Longitudinal Assessment of Hip Fractures-Diabetes Causality in Aging Populations: Evidence from the China Health and Retirement Longitudinal Study Cohort

Longitudinal Assessment of Hip Fractures-Diabetes Causality in Aging Populations: Evidence from the China Health and Retirement Longitudinal Study Cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Longitudinal Assessment of Hip Fractures-Diabetes Causality in Aging Populations: Evidence from the China Health and Retirement Longitudinal Study Cohort Yutong Lin, Frank F. Huang, Jiacong Xiao, Xingling Chen, Yuchen Wu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6803079/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective: To examine the longitudinal impact of hip fractures on the risk of diabetes in middle-aged and older individuals, and investigate the mediating effects of depression, sleep duration, and self-medication with traditional herbal medicines and tonic health supplements. Methods: Data from the China Health and Retirement Longitudinal Study (10,280 participants, aged 45+) were analyzed over a 7-year follow-up. Subgroup analyses were conducted by gender, age, and body mass index. After adjusting for relevant confounders, the Cox proportional hazards model was used to assessed the association between hip fractures and diabetes risk. Linear regression models were employed to conduct mediation analyses. Structural equation model was applied for sensitivity analysis. Results: At the 2011 baseline, 152 participants (1.48%) had a history of hip fractures. By 2018, 559 participants (5.44%) developed diabetes, including 10 (1.79%) individuals with prior hip fractures. Hip fractures were significantly associated with increased diabetes risk in males (HR = 2.43; 95% CI: 1.19, 4.96; P = 0.01) and obese participants (HR = 3.77; 95% CI: 1.42, 10.02; P < 0.01). No significant causal association was observed between hip fractures and diabetes in the overall population or in subgroups defined by female, age, or BMI < 28. Self-medication with herbal medicines and tonic supplements may partially explain the increased diabetes risk among obese individuals with hip fractures, while depression and sleep duration showed no significant mediating effects. Conclusion: Hip fractures were significantly associated with an increased risk of diabetes among middle-aged and older males, as well as individuals with obesity. To mitigate this risk, it was recommended to standardize the use of traditional herbal medicines and tonic health supplements under the guidance of healthcare professionals. hip fractures diabetes self-medication obesity herbal medicine Figures Figure 1 Figure 2 Figure 3 Figure 4 Key Points Males and obese individuals with hip fractures experience an increased risk of developing diabetes. Self-medication with traditional herbal medicines and tonic supplements may increase diabetes risk in obese individuals with hip fractures. Standardized use of herbal medicines and supplements is recommended under healthcare supervision to reduce diabetes risk. 1. Introduction Hip fractures are common among middle-aged and older populations. These fractures are linked to high rates of disability and mortality [ 1 , 2 ], as well as a range of complications during subsequent treatment, such as pulmonary infections, venous thrombosis, myocardial infarction, and renal dysfunction [ 3 – 5 ]. Most current studies have primarily focused on acute complications, such as infections, thrombosis [ 6 , 7 ].However, some studies have indicated that individuals with hip fractures may experience endocrine system disturbances due to prolonged bed rest, metabolic changes, and stress responses, thereby increasing the risk of developing diabetes [ 8 , 9 ]. Although research in this area remains insufficient, preliminary evidence suggests that post-fracture abnormalities in glucose metabolism may be an important consideration in the long-term health management of hip fractures [ 10 ]. Therefore, a thorough investigation into the intrinsic causality relationship between hip fractures and diabetes is of significant importance for the early identification of high-risk individuals and the implementation of timely prevention and interventions. In recent years, the role of traditional Chinese medicine and health supplements in the prevention of chronic diseases has attracted increasing attention [ 11 , 12 ]. Some studies have reported that these interventions may exert a protective effect against diabetes by modulating immune function and improving insulin sensitivity [ 13 , 14 ]. In addition, depressive symptoms and sleep duration have been considered as independent risk factors for diabetes. Depressive symptoms may increase the risk of diabetes through altered hormone secretion [ 15 ], while both excessively long and short sleep durations significantly elevate the risk of type 2 diabetes by affecting metabolic processes [ 16 ]. Given that these factors may play important mediating roles in the development of diabetes following hip fractures, the present study intended to examine them as potential mediators to explore the impact of multifactorial interactions on disease mechanisms. To comprehensively explore the relationship between hip fractures and diabetes, as well as to examine the influence of various mediating factors, this study conducted a retrospective longitudinal analysis of middle-aged and older adults in China using nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS). Additionally, we incorporated variables such as self-medication with traditional herbal medicines, tonic health supplements, depressive symptoms, and sleep duration to analyze their potential mediating roles in this relationship. By investigating the associations between hip fractures, and diabetes, we aimed to provide new theoretical insights for clinical interventions and long-term health management. 2. Methods 2.1. Study Design This study was a longitudinal analysis based on data from CHARLS and followed the guidelines of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [ 17 ]. CHARLS, initiated in 2011, aims to investigate the social, economic, and health status of the Chinese population aged 45 and older, with follow-up surveys conducted biennially. The study covers 28 provinces, 150 counties, and 450 communities, recruiting 17,708 participants. As an open public database, CHARLS is freely accessible to the public, and its detailed study design has been described in the relevant literature [ 18 ]. Additionally, the ethical approval for CHARLS was obtained from the Institutional Review Board (IRB) of Peking University (approval number: IRB00001052-11015), and written informed consent was obtained from all participants prior to their enrollment in the study [ 18 ]. 2.2. Participants This study used the CHARLS data from 2011 to 2018, covering a follow-up period of seven years. The 2011 survey included 17,596 participants. The exclusion criteria were: (1) participants who had diabetes at baseline; (2) participants with missing data on diabetes, hip fractures, follow-up, or mediators; (3) participants younger than 45 years. For variables with random missing data (height, weight, and chronic disease history), multiple imputation [ 19 ] was used to fill in the missing values. 2.3. Exposure The hip fracture event was defined as a hip fracture that occurred before the baseline survey. In the baseline survey household interviews, each participant was asked, “Have you ever fractured your hip?”[ 20 , 21 ] To assist participants in understanding the concept of hip fractures, the interviewer provided a detailed description of the hip’s location. If a participant responded affirmatively, it was interpreted as an indication that they had experienced a hip fracture. 2.4. Outcomes In both the baseline and follow-up surveys, all participants were asked whether they had ever been diagnosed with diabetes by a doctor, as well as the timing of their first diagnosis. In accordance with previous studies [ 22 ], a diabetes event was defined as a participant who, at the baseline survey, had not been diagnosed with diabetes or elevated blood glucose by a doctor, but was diagnosed with diabetes during the follow-up survey in 2018. 2.5. Covariates Known risk factors for hip fractures and diabetes were extracted from the China Health and Retirement Longitudinal Study (CHARLS) and included as covariates [ 20 , 23 , 24 ]. Socio-demographic factors included age, gender, height, weight, education level, and marital status. Among these, height and weight data were used to calculate Body Mass Index (BMI), which is determined by dividing weight (in kilograms) by the square of height (in meters). Lifestyle factors comprised smoking and alcohol consumption. Physical health factors include self-reported history of chronic diseases and falls. The assessment of fall events was based on participants’ self-reported responses to the question: “Have you fallen in the past two years?”[ 25 ] In addition, an assessment of physical function was also incorporated, including balance ability assessment and physical strength assessment. The specific evaluation method could be found in the Additional File 1. 2.6. Mediators Self-medication with tonic health supplements, self-medication with traditional herbal medicines, depression status, and sleep duration were selected as the mediators. Information regarding the use of traditional herbal medicines and health supplements was obtained through the question, “How did you treat yourself during the past month?” Notably, this question emphasized the exclusion of prescription medications, thereby focusing on self-medication practices rather than the use of health supplements and traditional herbal medicines under a doctor's guidance. Depression status, including the presence and severity of depressive symptoms, was assessed using the question, “Overall in the last month, how much of a problem did you have with feeling sad, low, or depressed?” Lunch nap duration and nighttime sleep duration were obtained through the interview questions: “During the past month, how long did you take a nap after lunch?” and “During the past month, how many hours of actual sleep did you get at night (average hours for one night)? (This may be shorter than the number of hours you spend in bed).” Referring to commonly used classifications for sleep duration in clinical studies of middle-aged and older adults [ 26 , 27 ], nap duration was categorized into four groups: 0 minutes, 1–30 minutes, 31–90 minutes, and ≥ 90 minutes. Nighttime sleep duration and total sleep duration (the sum of nap and nighttime sleep) were each categorized into five groups: 9 hours [ 26 , 27 ]. 2.7. Subgroup analysis The participants were divided into subgroups based on BMI, age, and gender for subgroup analysis. According to the BMI classification standard for the Chinese population [ 28 ], the study population was divided into four subgroups: underweight (BMI < 18.5), normal weight (BMI 18.5–23.9), overweight (BMI 24.0-27.9), and obesity (BMI ≥ 28.0). In addition, participants were classified into four age groups: 45–59 years, 60–69 years, 70–79 years, and 80 years and older [ 29 ]. 