Trends in prevalence, associated comorbid burden, and subsequent mortality of social isolation: A gender perspective

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However, gender differences in social isolation have not been thoroughly characterized. This study aimed to estimate the gender differences in the trends in the prevalence of social isolation, evaluate the gender-based differences in its comorbid burden, and examine their subsequent associated mortality by gender. Methods This nationwide cross-sectional and prospective cohort study used data from the China Health and Retirement Longitudinal Study. Social isolation was measured using 4 dichotomized indicators. The Cochran-Armitage trend test and multivariate Poisson regression models were constructed to analyze the trends in social isolation and the longitudinal associations between social isolation and mortality by gender. All analyses were weighted to account for the multistage, probability-proportional-to-size sampling scheme. Results Among the 10197 participants, the mean age was 60.0 years, and 48.1% were men. The prevalence of social isolation was 20.8%, with an average age-adjusted Charlson Comorbidity Index (ACCI) of 2.7 (± 1.8) and 1.5 (± 1.4) comorbidities. A significant downward trend in social isolation was observed in men, with a weighted prevalence of 19.4% (95% confidence interval (CI): 17.7, 21.3) in 2011 and 14.1% (95% CI: 12.9, 15.4) in 2018 (P for trend < 0.001). In contrast, a stable trend in social isolation was noted in women, with a weighted prevalence of 24.0% (95% CI: 22.5, 25.6) in 2011 and 24.1% (95% CI: 22.7, 25.6) in 2018 (P for trend = 0.154). A steeper increase in ACCI and number of comorbidities was observed in women compared to men. (P for gender-by-social isolation score interaction < 0.001) Over a 9-year follow-up period, females with social isolation had more than double the risk of mortality (incidence rate ratio (IRR): 2.05, 95% CI: 1.65, 2.53), while males with social isolation had only a 60% increased IRR (95% CI: 1.31, 1.95) of all-cause mortality (P for interaction = 0.032). Conclusion Several gender differences in social isolation were observed, including the higher prevalence, heavier comorbid burdens, and a more prominent impact on mortality noted in women, highlighting the importance of enhancing family and social support for older adults, particularly in improving the socioeconomic statuses and rights of women. Social isolation Gender differences Female Comorbidity Mortality Figures Figure 1 Figure 2 Figure 3 Introduction Social isolation, defined as objective physical separation from social activity and social networks, has been a pervasive public health concern with alarmingly high global prevalence. According to a recent meta-analysis, the pooled prevalence of social isolation is approximately 25.0% (95% confidence interval (CI): 21.0, 30.0) among community-dwelling older adults[ 1 ]. Similarly, it is estimated that nearly a quarter of American adults over 65 years old experience social isolation, which corresponds to approximately 7.7 million U.S. adults[ 2 ]. The situation is even worse in China, with around 40% (95% CI: 38.6, 41.3) of middle-aged and older individuals experiencing social isolation[ 3 ]. Social isolation is independently correlative with multiple chronic conditions. A recent study conducted in two prospective cohorts from Europe and China showed that social isolation was associated with an increased risk of type 2 diabetes mellitus. The potential causal relationship between social isolation and an increased risk of diabetes was confirmed through Mendelian randomization analysis[ 4 ]. Social isolation has also been demonstrated to be associated with higher risks of incident cardiovascular disease and heart failure[ 5 , 6 ], as well as functional limitations and cognitive health[ 7 , 8 ]. Therefore, individuals experiencing social isolation often bear a significant burden of comorbidities. Conversely, these multiple comorbidities can exacerbate the onset of social isolation[ 9 ]. Furthermore, individuals with social isolation had a 32% (95% CI: 1.26, 1.39), 24% (95% CI: 1.19 to 1.28), and 34% (95% CI: 1.25 to 1.44) elevated risk of all-cause mortality, cancer mortality, and cardiovascular mortality[ 10 ]. Despite this, there is little evidence regarding gender differences in the trends of social isolation prevalence. In addition, it remains unclear whether the comorbid burden of social isolation and its associated mortality differs between gender. Gender health inequalities remain a significant issue worldwide and may exacerbate the social isolation status between men and women[ 11 ]. Meanwhile, gender-based differences are noted in the epidemiology, pathophysiology, and progression of multiple diseases and mortality[ 12 ]. Therefore, it is reasonable to believe that social isolation may vary by gender and have different impacts on health and mortality. Comprehensively characterizing the gender differences in social isolation and its associated issues is essential for tailoring gender-specific preventive strategies and improving shared decision-making. To address this knowledge gap, our study utilized 5 waves of longitudinal data from a nationally representative sample of Chinese middle-aged and older community-dwelling adults to: (1) estimate the gender differences in the trends in the prevalence of social isolation; (2) evaluate the gender-based differences in its comorbid burden; and (3) examine their subsequent associated mortality by gender. Methods Study design and participants This study utilized nationwide, longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS). The CHARLS was launched in 2011 and employed a multistage, probability-proportional-to-size sampling method to recruit participants, ensuring a nationally representative sample. Participants who were over 45 years old and voluntarily completed a computer-assisted personal interview were eligible for recruitment. The first wave of CHARLS enrolled 17708 participants from 450 urban districts and 150 counties in 28 provinces across Mainland China. The follow-up was performed every 2 to 3 years through face-to-face interviews until 2020. Detailed information on the CHARLS has been described previously[13]. All participants provided written informed consent. The protocol of the CHARLS study was approved by the ethics review committee at Peking University, Beijing (IRB00001052-11015). This study involved two sections. First, we utilized the cross-sectional data from the baseline survey in 2011 and the subsequent 3 follow-up surveys in 2013, 2015, and 2018 to estimate the gender disparities in the trends of social isolation prevalence. Participants aged under 45 years old and those without data on social isolation were excluded from the analysis. In this part, we also assess the gender-based differences in the comorbid burden among individuals with different levels of social isolation. Given that more than 40% of participants had missing data on social isolation at each wave, Little's test of missing completely at random[14] was conducted on the remaining missing values using the ‘mcartest’ command in STATA, after excluding participants with over 20% missing data for social isolation. The results were not significant, indicating that the assumption of completely random missing data was satisfied[14]. Second, we merged the data from the baseline and all the follow-up surveys (i.e., follow-up data in 2013, 2015, 2018, and 2020) to examine the longitudinal association between social isolation and mortality among all participants and by gender. In this part, individuals without follow-up records were excluded. (Supplemental Figure 1) Assessment of social isolation Social activity and social networks, collected from the self-reported questionnaires, were employed to evaluate the social isolation index. The social isolation index included 4 dichotomized indicators, that is, living alone, being unmarried, having contact with children (in person, by phone, or by email) less than once a week, and participating in any social activities (such as attending sports, social, or other clubs, taking part in community-related organizations or an educational or training course, interacting with friends, going to a community club, playing Mahjong, chess, or cards, or doing voluntary work) less than once a month. A total score of social isolation was calculated by summing these 4 indicators and ranged from 0 to 4, with a higher score representing a higher level of social isolation. Individuals were identified as either experiencing social isolation (≥2) and or not experiencing social isolation (<2) based on the social isolation index[15]. All the questions used to identify the indicators of social isolation are consistent across all waves of CHALRS. Assessment of mortality The study outcome of the current study was all-cause mortality. Field investigations were conducted by trained staff to determine the survival status of participants by trained staff during follow-up. The survival status was determined by interviewing the recruited participants, their family members, or relatives who lived with the deceased. The date of the events was only available in the second follow-up survey in 2013. Therefore, only the mortality rate was used in our study. Covariates The information on demographics (i.e., age, gender, educational attainment, marital status, smoking and drinking status) and self-reported comorbidities was obtained from personal interviews using standard questionnaires. The comorbid burden was calculated using the age-adjusted Charlson Comorbidity Index (ACCI)[16] and the number of comorbidities. Physical measurements and anthropometrics were conducted to gather participants’ blood pressure levels, pulse rate, and body mass index (BMI) in accordance with the cohort profiles[13]. 8-mL samples of fasting venous blood were drawn and stored at -80℃. The laboratory tests were conducted at the central laboratory. