q.X., M.W., K.L., X.H., and Z.T. performed formal analysis. Z.T. acquired funding. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adhered to the ethical review requirements of the CHARLS project and was approved by the Ethics Review Committee of Peking University (Approval Number: IRB 00001052-11015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Science and Technology Fund Project of the Health Commission of Jiangxi Province (Project Number: 202410228).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the CHARLS research team for establishing and maintaining the cohort, and all participants for contributing their time and information. This study was made possible by their efforts. This research was funded by the Science and Technology Fund Project of the Health Commission of Jiangxi Province (Project Number: 202410228)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study are sourced from the China Health and Retirement Longitudinal Study (CHARLS), which is an openly accessible resource available to researchers worldwide. The data access address is: https://charls.pku.edu.cn/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLe Reste JY, Nabbe P, Manceau B, et al. The european general practice research network presents a comprehensive definition of multimorbidity in family medicine and long term care, following a systematic review of relevant literature. \u003cem\u003eJ Am Med Dir Assoc\u003c/em\u003e. 2013;14(5):319-325. doi:10.1016/j.jamda.2013.01.001\u003c/li\u003e\n\u003cli\u003ePLOS Medicine Editors. 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Association and mediation between educational attainment and respiratory diseases: A mendelian randomization study. \u003cem\u003eRespir Res\u003c/em\u003e. 2024;25(1):115. doi:10.1186/s12931-024-02722-4\u003c/li\u003e\n\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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cognitive function, Multimorbidity, Middle-aged and elderly, Latent class analysis, Prospective cohort study","lastPublishedDoi":"10.21203/rs.3.rs-8988548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8988548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eMultimorbidity\u003cstrong\u003e \u003c/strong\u003eis a major public health challenge in an aging society. There is a bidirectional relationship between cognitive function and chronic diseases, but there is still a lack of sufficient evidence regarding how cognitive function prospectively influences the occurrence and development of different multimorbidity\u003cstrong\u003e \u003c/strong\u003epatterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eThis study is based on the longitudinal data of the China Health and Retirement Longitudinal Study(CHARLS) from 2011 to 2020. A prospective cohort design was adopted, and participants aged 45 and above without multimorbidity\u003cstrong\u003e \u003c/strong\u003eat the baseline (n = 4,917) were included. The relationship between baseline cognitive function and the risk of multimorbidity\u003cstrong\u003e \u003c/strong\u003eonset was analyzed using the Cox proportional hazards model and restricted cubic spline analysis. The multimorbidity\u003cstrong\u003e \u003c/strong\u003epatterns were identified through latent class analysis, and then a multivariate unordered multinomial Logistic regression was used to analyze the differential effects of cognitive function and other factors on different patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eDuring the 9-year follow-up period, 56.7% of the participants developed new multimorbidity. After adjusting for confounding factors, higher cognitive function was an independent protective factor for the occurrence of multimorbidity (HR:0.839, 95% CI: (0.753,0.934)), and there was a non-linear relationship (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003eP \u003c/em\u003efor nonlinear \u0026lt;0.041). When the total cognitive function score was lower than 8.5 points, the risk significantly increased. The study identified four multimorbidity\u003cstrong\u003e \u003c/strong\u003epatterns: \"multisystem high multimorbidity group\", \"respiratory system disease dominant group\", \"mild to moderate joint-digestive system multimorbidity group\", and \"metabolic syndrome multimorbidity group\". The protective effect of cognitive function was particularly evident in significantly reducing the risk of the \"mild to moderate joint-digestive system multimorbidity pattern\" (OR:0.784, 95% CI :(0.615, 0.999)). In addition, central obesity was a strong risk factor for the \"metabolic syndrome multimorbidity group\", while a higher level of education was a protective factor for the \"respiratory system disease dominant group\".\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eBetter cognitive function is an important protective factor for preventing the occurrence of multiple multimorbidity in the elderly population, and it has specific protective effects on certain multimorbidity patterns such as the joint-digestive system pattern. Cognitive assessment can serve as an early warning tool for the risk of multimorbidity, and future comprehensive prevention strategies should place emphasis on maintaining cognitive health.\u003c/p\u003e","manuscriptTitle":"The Association between Cognitive Function and Multimorbidity in Middle-aged and Elderly Populations: A National Longitudinal Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 08:31:04","doi":"10.21203/rs.3.rs-8988548/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"160523985309861325889571603270368986941","date":"2026-04-13T11:18:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T10:06:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270630366098987645729502179639660342652","date":"2026-04-11T15:00:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-11T03:39:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279185380553791227034772265615826037644","date":"2026-04-10T09:58:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T09:25:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-05T07:00:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T14:03:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T14:03:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-02-27T13:16:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10d23a19-918c-44f3-a28b-5d2305b8eb35","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T08:31:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 08:31:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8988548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8988548","identity":"rs-8988548","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}