2.8. Directed Acyclic Graph Analysis Directed Acyclic Graph (DAG) analysis was performed to control the potential confounding or moderating effects of certain variables on the relationship between hip fractures and diabetes [ 30 , 31 ]. We hypothesized a causal relationship between hip fractures and diabetes, and selected risk factors as variables for the DAG model based on relevant international disease guidelines or consensus and previous research [ 32 , 33 ]. Using the web-based software DAGitty ( http://www.dagitty.net ), directed paths were constructed and the minimal sufficient adjustment set (MSAS) was visually presented. In subsequent model fitting and analysis, these MSAS variables were controlled for to reduce bias, enhancing the transparency and precision of the analysis [ 34 ]. 2.9. Statistical Analysis The Cox proportional hazards model [ 35 ], linear regression model [ 36 ], and structural equation model (SEM) [ 37 ]were employed for data analysis. All confounding factors identified by the DAG were adjusted for in the models, except for physical inactivity, which was excluded due to significant missing data. First, the Cox model was used to test whether there was a causal relationship between hip fractures and diabetes risk for all participants and within each subgroup. If the Hazard Ratio (HR) was greater than 1.00 and the p-value was less than 0.05, further mediation analysis was conducted using the linear regression to explain the underlying relationship between these two variables [ 38 ]. The proportional hazards assumption was assessed using the Schoenfeld residuals test [ 39 ]. Finally, the SEM approach was performed for sensitivity analysis to confirm the robustness of the results. Continuous variables were presented as the mean and standard deviation (SD), while categorical variables were presented as the proportion. Statistical analyses were performed using R software version 4.2.2, with P < 0.05 considered statistically significant. 3. Results 3.1. Participant Characteristics A total of 10,280 individuals (Age: 58.53 ± 8.75 years; BMI: 23.20 ± 4.36 kg/m 2 ; Male: n = 4,880 (47.47%)) were included in the analysis (Fig. 1 ). A total of 152 participants (1.48%) had a history of hip fractures, and 559 cases (5.44%) of new-onset diabetes were reported. Additionally, participants with a history of hip fractures were more likely to be older and may have a higher prevalence of chronic conditions, including chronic lung diseases, stomach or other digestive disorders, emotional or psychiatric disorders, memory diseases, and asthma (Table 1). 3.2. Longitudinal Causal Analysis According to the results derived from the DAG analysis (Fig. 2 ), several potential confounding factors were identified, including age, gender, BMI, falls, smoking, lack of physical activity, grip strength, balance ability, chair rise test, and other chronic diseases. The results of the Cox model analysis (Fig. 3 ) for all participants indicated that no statistically significant causal relationship was found between hip fractures and diabetes. In the female subgroup, all age subgroups, and all BMI < 28 subgroups, no significant causal association was found between hip fractures and diabetes. However, participants with a history of hip fractures had a significantly increased probability of developing diabetes in male (HR = 2.43; 95% CI: 1.19 to 4.96; P = 0.01) and obesity (HR = 3.77; 95% CI: 1.42 to 10.02; P < 0.01) subgroups (Fig. 4 ). The results of the Schoenfeld residuals test show that the p-values for all variables were greater than 0.05, indicating no statistically significant evidence of a violation of the proportional hazards assumption. Therefore, the constructed Cox model demonstrated acceptable applicability and robustness in this study, and the analytical results were considered reliable. Detailed information could be found in Additional file 1: Tables S1 to S12. 3.3. Mediation Analysis In the male and obesity subgroups, the indirect effects of self-medication with tonic health supplements, traditional herbal medicines, depressive mood, nighttime sleep duration, nap duration, and total sleep duration were not significant (all P > 0.05; Additional file 1: Table S13). 3.4. Sensitivity Analysis The results of the SEM model show that, in the male subgroup, participants with a history of hip fractures had an estimated value (EST) of 0.07 for the likelihood of developing diabetes (95% CI: 0.03 to 0.10; P < 0.01). Although the results suggest that some mediator variables have statistical significance between hip fractures and diabetes, these positive results were excluded due to the poor model fit in the male subgroup. In the obesity subgroup (BMI ≥ 28.0), participants with a history of hip fractures had an EST of 0.33 for the likelihood of developing diabetes (95% CI: 0.16 to 0.50; P < 0.01). Additionally, the results for the obesity subgroup showed that hip fractures had a significant impact on self-medication with tonic health supplements (EST = 0.14; 95% CI: 0.04 to 0.23; P = 0.01) and traditional herbal medicines (EST = 0.19; 95% CI: 0.09 to 0.29; P < 0.01), and traditional herbal medicines also had a significant effect on diabetes (EST = 0.07; 95% CI: 0.01 to 0.13; P = 0.03). The results for the remaining four mediator variables were not significant. Detailed model information was provided in Additional file 1: Tables S14 to S17. 4. Discussion This study is the first longitudinal cohort study with a follow-up period of seven years to investigate the causality between hip fractures and diabetes among middle-aged and older adults in China. Our findings indicate that a history of hip fractures was associated with diabetes among middle-aged and older males, as well as individuals with obesity in China. However, no significant causal association was observed between hip fractures and diabetes in the overall population, nor within the subgroups defined by female, age, and BMI < 28. Further mediation analysis revealed that hip fractures might lead to inappropriate use of tonic health supplements and traditional herbal medicines, thereby increasing the risk of developing diabetes. The entire sample did not show significant causal relationships, which may be primarily attributable to substantial heterogeneity within the sample. This heterogeneity likely resulted in null or even inverse effects in low-risk groups, thereby diluting the significant positive association observed in specific high-risk subgroups (e.g., males and obese individuals) [ 40 , 41 ]. Furthermore, factors such as gender and BMI may act as effect modifiers for diabetes risk following hip fractures [ 42 ], rendering these particular subgroups more susceptible to endocrine and metabolic abnormalities. In the male subgroup, the increased risk of diabetes following hip fractures may be related to changes in endocrine hormone levels and lifestyle modifications. After a hip fracture, the decline in testosterone levels [ 43 ], significant reduction in physical activity levels, weight gain, and lifestyle changes in males may exacerbate insulin resistance, thereby increasing the risk of developing diabetes. In contrast, women typically place greater emphasis on health management [ 44 , 45 ], which may help alleviate metabolic burdens and prevent similar trends. Gender differences in the systemic inflammatory response following a fracture could also play a role, as a more pronounced inflammatory response in males may further contribute to the development of diabetes [ 46 ]. Additionally, significant differences in drinking and smoking behaviors between males and females may influence the incidence of diabetes [ 47 , 48 ]. This gender difference can also be explained by unique physiological phenomena in women: hormonal changes during pregnancy contribute to the higher incidence of diabetes, and postmenopausal osteoporosis leads to a higher incidence of fractures in women (1.5–3 times higher than in men) [ 49 ], which may directly influence the complex relationship between hip fractures and diabetes, potentially masking the underlying causal connection. Notably, literature exploring gender differences in this context is scarce [ 50 , 51 ]. Therefore, further research is crucial to clarify the exact biological mechanisms behind this association and the gender differences involved. Compared to obese individuals, those with a lower BMI generally did not exhibit significant glucose metabolism disorders after hip fractures. This is ascribe that individuals with lower BMI typically have better insulin sensitivity, lower baseline inflammation levels, and more stable metabolic balance [ 52 ], which reduces the likelihood of glucose metabolism disturbances following a hip fracture. In contrast, obesity is associated with chronic low-grade inflammation, which promotes the development of both type 1 and type 2 diabetes [ 53 ]. Following a hip fracture, the increase in inflammatory factors, fluctuations in bone metabolism hormones (such as calcitonin and parathyroid hormone), and decreased physical activity may all reduce insulin sensitivity and affect metabolic balance [ 54 ]. Additionally, hip fractures, as major trauma, can trigger intense stress responses, leading to increased levels of stress hormones (such as cortisol), which further interfere with blood glucose regulation. Studies have shown that hip fractures can induce stress hyperglycemia, increasing the risk of diabetes [ 8 ]. In obese populations, these factors may act synergistically, significantly raising the risk of developing diabetes. Although the study did not find a significant causal association across age groups, this does not mean that there is no potential link between hip fractures and diabetes in different age groups. Research has shown that age is an important factor for diabetes risk, with the prevalence of diabetes and prediabetes significantly higher in the older group compared to the middle-aged group [ 55 ]. Moreover, as age increases, the risks of diabetes and prediabetes become more pronounced, with factors such as overweight and obesity, family history of diabetes, and high cholesterol contributing to this increased risk [ 55 ]. Based on the aforementioned analysis of the factors contributing to the increased diabetes risk after hip fractures, we believe that even in the absence of significant causal statistical associations, the metabolic health of older hip fracture participants should still be closely monitored, particularly about diabetes prevention and early intervention. However, some studies contradict our findings. A retrospective cohort study involving 46,628 participants aged 65 and above in Hong Kong [ 56 ] found that the risk of developing diabetes was significantly lower in the hip fracture group. This phenomenon may be related to differences in the study population and design. Compared to participants in mainland China, those in