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula was used to calculate the estimated glomerular filtration rate (eGFR)[17]. Statistical analysis Baseline comparisons of categorical and continuous variables between the social isolation and non-social isolation groups were analyzed using the Student’s t-test, chi-square test, and the Wilcoxon rank-sum test. Comparisons of continuous variables between different social isolation scores were analyzed using one-way ANOVA or the Kruskal–Wallis H‐test, accordingly. The Cochran-Armitage trend test was used to analyze the trends in the prevalence of social isolation over time among all participants and by gender. The trends in social isolation scores over time and the linear relationships between social isolation scores and comorbid burden among overall participants, as well as by gender, were illustrated using two-way linear prediction plots. The normal distribution of residuals was assessed by displaying the histograms of residuals and residual plots. Multivariate Poisson regression models were constructed to examine the longitudinal associations between social isolation and all-cause mortality by gender, with multiple adjustments for age, educational attainment, smoking and drinking status, systolic blood pressure (SBP), pulse rate, BMI, hemoglobin, triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), eGFR, hemoglobin A1c (HbA1c), fasting blood glucose (FBG), C-reactive protein, and self-reported hypertension, diabetes, dyslipidemia, stroke, heart disease, chronic liver disease, chronic lung disease or asthma, chronic kidney disease, memory-related disease, psychiatric disease, stomach or digestive disease, arthritis or rheumatism, and malignant tumor. Variance inflation factor (VIF) was calculated, and variables with a VIF over 10 were considered indicative of multicollinearity and were excluded from the multi-adjusted models. Incidence rate ratios (IRRs) and 95% CIs were reported. The interactions between gender and social isolation status were tested using interaction terms and the likelihood ratio tests. To clearly demonstrate the epidemiological implications of the analysis, the population attributable fractions (PAFs) for mortality attributed to social isolation were calculated by gender. The PAF could explain the proportion of all-cause mortality that could potentially be eliminated by preventing social isolation at baseline. Additionally, the absolute number of deaths that caused by social isolation was also calculated. All analyses, except for the comparisons between baseline characteristics, were weighted to account for the multistage, probability-proportional-to-size sampling scheme of the CHARLS cohort. This approach was implemented to mitigate the effects of variations in the internal composition of cross-sectional data across different years on the prevalence rate. All analyses were performed using Stata SE version 15.1 (StataCorp LLC, College Station, TX, USA). Two-tailed p-values <0.05 were considered statistically significant. Results Baseline characteristics of the study participants A total of 10197 participants aged over 45 years with a social isolation index were eventually included in the study. Among them, the mean age was 60.0 (±10.6) years, with 48.1% being men. The prevalence of social isolation was 20.8%, with an average ACCI of 2.7 (±1.8) and 1.5 (±1.4) comorbidities. Participants with social isolation were older, had lower educational attainment and BMI, and had higher SBP levels and higher ACCI in both genders, compared to those without social isolation. (Table 1) Women, in comparison to men, were younger, had lower educational attainment and higher BMI levels, and were less likely to be current smokers and alcohol drinkers. Although the ACCI was comparable between genders, women had a higher number of comorbidities than men. (Supplemental Table 1) The characteristics of participants according to their social isolation score were also presented in Supplemental Table 2. Gender differences in the trends in weighted prevalence of social isolation The overall weighted prevalence of social isolation was 21.8% (95% CI: 20.7, 23.0) in 2011, 15.2% (95% CI: 14.3, 16.2) in 2013, 16.4% (95% CI: 15.4, 17.4) in 2015, and 19.4% (95% CI: 18.4, 20.4) in 2018, showing a marginal borderline downward trend across time (P for trend = 0.083). (Supplemental Figure 2) When stratifying by gender, a significant downward trend was observed in men, with a weighted prevalence of 19.4% (95% CI: 17.7, 21.3) in 2011 and a weighted prevalence of 14.1% (95% CI: 12.9, 15.4) in 2018 (P for trend <0.001). In contrast, a stable trend was noted in women, with a weighted prevalence of 24.0% (95% CI: 22.5, 25.6) in 2011 and a weighted prevalence of 24.1% (95% CI: 22.7, 25.6) in 2018 (P for trend = 0.154). (Figure 1) In 2011, women had higher odds of living alone and being unmarried compared to men, while men showed greater likelihoods of having contact with children less than once a week and participating in social activities less than once a month. (Supplemental Figure 3) The overall weighted mean social isolation score was 0.92 (95% CI: 0.89, 0.95) in 2011 and 0.83 (95% CI: 0.80, 0.85) in 2018, indicating a significant downward trend over time. (Supplemental Figure 4 Panel A) Similarly, a significant downward trend in the weighted mean social isolation score was only observed in men, not in women. (Supplemental Figure 4 Panel B) Gender differences in the weighted comorbid burden of social isolation As depicted in Figure 2, the weighted ACCI was 2.46 (95% CI: 2.38, 2.54) and 2.15 (95% CI: 2.07, 2.23) for men and women with a social isolation score of 0, and 3.46 (95% CI: 3.00, 3.91) and 3.51 (95% CI: 3.15, 3.88) for men and women with a social isolation score of 4, respectively. A steeper increase in ACCI was observed in women compared to men (P for gender-by-social isolation score interaction <0.001). A similar steeper increased in the number of comorbidities was also noted among women. (Figure 2) The weighted linear relationships between ACCI and the number of comorbidities with social isolation scores among overall participants were shown in Supplemental Figure 5. The residuals plot and histogram of residuals showed a normal distribution, indicating that the assumptions of linear relationships were not violated. (Supplemental Figure 6) Figure 3 illustrates the weighted trends in the ACCI and the number of comorbidities among men and women with and without social isolation across time. The weighted differences in comorbid burden between the isolation statuses tended to be consistently more pronounced in women than in men. Supplemental Figure 7 displays the weighted trends in the ACCI and the number of comorbidities among all participants with and without social isolation over time. In addition, the weighted prevalence of each component of the comorbidity in the baseline survey among men and women with and without social isolation is also depicted in Supplemental Figure 8. Gender differences in the weighted mortality associated with social isolation During a 9-year follow-up, 1172 out of 9513 (12.3%) died. Per 1-point increase in social isolation score increase, there was a 29% (95% CI: 1.21, 1.37) higher IRR of all-cause mortality. Furthermore, individuals with social isolation had a 76% (95% CI: 1.52, 2.04) greater IRR of mortality compared to those without social isolation. Per 1-point increase in social isolation score was associated with a 21% (95% CI: 1.21, 1.37) and a 43% (95% CI: 1.31, 1.56) higher risk of mortality in men and women, with a 23% (95% CI: 1.09, 1.37) relative higher risk observing in women compared to men (P for interaction = 0.001). Compared to individuals without social isolation, females with social isolation had over double the risk of mortality (IRR: 2.05, 95% CI: 1.65, 2.53), whereas males with social isolation only had a 60% increased IRR (95% CI: 1.31, 1.95) of all-cause mortality (P for interaction = 0.032). (Table 2) Additionally, females were associated with approximately a 27% lower IRR of mortality than males. Nevertheless, social isolation diminished the protective effect of female sex on mortality. As the social isolation score increased, or among female individuals experiencing social isolation, women exhibited a mortality risk probability comparable to that of men. (Supplemental Table 3) No collinearity was found among the adjusted covariates (Supplemental Table 4). Overall, 13.1% (95% CI: 8.0, 13.0) of mortality was attributable to social isolation status, with women accounting for 21.0% (95% CI: 11.4, 29.5) of PAF, whereas men accounted for only 8.2% (95% CI: 2.5, 13.6) of PAF for all-cause mortality. (Supplemental Figure 9 Panel A) The absolute number of deaths attributed to social isolation is shown in Supplemental Figure 9 Panel B. Discussion This large, nationally representative, prospective cohort study has several novel and significant findings. First, nearly one-fifth of Chinese middle-aged and older community-dwelling adults experienced social isolation, with a higher prevalence noted in females. Second, a significant downward trend in the prevalence of social isolation was observed in men, while the prevalence in women remained consistently high. Third, the comorbid burden of social isolation was heavy in China, with a higher burden observed in women compared to men. Fourth, social isolation contributed to an adverse impact on all-cause mortality, with a greater effect presented in females than in males. Our current study unveiled that around 19.4% of Chinese middle-aged and older adults experienced social isolation in 2018, a figure consistent with those reported in the U.S.[ 2 ]. Furthermore, the prevalence of social isolation remained stable compared to 2011 and was even higher compared to 2013 and 2015. Given that China has a relatively large aging population, social isolation is expected to pose a significant public health threat and impede social and economic development. In the U.S., approximately 7.7 million adults aged over 65 experienced social isolation in 2011[ 2 ]. According to the latest national census data and the current study findings, it was estimated that around 42.2 million Chinese middle-aged and older adults experienced social isolation[ 18 ]. Additionally, our study and previous research have shown that social isolation is positively associated with heavier comorbid burdens, such as cognitive impairment[ 15 , 19 ], cardiovascular disease[ 5 , 6 ], depression[ 20 ], and a higher risk of mortality[ 10 , 21 ], compared to individuals who are not socially isolated. Although most Western policymakers have described social isolation as a significant public health and societal issue and are taking steps to intervene, there is still a long way to go[ 22 ], especially in low- and moderate-income countries. Another interesting finding of our study was that several gender differences were found in social isolation, including variations in prevalence, their comorbid burden, and subsequent mortality. It was the first study to systematically evaluate gender differences in social isolation from epidemiology to prognosis among middle-aged and older adults. Given the observational nature of this study, the underlying mechanisms of these observations were not further explored. Nevertheless, several hypothetical theories could be used to explain. First, gender inequalities have persisted for centuries, with females consistently experiencing unfavorable status and lower socioeconomic standing[ 