Association between sleep duration and hip fracture risk among the older adults: a cross-sectional study based on the NHANES

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However, it remains ambiguous regarding the association between HF risk and sleep duration. This study intended to explore the association between sleep duration and HF risk among the older adults. Methods The study assessed a cohort of 7,540 participants aged at least 60 years old using data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2010, as well as from 2013 to 2014. Two distinct groups of HF and non-HF were constructed on the basis of their history of HFs. Based on the self-reported sleep duration through a structured questionnaire, multivariate logistic regression analyses were conducted to examine the relationship between sleep duration and HF risk. In addition, restricted cubic splines (RCS) were used to assess linearity. The receiver operating characteristic (ROC) curve was used to explore the threshold of sleep duration for HF risk. Results Among 7540 participants over 60 years of age with mean age of 70.17 ±7.1 years, 129 had HF. Significant differences in sleep duration were observed between the HF and non-HF groups (7.73 ± 1.68 h vs. 7.11 ± 1.42 h; p=0.006). The multivariate analysis was adjusted for sociodemographic, behavioral lifestyle, and comorbidities. A one hour increase in sleep duration was associated with higher odds of having prior hip fractures in unadjusted models （OR=1.36; 1.11, 1.67; p=0.004), minimally adjusted models (OR=1.23; 1.03, 1.48; p=0.025), second adjusted models (OR=1.22; 1.03,1.45; p= 0.024) and fully adjusted models (OR=1.22; 1.03,1.44; p=0.024). The relationship remained consistent across all four models, indicating the correlation of a longer sleep duration with an elevated HF risk. RCS analysis revealed a statistically linear relationship between sleep duration and HF incidence (p-nonlinear=0.244, p-overall<0.01). In addition, the identified threshold of sleep duration linked to HF risk was determined to be 7.5 h among the older adults (AUC=0.611). Conclusion Sleep duration correlates with HF risk among the older adults. Findings in this study inspire that an appropriate sleep duration may reduce the risk of HF among the older adults. Figures Figure 1 Figure 2 Figure 3 Introduction Hip fractures (HFs) are prevalent, particularly among the elderly, showing significant increase in their incidence owing to the intensified aging globally(1,2). With 1.31 million of HF cases in 1990, its number is expected to rise to 6.26 million worldwide by 2050(3–5). Owing to serious complications as well as high mortality and disability rates, HFs are among the leading causes of loss of disability-adjusted life years among the older adults.(5). Even within a year of injury, the mortality rate is approximately 30%(1). Sufficient and adequate sleep is essential for maintaining optimal health and safety, whereas variations in sleep duration and timing may trigger metabolic, cardiovascular, endocrine, and neurological disorders(6–8). Circadian rhythmicity and sleep behaviors are documented to be associated with bone health(9–11). With disrupted circadian rhythms and impeded bone formation, sleep disorders can further increase the likelihood of fractures(12,13). Existing animal experiments have shown that long-term sleep restriction can hinder bone remodeling in laboratory rats(14,15). Therefore, disturbances in sleep physiology and the circadian rhythm may adversely affect bone health. So far, there are still insufficient observational studies of sleep behaviors and HF risk, with conflicting results as well(16–18). Meanwhile, people who sleep longer were reported to be more prone to falls and fractures compared with those with shorter sleep duration(16,17). Furthermore, a U-shaped relationship has been documented prospectively between sleep duration and fracture risk(19). Therefore, it highlights the significance of recognizing modifiable risk factors, including sleep duration, for reducing the incidence of HFs, particularly among the older adults. In view of the above interpretation, this study sought to explore the association between sleep duration and the incidence of HFs in individuals aged at least 60 years old. Materials and methods Data and participants The National Health and Nutrition Examination Survey represents a nationally representative cross-sectional study combining individual interviews, medical examinations and laboratory tests across diverse populations of America, orchestrated by the National Center for Health Statistics (NCHS) (20). NHANES-sourced data have been approved by the NCHS Ethics Review Committee, and detailed statistics can be found at https://www.cdc.gov/nchs/nhanes/ . Sleep duration of patients in 2011–2012 was lacking in the database. So in this study, 50,966 subjects from NHANES (2005–2010 and 2013–2014) were collected, with final identification of 7,540 participants through rigorous exclusion criteria: (1) under 60 years of age, (2) missing sleeping duration data, (3) missing fractured hip data, and (4) the age at which the fracture occurred was less than 60 years (Fig. 1 ). Finally, among the 7,540 eligible individuals enrolled in this study, 7,411 did not suffer from HFs, while 129 sustained HFs after age 60. HF This study obtained data related to self-reported HF history and age at first HF provided in the NHANES interviews. Participants were asked to answer, "Has a doctor ever told you that you had broken or fractured hip?" Patients with HF history were asked the following questions “How old were you when you fractured your hip the first time?” Cases of elderly HF were included if they had HFs and were older than 60 years of age for the first time (21,22). Sleep duration The sleep duration of the included participants was self-reported by answering the question of “How much sleep do you usually get at night on weekdays or workdays?” The recorded duration was a continuous variable and served as an independent variable(23). Covariates The NHANES was searched to acquire self-reported demographic data such as age, sex, race/ethnicity, education, marital status, and poverty-to-income ratio (PIR). Covariates included common risk factors, such as body mass index (BMI; normal, 30 kg/m 2 ), alcohol consumption, and smoking. The smoking status was classified as either “smoker” or “non-smoker” by a questionnaire. Alcohol consumption was measured by a question “During the past 12 months, on those days that drank alcoholic beverages, on the average, how many drinks did you have?” Diabetes was determined if any of the following criteria were met by the participants: a self-reported history of diabetes, a history of diabetes medication (DM), a fasting blood glucose > 7.0 mmol/L, a random blood glucose > 11.0 mmol/L, a glycosylated hemoglobin > 6.5%, and a 2-hour OGTT glucose level > 11.1 mmoL/L(24,25). Meanwhile, hypertension would exist if participants had self-reported history of hypertension, history of medication for hypertension, three times of mean diastolic blood pressure > 90mmHg, or three times of the mean systolic blood pressure > 140 mmHg(24,26). In addition, cardiovascular disease (CVD) included self-reported congestive coronary heart disease, heart failure, stroke, heart attack, and angina. Statistical analysis Given the complex multistage sampling design of the NHANES, weighted analyses were appropriately conducted via the R survey package to better represent the overall characteristics of the US population. According to the NHANES recommended Two-Year Sample Weights for MEC Examination (WTMEC2YR) records, sample weights of individuals were determined by WTMEC2YR/4. Statistical analyses in this study were completed by utilizing R 4.3.0 and SPSS 29. Normally distributed continuous variables were reported as mean ± standard deviation (SD) and compared using two