Hong Kong may have received more stringent health management and medical interventions after their fractures [ 57 , 58 ], which could have reduced the risk of diabetes. More prospective studies are needed in the future to further clarify the bidirectional relationship between fractures and diabetes and its potential biological mechanisms, to guide health intervention strategies for specific populations. Regarding mediating factors, previous studies have indicated that self-medication among middle-aged and older individuals is associated with high risks and poor quality [ 59 ]. This provides a theoretical basis for further exploring the tendency of middle-aged and older fracture patients to rely on tonic health supplements and traditional herbal medicines for self-regulation during their recovery process. On one hand, although many tonic health supplements and traditional herbal medicines are promoted as capable of improving diabetes, most lack support from large-scale randomized controlled trials. Improper use of these products may disrupt insulin receptor signaling, reduce insulin sensitivity, and exacerbate insulin resistance [ 60 , 61 ]. On the other hand, the active or hormonal components in traditional herbal medicines or supplements may directly damage pancreatic β -cells, thereby triggering diabetes [ 62 , 63 ]; some traditional herbal medicines components may further reduce insulin sensitivity or induce insulin resistance by modulating gut microbiota, bile acid metabolism, short-chain fatty acid synthesis, and inflammation levels [ 64 ]. We speculate that the self-medication behavior of elderly fracture patients, who show a marked preference for traditional Chinese medicine [ 65 ], is closely associated with the improper use of tonic health supplements and traditional herbal medicines, which may collectively contribute to an increased incidence of diabetes. In this study, we found that patients with hip fractures frequently engage in the inappropriate self-administration of traditional Chinese herbal medicines and health supplements during their rehabilitation process, a phenomenon particularly prevalent in China, where traditional Chinese medicine culture is deeply rooted. In Chinese traditional culture, traditional Chinese medicine is widely accepted and highly trusted, and many elderly patients tend to use traditional Chinese herbal medicines and health supplements for self-regulation and wellness [ 66 – 68 ]. This cultural background may lead patients to take these substances indiscriminately without professional guidance, thereby increasing the risk of adverse effects and drug interactions [ 66 , 69 ]. Additionally, the traditional Chinese health preservation concept emphasizes the principle of “homology of medicine and food” [ 70 ]. Many middle-aged and older individuals tend to take traditional Chinese medicine on their own in daily life to regulate their health. This belief may lead fracture patients to actively seek self-treatment with herbal medicines. However, if such practices lack scientific evidence and proper regulation, they may result in improper medication use, increasing the burden on the body. 5. Study Strengths and Limitations The study offers the following advantages: First, it is a large-scale, population-based longitudinal study covering 150 county-level units in China, with a representative sample of middle-aged and older individuals nationwide, ensuring the generalizability and long-term stability of the results. Rigorous multi-stage probability sampling and long-term follow-up data enhance the credibility of causal inference and provide a solid foundation for chronic disease risk assessment, making the results more broadly applicable compared to hospital-based studies. Second, this study is the first to explore the relationship between hip fractures and diabetes risk in middle and older adults, and innovatively incorporates self-reported use of tonic health supplements, traditional herbal medicines, depressive symptoms, and sleep duration in the analysis. Previous orthopedic studies have primarily focused on acute complications following fractures, such as infections, thrombosis, and functional recovery, with limited attention given to the systemic endocrine and metabolic disruptions that fractures may cause. Diabetes, as a complication of fractures, may delay fracture healing and increase the risk of re-fractures. Therefore, exploring the relationship between hip fractures and diabetes is crucial for optimizing long-term prognosis. However, the study also has some limitations. First, the assessment of hip fractures relies on self-reporting, and the participants’ relatively low educational level may affect the accuracy of these reports. Second, fractures may lead to bed rest or prolonged hospitalization, introducing selection bias. Third, the CHARLS did not distinguish specific types of diabetes [ 71 ]. Third, the survey data does not include detailed information on post-fracture treatment and rehabilitation, limiting the understanding of how different post-fracture conditions influence changes in diabetes risk. Future research should be based on more comprehensive and detailed clinical data to further investigate the causal relationship between hip fractures and diabetes in this population. 6. Conclusions In this seven-year longitudinal study based on a Chinese population, hip fractures were found to be associated with an increased prevalence of diabetes among middle-aged and older males, as well as individuals with obesity. Factors such as tonic health supplements and traditional herbal medicines may serve as mediating factors in this relationship. A deeper understanding of the relationship between hip fractures and diabetes could provide valuable insights for clinical practice and public health policies, facilitating the proactive prevention of complications arising from hip fractures, regulating patients' self-medication behaviors, and ultimately improving the long-term health outcomes of middle-aged and older patients with hip fractures. However, further research is necessary to investigate the impact of fractures on diabetes prevalence, as well as to conduct multicenter longitudinal studies in other countries and regions to validate the findings. Declarations Ethics approval and consent to participate CHARLS was ethically approved by the Institutional Review Board at Peking University (00001052–11015). Consent for publication Not applicable. Data availability The data supporting the findings of this study are publicly available on the website of the China Health and Retirement Longitudinal Study (CHARLS) at http://charls.pku.edu.cn/. Competing interests The authors declare that they have no competing interests Funding Not applicable. Authors’ contributions YL, JX and FFH were responsible for organizing the data information of CHARLS database participants and performed data analysis using software. DKYZ and JRC optimized the research methodology and verified the research conclusions. YL, FFH, and XC were the primary contributors to manuscript writing and supplementary materials preparation. YL and YW were responsible for creating research result tables and data visualization. HW and JH participated in manuscript review. All authors read and approved the final manuscript. Footnotes Not applicable. Clinical trial number Not applicable. References Kanis JA, Odén A, McCloskey EV, et al. 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The complications of male hypogonadism: is it just a matter of low testosterone? Front Endocrinol (Lausanne). 2023;14:1201313. Deeks A, Lombard C, Michelmore J, et al. The effects of gender and age on health related behaviors. BMC Public Health. 2009;9:213. Bärebring L, Palmqvist M, Winkvist A, et al. Gender differences in perceived food healthiness and food avoidance in a Swedish population-based survey: a cross sectional study. Nutr J. 2020;19:140. Osipov B, Emami AJ, Christiansen BA. Systemic Bone Loss After Fracture. Clin Rev Bone Min Metab. 2018;16:116–30. Acheson A, Mahler SV, Chi H, et al. Differential effects of nicotine on alcohol consumption in men and women. Psychopharmacology. 2006;186:54–63. Stockton MC, McMahon SD, Jason LA. Gender and smoking behavior in a worksite smoking cessation program. Addict Behav. 2000;25:347–60. Rinonapoli G, Ruggiero C, Meccariello L, et al. Osteoporosis in Men: A Review of an Underestimated Bone Condition. Int J Mol Sci. 2021;22:2105. Ciarambino T, Crispino P, Leto G, et al. Influence of Gender in Diabetes Mellitus and Its Complication. Int J Mol Sci. 2022;23:8850. Kautzky-Willer A, Harreiter J, Pacini G. Sex and Gender Differences in Risk, Pathophysiology and Complications of Type 2 Diabetes Mellitus. Endocr Rev. 2016;37:278–316. Smith GI, Mittendorfer B, Klein S. Metabolically healthy obesity: facts and fantasies. J Clin Invest. 2019;129:3978–89. Tsalamandris S, Antonopoulos AS, Oikonomou E, et al. The Role of Inflammation in Diabetes: Current Concepts and Future Perspectives. Eur Cardiol. 2019;14:50–9. Beloosesky Y, Hendel D, Weiss A, et al. Cytokines and C-reactive protein production in hip-fracture-operated elderly patients. J Gerontol Biol Sci Med Sci. 2007;62:420–6. Bellary S, Kyrou I, Brown JE, et al. Type 2 diabetes mellitus in older adults: clinical considerations and management. Nat Rev Endocrinol. 2021;17:534–48. Krishnamoorthy S, Tang CT-L, Hsu WW-Q, et al. Hip fracture is associated with a reduced risk of type 2 diabetes: A retrospective cohort study. Osteoporos Sarcopenia. 2024;10:60–5. Maw K-CC, Lo S-V, Leung P-Y. Integrating Medical and Social Support for Elderly in Hong Kong - System and Technology Enabled Service Innovations. World Hosp Health Serv. 2017;53:7–10. Wong RMY, Ho WT, Wai LS, et al. Fragility fractures and imminent fracture risk in Hong Kong: one of the cities with longest life expectancies. Arch Osteoporos. 2019;14:104. Chang J, Wang Q, Fang Y. Socioeconomic differences in self-medication among middle-aged and older people: data from the China health and retirement longitudinal study. BMJ Open. 2017;7:e017306. Forouhar E, Sack P. Non-traditional therapies for diabetes: fact or fiction. J Community Hosp Intern Med Perspect. 2012;2. 10.3402/jchimp.v2i2.18447 . Kiani AK, Donato K, Dhuli K, et al. Dietary supplements for polycystic ovary syndrome. J Prev Med Hyg. 2022;63:E206–13. Hassen G, Belete G, Carrera KG, et al. Clinical Implications of Herbal Supplements in Conventional Medical Practice: A US Perspective. Cureus. 2022;14:e26893. Wang Z, Wang J, Chan P. Treating type 2 diabetes mellitus with traditional chinese and Indian medicinal herbs. Evid Based Complement Alternat Med. 2013;2013:343594. Zhang L, Chu J, Hao W, et al. Gut Microbiota and Type 2 Diabetes Mellitus: Association, Mechanism, and Translational Applications. Mediators Inflamm. 