11 , 23 ], which may lead to increased social pressure on women. Social stress can spontaneously lead to depressive-like behaviors and neural adaptations[ 24 ], ultimately resulting in social isolation. Furthermore, women not only face unfavorable socioeconomic status but also encounter challenges in disease prevention, both in primary and secondary care[ 25 ], as well as in clinical trials[ 26 ], resulting in a heavier comorbid burden among women. In addition, women are generally at a greater disadvantage than men regarding their knowledge and authority to make informed decisions about healthcare worldwide, regardless of their geographic location or economic status[ 27 ]. Second, chronic social isolation pressure has been demonstrated to have different effects on behavior in a gender-specific manner. Male mice tend to exhibit escalated aggression, while females tend to social withdrawl[ 28 ]. Similar gender differences in stress responses have also been observed in humans[ 29 ]. These gender-specific physiological mechanisms could further exaggerate social stress and lead to a higher prevalence of social isolation in women. Third, previous epidemiological studies have shown that women are more susceptible to mental disorders, whereas men mainly suffer from cardiovascular disease[ 30 , 31 ]. Meanwhile, females are more prone to be affected by social stress and environmental pressure compared to men[ 32 ], indicating that women are vulnerable populations. Taken together, these factors could explain why women have a higher prevalence of social isolation and why their comorbid burden and associated mortality rates are higher than those of men. Our study has several important public health implications. First, social isolation has emerged as a significant public health concern globally, with high prevalence and substantial comorbid burden, along with an elevated risk of mortality. Therefore, enhancing family and social support for older adults and reducing social isolation could be a cost-effective strategy. Previous studies have indicated that social isolation increases the risk of nursing home admission and the utilization of primary health services[ 33 , 34 ]. Second, describing the gender differences in social isolation could underscore the unfavorable socioeconomic and health status among females. It is high time to enhance the statuses and rights of women because they are significantly associated with the social, economic, and sustainable development, as well as the health indicators of countries[ 35 ]. Third, identifying gender-specific high-risk populations for social isolation and its associated increased mortality could help inform policymakers to implement targeted interventions in a gender-specific manner, effectively improving human health. As shown in the estimated PAFs in the current study, avoiding social isolation could potentially prevent 13% of mortalities among general adults and over one-fifth of mortalities in women. Limitations Our study has several noteworthy limitations. First, this was an observational cohort study, making it difficult to draw causal conclusions. Even though our study shows that avoiding social isolation could theoretically prevent a considerable proportion of deaths, the conclusion warrants further clinical trials to confirm, due to the nonrandomized data of the present study. Meanwhile, some residual and unmeasured factors, such as the duration of social isolation, cannot be avoided. Second, the prevalence of social isolation, as categorized using several self-reported questions, was likely underreported, because men found it more challenging to express feelings of loneliness compared to women[ 36 ], and socially isolated adults were less likely to enroll. Third, relying on self-reported comorbidity may also result in inaccurate estimations of comorbid prevalence. However, prior research has found good agreement between self-reported data and administrative records[ 37 ]. Fourth, the CHARLS data only included Chinese middle-aged and older individuals. Therefore, caution should be exercised when generalizing the conclusions to other populations with varying regions, races, and age groups. Conclusions In summary, this nationwide study revealed that a large proportion of middle-aged and older community-dwelling adults in China experienced social isolation. In addition, several gender differences in social isolation were observed, including the higher prevalence, heavier comorbid burdens, and a more prominent impact on mortality noted in women, highlighting the importance of improving family and social support for older adults, especially in enhancing female socioeconomic statuses and rights. Declarations Ethics approval and consent to participate The CHARLS study protocol was approved by the ethics review committee at Peking University, Beijing, China (IRB00001052-11015). Written informed consent was obtained from all participants. Consent for publication Not applicable. Availability of data and materials The data analyzed in this study are available from the Institute of Social Science Survey, Peking University, Beijing, China. (http://charls.pku.edu.cn). Competing interests None declared. Funding Not applicable. Author’s contributions S.X.K and S.Z.H contributed to the data acquisition, concept and design, analysis and drafted the manuscript. Y.Y.P and H.H.M contributed to the data acquisition and analysis. G.Q, Y.L.J, and Q.W.D provided study concept and design, and revised the manuscript. Z.Z and W.S.K contributed to funding obtained and provide administrative, technical, or material support. W.S.K also contributed to supervision of the work. Acknowledgments We thank the China Health and Retirement Longitudinal Study for sharing their data and all the staffs participated in the CHARLS for their contributions to this work. 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Pomeroy ML, Cudjoe TKM, Cuellar AE, Ihara ES, Ornstein KA, Bollens-Lund E, Kotwal AA, Gimm GW: Association of Social Isolation With Hospitalization and Nursing Home Entry Among Community-Dwelling Older Adults . JAMA internal medicine 2023, 183 (9):955-962. Xie X, Lyu Y, Li X, Zhuang Z, Xu A: Exploring the association between social isolation and utilization of primary health services by older adults: evidence from China . Frontiers in public health 2024, 12 :1341304. Alaei K, Akgüngör S, Chao WF, Hasan S, Marshall A, Schultz E, Alaei A: Cross-country analysis of correlation between protection of women's economic and social rights, health improvement and sustainabledevelopment . BMJ open 2019, 9 (6):e021350. Ratcliffe J, Galdas P, Kanaan M: Older men and loneliness: a cross-sectional study of sex differences in the English Longitudinal Study of Ageing . BMC public health 2024, 24 (1):354. Baumeister H, Kriston L, Bengel J, Härter M: High agreement of self-report and physician-diagnosed somatic conditions yields limited bias in examining mental-physical comorbidity . Journal of clinical epidemiology 2010, 63 (5):558-565. Tables Table 1. Baseline characteristics of participants. Baseline characteristics at 2011 Overall (N=10197) Men (N=4902) Women (N=5295) Without social isolation (N=3992) With social isolation (N=910) P-value Without social isolation (N=4081) With social isolation (N=1214) P-value Demographic Age (years) 60.0±10.6 60.1±9.6 63.4±10.4 <0.001 57.5±10.4 66.0±11.6 <0.001 Male, n(%) 4902 (48.1) - - - - - - Married, n(%) 7951 (78.0) 3815 (95.6) 298 (32.8) <0.001 3642 (89.2) 196 (16.1) <0.001 Education ≥High school, n(%) 1311 (12.9) 684 (17.1) 79 (8.7) <0.001 470 (11.5) 78 (6.4) <0.001 Current smoker, n(%) 3074 (30.4) 2242 (56.5) 505 (55.9) 0.752 231 (5.7) 96 (8.0) 0.004 Current drinker, n(%) 3253 (32.2) 2180 (55.1) 441 (49.0) 0.001 500 (12.4) 132 (11.0) 0.186 Physical measurements and anthropometrics Systolic blood pressure (mmHg) 131.3±21.7 130.9±20.7 133.3±22.4 0.006 129.6±21.2 136.4±24.5 <0.001 Diastolic blood pressure (mmHg) 76.0±12.1 76.5±12.5 76.7±12.6 0.693 75.6±11.7 75.6±11.9 0.924 Pulse (beat per minute) 72.5±10.6 71.9±11.1 73.2±11.3 0.004 72.6±9.8 73.1±10.5 0.125 Body mass index (kg/m 2 ) 23.4±3.8 23.0±3.6 21.8±3.2 <0.001 24.3±4.0 22.9±4.1 <0.001 Laboratory Hemoglobin (g/dL) 14.4±2.2 15.2±2.1 15.0±2.2 0.015 13.6±1.9 13.7±2.4 0.562 Triglyceride (mg/dL) 133.7±104.7 129.8±112.4 113.0±81.0 <0.001 141.1±101.1 136.4±103.9 0.250 Total cholesterol (mg/dL) 193.5±38.0 189.7±36.8 182.7±35.7 <0.001 197.9±38.8 199.5±38.3 0.306 LDL-C (mg/dL) 116.7±34.8 114.0±34.4 108.6±32.3 <0.001 120.0±34.9 120.9±35.7 0.508 HDL-C (mg/dL) 51.1±15.4 50.5±16.1 52.1±16.7 0.027 50.9±14.4 52.9±14.9 <0.001 eGFR (ml/min/1.72m2) 80.0±26.5 81.0±24.9 74.3±23.3 <0.001 83.5±28.1 69.1±25.1 <0.001 FBG (mg/dL) 110.0±35.5 110.4±36.1 110.4±32.3 0.964 110.2±37.8 108.0±26.0 0.119 HbA1c (%) 5.3±0.8 5.2±0.8 5.2±0.7 0.376 5.3±0.9 5.2±0.7 0.010 C-reactive protein (mg/l)* 1.0 (0.6, 2.3) 1.1 (0.6, 2.4) 1.1 (0.6, 2.6) 0.632 1.0 (0.5, 2.0) 1.1 (0.5, 2.6) 0.082 Self-reported comorbidity Hypertension, n(%) 2571 (25.2) 947 (23.7) 190 (20.9) 0.067 1072 (26.3) 362 (29.8) 0.015 Diabetes mellitus, n(%) 578 (5.7) 207 (5.2) 36 (4.0) 0.123 267 (6.5) 68 (5.6) 0.237 Dyslipidemia, n(%) 973 (9.5) 379 (9.5) 55 (6.0) 0.001 429 (10.5) 110 (9.1) 0.142 Stroke, n(%) 233 (2.3) 81 (2.0) 26 (2.9) 0.123 92 (2.3) 34 (2.8) 0.273 Heart disease, n(%) 1271 (12.5) 402 (10.1) 93 (10.2) 0.892 579 (14.2) 197 (16.2) 0.078 Liver disease, n(%) 416 (4.1) 196 (4.9) 39 (4.3) 0.426 145 (3.6) 36 (3.0) 0.323 Lung disease or asthma, n(%) 1453 (14.2) 626 (15.7) 185 (20.3) 0.001 451 (11.1) 191 (15.7) <0.001 Kidney disease, n(%) 711 (7.0) 310 (7.8) 72 (7.9) 0.882 261 (6.4) 68 (5.6) 0.314 Memory-related disease, n(%) 183 (1.8) 71 (1.8) 27 (3.0) 0.021 52 (1.3) 33 (2.7) <0.001 Stomach or digestive disease, n(%) 2383 (23.4) 853 (21.4) 188 (20.7) 0.637 1043 (25.6) 299 (24.6) 0.514 Malignant tumor, n(%) 108 (1.1) 38 (1.0) 4 (0.4) 0.130 56 (1.4) 10 (0.8) 0.130 Arthritis and rheumatism 3599 (35.3) 1227 (30.7) 318 (35.0) 0.014 1548 (37.9) 506 (41.7) 0.019 Psychiatric disease 197 (1.9) 48 (1.2) 32 (3.5) <0.001 77 (1.9) 40 (3.3) 0.003 Age-adjusted Charlson Comorbidity Index 2.7±1.8 2.6±1.7 3.0±1.7 <0.001 2.4±1.8 3.4±1.7 <0.001 Number of comorbidity 1.5±1.4 1.4±1.4 1.4±1.4 0.223 1.5±1.5 1.8±1.5 <0.001 LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; HbA1c, hemoglobin A1c. * Present as median (interquartile range) Table 2. The weighted association between social isolation and all-cause mortality among overall participants and by gender. Social isolation status Overall Men Women Women vs men: relative IRR (95% CI) P for interaction Cases/Total (%) Adjusted IRR (95% CI) Cases/Total (%) Adjusted IRR (95% CI) Cases/Total (%) Adjusted IRR (95% CI) Per 1-social isolation score 1172/9513 (12.3) 1.29 (1.21, 1.37) 714/4588 (15.6) 1.21 (1.11, 1.32) 458/4925 (9.3) 1.43 (1.31, 1.56) 1.23 (1.09, 1.37) 0.001 0 297/3576 (8.3) Reference 215/1780 (12.1) Reference 82/1796 (4.6) Reference - - 1 503/3990 (12.6) 1.45 (1.24, 1.71) 307/1964 (15.6) 1.19 (0.98, 1.45) 196/2026 (9.7) 2.06 (1.55, 2.73) 1.43 (1.20, 1.71) <0.001 2 258/1383 (18.7) 2.39 (1.97, 2.89) 129/584 (22.1) 1.89 (1.47, 2.44) 129/799 (16.2) 3.51 (2.54, 4.84) 2.01 (1.51, 2.67) <0.001 3 92/450 (20.4) 1.90 (1.44, 2.52) 52/209 (24.9) 1.57 (1.07, 2.30) 40/241 (16.6) 2.99 (1.97, 4.54) 1.86 (1.22, 2.83) 0.004 4 22/114 (19.3) 1.84 (1.24, 2.73) 11/51 (21.6) 1.44 (0.85, 2.43) 11/63 (17.5) 2.91 (1.53, 5.53) 1.90 (1.05, 3.44) 0.033 Non-social isolation 800/7566 (10.6) Reference 522/3744 (13.9) Reference 278/3822 (7.3) Reference - - Social isolation 372/1947 (19.1) 1.76 (1.52, 2.04) 192/844 (22.8) 1.60 (1.31, 1.95) 180/1103 (16.3) 2.05 (1.65, 2.53) 1.35 (1.03, 1.79) 0.032 IRR, incidence rate ratio; CI, confidence interval. The numbers in bold font indicate significance. Additional Declarations No competing interests reported. Supplementary Files Supplementalmaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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09:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5452833/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5452833/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71757025,"identity":"472d78a6-598b-46d2-80e9-b00f07a62d86","added_by":"auto","created_at":"2024-12-18 10:17:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeighted trend in the prevalence of social isolation among men and women.