independent samples t-test between groups. Categorical variables were represented as n (%), and compared by chi-square test or Fisher's exact test. The p value of < 0.05 was considered statistically significant. In the multivariable regression analyses, Model 1 was adjusted for covariates such as age, sex, education, marital status, race/ethnicity, and PIR. Based on Model 1, Model 2 incorporated adjustments for BMI, smoking, and alcohol consumption. Finally, Model 3 was additionally adjusted for diabetes, hypertension, and CVD. Furthermore, the optimal cutoff value for sleep duration was determined by receiver operating characteristic (ROC) curve analyses. Based on the computation of the area under the curve (AUC), an AUC value of ≥ 0.81, 0.71–0.80, 0.61–0.70, and ≤ 0.6 indicated high, fair, poor accuracy, and a lack of discriminatory capability, respectively (27). The Youden index was used to determine the optimal threshold for distinguishing patients if the AUC exceeded 0.60. In addition, a restricted cubic spline (RCS) with four nodes at the 5th, 35th, 65th, and 95th percentiles was employed to identify a linear or nonlinear relationship between sleep duration and HF risk. Results Table 2 presents significant inter-group differences concerning various demographic and lifestyle factors. On average, individuals in the HF group were older than those in the control group (76.93 ± 6.13 years vs. 70.17 ± 7.10 years; p<0.001). The HF group included a significantly greater percentage of female participants (71.13% vs. 55.20%; p=0.002). Meanwhile, compared to the control group, the HF group presented a longer average sleep duration (7.73 ± 1.68 h vs. 7.11 ± 1.42 h; p=0.006), and a lower level of alcohol consumption (0.49±0.94 vs. 0.94±1.34 glasses/day; p<0.001). The incidence of CVD was also elevated in the HF group (37.44% vs. 23.84%; p<0.001). Furthermore, four logistical regression models were constructed to clarify the correlation between sleep duration and HF risk among the older adults. As presented in Table 3, longer sleep duration correlated with an increased risk of HF in the crude model [OR=1.36,(1.11,1.67); p= 0.004]. Consistently, longer sleep duration was associated with an increased risk of HF in the adjusted Model 1 [OR=1.23(1.03,1.48); p= 0.025], Model 2 [OR=1.22(1.03,1.45); p= 0.024], and Model 3 [OR=1.22(1.03,1.44); p=0.021]. After adjustment for confounding variables, the OR values of the three models decreased compared with those of the crude model, yet with the same association remained. The ROC curve analysis (Fig.2) revealed an AUC of 0.611, indicating that sleep duration had a low predictive accuracy for HF occurrence. The optimal threshold was determined to be 7.5 h, resulting in a Youden index of 0.192, corresponding to a sensitivity of 0.597 and a specificity of 0.595. In addition, RCS curves were plotted to examine the potential nonlinearity in the studied relationship. The generated approximate U-shaped image suggested that either long or short sleep duration would increase the risk of HF. However, the RCS curve (Fig.3) still showed a linear association (p-overall<0.01, p-nonlinear=0.244). Table1 Participant characteristics in NHANES (2005-2010 and 2013-2014). Variables Total, N = 7,540 Control, N = 7,411 Case, N = 129 P value Age,Mean ± SD 70.17±7.10 70.07±7.07 76.93±6.13 <0.001 Sleep h,Mean ± SD 7.11±1.42 7.11±1.42 7.73±1.68 0.006 Alcohol consumption,Mean ± SD 0.93±1.34 0.94±1.34 0.49±0.94 <0.001 Gender, n (%) 0.002 Male 3,680 (44.55) 3,629 (44.80) 51 (28.87) Female 3,860 (55.45) 3,782 (55.20) 78 (71.13) Race, n (%) 0.269 Non-hispanic white 4,118 (79.74) 4,022 (79.67) 96 (84.06) Non-hispanic black 1,482 (8.88) 1,469 (8.94) 13 (4.88) Mexican American 1,005 (4.27) 991 (4.26) 14 (4.54) Other Hispanic 558 (2.63) 558 (2.67) 0 (0) Other 377 (4.48) 371 (4.45) 6 (6.52) Education, n (%) 0.013 College graduate or above 1,403 (25.01) 1,389 (25.22) 14 (11.75) High school grade 1,825 (25.78) 1,793 (25.73) 32 (28.86) 9-11 th grade 1,220 (13.05) 1,192 (12.96) 28 (19.09) Some college or AA degree 1,758 (26.47) 1,725 (26.48) 33 (26) Less than 9 th grade 1,334 (9.69) 1,312 (9.62) 22 (14.31) DM, n (%) 0.185 No 5,114 (72.24) 5,011 (72.12) 103 (80.09) Yes 2,426 (27.76) 2,400 (27.88) 26 (19.91) CVD, n (%) <0.001 No 5,630 (75.95) 5,553 (76.16) 77 (62.56) Yes 1,910 (24.05) 1,858 (23.84) 52 (37.44) PIR, n (%) 0.217 ≤1.0 1,261 (9.87) 1,240 (9.83) 21 (12.60) 1.0-3.0 3,653 (44.42) 3,583 (44.32) 70 (50.65) ≥3.0 2,626 (45.71) 2,588 (45.85) 38 (36.74) BMI group, n (%) <0.001 Normal 1,864 (26.26) 1,811 (25.95) 53 (46) Overweight 3,088 (38.11) 3,028 (38.04) 60 (42.56) Obesity 2,588 (35.62) 2,572 (36.01) 16 (11.44) Smoking, n (%) 0.656 No 6,618 (89.04) 6,505 (89.06) 113 (87.50) Yes 922 (10.96) 906 (10.94) 16 (12.50) Hypertension, n (%) 0.102 No 2,225 (31.14) 2,192 (31.27) 33 (22.88) Yes 5,315 (68.86) 5,219 (68.73) 96 (77.12) Marital Status, n (%) <0.001 Married 4,142 (60.78) 4,094 (61.15) 48 (37.51) Widowed 1,802 (21.51) 1,736 (21.01) 66 (53.07) Divorced 921 (11.34) 911 (11.43) 10 (5.61) Separated 178 (1.19) 177 (1.19) 1 (0.69) Living with partner 154 (1.64) 154 (1.67) 0 (0) Never married 343 (3.55) 339 (3.56) 4 (3.12) Table 2 Association between sleep duration and HF risk among the older adults Crude Model 1 Model 2 Model 3 OR(95％CI) p OR(95％CI) p OR(95％CI) p OR(95％CI) p Sleep duration 1.36 (1.11, 1.67) 0.004 1.23 (1.03, 1.48) 0.025 1.22 (1.03, 1.45) 0.024 1.22 (1.03, 1.44) 0.021 Crude represents an unadjusted model. Model 1 was adjusted for age, gender, race, marital status, education, and PIR. Model 2 was adjusted for alcohol consumption past 12 months, smoking past 30 days, and BMI groups, based on Model 1. Model 3 was adjusted for diabetes, CVD, and hypertension, based on Model 2. Discussion In this population-based retrospective cross-sectional study, sleep duration was associated with HF risk among the older adults according to analyses based on logistic regression models. There was a linear correlation between sleep duration and HF risk, as supported by multiple logistic regression analysis and the RCS curve. This association remained consistent across the three models that accounted for confounding factors. The threshold of sleep duration was 7.5 h, which, however, cannot be used as a diagnostic criterion owing to a low reliability in our study. These findings have crucial clinical implications. Longer sleep duration is an independent risk factor for HF according to existing epidemiological studies. For instance, in a study on osteoporotic fractures by including 8,101 postmenopausal women, significantly increased risk of HFs was observed in postmenopausal women who slept longer than 10 hours(17). In our study, women accounted for a greater proportion of HFs among the older adults, which was in line with the findings of this study. A substantial prospective cohort study from the China Health and Retirement Longitudinal Study indicated a mitigated risk of HF by integrating brief sleep duration with afternoon napping(28). A recent meta-analysis in China also revealed a significant correlation of both insufficient and excessive sleep duration with the incidence of falls, with a notably stronger association between prolonged sleep duration and falls in the white population (29). HFs are caused mainly by falls among the older adults, which, to some extent, explain the increased risk of HF in case of prolonged sleep duration(30,31). HF is significantly correlated with decreased bone mineral density (BMD)(32), which facilitated the elucidation of our findings. For example, Specker et al. conducted a cross-sectional investigation involving 1,146 participants, comprising both genders aged between 20 and 60 years. Individuals who reported a nightly sleep duration of under 6.5 h had lower BMD than those whose slept over 6.5 h per night. Nevertheless, no significant distinctions in BMD at the spinal or hip areas were identified