2021;2021:5110276. Wang Y-C, Chiang J-H, Hsu H-C, et al. Decreased fracture incidence with traditional Chinese medicine therapy in patients with osteoporosis: a nationwide population-based cohort study. BMC Complement Altern Med. 2019;19:42. Dong X, Bergren SM, Chang E-S. Traditional Chinese Medicine Use and Health in Community-Dwelling Chinese-American Older Adults in Chicago. J Am Geriatr Soc. 2015;63:2588–95. Zhang H, Zhang Y, Yan Y, et al. Traditional Chinese medicine health literacy among rural older adults: a cross-sectional study. Front Public Health. 2024;12:1361572. Liang W, Lee AH, Binns CW. Dietary supplementation by older adults in southern China: a hospital outpatient clinic study. BMC Complement Altern Med. 2009;9:39. Melchart D, Hager S, Albrecht S, et al. Herbal Traditional Chinese Medicine and suspected liver injury: A prospective study. World J Hepatol. 2017;9:1141–57. Chen J. Essential role of medicine and food homology in health and wellness. Chin Herb Med. 2023;15:347–8. Buzzetti R, Tuomi T, Mauricio D, et al. Management of Latent Autoimmune Diabetes in Adults: A Consensus Statement From an International Expert Panel. Diabetes. 2020;69:2037–47. Tables Table.1 The baseline characteristics of the study population in the China Health and Retirement Longitudinal Study (CHARLS). Variables Total (n = 10,280) No hip-fracture (n = 10,128) Hip fracture (n = 152) t/x 2 value P -value Age, mean ± SD 58.53 ± 8.75 58.50 ± 8.73 60.26 ± 9.57 -2.253 0.026 Weight, mean ± SD 58.22 ± 12.78 58.23 ± 12.78 57.46 ± 12.81 0.736 0.463 Height, mean ± SD 158.16 ± 8.85 158.18 ± 8.85 156.79 ± 9.27 1.836 0.068 Body mass index, mean ± SD 23.20 ± 4.36 23.20 ± 4.36 23.33 ± 4.29 -0.371 0.711 Gender, n (%) 0.358 0.550 Male 4880 (47.47) 4812 (47.51) 68 (44.74) Female 5400 (52.53) 5316 (52.49) 84 (55.26) Education, n (%) 3.352 0.327 Elementary school or below 6914 (67.26) 6804 (67.18) 110 (72.37) Middle school 2172 (21.13) 2141 (21.14) 31 (20.39) High school or vocational school 1020 (9.92) 1010 (9.97) 10 (6.58) College or above 174 (1.69) 173 (1.71) 1 (0.66) Marriage, n (%) 5.240 0.062 Single 73 (0.71) 71 (0.71) 2 (1.32) Married 9170 (89.2) 9043 (89.29) 127 (83.55) Divorced or widowed or others 1037 (10.09) 1014 (10.01) 23 (15.13) Drinking, n (%) 2.019 0.365 Current drinking 3256 (31.67) 3201 (31.61) 55 (36.18) Ever drinking 769 (7.48) 756 (7.46) 13 (8.55) Never drinking 6255 (60.85) 6171 (60.93) 84 (55.26) Smoking, n (%) 4.042 0.133 Current smoking 3262 (31.73) 3222 (31.81) 40 (26.32) Ever smoking 786 (7.65) 769 (7.59) 17 (11.18) Never smoking 6232 (60.62) 6137 (60.59) 95 (62.50) Hypertension, n (%) 0.743 0.389 No 8130 (79.09) 8005 (79.04) 125 (82.24) Yes 2150 (20.91) 2123 (20.96) 27 (17.76) Dyslipidemia, n (%) 2.929 0.087 No 9532 (92.72) 9397 (92.78) 135 (88.82) Yes 748 (7.28) 731 (7.22) 17 (11.18) Cancer, n (%) 0.054 1 No 10,195 (99.17) 10,044 (99.17) 151 (99.34) Yes 85 (0.83) 84 (0.83) 1 (0.66) Chronic lung diseases, n (%) 4.944 0.026 No 9353 (90.98) 9223 (91.06) 130 (85.53) Yes 927 (9.02) 905 (8.94) 22 (14.47) Heart disease, n (%) 0.150 0.698 No 9195 (89.45) 9061 (89.46) 134 (88.16) Yes 1085 (10.55) 1067 (10.54) 18 (11.84) Stroke, n (%) 2.772 0.099 No 10,115 (98.39) 9968 (98.42) 147 (96.71) Yes 165 (1.61) 160 (1.58) 5 (3.29) Liver disease, n (%) 1.701 0.192 No 9907 (96.37) 9764 (96.41) 143 (94.08) Yes 373 (3.63) 364 (3.59) 9 (5.92) Stomach or other digest disease, n (%) 7.075 0.008 No 7925 (77.09) 7822 (77.23) 103 (67.76) Yes 2355 (22.91) 2306 (22.77) 49 (32.24) Kidney disease, n (%) 2.582 0.108 No 9679 (94.15) 9541 (94.20) 138 (90.79) Yes 601 (5.85) 587 (5.80) 14 (9.21) Emotional or psychiatric disease, n (%) 10.989 0.007 No 10,164 (98.87) 10,018 (98.91) 146 (96.05) Yes 116 (1.13) 110 (1.09) 6 (3.95) Arthritis or rheumatism, n (%) 3.202 0.074 No 6756 (65.72) 6667 (65.83) 89 (58.55) Yes 3524 (34.28) 3461 (34.17) 63 (41.45) Memory disease, n (%) 5.132 0.041 No 10,187 (99.10) 10,039 (99.12) 148 (97.37) Yes 93 (0.90) 89 (0.88) 4 (2.63) Asthma, n (%) 8.974 0.007 No 9964 (96.93) 9823 (96.70) 141 (92.76) Yes 316 (3.07) 305(3.30) 11 (7.24) Diabetes, n (%) 0.198 0.656 No 9721 (94.56) 9579 (94.58) 142 (93.42) Yes 559 (5.44) 549 (5.42) 10 (6.58) Additional Declarations No competing interests reported. Supplementary Files AdditionalFile1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Jul, 2025 Reviewers agreed at journal 12 Jul, 2025 Reviewers invited by journal 10 Jul, 2025 Editor invited by journal 09 Jun, 2025 Editor assigned by journal 05 Jun, 2025 Submission checks completed at journal 05 Jun, 2025 First submitted to journal 02 Jun, 2025 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6803079","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484404137,"identity":"b03e1a34-0f5e-4514-87c1-88c53d7f6f14","order_by":0,"name":"Yutong Lin","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yutong","middleName":"","lastName":"Lin","suffix":""},{"id":484404138,"identity":"48dceb7e-12d6-4c23-999f-237aea3b2efd","order_by":1,"name":"Frank F. Huang","email":"","orcid":"","institution":"Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"F.","lastName":"Huang","suffix":""},{"id":484404139,"identity":"a79ddb48-139c-499f-9dcf-0b16cb1909ee","order_by":2,"name":"Jiacong Xiao","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiacong","middleName":"","lastName":"Xiao","suffix":""},{"id":484404140,"identity":"2372cf8f-4df2-4d9a-8df9-7c256145c113","order_by":3,"name":"Xingling Chen","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xingling","middleName":"","lastName":"Chen","suffix":""},{"id":484404141,"identity":"d8a4974f-7741-4f53-a92d-24e51e728c3f","order_by":4,"name":"Yuchen Wu","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuchen","middleName":"","lastName":"Wu","suffix":""},{"id":484404142,"identity":"0261ef30-4d64-458a-94de-b2f2aab9bd3a","order_by":5,"name":"Daniel K.Y. Zheng","email":"","orcid":"","institution":"Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"K.Y.","lastName":"Zheng","suffix":""},{"id":484404143,"identity":"4b377979-8ab2-46f0-8605-5ba0d46be7b1","order_by":6,"name":"Jeremy R. Chang","email":"","orcid":"","institution":"Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Jeremy","middleName":"R.","lastName":"Chang","suffix":""},{"id":484404145,"identity":"2bf32869-c370-45c1-946f-f1bc523615ed","order_by":7,"name":"Haibin Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Haibin","middleName":"","lastName":"Wang","suffix":""},{"id":484404146,"identity":"26ceff47-71ce-4812-895e-f4b46e94fa21","order_by":8,"name":"Jiandong He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACA4YDBgc+VPyT42dvIF6L4cEZZw4YS/YcIFoLg/Fh3rYDiRtuJBCpxZzx8IbDPGfuMM6c+XjjDYYam2iCWiwbjhUcnFPxjJlfOq3YguFYWm4DQYcdOGNw4M0ZZjbJ2TlmEowNh4nUwtvGzGNw8wwJWg7yth2WMLjBQ7QWoF9mnEkzkOwB+iWBKL/cOLz5w4cKm/p+9sMbb3yosSGshUHiAEK7RAJB5SDAjzDVQIIoHaNgFIyCUTDiAABOFE37ZmT4HwAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jiandong","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-06-02 14:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6803079/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6803079/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86762296,"identity":"3f5aa8aa-381d-4be5-84b0-3aa855a2d57e","added_by":"auto","created_at":"2025-07-15 10:31:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":169864,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participants selection in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: \u003c/strong\u003eCHARLS, China Health and Retirement Longitudinal Study.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6803079/v1/df46aadf9ebd00f08325f2eb.png"},{"id":86762298,"identity":"1e57e634-f3ac-4c4b-b1f6-136675b22209","added_by":"auto","created_at":"2025-07-15 10:31:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":293228,"visible":true,"origin":"","legend":"\u003cp\u003eDirected acyclic graph (DAG) model of hip fracture and diabetes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes：\u003c/strong\u003e A directed acyclic graph shows the relationships between covariates, exposure, and outcome. Pink circles indicate confounders, blue circles represent causal determinants of the outcome, green lines show causal paths, and pink lines represent biasing paths.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6803079/v1/550582868dee0772c60acad1.png"},{"id":86762300,"identity":"459bed2f-8f0f-4215-b4a8-73f66c05d8b7","added_by":"auto","created_at":"2025-07-15 10:31:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":617595,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot of the Cox regression model for all participants and the other subgroups.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6803079/v1/a17fd3b7855a345627e62ad1.png"},{"id":86763618,"identity":"5e76b24b-ace4-498e-bb35-fd3cdfc44bf0","added_by":"auto","created_at":"2025-07-15 10:39:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":261541,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot of the Cox regression model for the male subgroup and the obesity subgroup.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6803079/v1/2491a310d1e2ec52cdf58547.png"},{"id":86764819,"identity":"fe9d2855-a1b2-4f67-99d8-69b313acac56","added_by":"auto","created_at":"2025-07-15 10:55:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2142391,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6803079/v1/fff379c8-cd86-4d8c-bd40-d35380a4a71b.pdf"},{"id":86762297,"identity":"da160a11-63ea-4237-b053-db4eb701b6f3","added_by":"auto","created_at":"2025-07-15 10:31:47","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":163555,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6803079/v1/e50bd23b386f65a93b63ab66.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Longitudinal Assessment of Hip Fractures-Diabetes Causality in Aging Populations: Evidence from the China Health and Retirement Longitudinal Study Cohort","fulltext":[{"header":"Key Points","content":"\u003cul\u003e\n\u003cli\u003eMales and obese individuals with hip fractures experience an increased risk of developing diabetes.\u003c/li\u003e\n\u003cli\u003eSelf-medication with traditional herbal medicines and tonic supplements may increase diabetes risk in obese individuals with hip fractures.\u003c/li\u003e\n\u003cli\u003eStandardized use of herbal medicines and supplements is recommended under healthcare supervision to reduce diabetes risk.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eHip fractures are common among middle-aged and older populations. These fractures are linked to high rates of disability and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], as well as a range of complications during subsequent treatment, such as pulmonary infections, venous thrombosis, myocardial infarction, and renal dysfunction [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Most current studies have primarily focused on acute complications, such as infections, thrombosis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].However, some studies have indicated that individuals with hip fractures may experience endocrine system disturbances due to prolonged bed rest, metabolic changes, and stress responses, thereby increasing the risk of developing diabetes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Although research in this area remains insufficient, preliminary evidence suggests that post-fracture abnormalities in glucose metabolism may be an important consideration in the long-term health management of hip fractures [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, a thorough investigation into the intrinsic causality relationship between hip fractures and diabetes is of significant importance for the early identification of high-risk individuals and the implementation of timely prevention and interventions.