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5452833/v1/b2b1fcd3dcf9c0fe78d10463.png"},{"id":71757029,"identity":"154e84a3-4a73-42a8-b61d-01ea7fd85386","added_by":"auto","created_at":"2024-12-18 10:17:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":465293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeighted linear relationship between age-adjusted Charlson Comorbidity Index (A), number of comorbidity (B) and the social isolation score among men and women.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5452833/v1/0b9e8bccbc71d010f6c858bd.png"},{"id":71757030,"identity":"8e6afe2c-01e0-4f6f-ac47-885b61956ab3","added_by":"auto","created_at":"2024-12-18 10:17:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":489374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeighted trends in the age-adjusted Charlson Comorbidity Index (A) and the number of comorbidities (B) among men and women with and without social isolation.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5452833/v1/54100ae50b4c698a212c3f16.png"},{"id":72711592,"identity":"4d0db19c-4324-4192-8092-50f73fb90d12","added_by":"auto","created_at":"2024-12-31 15:46:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2964682,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5452833/v1/a576a8b1-69bc-424f-86aa-288e581e47aa.pdf"},{"id":71757028,"identity":"489501fe-1bfe-425f-b55d-100ca9155500","added_by":"auto","created_at":"2024-12-18 10:17:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":630209,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5452833/v1/133561d89844bc9b26eaff74.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends in prevalence, associated comorbid burden, and subsequent mortality of social isolation: A gender perspective","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSocial isolation, defined as objective physical separation from social activity and social networks, has been a pervasive public health concern with alarmingly high global prevalence. According to a recent meta-analysis, the pooled prevalence of social isolation is approximately 25.0% (95% confidence interval (CI): 21.0, 30.0) among community-dwelling older adults[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Similarly, it is estimated that nearly a quarter of American adults over 65 years old experience social isolation, which corresponds to approximately 7.7\u0026nbsp;million U.S. adults[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The situation is even worse in China, with around 40% (95% CI: 38.6, 41.3) of middle-aged and older individuals experiencing social isolation[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSocial isolation is independently correlative with multiple chronic conditions. A recent study conducted in two prospective cohorts from Europe and China showed that social isolation was associated with an increased risk of type 2 diabetes mellitus. The potential causal relationship between social isolation and an increased risk of diabetes was confirmed through Mendelian randomization analysis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Social isolation has also been demonstrated to be associated with higher risks of incident cardiovascular disease and heart failure[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], as well as functional limitations and cognitive health[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, individuals experiencing social isolation often bear a significant burden of comorbidities. Conversely, these multiple comorbidities can exacerbate the onset of social isolation[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, individuals with social isolation had a 32% (95% CI: 1.26, 1.39), 24% (95% CI: 1.19 to 1.28), and 34% (95% CI: 1.25 to 1.44) elevated risk of all-cause mortality, cancer mortality, and cardiovascular mortality[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Despite this, there is little evidence regarding gender differences in the trends of social isolation prevalence. In addition, it remains unclear whether the comorbid burden of social isolation and its associated mortality differs between gender.\u003c/p\u003e \u003cp\u003eGender health inequalities remain a significant issue worldwide and may exacerbate the social isolation status between men and women[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Meanwhile, gender-based differences are noted in the epidemiology, pathophysiology, and progression of multiple diseases and mortality[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, it is reasonable to believe that social isolation may vary by gender and have different impacts on health and mortality. Comprehensively characterizing the gender differences in social isolation and its associated issues is essential for tailoring gender-specific preventive strategies and improving shared decision-making.\u003c/p\u003e \u003cp\u003eTo address this knowledge gap, our study utilized 5 waves of longitudinal data from a nationally representative sample of Chinese middle-aged and older community-dwelling adults to: (1) estimate the gender differences in the trends in the prevalence of social isolation; (2) evaluate the gender-based differences in its comorbid burden; and (3) examine their subsequent associated mortality by gender.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized nationwide, longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS). The CHARLS was launched in 2011 and employed a multistage, probability-proportional-to-size sampling method to recruit participants, ensuring a nationally representative sample. Participants who were over 45 years old and voluntarily completed a computer-assisted personal interview were eligible for recruitment. The first wave of CHARLS enrolled 17708 participants from 450 urban districts and 150 counties in 28 provinces across Mainland China. The follow-up was performed every 2 to 3 years through face-to-face interviews until 2020. Detailed information on the CHARLS has been described previously[13]. All participants provided written informed consent. The protocol of the CHARLS study was approved by the ethics review committee at Peking University, Beijing (IRB00001052-11015).\u003c/p\u003e\n\u003cp\u003eThis study involved two sections. First, we utilized the cross-sectional data from the baseline survey in 2011 and the subsequent 3 follow-up surveys in 2013, 2015, and 2018 to estimate the gender disparities in the trends of social isolation prevalence. Participants aged under 45 years old and those without data on social isolation were excluded from the analysis. In this part, we also assess the gender-based differences in the comorbid burden among individuals with different levels of social isolation. Given that more than 40% of participants had missing data on social isolation at each wave, Little\u0026apos;s test of missing completely at random[14] was conducted on the remaining missing values using the \u0026lsquo;mcartest\u0026rsquo; command in STATA, after excluding participants with over 20% missing data for social isolation. The results were not significant, indicating that the assumption of completely random missing data was satisfied[14]. Second, we merged the data from the baseline and all the follow-up surveys (i.e., follow-up data in 2013, 2015, 2018, and 2020) to examine the longitudinal association between social isolation and mortality among all participants and by gender. In this part, individuals without follow-up records were excluded. (Supplemental Figure 1)\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAssessment of social isolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSocial activity and social networks, collected from the self-reported questionnaires, were employed to evaluate the social isolation index. The social isolation index included 4 dichotomized indicators, that is, living alone, being unmarried, having contact with children (in person, by phone, or by email) less than once a week, and participating in any social activities (such as attending sports, social, or other clubs, taking part in community-related organizations or an educational or training course, interacting with friends, going to a community club, playing Mahjong, chess, or cards, or doing voluntary work) less than once a month. A total score of social isolation was calculated by summing these 4 indicators and ranged from 0 to 4, with a higher score representing a higher level of social isolation. Individuals were identified as either experiencing social isolation (\u0026ge;2) and or not experiencing social isolation (\u0026lt;2) based on the social isolation index[15]. All the questions used to identify the indicators of social isolation are consistent across all waves of CHALRS.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAssessment of mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study outcome of the current study was all-cause mortality. Field investigations were conducted by trained staff to determine the survival status of participants by trained staff during follow-up. The survival status was determined by interviewing the recruited participants, their family members, or relatives who lived with the deceased. The date of the events was only available in the second follow-up survey in 2013. Therefore, only the mortality rate was used in our study.