between sleep-deprived individuals of either sexes. Additional cross-sectional analyses in Japan and China both indicated that individuals with self-reported sleep duration of over 8 h were at a greater risk of developing osteoporosis than those less than 8 h in sleeping(33–35). Noticeably, even though there was a linear association between sleep duration and risk of HF, the RCS curve was close to a “U-shape”. It can be observed with no obvious change in HF risk when the sleep duration was less than 7.5 h, but increased risk when the sleep duration was over 7.5 h. In contrast, Yu et al. reported a U-shaped correlation between sleep duration and HF risk, with the lowest HF risk detected in participants who slept 7-8 h daily (19). These findings may indicate the unique nature of the association between HF and sleep duration, which can vary according to fracture location(36). Furthermore, the effect of sleep on HF risk involves many physiological and pathological processes, with unclear mechanism so far. Both short and long sleep durations are associated with the risk factors of HFs, such as osteoporosis, falls, psychological disorders, etc., all of which can mediate the effect on HF risk(10,11,37–39). Specifically, patients with long sleep duration at night predispose to depression. Cizza et al. proposed that depressed patients might present with bone loss and osteoporosis symptoms, which could be explained primarily by specific immune responses and endocrine mechanisms(40). We reckon that it may be one of the important reasons for the increased risk of HF caused by longer sleep duration. Moreover, multiple risk factors for HF, such as age, BMI, sex, and country, pose challenges to predict risk via a single variable, which may explain the limited predictive ability of ROC curve(32,41). This study firstly explored the relationship between sleep duration and HF risk among the older adults based on recent extensive datasets. For a better representation of the overall characteristics of the US population, this study utilized weights provided by NHANES to weight the sample data, in addition to data derived from a nationally representative sample. Therefore, the concluded results hold the potential to be generalized across the entire U.S. population. However, our study still has some limitations that must be acknowledged. First, this study was a cross-sectional study using the NHANES database, which was impossible to establish a causal link between sleep duration and HF risk among the older adults. Our analyses based on self-reported sleep duration might introduce recall bias and subjectivity. And this large sample size study may have overstated the statistical effect, and more clinical studies are needed to demonstrate the relationship between sleep duration and hip fracture in older adults, for example using propensity matching analysis. Moreover, the 2013-2014 NHANES data used in this study contains accelerometer data that has been used to derive multiple measures of sleep health (duration, efficiency, regularity, timing) in recent work, so this can affect the consistency of sleep duration records in this study. Finally, sleep health is multi-dimensional, but this study used self-reported sleep data and only considered one measure of sleep health, and further studies are needed to explore the effects of sleep on HF risk by including more sleep characteristics as variables such as daytime naps, snoring, difficulty falling asleep, etc(42,43). Conclusion In summary,excessive sleep duration is linearly associated with higher HF risk among the older adults based on a nationally representative study. But sleep duration cannot be used as part of a diagnostic tool. These findings suggest that an appropriate sleep duration may reduce the risk of HF among the older adults. Abbreviations HFs hip fractures HF hip fracture NHANES National Health and Nutrition Examination Survey NCHS National Center for Health Statistics RCS restricted cubic splines ROC receiver Operating Characteristic PIR poverty-to-income ratio BMI body mass index CI confidence interval AUC area under the curve OR odds ratio SD standard deviation CVD Cardiovascular disease DM diabetes medication CHARLS China Health and Retirement Longitudinal Study BMD bone mineral density Declarations Acknowledgements We acknowledge NHANES database for providing their platforms and contributors for uploading their meaningful datasets. And we thank all participants included in our present study. Data Availability All data analyzed in the current study are freely accessible on the NHANES website (https://www.cdc.gov/nchs/nhanes/index.htm). Author contributions Substantial contributions to study conception and design：HZ,ST,LT,BY. Substantial contributions to acquisition of data:HZ,LK,ST. Substantial contributions to analysis and interpretation of data: HZ,LK,ST. Drafting the article or revising it critically for important intellectual content:HZ,BY,QL. All authors read and approved the final manuscript. Final approval of the version of the article to be published:HZ,ST,LK,LT,BY,QL. Funding Not applicable. Ethics approval and consent to participate Use of the dateset from the NHANES was approved by the National Center for Health. The study was conducted in accordance with the revised Declaration of Helsinki. All informed consents were obtained prior to data collection. Consent for publication Not applicable in the declarations section. Competing interests The authors declare no competing interests. References Roberts SE, Goldacre MJ. Time trends and demography of mortality after fractured neck of femur in an English population, 1968-98: database study. BMJ. 2003 Oct 4;327(7418):771. Gullberg B, Johnell O, Kanis JA. World-wide Projections for Hip Fracture. 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Sleep. 2023 Sep 8;46(9):zsad075. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 May, 2025 Read the published version in BMC Musculoskeletal Disorders → Version 1 posted Editorial decision: Revision requested 10 Apr, 2025 Reviews received at journal 10 Apr, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviews received at journal 26 Mar, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviews received at journal 25 Mar, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviewers invited by journal 25 Mar, 2025 Submission checks completed at journal 25 Mar, 2025 First submitted to journal 24 Mar, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5688645","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434203968,"identity":"d6b86963-1c83-4dac-92d2-a992773630ea","order_by":0,"name":"Hengbo Zhang","email":"","orcid":"","institution":"Zhujiang Hospital, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hengbo","middleName":"","lastName":"Zhang","suffix":""},{"id":434203971,"identity":"d3e0d2e9-79f6-43f6-9f9f-b14284f17fed","order_by":1,"name":"Sijing Tang","email":"","orcid":"","institution":"Zhujiang Hospital, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sijing","middleName":"","lastName":"Tang","suffix":""},{"id":434203972,"identity":"3102ecff-cfc3-4c86-b819-f79774a1dfa3","order_by":2,"name":"Lingkai Kong","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lingkai","middleName":"","lastName":"Kong","suffix":""},{"id":434203973,"identity":"361c8f9d-df6f-4154-aaea-63624ff9817e","order_by":3,"name":"Lu Tang","email":"","orcid":"","institution":"Zhujiang Hospital, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Tang","suffix":""},{"id":434203974,"identity":"c34bc950-fca3-4467-8fd1-1dcc44290084","order_by":4,"name":"Qiaolan Liu","email":"","orcid":"","institution":"Zhujiang Hospital, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiaolan","middleName":"","lastName":"Liu","suffix":""},{"id":434203975,"identity":"6fc6d2eb-05e8-4644-b3e3-04c67fbb7726","order_by":5,"name":"Bo Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYJACZgYGGx5+/gbStKTJSM44QJqWwzYGDQlEKpf3P2MmXVBxnseA4QDjh485RGgxPADUMuPMbR5z5gZmyZnbiNHS2GMmzdt2m8ey4QAbMy9RWpp5QFrO8RgcSCBSizwbWMsBErQY8LAVW884k8wjOeNgM3F+ke8/vPF2QYWdPT9/88EPH4my5QCHAZTJ2ECEepAtDewPiFM5CkbBKBgFIxcAAFCPMYtz2B1TAAAAAElFTkSuQmCC","orcid":"","institution":"Zhujiang