\u003c/p\u003e\u003cp\u003eIn recent years, the role of traditional Chinese medicine and health supplements in the prevention of chronic diseases has attracted increasing attention [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Some studies have reported that these interventions may exert a protective effect against diabetes by modulating immune function and improving insulin sensitivity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, depressive symptoms and sleep duration have been considered as independent risk factors for diabetes. Depressive symptoms may increase the risk of diabetes through altered hormone secretion [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while both excessively long and short sleep durations significantly elevate the risk of type 2 diabetes by affecting metabolic processes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Given that these factors may play important mediating roles in the development of diabetes following hip fractures, the present study intended to examine them as potential mediators to explore the impact of multifactorial interactions on disease mechanisms.\u003c/p\u003e\u003cp\u003eTo comprehensively explore the relationship between hip fractures and diabetes, as well as to examine the influence of various mediating factors, this study conducted a retrospective longitudinal analysis of middle-aged and older adults in China using nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS). Additionally, we incorporated variables such as self-medication with traditional herbal medicines, tonic health supplements, depressive symptoms, and sleep duration to analyze their potential mediating roles in this relationship. By investigating the associations between hip fractures, and diabetes, we aimed to provide new theoretical insights for clinical interventions and long-term health management.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study Design\u003c/h2\u003e\u003cp\u003eThis study was a longitudinal analysis based on data from CHARLS and followed the guidelines of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. CHARLS, initiated in 2011, aims to investigate the social, economic, and health status of the Chinese population aged 45 and older, with follow-up surveys conducted biennially. The study covers 28 provinces, 150 counties, and 450 communities, recruiting 17,708 participants. As an open public database, CHARLS is freely accessible to the public, and its detailed study design has been described in the relevant literature [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, the ethical approval for CHARLS was obtained from the Institutional Review Board (IRB) of Peking University (approval number: IRB00001052-11015), and written informed consent was obtained from all participants prior to their enrollment in the study [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Participants\u003c/h2\u003e\u003cp\u003eThis study used the CHARLS data from 2011 to 2018, covering a follow-up period of seven years. The 2011 survey included 17,596 participants. The exclusion criteria were: (1) participants who had diabetes at baseline; (2) participants with missing data on diabetes, hip fractures, follow-up, or mediators; (3) participants younger than 45 years. For variables with random missing data (height, weight, and chronic disease history), multiple imputation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] was used to fill in the missing values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Exposure\u003c/h2\u003e\u003cp\u003eThe hip fracture event was defined as a hip fracture that occurred before the baseline survey. In the baseline survey household interviews, each participant was asked, \u0026ldquo;Have you ever fractured your hip?\u0026rdquo;[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] To assist participants in understanding the concept of hip fractures, the interviewer provided a detailed description of the hip\u0026rsquo;s location. If a participant responded affirmatively, it was interpreted as an indication that they had experienced a hip fracture.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Outcomes\u003c/h2\u003e\u003cp\u003eIn both the baseline and follow-up surveys, all participants were asked whether they had ever been diagnosed with diabetes by a doctor, as well as the timing of their first diagnosis. In accordance with previous studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], a diabetes event was defined as a participant who, at the baseline survey, had not been diagnosed with diabetes or elevated blood glucose by a doctor, but was diagnosed with diabetes during the follow-up survey in 2018.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e2.5. Covariates\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eKnown risk factors for hip fractures and diabetes were extracted from the China Health and Retirement Longitudinal Study (CHARLS) and included as covariates [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Socio-demographic factors included age, gender, height, weight, education level, and marital status. Among these, height and weight data were used to calculate Body Mass Index (BMI), which is determined by dividing weight (in kilograms) by the square of height (in meters). Lifestyle factors comprised smoking and alcohol consumption. Physical health factors include self-reported history of chronic diseases and falls. The assessment of fall events was based on participants\u0026rsquo; self-reported responses to the question: \u0026ldquo;Have you fallen in the past two years?\u0026rdquo;[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] In addition, an assessment of physical function was also incorporated, including balance ability assessment and physical strength assessment. The specific evaluation method could be found in the Additional File 1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e2.6. Mediators\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eSelf-medication with tonic health supplements, self-medication with traditional herbal medicines, depression status, and sleep duration were selected as the mediators. Information regarding the use of traditional herbal medicines and health supplements was obtained through the question, \u0026ldquo;How did you treat yourself during the past month?\u0026rdquo; Notably, this question emphasized the exclusion of prescription medications, thereby focusing on self-medication practices rather than the use of health supplements and traditional herbal medicines under a doctor's guidance. Depression status, including the presence and severity of depressive symptoms, was assessed using the question, \u0026ldquo;Overall in the last month, how much of a problem did you have with feeling sad, low, or depressed?\u0026rdquo; Lunch nap duration and nighttime sleep duration were obtained through the interview questions: \u0026ldquo;During the past month, how long did you take a nap after lunch?\u0026rdquo; and \u0026ldquo;During the past month, how many hours of actual sleep did you get at night (average hours for one night)? (This may be shorter than the number of hours you spend in bed).\u0026rdquo; Referring to commonly used classifications for sleep duration in clinical studies of middle-aged and older adults [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], nap duration was categorized into four groups: 0 minutes, 1\u0026ndash;30 minutes, 31\u0026ndash;90 minutes, and \u0026ge;\u0026thinsp;90 minutes. Nighttime sleep duration and total sleep duration (the sum of nap and nighttime sleep) were each categorized into five groups: \u0026lt; 6 hours, 6\u0026ndash;7 hours, 7\u0026ndash;8 hours, 8\u0026ndash;9 hours, and \u0026gt;\u0026thinsp;9 hours [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Subgroup analysis\u003c/h2\u003e\u003cp\u003eThe participants were divided into subgroups based on BMI, age, and gender for subgroup analysis. According to the BMI classification standard for the Chinese population [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the study population was divided into four subgroups: underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5), normal weight (BMI 18.5\u0026ndash;23.9), overweight (BMI 24.0-27.9), and obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28.0). In addition, participants were classified into four age groups: 45\u0026ndash;59 years, 60\u0026ndash;69 years, 70\u0026ndash;79 years, and 80 years and older [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8. Directed Acyclic Graph Analysis\u003c/h2\u003e\u003cp\u003eDirected Acyclic Graph (DAG) analysis was performed to control the potential confounding or moderating effects of certain variables on the relationship between hip fractures and diabetes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We hypothesized a causal relationship between hip fractures and diabetes, and selected risk factors as variables for the DAG model based on relevant international disease guidelines or consensus and previous research [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Using the web-based software DAGitty (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.dagitty.net\u003c/span\u003e\u003cspan address=\"http://www.dagitty.