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe information on demographics (i.e., age, gender, educational attainment, marital status, smoking and drinking status) and self-reported comorbidities was obtained from personal interviews using standard questionnaires. The comorbid burden was calculated using the age-adjusted Charlson Comorbidity Index (ACCI)[16] and the number of comorbidities. Physical measurements and anthropometrics were conducted to gather participants\u0026rsquo; blood pressure levels, pulse rate, and body mass index (BMI) in accordance with the cohort profiles[13]. 8-mL samples of fasting venous blood were drawn and stored at -80℃. The laboratory tests were conducted at the central laboratory. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula was used to calculate the estimated glomerular filtration rate (eGFR)[17].\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline comparisons of categorical and continuous variables between the social isolation and non-social isolation groups were analyzed using the Student\u0026rsquo;s t-test, chi-square test, and the Wilcoxon rank-sum test. Comparisons of continuous variables between different social isolation scores were analyzed using one-way ANOVA or the Kruskal\u0026ndash;Wallis H‐test, accordingly.\u003c/p\u003e\n\u003cp\u003eThe Cochran-Armitage trend test was used to analyze the trends in the prevalence of social isolation over time among all participants and by gender. The trends in social isolation scores over time and the linear relationships between social isolation scores and comorbid burden among overall participants, as well as by gender, were illustrated using two-way linear prediction plots. The normal distribution of residuals was assessed by displaying the histograms of residuals and residual plots.\u003c/p\u003e\n\u003cp\u003eMultivariate Poisson regression models were constructed to examine the longitudinal associations between social isolation and all-cause mortality by gender, with multiple adjustments for age, educational attainment, smoking and drinking status, systolic blood pressure (SBP), pulse rate, BMI, hemoglobin, triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), eGFR, hemoglobin A1c (HbA1c), fasting blood glucose (FBG), C-reactive protein, and self-reported hypertension, diabetes, dyslipidemia, stroke, heart disease, chronic liver disease, chronic lung disease or asthma, chronic kidney disease, memory-related disease, psychiatric disease, stomach or digestive disease, arthritis or rheumatism, and malignant tumor. Variance inflation factor (VIF) was calculated, and variables with a VIF over 10 were considered indicative of multicollinearity and were excluded from the multi-adjusted models. Incidence rate ratios (IRRs) and 95% CIs were reported. The interactions between gender and social isolation status were tested using interaction terms and the likelihood ratio tests. To clearly demonstrate the epidemiological implications of the analysis, the population attributable fractions (PAFs) for mortality attributed to social isolation were calculated by gender. The PAF could explain the proportion of all-cause mortality that could potentially be eliminated by preventing social isolation at baseline. Additionally, the absolute number of deaths that caused by social isolation was also calculated.\u003c/p\u003e\n\u003cp\u003eAll analyses, except for the comparisons between baseline characteristics, were weighted to account for the multistage, probability-proportional-to-size sampling scheme of the CHARLS cohort. This approach was implemented to mitigate the effects of variations in the internal composition of cross-sectional data across different years on the prevalence rate. All analyses were performed using Stata SE version 15.1 (StataCorp LLC, College Station, TX, USA). Two-tailed p-values \u0026lt;0.05 were considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of the study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 10197 participants aged over 45 years with a social isolation index were eventually included in the study. Among them, the mean age was 60.0 (\u0026plusmn;10.6) years, with 48.1% being men. The prevalence of social isolation was 20.8%, with an average ACCI of 2.7 (\u0026plusmn;1.8) and 1.5 (\u0026plusmn;1.4) comorbidities. Participants with social isolation were older, had lower educational attainment and BMI, and had higher SBP levels and higher ACCI in both genders, compared to those without social isolation. (Table 1) Women, in comparison to men, were younger, had lower educational attainment and higher BMI levels, and were less likely to be current smokers and alcohol drinkers. Although the ACCI was comparable between genders, women had a higher number of comorbidities than men. (Supplemental Table 1) The characteristics of participants according to their social isolation score were also presented in Supplemental Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGender differences in the trends in weighted prevalence of social isolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall weighted prevalence of social isolation was 21.8% (95% CI: 20.7, 23.0) in 2011, 15.2% (95% CI: 14.3, 16.2) in 2013, 16.4% (95% CI: 15.4, 17.4) in 2015, and 19.4% (95% CI: 18.4, 20.4) in 2018, showing a marginal borderline downward trend across time (P for trend = 0.083). (Supplemental Figure 2) When stratifying by gender, a significant downward trend was observed in men, with a weighted prevalence of 19.4% (95% CI: 17.7, 21.3) in 2011 and a weighted prevalence of 14.1% (95% CI: 12.9, 15.4) in 2018 (P for trend \u0026lt;0.001). In contrast, a stable trend was noted in women, with a weighted prevalence of 24.0% (95% CI: 22.5, 25.6) in 2011 and a weighted prevalence of 24.1% (95% CI: 22.7, 25.6) in 2018 (P for trend = 0.154). (Figure 1) In 2011, women had higher odds of living alone and being unmarried compared to men, while men showed greater likelihoods of having contact with children less than once a week and participating in social activities less than once a month. (Supplemental Figure 3)\u003c/p\u003e\n\u003cp\u003eThe overall weighted mean social isolation score was 0.92 (95% CI: 0.89, 0.95) in 2011 and 0.83 (95% CI: 0.80, 0.85) in 2018, indicating a significant downward trend over time. (Supplemental Figure 4 Panel A) Similarly, a significant downward trend in the weighted mean social isolation score was only observed in men, not in women. (Supplemental Figure 4 Panel B)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGender differences in the weighted comorbid burden of social isolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs depicted in Figure 2, the weighted ACCI was 2.46 (95% CI: 2.38, 2.54) and 2.15 (95% CI: 2.07, 2.23) for men and women with a social isolation score of 0, and 3.46 (95% CI: 3.00, 3.91) and 3.51 (95% CI: 3.15, 3.88) for men and women with a social isolation score of 4, respectively. A steeper increase in ACCI was observed in women compared to men (P for gender-by-social isolation score interaction \u0026lt;0.001). A similar steeper increased in the number of comorbidities was also noted among women. (Figure 2) The weighted linear relationships between ACCI and the number of comorbidities with social isolation scores among overall participants were shown in Supplemental Figure 5. The residuals plot and histogram of residuals showed a normal distribution, indicating that the assumptions of linear relationships were not violated. (Supplemental Figure 6)\u003c/p\u003e\n\u003cp\u003eFigure 3 illustrates the weighted trends in the ACCI and the number of comorbidities among men and women with and without social isolation across time. The weighted differences in comorbid burden between the isolation statuses tended to be consistently more pronounced in women than in men. Supplemental Figure 7 displays the weighted trends in the ACCI and the number of comorbidities among all participants with and without social isolation over time. In addition, the weighted prevalence of each component of the comorbidity in the baseline survey among men and women with and without social isolation is also depicted in Supplemental Figure 8.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGender differences in the weighted mortality associated with social isolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring a 9-year follow-up, 1172 out of 9513 (12.3%) died. Per 1-point increase in social isolation score increase, there was a 29% (95% CI: 1.21, 1.37) higher IRR of all-cause mortality. Furthermore, individuals with social isolation had a 76% (95% CI: 1.52, 2.04) greater IRR of mortality compared to those without social isolation. Per 1-point increase in social isolation score was associated with a 21% (95% CI: 1.21, 1.37) and a 43% (95% CI: 1.31, 1.56) higher risk of mortality in men and women, with a 23% (95% CI: 1.09, 1.37) relative higher risk observing in women compared to men (P for interaction = 0.001). Compared to individuals without social isolation, females with social isolation had over double the risk of mortality (IRR: 2.05, 95% CI: 1.65, 2.53), whereas males with social isolation only had a 60% increased IRR (95% CI: 1.31, 1.95) of all-cause mortality (P for interaction = 0.032). (Table 2)\u003c/p\u003e\n\u003cp\u003eAdditionally, females were associated with approximately a 27% lower IRR of mortality than males.\u0026nbsp;Nevertheless, social isolation diminished the protective effect of female sex on mortality. As the social isolation score increased, or among female individuals experiencing social isolation, women exhibited a mortality risk probability comparable to that of men.\u0026nbsp;(Supplemental Table 3) No collinearity was found among the adjusted covariates (Supplemental Table 4).\u003c/p\u003e\n\u003cp\u003eOverall, 13.1% (95% CI: 8.0, 13.0) of mortality was attributable to social isolation status, with women accounting for 21.0% (95% CI: 11.4, 29.5) of PAF, whereas men accounted for only 8.2% (95% CI: 2.5, 13.6) of PAF for all-cause mortality. (Supplemental Figure 9 Panel A) The absolute number of deaths attributed to social isolation is shown in Supplemental Figure 9 Panel B.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis large, nationally representative, prospective cohort study has several novel and significant findings. First, nearly one-fifth of Chinese middle-aged and older community-dwelling adults experienced social isolation, with a higher prevalence noted in females. Second, a significant downward trend in the prevalence of social isolation was observed in men, while the prevalence in women remained consistently high. Third, the comorbid burden of social isolation was heavy in China, with a higher burden observed in women compared to men. Fourth, social isolation contributed to an adverse impact on all-cause mortality, with a greater effect presented in females than in males.