Construction and validation of a nomogram to predict frailty in Chinese patients with hypertension: evidence from the China Health and Retirement Longitudinal Study (CHARLS)

Construction and validation of a nomogram to predict frailty in Chinese patients with hypertension: evidence from the China Health and Retirement Longitudinal Study (CHARLS) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Construction and validation of a nomogram to predict frailty in Chinese patients with hypertension: evidence from the China Health and Retirement Longitudinal Study (CHARLS) Guiping Wu, Huarong Wu, Zexue Shen, Ruizhe Chen, Xiaowen Che This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4853180/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Frailty is common in patients with hypertension and predisposes patients to poor postoperative outcomes. However, there is a lack of a prediction model for frailty in patients with hypertension. Therefore, we established a nomogram to identify those at risk for frailty in hypertension, providing implications for health interventions and community services. Methods The patients diagnosed with hypertension were collected from the the China Health and Retirement Longitudinal Study (CHARLS) database and were randomly divided into a training cohort and a validation group at a ratio of 7:3. The independent risk factors of frailty were determined by LASSO regression and multivariable logistic regression and a nomogram predict model for frailty was established. The performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA). Results The content of the nomogram includes age, health status, cognitive function, and the Center for Epidemiological Studies Depression (CES-D). The AUCs of the training cohort and validation cohort (0.830 and 0.854) suggested good discrimination of the nomogram. Calibration curves and DCA curves proved accuracy and clinical applicability. Conclusion The nomogram we created for the frailty in hypertensive patients had good performance and applicability, which could help clinicians in the medical decision-making process. hypertension frailty nomogram Lasso-logistic regression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Hypertension is the main risk factor for inducing cardiovascular diseases (CVDs), which are the most common cause of death from non-communicable diseases 1,2 . Hypertension has emerged as the leading cause of death, accounting for 9.4 million deaths each year 3 . In China, high systolic BP (SBP) was the top risk factor for both the number of deaths and the percentage of the disability-adjusted life years (DALYs) 4 . Although 46.9% of hypertensive patients were aware of their condition and 40.7% were taking prescription antihypertensive medications, only 15.3% achieved blood control 5 . Hypertension results in substantial disability and places a heavy burden on the healthcare system. The pathophysiology of hypertension is shaped by a combination of environmental, genetic, anatomical, neurologic, endocrine, humoral, and hemodynamic factors 6 . In addition, psychosocial factors, including occupational stress, socioeconomic stress, anxiety, and depression, are possible enhancers and triggers of hypertension. Higher levels of psychosocial stress occur in those with hypertension compared to those with normal blood pressure 7 . Defined as a state of decreased physiological reserve and increased vulnerability to possible intrinsic or extrinsic stressors, frailty involves several domains: physical, psychological, social, and others, which amplifies the risk of adverse health outcomes 8–10 . Moreover, frailty has been proven to be associated with cardiovascular mortality and morbidity, and multiple mechanisms (physical inactivity, subclinical vascular and cardiac alterations, oxidative stress, deoxyribonucleic acid damage and telomere length shortening inflammatory markers, endocrine dysregulation, accelerated cellular senescence, and epigenetic modifications) contribute to the association between frailty and symptomatic cardiovascular disease 11–13 . Recognition of frailty status can help physicians determine surgical risk and prognosis 14 . However, there is a lack of a prediction model for frailty in patients with hypertension. Since frailty is reversible, early intervention can improve patient prognosis. Therefore, we established a nomogram to identify those at risk for frailty in hypertension, providing implications for health interventions and community services that can slow the progression of frailty and improve the quality of life in the elderly population. Result 2.1 Participant characteristics In 2013 and 2015, 4725 and 5193 patients with hypertension were recruited, removing duplicates for a total of 4501 patients from the China Health and Retirement Longitudinal Study (CHARLS). The mean age of the 4501 enrolled adults with hypertension