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), directed paths were constructed and the minimal sufficient adjustment set (MSAS) was visually presented. In subsequent model fitting and analysis, these MSAS variables were controlled for to reduce bias, enhancing the transparency and precision of the analysis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9. Statistical Analysis\u003c/h2\u003e\u003cp\u003eThe Cox proportional hazards model [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], linear regression model [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and structural equation model (SEM) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]were employed for data analysis. All confounding factors identified by the DAG were adjusted for in the models, except for physical inactivity, which was excluded due to significant missing data. First, the Cox model was used to test whether there was a causal relationship between hip fractures and diabetes risk for all participants and within each subgroup. If the Hazard Ratio (HR) was greater than 1.00 and the p-value was less than 0.05, further mediation analysis was conducted using the linear regression to explain the underlying relationship between these two variables [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The proportional hazards assumption was assessed using the Schoenfeld residuals test [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Finally, the SEM approach was performed for sensitivity analysis to confirm the robustness of the results. Continuous variables were presented as the mean and standard deviation (SD), while categorical variables were presented as the proportion. Statistical analyses were performed using R software version 4.2.2, with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Participant Characteristics\u003c/h2\u003e\n\u003cp\u003eA total of 10,280 individuals (Age: 58.53\u0026thinsp;\u0026plusmn;\u0026thinsp;8.75 years; BMI: 23.20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36 kg/m\u003csup\u003e2\u003c/sup\u003e; Male: n\u0026thinsp;=\u0026thinsp;4,880 (47.47%)) were included in the analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 152 participants (1.48%) had a history of hip fractures, and 559 cases (5.44%) of new-onset diabetes were reported. Additionally, participants with a history of hip fractures were more likely to be older and may have a higher prevalence of chronic conditions, including chronic lung diseases, stomach or other digestive disorders, emotional or psychiatric disorders, memory diseases, and asthma (Table\u0026nbsp;1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Longitudinal Causal Analysis\u003c/h2\u003e\n\u003cp\u003eAccording to the results derived from the DAG analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), several potential confounding factors were identified, including age, gender, BMI, falls, smoking, lack of physical activity, grip strength, balance ability, chair rise test, and other chronic diseases. The results of the Cox model analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) for all participants indicated that no statistically significant causal relationship was found between hip fractures and diabetes. In the female subgroup, all age subgroups, and all BMI\u0026thinsp;\u0026lt;\u0026thinsp;28 subgroups, no significant causal association was found between hip fractures and diabetes. However, participants with a history of hip fractures had a significantly increased probability of developing diabetes in male (HR\u0026thinsp;=\u0026thinsp;2.43; 95% CI: 1.19 to 4.96; P\u0026thinsp;=\u0026thinsp;0.01) and obesity (HR\u0026thinsp;=\u0026thinsp;3.77; 95% CI: 1.42 to 10.02; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) subgroups (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The results of the Schoenfeld residuals test show that the p-values for all variables were greater than 0.05, indicating no statistically significant evidence of a violation of the proportional hazards assumption. Therefore, the constructed Cox model demonstrated acceptable applicability and robustness in this study, and the analytical results were considered reliable. Detailed information could be found in Additional file 1: Tables S1 to S12.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3. Mediation Analysis\u003c/h2\u003e\n\u003cp\u003eIn the male and obesity subgroups, the indirect effects of self-medication with tonic health supplements, traditional herbal medicines, depressive mood, nighttime sleep duration, nap duration, and total sleep duration were not significant (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Additional file 1: Table S13).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4. Sensitivity Analysis\u003c/h2\u003e\n\u003cp\u003eThe results of the SEM model show that, in the male subgroup, participants with a history of hip fractures had an estimated value (EST) of 0.07 for the likelihood of developing diabetes (95% CI: 0.03 to 0.10; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Although the results suggest that some mediator variables have statistical significance between hip fractures and diabetes, these positive results were excluded due to the poor model fit in the male subgroup. In the obesity subgroup (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28.0), participants with a history of hip fractures had an EST of 0.33 for the likelihood of developing diabetes (95% CI: 0.16 to 0.50; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, the results for the obesity subgroup showed that hip fractures had a significant impact on self-medication with tonic health supplements (EST\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.14; 95% CI: 0.04 to 0.23; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) and traditional herbal medicines (EST\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.19; 95% CI: 0.09 to 0.29; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and traditional herbal medicines also had a significant effect on diabetes (EST\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.07; 95% CI: 0.01 to 0.13; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03). The results for the remaining four mediator variables were not significant. Detailed model information was provided in Additional file 1: Tables S14 to S17.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study is the first longitudinal cohort study with a follow-up period of seven years to investigate the causality between hip fractures and diabetes among middle-aged and older adults in China. Our findings indicate that a history of hip fractures was associated with diabetes among middle-aged and older males, as well as individuals with obesity in China. However, no significant causal association was observed between hip fractures and diabetes in the overall population, nor within the subgroups defined by female, age, and BMI\u0026thinsp;\u0026lt;\u0026thinsp;28. Further mediation analysis revealed that hip fractures might lead to inappropriate use of tonic health supplements and traditional herbal medicines, thereby increasing the risk of developing diabetes.\u003c/p\u003e\u003cp\u003eThe entire sample did not show significant causal relationships, which may be primarily attributable to substantial heterogeneity within the sample. This heterogeneity likely resulted in null or even inverse effects in low-risk groups, thereby diluting the significant positive association observed in specific high-risk subgroups (e.g., males and obese individuals) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Furthermore, factors such as gender and BMI may act as effect modifiers for diabetes risk following hip fractures [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], rendering these particular subgroups more susceptible to endocrine and metabolic abnormalities.\u003c/p\u003e\u003cp\u003eIn the male subgroup, the increased risk of diabetes following hip fractures may be related to changes in endocrine hormone levels and lifestyle modifications. After a hip fracture, the decline in testosterone levels [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], significant reduction in physical activity levels, weight gain, and lifestyle changes in males may exacerbate insulin resistance, thereby increasing the risk of developing diabetes. In contrast, women typically place greater emphasis on health management [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], which may help alleviate metabolic burdens and prevent similar trends. Gender differences in the systemic inflammatory response following a fracture could also play a role, as a more pronounced inflammatory response in males may further contribute to the development of diabetes [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Additionally, significant differences in drinking and smoking behaviors between males and females may influence the incidence of diabetes [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This gender difference can also be explained by unique physiological phenomena in women: hormonal changes during pregnancy contribute to the higher incidence of diabetes, and postmenopausal osteoporosis leads to a higher incidence of fractures in women (1.5\u0026ndash;3 times higher than in men) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], which may directly influence the complex relationship between hip fractures and diabetes, potentially masking the underlying causal connection. Notably, literature exploring gender differences in this context is scarce [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Therefore, further research is crucial to clarify the exact biological mechanisms behind this association and the gender differences involved.\u003c/p\u003e\u003cp\u003eCompared to obese individuals, those with a lower BMI generally did not exhibit significant glucose metabolism disorders after hip fractures. This is ascribe that individuals with lower BMI typically have better insulin sensitivity, lower baseline inflammation levels, and more stable metabolic balance [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], which reduces the likelihood of glucose metabolism disturbances following a hip fracture. In contrast, obesity is associated with chronic low-grade inflammation, which promotes the development of both type 1 and type 2 diabetes [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Following a hip fracture, the increase in inflammatory factors, fluctuations in bone metabolism hormones (such as calcitonin and parathyroid hormone), and decreased physical activity may all reduce insulin sensitivity and affect metabolic balance [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Additionally, hip fractures, as major trauma, can trigger intense stress responses, leading to increased levels of stress hormones (such as cortisol), which further interfere with blood glucose regulation. Studies have shown that hip fractures can induce stress hyperglycemia, increasing the risk of diabetes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In obese populations, these factors may act synergistically, significantly raising the risk of developing diabetes.\u003c/p\u003e\u003cp\u003eAlthough the study did not find a significant causal association across age groups, this does not mean that there is no potential link between hip fractures and diabetes in different age groups. Research has shown that age is an important factor for diabetes risk, with the prevalence of diabetes and prediabetes significantly higher in the older group compared to the middle-aged group [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Moreover, as age increases, the risks of diabetes and prediabetes become more pronounced, with factors such as overweight and obesity, family history of diabetes, and high cholesterol contributing to this increased risk [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Based on the aforementioned analysis of the factors contributing to the increased diabetes risk after hip fractures, we believe that even in the absence of significant causal statistical associations, the metabolic health of older hip fracture participants should still be closely monitored, particularly about diabetes prevention and early intervention.