\u003c/p\u003e \u003cp\u003eOur current study unveiled that around 19.4% of Chinese middle-aged and older adults experienced social isolation in 2018, a figure consistent with those reported in the U.S.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Furthermore, the prevalence of social isolation remained stable compared to 2011 and was even higher compared to 2013 and 2015. Given that China has a relatively large aging population, social isolation is expected to pose a significant public health threat and impede social and economic development. In the U.S., approximately 7.7\u0026nbsp;million adults aged over 65 experienced social isolation in 2011[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the latest national census data and the current study findings, it was estimated that around 42.2\u0026nbsp;million Chinese middle-aged and older adults experienced social isolation[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, our study and previous research have shown that social isolation is positively associated with heavier comorbid burdens, such as cognitive impairment[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], cardiovascular disease[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], depression[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and a higher risk of mortality[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], compared to individuals who are not socially isolated. Although most Western policymakers have described social isolation as a significant public health and societal issue and are taking steps to intervene, there is still a long way to go[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], especially in low- and moderate-income countries.\u003c/p\u003e \u003cp\u003eAnother interesting finding of our study was that several gender differences were found in social isolation, including variations in prevalence, their comorbid burden, and subsequent mortality. It was the first study to systematically evaluate gender differences in social isolation from epidemiology to prognosis among middle-aged and older adults. Given the observational nature of this study, the underlying mechanisms of these observations were not further explored. Nevertheless, several hypothetical theories could be used to explain. First, gender inequalities have persisted for centuries, with females consistently experiencing unfavorable status and lower socioeconomic standing[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which may lead to increased social pressure on women. Social stress can spontaneously lead to depressive-like behaviors and neural adaptations[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], ultimately resulting in social isolation. Furthermore, women not only face unfavorable socioeconomic status but also encounter challenges in disease prevention, both in primary and secondary care[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], as well as in clinical trials[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], resulting in a heavier comorbid burden among women. In addition, women are generally at a greater disadvantage than men regarding their knowledge and authority to make informed decisions about healthcare worldwide, regardless of their geographic location or economic status[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Second, chronic social isolation pressure has been demonstrated to have different effects on behavior in a gender-specific manner. Male mice tend to exhibit escalated aggression, while females tend to social withdrawl[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Similar gender differences in stress responses have also been observed in humans[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These gender-specific physiological mechanisms could further exaggerate social stress and lead to a higher prevalence of social isolation in women. Third, previous epidemiological studies have shown that women are more susceptible to mental disorders, whereas men mainly suffer from cardiovascular disease[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Meanwhile, females are more prone to be affected by social stress and environmental pressure compared to men[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], indicating that women are vulnerable populations. Taken together, these factors could explain why women have a higher prevalence of social isolation and why their comorbid burden and associated mortality rates are higher than those of men.\u003c/p\u003e \u003cp\u003eOur study has several important public health implications. First, social isolation has emerged as a significant public health concern globally, with high prevalence and substantial comorbid burden, along with an elevated risk of mortality. Therefore, enhancing family and social support for older adults and reducing social isolation could be a cost-effective strategy. Previous studies have indicated that social isolation increases the risk of nursing home admission and the utilization of primary health services[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Second, describing the gender differences in social isolation could underscore the unfavorable socioeconomic and health status among females. It is high time to enhance the statuses and rights of women because they are significantly associated with the social, economic, and sustainable development, as well as the health indicators of countries[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Third, identifying gender-specific high-risk populations for social isolation and its associated increased mortality could help inform policymakers to implement targeted interventions in a gender-specific manner, effectively improving human health. As shown in the estimated PAFs in the current study, avoiding social isolation could potentially prevent 13% of mortalities among general adults and over one-fifth of mortalities in women.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOur study has several noteworthy limitations. First, this was an observational cohort study, making it difficult to draw causal conclusions. Even though our study shows that avoiding social isolation could theoretically prevent a considerable proportion of deaths, the conclusion warrants further clinical trials to confirm, due to the nonrandomized data of the present study. Meanwhile, some residual and unmeasured factors, such as the duration of social isolation, cannot be avoided. Second, the prevalence of social isolation, as categorized using several self-reported questions, was likely underreported, because men found it more challenging to express feelings of loneliness compared to women[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and socially isolated adults were less likely to enroll. Third, relying on self-reported comorbidity may also result in inaccurate estimations of comorbid prevalence. However, prior research has found good agreement between self-reported data and administrative records[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Fourth, the CHARLS data only included Chinese middle-aged and older individuals. Therefore, caution should be exercised when generalizing the conclusions to other populations with varying regions, races, and age groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this nationwide study revealed that a large proportion of middle-aged and older community-dwelling adults in China experienced social isolation. In addition, several gender differences in social isolation were observed, including the higher prevalence, heavier comorbid burdens, and a more prominent impact on mortality noted in women, highlighting the importance of improving family and social support for older adults, especially in enhancing female socioeconomic statuses and rights.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS study protocol was approved by the ethics review committee at Peking University, Beijing, China (IRB00001052-11015). Written informed consent was obtained from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analyzed in this study are available from the Institute of Social Science Survey, Peking University, Beijing, China. (http://charls.pku.edu.cn). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.X.K and S.Z.H contributed to the data acquisition, concept and design, analysis and drafted the manuscript. Y.Y.P and H.H.M contributed to the data acquisition and analysis. G.Q, Y.L.J, and Q.W.D provided study concept and design, and revised the manuscript. Z.Z and W.S.K contributed to funding obtained and provide administrative, technical, or material support. W.S.K also contributed to supervision of the work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the China Health and Retirement Longitudinal Study for sharing their data and all the staffs participated in the CHARLS for their contributions to this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTeo RH, Cheng WH, Cheng LJ, Lau Y, Lau ST: \u003cstrong\u003eGlobal prevalence of social isolation among community-dwelling older adults: A systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eArchives of gerontology and geriatrics \u003c/em\u003e2023, \u003cstrong\u003e107\u003c/strong\u003e:104904.\u003c/li\u003e\n\u003cli\u003eCudjoe TKM, Roth DL, Szanton SL, Wolff JL, Boyd CM, Thorpe RJ: \u003cstrong\u003eThe Epidemiology of Social Isolation: National Health and Aging Trends Study\u003c/strong\u003e. \u003cem\u003eThe journals of gerontology Series B, 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Marshall A, Schultz E, Alaei A: \u003cstrong\u003eCross-country analysis of correlation between protection of women\u0026apos;s economic and social rights, health improvement and sustainabledevelopment\u003c/strong\u003e. \u003cem\u003eBMJ open \u003c/em\u003e2019, \u003cstrong\u003e9\u003c/strong\u003e(6):e021350.\u003c/li\u003e\n\u003cli\u003eRatcliffe J, Galdas P, Kanaan M: \u003cstrong\u003eOlder men and loneliness: a cross-sectional study of sex differences in the English Longitudinal Study of Ageing\u003c/strong\u003e. \u003cem\u003eBMC public health \u003c/em\u003e2024, \u003cstrong\u003e24\u003c/strong\u003e(1):354.\u003c/li\u003e\n\u003cli\u003eBaumeister H, Kriston L, Bengel J, H\u0026auml;rter M: \u003cstrong\u003eHigh agreement of self-report and physician-diagnosed somatic conditions yields limited bias in examining mental-physical comorbidity\u003c/strong\u003e. \u003cem\u003eJournal of clinical epidemiology \u003c/em\u003e2010, \u003cstrong\u003e63\u003c/strong\u003e(5):558-565.