Hospital, Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-12-21 08:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5688645/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5688645/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12891-025-08721-w","type":"published","date":"2025-05-15T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79345617,"identity":"afb39f67-d8a4-4cd7-82d6-ad80a6d64f95","added_by":"auto","created_at":"2025-03-27 09:31:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":123706,"visible":true,"origin":"","legend":"\u003cp\u003eScreening procedure flowchart\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5688645/v1/30f79f423d9c6e7c733d0f41.png"},{"id":79345619,"identity":"890e4333-e342-43c8-953b-40754a3ab776","added_by":"auto","created_at":"2025-03-27 09:31:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":374565,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for sleep duration with poor accuracy (AUC :0.61-0.7 ) for distinguishing between controls and HF patients.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5688645/v1/b73a3cb5fbf62ae261e2d332.png"},{"id":79346652,"identity":"690e0e13-a3f9-4489-9ccc-a57c18ed3372","added_by":"auto","created_at":"2025-03-27 09:39:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":852792,"visible":true,"origin":"","legend":"\u003cp\u003eThe RCS curve of the association between sleep duration and HF risk among all the study participants. RCS regression was adjusted for age, gender, race, marital status, education, PIR, alcohol consumption, smoking, BMI, diabetes, CVD, and hypertension.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5688645/v1/7aa7ce4c26fe8b1f8cd45d9a.png"},{"id":83067846,"identity":"b262ba7f-9dfa-4a02-af52-d76691fb7857","added_by":"auto","created_at":"2025-05-19 16:06:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2199488,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5688645/v1/197b7a2e-d9bd-4e07-a1fb-27acc79b2de0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between sleep duration and hip fracture risk among the older adults: a cross-sectional study based on the NHANES","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHip fractures (HFs) are prevalent, particularly among the elderly, showing significant increase in their incidence owing to the intensified aging globally(1,2). With 1.31\u0026nbsp;million of HF cases in 1990, its number is expected to rise to 6.26\u0026nbsp;million worldwide by 2050(3\u0026ndash;5). Owing to serious complications as well as high mortality and disability rates, HFs are among the leading causes of loss of disability-adjusted life years among the older adults.(5). Even within a year of injury, the mortality rate is approximately 30%(1).\u003c/p\u003e \u003cp\u003eSufficient and adequate sleep is essential for maintaining optimal health and safety, whereas variations in sleep duration and timing may trigger metabolic, cardiovascular, endocrine, and neurological disorders(6\u0026ndash;8). Circadian rhythmicity and sleep behaviors are documented to be associated with bone health(9\u0026ndash;11). With disrupted circadian rhythms and impeded bone formation, sleep disorders can further increase the likelihood of fractures(12,13). Existing animal experiments have shown that long-term sleep restriction can hinder bone remodeling in laboratory rats(14,15). Therefore, disturbances in sleep physiology and the circadian rhythm may adversely affect bone health.\u003c/p\u003e \u003cp\u003eSo far, there are still insufficient observational studies of sleep behaviors and HF risk, with conflicting results as well(16\u0026ndash;18). Meanwhile, people who sleep longer were reported to be more prone to falls and fractures compared with those with shorter sleep duration(16,17). Furthermore, a U-shaped relationship has been documented prospectively between sleep duration and fracture risk(19). Therefore, it highlights the significance of recognizing modifiable risk factors, including sleep duration, for reducing the incidence of HFs, particularly among the older adults. In view of the above interpretation, this study sought to explore the association between sleep duration and the incidence of HFs in individuals aged at least 60 years old.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData and participants\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey represents a nationally representative cross-sectional study combining individual interviews, medical examinations and laboratory tests across diverse populations of America, orchestrated by the National Center for Health Statistics (NCHS) (20). NHANES-sourced data have been approved by the NCHS Ethics Review Committee, and detailed statistics can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSleep duration of patients in 2011\u0026ndash;2012 was lacking in the database. So in this study, 50,966 subjects from NHANES (2005\u0026ndash;2010 and 2013\u0026ndash;2014) were collected, with final identification of 7,540 participants through rigorous exclusion criteria: (1) under 60 years of age, (2) missing sleeping duration data, (3) missing fractured hip data, and (4) the age at which the fracture occurred was less than 60 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Finally, among the 7,540 eligible individuals enrolled in this study, 7,411 did not suffer from HFs, while 129 sustained HFs after age 60.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHF\u003c/h3\u003e\n\u003cp\u003eThis study obtained data related to self-reported HF history and age at first HF provided in the NHANES interviews. Participants were asked to answer, \"Has a doctor ever told you that you had broken or fractured hip?\" Patients with HF history were asked the following questions \u0026ldquo;How old were you when you fractured your hip the first time?\u0026rdquo; Cases of elderly HF were included if they had HFs and were older than 60 years of age for the first time (21,22).\u003c/p\u003e\n\u003ch3\u003eSleep duration\u003c/h3\u003e\n\u003cp\u003eThe sleep duration of the included participants was self-reported by answering the question of \u0026ldquo;How much sleep do you usually get at night on weekdays or workdays?\u0026rdquo; The recorded duration was a continuous variable and served as an independent variable(23).\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThe NHANES was searched to acquire self-reported demographic data such as age, sex, race/ethnicity, education, marital status, and poverty-to-income ratio (PIR). Covariates included common risk factors, such as body mass index (BMI; normal, \u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e; overweight, 25\u0026ndash;30 kg/m\u003csup\u003e2\u003c/sup\u003e; and obesity: \u0026gt;30 kg/m\u003csup\u003e2\u003c/sup\u003e), alcohol consumption, and smoking. The smoking status was classified as either \u0026ldquo;smoker\u0026rdquo; or \u0026ldquo;non-smoker\u0026rdquo; by a questionnaire. Alcohol consumption was measured by a question \u0026ldquo;During the past 12 months, on those days that drank alcoholic beverages, on the average, how many drinks did you have?\u0026rdquo; Diabetes was determined if any of the following criteria were met by the participants: a self-reported history of diabetes, a history of diabetes medication (DM), a fasting blood glucose\u0026thinsp;\u0026gt;\u0026thinsp;7.0 mmol/L, a random blood glucose\u0026thinsp;\u0026gt;\u0026thinsp;11.0 mmol/L, a glycosylated hemoglobin\u0026thinsp;\u0026gt;\u0026thinsp;6.5%, and a 2-hour OGTT glucose level\u0026thinsp;\u0026gt;\u0026thinsp;11.1 mmoL/L(24,25). Meanwhile, hypertension would exist if participants had self-reported history of hypertension, history of medication for hypertension, three times of mean diastolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;90mmHg, or three times of the mean systolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;140 mmHg(24,26). In addition, cardiovascular disease (CVD) included self-reported congestive coronary heart disease, heart failure, stroke, heart attack, and angina.