\u003c/p\u003e\u003cp\u003eHowever, some studies contradict our findings. A retrospective cohort study involving 46,628 participants aged 65 and above in Hong Kong [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] found that the risk of developing diabetes was significantly lower in the hip fracture group. This phenomenon may be related to differences in the study population and design. Compared to participants in mainland China, those in Hong Kong may have received more stringent health management and medical interventions after their fractures [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], which could have reduced the risk of diabetes. More prospective studies are needed in the future to further clarify the bidirectional relationship between fractures and diabetes and its potential biological mechanisms, to guide health intervention strategies for specific populations.\u003c/p\u003e\u003cp\u003eRegarding mediating factors, previous studies have indicated that self-medication among middle-aged and older individuals is associated with high risks and poor quality [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. This provides a theoretical basis for further exploring the tendency of middle-aged and older fracture patients to rely on tonic health supplements and traditional herbal medicines for self-regulation during their recovery process. On one hand, although many tonic health supplements and traditional herbal medicines are promoted as capable of improving diabetes, most lack support from large-scale randomized controlled trials. Improper use of these products may disrupt insulin receptor signaling, reduce insulin sensitivity, and exacerbate insulin resistance [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. On the other hand, the active or hormonal components in traditional herbal medicines or supplements may directly damage pancreatic \u003cem\u003eβ\u003c/em\u003e-cells, thereby triggering diabetes [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]; some traditional herbal medicines components may further reduce insulin sensitivity or induce insulin resistance by modulating gut microbiota, bile acid metabolism, short-chain fatty acid synthesis, and inflammation levels [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. We speculate that the self-medication behavior of elderly fracture patients, who show a marked preference for traditional Chinese medicine [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], is closely associated with the improper use of tonic health supplements and traditional herbal medicines, which may collectively contribute to an increased incidence of diabetes.\u003c/p\u003e\u003cp\u003eIn this study, we found that patients with hip fractures frequently engage in the inappropriate self-administration of traditional Chinese herbal medicines and health supplements during their rehabilitation process, a phenomenon particularly prevalent in China, where traditional Chinese medicine culture is deeply rooted. In Chinese traditional culture, traditional Chinese medicine is widely accepted and highly trusted, and many elderly patients tend to use traditional Chinese herbal medicines and health supplements for self-regulation and wellness [\u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. This cultural background may lead patients to take these substances indiscriminately without professional guidance, thereby increasing the risk of adverse effects and drug interactions [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Additionally, the traditional Chinese health preservation concept emphasizes the principle of \u0026ldquo;homology of medicine and food\u0026rdquo; [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Many middle-aged and older individuals tend to take traditional Chinese medicine on their own in daily life to regulate their health. This belief may lead fracture patients to actively seek self-treatment with herbal medicines. However, if such practices lack scientific evidence and proper regulation, they may result in improper medication use, increasing the burden on the body.\u003c/p\u003e"},{"header":"5. Study Strengths and Limitations","content":"\u003cp\u003eThe study offers the following advantages: First, it is a large-scale, population-based longitudinal study covering 150 county-level units in China, with a representative sample of middle-aged and older individuals nationwide, ensuring the generalizability and long-term stability of the results. Rigorous multi-stage probability sampling and long-term follow-up data enhance the credibility of causal inference and provide a solid foundation for chronic disease risk assessment, making the results more broadly applicable compared to hospital-based studies. Second, this study is the first to explore the relationship between hip fractures and diabetes risk in middle and older adults, and innovatively incorporates self-reported use of tonic health supplements, traditional herbal medicines, depressive symptoms, and sleep duration in the analysis. Previous orthopedic studies have primarily focused on acute complications following fractures, such as infections, thrombosis, and functional recovery, with limited attention given to the systemic endocrine and metabolic disruptions that fractures may cause. Diabetes, as a complication of fractures, may delay fracture healing and increase the risk of re-fractures. Therefore, exploring the relationship between hip fractures and diabetes is crucial for optimizing long-term prognosis.\u003c/p\u003e\u003cp\u003eHowever, the study also has some limitations. First, the assessment of hip fractures relies on self-reporting, and the participants\u0026rsquo; relatively low educational level may affect the accuracy of these reports. Second, fractures may lead to bed rest or prolonged hospitalization, introducing selection bias. Third, the CHARLS did not distinguish specific types of diabetes [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Third, the survey data does not include detailed information on post-fracture treatment and rehabilitation, limiting the understanding of how different post-fracture conditions influence changes in diabetes risk. Future research should be based on more comprehensive and detailed clinical data to further investigate the causal relationship between hip fractures and diabetes in this population.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eIn this seven-year longitudinal study based on a Chinese population, hip fractures were found to be associated with an increased prevalence of diabetes among middle-aged and older males, as well as individuals with obesity. Factors such as tonic health supplements and traditional herbal medicines may serve as mediating factors in this relationship. A deeper understanding of the relationship between hip fractures and diabetes could provide valuable insights for clinical practice and public health policies, facilitating the proactive prevention of complications arising from hip fractures, regulating patients' self-medication behaviors, and ultimately improving the long-term health outcomes of middle-aged and older patients with hip fractures. However, further research is necessary to investigate the impact of fractures on diabetes prevalence, as well as to conduct multicenter longitudinal studies in other countries and regions to validate the findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCHARLS was ethically approved by the Institutional Review Board at Peking University (00001052\u0026ndash;11015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are publicly available on the website of the China Health and Retirement Longitudinal Study (CHARLS) at http://charls.pku.edu.cn/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL, JX and FFH were responsible for organizing the data information of CHARLS database participants and performed data analysis using software. DKYZ and JRC optimized the research methodology and verified the research conclusions. YL, FFH, and XC were the primary contributors to manuscript writing and supplementary materials preparation. YL and YW were responsible for creating research result tables and data visualization. HW and JH participated in manuscript review. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFootnotes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKanis JA, Od\u0026eacute;n A, McCloskey EV, et al. A systematic review of hip fracture incidence and probability of fracture worldwide. 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Diabetes. 2020;69:2037\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cb\u003eTable.1\u003c/b\u003e The baseline characteristics of the study population in the China Health and Retirement Longitudinal Study (CHARLS).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;10,280)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo hip-fracture (n\u0026thinsp;=\u0026thinsp;10,128)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHip fracture (n\u0026thinsp;=\u0026thinsp;152)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et/x\u003csup\u003e2\u003c/sup\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.53\u0026thinsp;\u0026plusmn;\u0026thinsp;8.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.50\u0026thinsp;\u0026plusmn;\u0026thinsp;8.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.26\u0026thinsp;\u0026plusmn;\u0026thinsp;9.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.22\u0026thinsp;\u0026plusmn;\u0026thinsp;12.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.23\u0026thinsp;\u0026plusmn;\u0026thinsp;12.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.46\u0026thinsp;\u0026plusmn;\u0026thinsp;12.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e158.16\u0026thinsp;\u0026plusmn;\u0026thinsp;8.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e158.18\u0026thinsp;\u0026plusmn;\u0026thinsp;8.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e156.79\u0026thinsp;\u0026plusmn;\u0026thinsp;9.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody mass index, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.711\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4880 (47.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4812 (47.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (44.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5400 (52.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5316 (52.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84 (55.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElementary school or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6914 (67.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6804 (67.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110 (72.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2172 (21.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2141 (21.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (20.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or vocational school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1020 (9.