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics of participants.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"973\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline characteristics at 2011\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (N=10197)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMen (N=4902)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen (N=5295)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithout social isolation (N=3992)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith social isolation (N=910)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithout social isolation (N=4081)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith social isolation (N=1214)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 973px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e60.0\u0026plusmn;10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e60.1\u0026plusmn;9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e63.4\u0026plusmn;10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e57.5\u0026plusmn;10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e66.0\u0026plusmn;11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eMale, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4902 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eMarried, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e7951 (78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3815 (95.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e298 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3642 (89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e196 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eEducation \u0026ge;High school, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1311 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e684 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e79 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e470 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e78 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eCurrent smoker, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3074 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2242 (56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e505 (55.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e231 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e96 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eCurrent drinker, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3253 (32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2180 (55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e441 (49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e500 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e132 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 973px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical measurements and anthropometrics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e131.3\u0026plusmn;21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e130.9\u0026plusmn;20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e133.3\u0026plusmn;22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e129.6\u0026plusmn;21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e136.4\u0026plusmn;24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e76.0\u0026plusmn;12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e76.5\u0026plusmn;12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e76.7\u0026plusmn;12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e75.6\u0026plusmn;11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e75.6\u0026plusmn;11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003ePulse (beat per minute)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e72.5\u0026plusmn;10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e71.9\u0026plusmn;11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e73.2\u0026plusmn;11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e72.6\u0026plusmn;9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e73.1\u0026plusmn;10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e23.4\u0026plusmn;3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e23.0\u0026plusmn;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e21.8\u0026plusmn;3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e24.3\u0026plusmn;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e22.9\u0026plusmn;4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 973px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e14.4\u0026plusmn;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e15.2\u0026plusmn;2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e15.0\u0026plusmn;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e13.6\u0026plusmn;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e13.7\u0026plusmn;2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eTriglyceride (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e133.7\u0026plusmn;104.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e129.8\u0026plusmn;112.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e113.0\u0026plusmn;81.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e141.1\u0026plusmn;101.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e136.4\u0026plusmn;103.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e193.5\u0026plusmn;38.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e189.7\u0026plusmn;36.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e182.7\u0026plusmn;35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e197.9\u0026plusmn;38.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e199.5\u0026plusmn;38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e116.7\u0026plusmn;34.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e114.0\u0026plusmn;34.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e108.6\u0026plusmn;32.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e120.0\u0026plusmn;34.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e120.9\u0026plusmn;35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e51.1\u0026plusmn;15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e50.5\u0026plusmn;16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e52.1\u0026plusmn;16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e50.9\u0026plusmn;14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e52.9\u0026plusmn;14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eeGFR (ml/min/1.72m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e80.0\u0026plusmn;26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e81.0\u0026plusmn;24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e74.3\u0026plusmn;23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e83.5\u0026plusmn;28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e69.1\u0026plusmn;25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eFBG (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e110.0\u0026plusmn;35.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e110.4\u0026plusmn;36.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e110.4\u0026plusmn;32.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e110.2\u0026plusmn;37.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e108.0\u0026plusmn;26.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5.3\u0026plusmn;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5.2\u0026plusmn;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5.2\u0026plusmn;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5.3\u0026plusmn;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5.2\u0026plusmn;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eC-reactive protein (mg/l)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.0 (0.6, 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.1 (0.6, 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.1 (0.6, 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.0 (0.5, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.1 (0.5, 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 973px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-reported comorbidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eHypertension, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2571 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e947 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e190 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1072 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e362 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eDiabetes mellitus, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e578 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e207 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e36 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e267 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e68 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eDyslipidemia, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e973 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e379 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e55 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e429 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e110 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eStroke, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e233 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e81 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e26 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e92 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e34 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eHeart disease, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1271 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e402 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e93 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e579 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e197 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eLiver disease, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e416 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e196 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e39 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e145 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e36 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eLung disease or asthma, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1453 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e626 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e185 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e451 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e191 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eKidney disease, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e711 