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eGiven the complex multistage sampling design of the NHANES, weighted analyses were appropriately conducted via the R survey package to better represent the overall characteristics of the US population. According to the NHANES recommended Two-Year Sample Weights for MEC Examination (WTMEC2YR) records, sample weights of individuals were determined by WTMEC2YR/4. Statistical analyses in this study were completed by utilizing R 4.3.0 and SPSS 29. Normally distributed continuous variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared using two independent samples t-test between groups. Categorical variables were represented as n (%), and compared by chi-square test or Fisher's exact test. The p value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eIn the multivariable regression analyses, Model 1 was adjusted for covariates such as age, sex, education, marital status, race/ethnicity, and PIR. Based on Model 1, Model 2 incorporated adjustments for BMI, smoking, and alcohol consumption. Finally, Model 3 was additionally adjusted for diabetes, hypertension, and CVD. Furthermore, the optimal cutoff value for sleep duration was determined by receiver operating characteristic (ROC) curve analyses. Based on the computation of the area under the curve (AUC), an AUC value of \u0026ge;\u0026thinsp;0.81, 0.71\u0026ndash;0.80, 0.61\u0026ndash;0.70, and \u0026le;\u0026thinsp;0.6 indicated high, fair, poor accuracy, and a lack of discriminatory capability, respectively (27). The Youden index was used to determine the optimal threshold for distinguishing patients if the AUC exceeded 0.60. In addition, a restricted cubic spline (RCS) with four nodes at the 5th, 35th, 65th, and 95th percentiles was employed to identify a linear or nonlinear relationship between sleep duration and HF risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable 2 presents significant inter-group differences concerning various demographic and lifestyle factors. On average, individuals in the HF group were older than those in the control group (76.93 \u0026plusmn; 6.13 years vs. 70.17 \u0026plusmn; 7.10 years; p\u0026lt;0.001). The HF group included a significantly greater percentage of female participants (71.13% vs. 55.20%; p=0.002). Meanwhile, compared to the control group, the HF group presented a longer average sleep duration (7.73 \u0026plusmn; 1.68 h vs. 7.11 \u0026plusmn; 1.42 h; p=0.006), and a lower level of alcohol consumption (0.49\u0026plusmn;0.94 vs. 0.94\u0026plusmn;1.34 glasses/day; p\u0026lt;0.001). The incidence of CVD was also elevated in the HF group (37.44% vs. 23.84%; p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eFurthermore, four logistical regression models were constructed to clarify the correlation between sleep duration and HF risk among the older adults. As presented in Table 3, longer sleep duration correlated with an increased risk of HF in the crude model [OR=1.36,(1.11,1.67); p= 0.004]. Consistently, longer sleep duration was associated with an increased risk of HF in the adjusted Model 1 [OR=1.23(1.03,1.48); p= 0.025], Model 2 [OR=1.22(1.03,1.45); p= 0.024], and Model 3 [OR=1.22(1.03,1.44); p=0.021]. After adjustment for confounding variables, the OR values of the three models decreased compared with those of the crude model, yet with the same association remained.\u003c/p\u003e\n\u003cp\u003eThe ROC curve analysis (Fig.2) revealed an AUC of 0.611, indicating that sleep duration had a low predictive accuracy for HF occurrence. The optimal threshold was determined to be 7.5 h, resulting in a Youden index of 0.192, corresponding to a sensitivity of 0.597 and a specificity of 0.595. In addition, RCS curves were plotted to examine the potential nonlinearity in the studied relationship. The generated approximate U-shaped image suggested that either long or short sleep duration would increase the risk of HF. However, the RCS curve (Fig.3) still showed a linear association\u0026nbsp;(p-overall\u0026lt;0.01, p-nonlinear=0.244).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"731\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 90.8219%;\"\u003e\n \u003cp\u003e\u0026nbsp;Table1 Participant characteristics in NHANES (2005-2010 and 2013-2014).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0411%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal, N = 7,540\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl, N = 7,411\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase, N = 129\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eAge,Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e70.17\u0026plusmn;7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e70.07\u0026plusmn;7.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e76.93\u0026plusmn;6.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eSleep h,Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e7.11\u0026plusmn;1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e7.11\u0026plusmn;1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e7.73\u0026plusmn;1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eAlcohol consumption,Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e0.93\u0026plusmn;1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e0.94\u0026plusmn;1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e0.49\u0026plusmn;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e3,680 (44.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e3,629 (44.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e51 (28.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e3,860 (55.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e3,782 (55.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e78 (71.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eRace, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eNon-hispanic white\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e4,118 (79.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e4,022 (79.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e96 (84.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eNon-hispanic black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e1,482 (8.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e1,469 (8.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e13 (4.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e1,005 (4.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e991 (4.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e14 (4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e558 (2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e558 (2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e377 (4.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e371 (4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e6 (6.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eEducation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eCollege graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e1,403 (25.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e1,389 (25.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e14 (11.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eHigh school grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e1,825 (25.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e1,793 (25.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e32 (28.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003e9-11\u003csup\u003eth\u003c/sup\u003e grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e1,220 (13.