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1010 (9.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (6.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174 (1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e173 (1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarriage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73 (0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71 (0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9170 (89.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9043 (89.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e127 (83.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced or widowed or others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1037 (10.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1014 (10.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (15.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.365\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent drinking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3256 (31.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3201 (31.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55 (36.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEver drinking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e769 (7.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e756 (7.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (8.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever drinking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6255 (60.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6171 (60.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84 (55.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3262 (31.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3222 (31.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (26.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEver smoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e786 (7.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e769 (7.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (11.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever smoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6232 (60.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6137 (60.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95 (62.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8130 (79.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8005 (79.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125 (82.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2150 (20.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2123 (20.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (17.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9532 (92.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9397 (92.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e135 (88.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e748 (7.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e731 (7.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (11.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,195 (99.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10,044 (99.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e151 (99.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic lung diseases, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9353 (90.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9223 (91.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130 (85.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e927 (9.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e905 (8.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (14.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9195 (89.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9061 (89.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e134 (88.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1085 (10.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1067 (10.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (11.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,115 (98.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9968 (98.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e147 (96.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e165 (1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160 (1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9907 (96.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9764 (96.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e143 (94.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e373 (3.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e364 (3.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (5.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStomach or other digest disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7925 (77.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7822 (77.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103 (67.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2355 (22.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2306 (22.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (32.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9679 (94.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9541 (94.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138 (90.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e601 (5.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e587 (5.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (9.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmotional or psychiatric disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,164 (98.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10,018 (98.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e146 (96.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110 (1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (3.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArthritis or rheumatism, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6756 (65.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6667 (65.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89 (58.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3524 (34.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3461 (34.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63 (41.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMemory disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,187 (99.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10,039 (99.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e148 (97.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 (0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (2.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsthma, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9964 (96.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9823 (96.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e141 (92.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e316 (3.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e305(3.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (7.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9721 (94.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9579 (94.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e142 (93.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e559 (5.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e549 (5.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (6.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hip fractures, diabetes, self-medication, obesity, herbal medicine","lastPublishedDoi":"10.21203/rs.3.rs-6803079/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6803079/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e\u003cbr\u003e\n To examine the longitudinal impact of hip fractures on the risk of diabetes in middle-aged and older individuals, and investigate the mediating effects of depression, sleep duration, and self-medication with traditional herbal medicines and tonic health supplements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003cbr\u003e\n Data from the China Health and Retirement Longitudinal Study (10,280 participants, aged 45+) were analyzed over a 7-year follow-up. Subgroup analyses were conducted by gender, age, and body mass index. After adjusting for relevant confounders, the Cox proportional hazards model was used to assessed the association between hip fractures and diabetes risk. Linear regression models were employed to conduct mediation analyses. Structural equation model was applied for sensitivity analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the 2011 baseline, 152 participants (1.48%) had a history of hip fractures. By 2018, 559 participants (5.44%) developed diabetes, including 10 (1.79%) individuals with prior hip fractures. Hip fractures were significantly associated with increased diabetes risk in males (HR = 2.43; 95% CI: 1.19, 4.96; \u003cem\u003eP\u003c/em\u003e = 0.01) and obese participants (HR = 3.77; 95% CI: 1.42, 10.02; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). No significant causal association was observed between hip fractures and diabetes in the overall population or in subgroups defined by female, age, or BMI \u0026lt; 28. Self-medication with herbal medicines and tonic supplements may partially explain the increased diabetes risk among obese individuals with hip fractures, while depression and sleep duration showed no significant mediating effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003cbr\u003e\n Hip fractures were significantly associated with an increased risk of diabetes among middle-aged and older males, as well as individuals with obesity. To mitigate this risk, it was recommended to standardize the use of traditional herbal medicines and tonic health supplements under the guidance of healthcare professionals.\u003c/p\u003e","manuscriptTitle":"Longitudinal Assessment of Hip Fractures-Diabetes Causality in Aging Populations: Evidence from the China Health and Retirement Longitudinal Study Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 10:31:42","doi":"10.21203/rs.3.rs-6803079/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-07-24T15:25:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74711232285845879567913438449418290295","date":"2025-07-12T10:36:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-10T09:45:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-09T07:53:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-05T05:50:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-05T05:48:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-06-02T14:15:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11c2631a-60b7-4e9a-964a-731c8c1ed548","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-15T10:31:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-15 10:31:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6803079","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6803079","identity":"rs-6803079","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}