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e310 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e72 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e261 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e68 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eMemory-related disease, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e183 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e71 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e27 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e52 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e33 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eStomach or digestive disease, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2383 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e853 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e188 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1043 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e299 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eMalignant tumor, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e108 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e38 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e56 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e10 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eArthritis and rheumatism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3599 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1227 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e318 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1548 (37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e506 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003ePsychiatric disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e197 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e48 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e32 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e77 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e40 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eAge-adjusted Charlson Comorbidity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.7\u0026plusmn;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.6\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3.0\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.4\u0026plusmn;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3.4\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eNumber of comorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.5\u0026plusmn;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.4\u0026plusmn;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.4\u0026plusmn;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.5\u0026plusmn;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.8\u0026plusmn;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; HbA1c, hemoglobin A1c.\u003c/p\u003e\n\u003cp\u003e* Present as median (interquartile range)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. The weighted association between social isolation and all-cause mortality among overall participants and by gender.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"977\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial isolation status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen vs men: relative IRR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases/Total (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted IRR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases/Total (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted IRR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases/Total (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted IRR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer 1-social isolation score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1172/9513 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.29 (1.21, 1.37)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e714/4588 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.21 (1.11, 1.32)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e458/4925 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.43 (1.31, 1.56)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.23 (1.09, 1.37)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e297/3576 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e215/1780 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e82/1796 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e503/3990 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.45 (1.24, 1.71)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e307/1964 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1.19 (0.98, 1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e196/2026 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.06 (1.55, 2.73)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.43 (1.20, 1.71)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e258/1383 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.39 (1.97, 2.89)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e129/584 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.89 (1.47, 2.44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e129/799 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.51 (2.54, 4.84)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.01 (1.51, 2.67)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e92/450 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.90 (1.44, 2.52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e52/209 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.57 (1.07, 2.30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e40/241 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.99 (1.97, 4.54)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.86 (1.22, 2.83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e22/114 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.84 (1.24, 2.73)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e11/51 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1.44 (0.85, 2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e11/63 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.91 (1.53, 5.53)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.90 (1.05, 3.44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-social isolation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e800/7566 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e522/3744 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e278/3822 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial isolation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e372/1947 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.76 (1.52, 2.04)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e192/844 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.60 (1.31, 1.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e180/1103 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.05 (1.65, 2.53)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.35 (1.03, 1.79)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIRR, incidence rate ratio; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003eThe numbers in \u003cstrong\u003ebold font\u003c/strong\u003e indicate significance.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Social isolation, Gender differences, Female, Comorbidity, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-5452833/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5452833/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSocial isolation has been a major public health issue associated with increased mortality. However, gender differences in social isolation have not been thoroughly characterized. This study aimed to estimate the gender differences in the trends in the prevalence of social isolation, evaluate the gender-based differences in its comorbid burden, and examine their subsequent associated mortality by gender.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis nationwide cross-sectional and prospective cohort study used data from the China Health and Retirement Longitudinal Study. Social isolation was measured using 4 dichotomized indicators. The Cochran-Armitage trend test and multivariate Poisson regression models were constructed to analyze the trends in social isolation and the longitudinal associations between social isolation and mortality by gender. All analyses were weighted to account for the multistage, probability-proportional-to-size sampling scheme.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 10197 participants, the mean age was 60.0 years, and 48.1% were men. The prevalence of social isolation was 20.8%, with an average age-adjusted Charlson Comorbidity Index (ACCI) of 2.7 (\u0026plusmn;\u0026thinsp;1.8) and 1.5 (\u0026plusmn;\u0026thinsp;1.4) comorbidities. A significant downward trend in social isolation was observed in men, with a weighted prevalence of 19.4% (95% confidence interval (CI): 17.7, 21.3) in 2011 and 14.1% (95% CI: 12.9, 15.4) in 2018 (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, a stable trend in social isolation was noted in women, with a weighted prevalence of 24.0% (95% CI: 22.5, 25.6) in 2011 and 24.1% (95% CI: 22.7, 25.6) in 2018 (P for trend\u0026thinsp;=\u0026thinsp;0.154). A steeper increase in ACCI and number of comorbidities was observed in women compared to men. (P for gender-by-social isolation score interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001) Over a 9-year follow-up period, females with social isolation had more than double the risk of mortality (incidence rate ratio (IRR): 2.05, 95% CI: 1.65, 2.53), while males with social isolation had only a 60% increased IRR (95% CI: 1.31, 1.95) of all-cause mortality (P for interaction\u0026thinsp;=\u0026thinsp;0.032).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSeveral gender differences in social isolation were observed, including the higher prevalence, heavier comorbid burdens, and a more prominent impact on mortality noted in women, highlighting the importance of enhancing family and social support for older adults, particularly in improving the socioeconomic statuses and rights of women.\u003c/p\u003e","manuscriptTitle":"Trends in prevalence, associated comorbid burden, and subsequent mortality of social isolation: A gender perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 10:17:01","doi":"10.21203/rs.3.rs-5452833/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2ca8d08e-ba02-4caa-a55b-f50c7d8609c7","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-31T15:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-18 10:17:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5452833","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5452833","identity":"rs-5452833","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}