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e1,192 (12.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e28 (19.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eSome college or AA degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e1,758 (26.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e1,725 (26.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e33 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eLess than 9\u003csup\u003eth\u003c/sup\u003e grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e1,334 (9.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e1,312 (9.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e22 (14.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eDM, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e5,114 (72.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e5,011 (72.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e103 (80.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e2,426 (27.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e2,400 (27.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e26 (19.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eCVD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e5,630 (75.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e5,553 (76.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e77 (62.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e1,910 (24.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e1,858 (23.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e52 (37.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003ePIR, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003e\u0026le;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e1,261 (9.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e1,240 (9.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e21 (12.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003e1.0-3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e3,653 (44.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e3,583 (44.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e70 (50.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003e\u0026ge;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e2,626 (45.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e2,588 (45.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e38 (36.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eBMI group, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e1,864 (26.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e1,811 (25.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e53 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e3,088 (38.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e3,028 (38.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e60 (42.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e2,588 (35.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e2,572 (36.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e16 (11.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eSmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e6,618 (89.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e6,505 (89.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e113 (87.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e922 (10.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e906 (10.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e16 (12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e2,225 (31.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e2,192 (31.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e33 (22.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e5,315 (68.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e5,219 (68.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e96 (77.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eMarital Status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e4,142 (60.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e4,094 (61.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e48 (37.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e1,802 (21.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e1,736 (21.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e66 (53.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e921 (11.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e911 (11.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e10 (5.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e178 (1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e177 (1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e1 (0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eLiving with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e154 (1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e154 (1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.0411%;\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1507%;\"\u003e\n \u003cp\u003e343 (3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3288%;\"\u003e\n \u003cp\u003e339 (3.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3014%;\"\u003e\n \u003cp\u003e4 (3.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.17808%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 Association between sleep duration and HF risk among the older adults\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"130%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eOR(95％CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eOR(95％CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eOR(95％CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eOR(95％CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eSleep duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1.36 (1.11, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.23 (1.03, 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1.22 (1.03, 1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1.22 (1.03, 1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 100px;\"\u003e\n \u003cp\u003eCrude represents an unadjusted model. Model 1 was adjusted for age, gender, race, marital status, education, and PIR. Model 2 was adjusted for alcohol consumption past 12 months, smoking past 30 days, and BMI groups, based on Model 1. Model 3 was adjusted for diabetes, CVD, and hypertension, based on Model 2.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this population-based retrospective cross-sectional study, sleep duration was associated with HF risk among the older adults according to analyses based on logistic regression models. There was a linear correlation between sleep duration and HF risk, as supported by multiple logistic regression analysis and the RCS curve. This association remained consistent across the three models that accounted for confounding factors. The threshold of sleep duration was 7.5 h, which, however, cannot be used as a diagnostic criterion owing to a low reliability in our study.\u003c/p\u003e\n\u003cp\u003eThese findings have crucial clinical implications. Longer sleep duration is an independent risk factor for HF according to existing epidemiological studies. For instance, in a study on osteoporotic fractures by including 8,101 postmenopausal women, significantly increased risk of HFs was observed in postmenopausal women who slept longer than 10 hours(17). In our study, women accounted for a greater proportion of HFs among the older adults, which was in line with the findings of this study. A substantial prospective cohort study from the China Health and Retirement Longitudinal Study indicated a mitigated risk of HF by integrating brief sleep duration with afternoon napping(28). A recent meta-analysis in China also revealed a significant correlation of both insufficient and excessive sleep duration with the incidence of falls, with a notably stronger association between prolonged sleep duration and falls in the white population (29). HFs are caused mainly by falls among the older adults, which, to some extent, explain the increased risk of HF in case of prolonged sleep duration(30,31). HF is significantly correlated with decreased bone mineral density (BMD)(32), which facilitated the elucidation of our findings. For example, Specker et al. conducted a cross-sectional investigation involving 1,146 participants, comprising both genders aged between 20 and 60 years. Individuals who reported a nightly sleep duration of under 6.5 h had lower BMD than those whose slept over 6.5 h per night. Nevertheless, no significant distinctions in BMD at the spinal or hip areas were identified between sleep-deprived individuals of either sexes. Additional cross-sectional analyses in Japan and China both indicated that individuals with self-reported sleep duration of over 8 h were at a greater risk of developing osteoporosis than those less than 8 h in sleeping(33\u0026ndash;35).\u003c/p\u003e\n\u003cp\u003eNoticeably, even though there was a linear association between sleep duration and risk of HF, the RCS curve was close to a \u0026ldquo;U-shape\u0026rdquo;. It can be observed with no obvious change in HF risk when the sleep duration was less than 7.5 h, but increased risk when the sleep duration was over 7.5 h. In contrast, Yu et al. reported a U-shaped correlation between sleep duration and HF risk, with the lowest HF risk detected in participants who slept 7-8 h daily\u0026nbsp;(19). These findings may indicate the unique nature of the association between HF and sleep duration, which can vary according to fracture location(36).\u003c/p\u003e\n\u003cp\u003eFurthermore, the effect of sleep on HF risk involves many physiological and pathological processes, with unclear mechanism so far. Both short and long sleep durations are associated with the risk factors of HFs, such as osteoporosis, falls, psychological disorders, etc., all of which can mediate the effect on HF risk(10,11,37\u0026ndash;39). Specifically, patients with long sleep duration at night predispose to depression. Cizza et al. proposed that depressed patients might present with bone loss and osteoporosis symptoms, which could be explained primarily by specific immune responses and endocrine mechanisms(40). We reckon that it may be one of the important reasons for the increased risk of HF caused by longer sleep duration. Moreover, multiple risk factors for HF, such as age, BMI, sex, and country, pose challenges to predict risk via a single variable, which may explain the limited predictive ability of ROC curve(32,41).\u003c/p\u003e\n\u003cp\u003eThis study firstly explored the relationship between sleep duration and HF risk among the older adults based on recent extensive datasets. For a better representation of the overall characteristics of the US population, this study utilized weights provided by NHANES to weight the sample data, in addition to data derived from a nationally representative sample. Therefore, the concluded results hold the potential to be generalized across the entire U.S. population. However, our study still has some limitations that must be acknowledged. First, this study was a cross-sectional study using the NHANES database, which was impossible to establish a causal link between sleep duration and HF risk among the older adults. Our analyses based on self-reported sleep duration might introduce recall bias and subjectivity. And this large sample size study may have overstated the statistical effect, and more clinical studies are needed to demonstrate the relationship between sleep duration and hip fracture in older adults, for example using propensity matching analysis. Moreover, the 2013-2014 NHANES data used in this study contains accelerometer data that has been used to derive multiple measures of sleep health (duration, efficiency, regularity, timing) in recent work, so this can affect the consistency of sleep duration records in this study. Finally, sleep health is multi-dimensional, but this study used self-reported sleep data and only considered one measure of sleep health, and further studies are needed to explore the effects of sleep on HF risk by including more sleep characteristics as variables such as daytime naps, snoring, difficulty falling asleep, etc(42,43).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary,excessive sleep duration is linearly associated with higher HF risk among the older adults based on a nationally representative study. But sleep duration cannot be used as part of a diagnostic tool. These findings suggest that an appropriate sleep duration may reduce the risk of HF among the older adults.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHFs \u0026nbsp; \u0026nbsp;hip fractures\u003c/p\u003e\n\u003cp\u003eHF \u0026nbsp; \u0026nbsp;hip fracture\u003c/p\u003e\n\u003cp\u003eNHANES \u0026nbsp; \u0026nbsp;National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003eNCHS \u0026nbsp; \u0026nbsp;National Center for Health Statistics\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRCS \u0026nbsp; \u0026nbsp;restricted cubic splines\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003ePIR \u0026nbsp; \u0026nbsp;poverty-to-income ratio\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp;body mass index\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp;confidence interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp;area under the curve\u003c/p\u003e\n\u003cp\u003eOR \u0026nbsp; \u0026nbsp;odds ratio\u003c/p\u003e\n\u003cp\u003eSD \u0026nbsp; \u0026nbsp;standard deviation\u003c/p\u003e\n\u003cp\u003eCVD \u0026nbsp; \u0026nbsp;Cardiovascular disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDM \u0026nbsp; \u0026nbsp;diabetes medication\u003c/p\u003e\n\u003cp\u003eCHARLS \u0026nbsp; \u0026nbsp;China Health and Retirement Longitudinal Study\u003c/p\u003e\n\u003cp\u003eBMD \u0026nbsp; \u0026nbsp;bone mineral density\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge NHANES database for providing their platforms and contributors for uploading their meaningful datasets. And we thank all participants included in our present study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analyzed in the current study are freely accessible on the NHANES website (https://www.cdc.gov/nchs/nhanes/index.htm).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubstantial contributions to study conception and design：HZ,ST,LT,BY. \u0026nbsp;Substantial contributions to acquisition of data:HZ,LK,ST.\u0026nbsp;Substantial contributions to analysis and interpretation of data:\u0026nbsp;HZ,LK,ST.\u0026nbsp;Drafting the article or revising it critically for important intellectual content:HZ,BY,QL.\u0026nbsp;All authors read and approved the final manuscript.\u0026nbsp;Final approval of the version of the article to be published:HZ,ST,LK,LT,BY,QL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse of the dateset from the NHANES was approved by the National Center for Health. The study was conducted in accordance with the revised Declaration of Helsinki. All informed consents were obtained prior to data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable in the declarations section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRoberts SE, Goldacre MJ. Time trends and demography of mortality after fractured neck of femur in an English population, 1968-98: database study. BMJ. 2003 Oct 4;327(7418):771. \u003c/li\u003e\n\u003cli\u003eGullberg B, Johnell O, Kanis JA. World-wide Projections for Hip Fracture. Osteoporos Int. 1997 Sep 1;7(5):407\u0026ndash;13. \u003c/li\u003e\n\u003cli\u003eCooper C, Campion G, Melton LJ. 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Front Endocrinol. 2023 Nov 28;14:1298345. \u003c/li\u003e\n\u003cli\u003eLiu X, Jin X, Cen W, Liu Y, Luo S, You J, et al. Building a predictive model for depression risk in fracture patients: insights from cross-sectional NHANES 2005\u0026ndash;2020 data and an external hospital-based dataset. BMC Public Health. 2024 Aug 27;24:2328. \u003c/li\u003e\n\u003cli\u003eNahm FS. Receiver operating characteristic curve: overview and practical use for clinicians. Korean J Anesthesiol. 2022 Jan 18;75(1):25. \u003c/li\u003e\n\u003cli\u003eZhu C, Sun J, Huang Y, Lian Z. Sleep and risk of hip fracture and falls among middle-aged and older Chinese. Sci Rep. 2024 Oct 7;14:23273. \u003c/li\u003e\n\u003cli\u003eWu L, Sun D. Sleep duration and falls: a systemic review and meta-analysis of observational studies. J Sleep Res. 2017;26(3):293\u0026ndash;301. \u003c/li\u003e\n\u003cli\u003eNasiri Sarvi M, Luo Y. Sideways fall-induced impact force and its effect on hip fracture risk: a review. Osteoporos Int. 2017 Oct 1;28(10):2759\u0026ndash;80. \u003c/li\u003e\n\u003cli\u003eGreen C, Molony D, Fitzpatrick C, O\u0026rsquo;Rourke K. Age-specific incidence of hip fracture among the older adults: a healthy decline. Surg J R Coll Surg Edinb Irel. 2010 Dec;8(6):310\u0026ndash;3. \u003c/li\u003e\n\u003cli\u003eCummings SR, Melton LJ. Epidemiology and outcomes of osteoporotic fractures. Lancet Lond Engl. 2002 May 18;359(9319):1761\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eKobayashi D, Takahashi O, Deshpande GA, Shimbo T, Fukui T. Association between osteoporosis and sleep duration in healthy middle-aged and elderly adults: A large-scale, cross-sectional study in Japan. Sleep Breath. 2012;16(2):579\u0026ndash;83. \u003c/li\u003e\n\u003cli\u003eFu X, Zhao X, Lu H, Jiang F, Ma X, Zhu S. Association between sleep duration and bone mineral density in Chinese women. Bone. 2011 Nov;49(5):1062\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eMoradi S, Shab-Bidar S, Alizadeh S, Djafarian K. Association between sleep duration and osteoporosis risk in middle-aged and elderly women: A systematic review and meta-analysis of observational studies. Metabolism. 2017 Apr;69:199\u0026ndash;206. \u003c/li\u003e\n\u003cli\u003eHuang T, Tworoger SS, Redline S, Curhan GC, Paik JM. Obstructive Sleep Apnea and Risk For Incident Vertebral and Hip Fracture in Women. J Bone Miner Res Off J Am Soc Bone Miner Res. 2020 Sep 9;35(11):2143. \u003c/li\u003e\n\u003cli\u003eSwanson CM. Sleep disruptions and bone health: what do we know so far? Curr Opin Endocrinol Diabetes Obes. 2021 Aug 1;28(4):348\u0026ndash;53. \u003c/li\u003e\n\u003cli\u003eDepner CM, Rice JD, Tussey EJ, Eckel RH, Bergman BC, Higgins JA, et al. Bone turnover marker responses to sleep restriction and weekend recovery sleep. Bone. 2021 Nov;152:116096. \u003c/li\u003e\n\u003cli\u003eHughes JM, Smith MA, Henning PC, Scofield DE, Spiering BA, Staab JS, et al. Bone formation is suppressed with multi-stressor military training. Eur J Appl Physiol. 2014 Nov;114(11):2251\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eCizza G, Primma S, Csako G. Depression as a risk factor for osteoporosis. Trends Endocrinol Metab. 2009 Oct 1;20(8):367\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eKanis JA, Od\u0026eacute;n A, McCloskey EV, Johansson H, Wahl DA, Cooper C, et al. A systematic review of hip fracture incidence and probability of fracture worldwide. Osteoporos Int. 2012 Mar 15;23(9):2239. \u003c/li\u003e\n\u003cli\u003eBuysse DJ. Sleep health: can we define it? Does it matter? Sleep. 2014 Jan 1;37(1):9\u0026ndash;17. \u003c/li\u003e\n\u003cli\u003eLee S, Kaufmann CN. Multidimensional sleep health approach to evaluate the risk of morbidity and mortality in diverse adult populations. Sleep. 2023 Sep 8;46(9):zsad075.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5688645/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5688645/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere has been sharp increase in the incidence of hip fractures (HFs) with the increasing aging globally. However, it remains ambiguous regarding the association between HF risk and sleep duration. This study intended to explore the association between sleep duration and HF risk among the older adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study assessed a cohort of 7,540 participants aged at least 60 years old using data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2010, as well as from 2013 to 2014. Two distinct groups of HF and non-HF were constructed on the basis of their history of HFs. Based on the self-reported sleep duration through a structured questionnaire, multivariate logistic regression analyses were conducted to examine the relationship between sleep duration and HF risk. In addition, restricted cubic splines (RCS) were used to assess linearity. The receiver operating characteristic (ROC) curve was used to explore the threshold of sleep duration for HF risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 7540 participants over 60 years of age with mean age of 70.17 ±7.1 years, 129 had HF. Significant differences in sleep duration were observed between the HF and non-HF groups (7.73 ± 1.68 h vs. 7.11 ± 1.42 h; p=0.006). The multivariate analysis was adjusted for sociodemographic, behavioral lifestyle, and comorbidities. A one hour increase in sleep duration was associated with higher odds of having prior hip fractures in unadjusted models （OR=1.36; 1.11, 1.67; p=0.004), minimally adjusted models (OR=1.23; 1.03, 1.48; p=0.025), second adjusted models (OR=1.22; 1.03,1.45; p= 0.024) and fully adjusted models (OR=1.22; 1.03,1.44; p=0.024). The relationship remained consistent across all four models, indicating the correlation of a longer sleep duration with an elevated HF risk. RCS analysis revealed a statistically linear relationship between sleep duration and HF incidence (p-nonlinear=0.244, p-overall\u0026lt;0.01). In addition, the identified threshold of sleep duration linked to HF risk was determined to be 7.5 h among the older adults (AUC=0.611).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSleep duration correlates with HF risk among the older adults. Findings in this study inspire that an appropriate sleep duration may reduce the risk of HF among the older adults.\u003c/p\u003e","manuscriptTitle":"Association between sleep duration and hip fracture risk among the older adults: a cross-sectional study based on the NHANES","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 09:31:45","doi":"10.21203/rs.3.rs-5688645/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-10T16:44:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T14:49:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316485900674702706655742029738943453281","date":"2025-03-31T22:30:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-26T09:36:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12586356119237039937815707522440951931","date":"2025-03-26T09:30:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-26T01:00:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309597612769565964882920195063597810945","date":"2025-03-26T00:26:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-25T21:53:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T16:22:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Musculoskeletal Disorders","date":"2025-03-24T16:59:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4cd52ff6-3c27-44e4-813a-88fb14fbedc8","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T16:02:03+00:00","versionOfRecord":{"articleIdentity":"rs-5688645","link":"https://doi.org/10.1186/s12891-025-08721-w","journal":{"identity":"bmc-musculoskeletal-disorders","isVorOnly":false,"title":"BMC Musculoskeletal Disorders"},"publishedOn":"2025-05-15 15:57:54","publishedOnDateReadable":"May 15th, 2025"},"versionCreatedAt":"2025-03-27 09:31:45","video":"","vorDoi":"10.1186/s12891-025-08721-w","vorDoiUrl":"https://doi.org/10.1186/s12891-025-08721-w","workflowStages":[]},"version":"v1","identity":"rs-5688645","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5688645","identity":"rs-5688645","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}