was 61.8 years (SD: ± 9.3), with 2103 (46.7%) male and 2398 (53.5%) female. For health factors, there were 583 (13.0%) patients with diabetes and 374 (8.3%) with kidney disease. In addition, the mean cognition score was 10.7 (SD: ± 4.1), and 1640 (36.4%) of the participants had depression (Table 1 ). The prevalence of frailty in hypertension was 11.9% (534/4501). Most variables were significantly different when compared between older hypertension patients with frailty and non-frailty (Table 1 ). Baseline data showed that compared to the non-frailty patients, the frailty patients mainly comprised women (66.5% VS. 51.5%, P < 0.001) and patients of a higher average age (64.4 ± 9.5 VS. 61.4 ± 9.2, P < 0.001). It was noteworthy that patients with frailty had more chronic conditions, along with poor cognition score (8.4 ± 4.2 VS. 11.0 ± 4.0, P < 0.001) and a significant rate of depression (22.3% VS. 69.1%, P < 0.001). All patients were completely randomized in a ratio of 7:3 into a training cohort (N = 3151) and a validation cohort (N = 1350). There was no significant difference among the following variables between the training cohort and validation cohort (P > 0.05), indicating that the random grouping was rational (Table 2 ). 2.2 Prediction model built based on Lasso-logistic regression 2.2.1 Independent risk factors for hypertension patients with frailty The training cohort was used to establish a prognostic model, and the validation cohort was used for validation purposes. The parameters were firstly screened by a LASSO regression analysis, and the variation characteristics of the coefficient of these variables were shown in Fig. 1 . The model performance was excellent with the λ of the standard error (0.02293). Variables based on LASSO regression were further analyzed by multivariate logistic regression to screen independent risk factors for frailty, including age (OR: 1.04, 95%CI: 1.02–1.05, P < 0.001), health status (OR: 0.53, 95%CI: 0.43–0.65, P < 0.001), cognitive function (OR: 0.93, 95%CI: 0.43–0.96, P < 0.001), and the Center for Epidemiological Studies Depression (CES-D, OR: 1.15, 95%CI: 1.13–1.17, P < 0.001) (Table 3 ). 2.2.2 Construct the nomogram A nomogram was developed based on the independent factors of the LASSO-logistic regression (Fig. 2 ). The AUC of the predictive model was 0.830 (Fig. 3 ). The calibration curve showed the agreement between the predicted and actual results (Fig. 4 ). Additionally, the clinical validity of the nomogram was evaluated by the decision curve analysis (DCA). From the DCA curve, the nomogram could acquire the net benefit in most of the reasonable threshold probabilities (Fig. 5 ). 2.2.3 Validate the nomogram In order to verify the robustness of the nomogram, an internal validation cohort was performed. The AUC in the validation set was 0.854 (Fig. 3 ). Good linearity and clinical applicability of the nomogram were found in the calibration and DCA curves (Figs. 4 and 5 ). Discussion Frailty represents a global health issue with high prevalence and adverse health effects 15,16 . In our study, the probability of frailty in hypertensive patients was 11.9%. For patients with hypertension, accurate and prompt diagnosis of frailty is important because it allows for effective interventions and treatments 17 . Therefore, we established a reliable model to identify the risk of frailty in hypertensive patients and assist in its management to help clinicians and patients share decisions. In our research, we utilized LASSO regression to screen variables and adjust for complexity. Compared to the univariate regression analysis, Lasso regression can minimize the multicollinearity in variables. The nomogram contains four risk factors, including age, health status, cognitive function, and CES-D. The use of the nomogram is that the corresponding predicted values are first transformed into the corresponding nomogram scores. The scores are summed, and the probability of a hypertensive patient being frail is derived from the realistic values at the intersection points. The AUCs and calibration curves suggested that the nomogram had good discrimination and accuracy in both the training and validation cohorts, while the DCA curves further showed that the nomogram conferred high clinical net benefits. At present, most of the studies on frailty have been done on the Western population; few have been done on the Chinese or oriental populations. Recently, standardized definitions of frailty have been proposed, which were characterized by unintentional weight loss, self-reported fatigue, weakness, slower walking speed, and reduced physical activity 8,18 . Due to the aging and increasingly complex nature of cardiovascular patients, there is growing awareness of frailty in cardiovascular medicine 14,19 . The selection of hypertension treatment might also be influenced by frailty. Frail older adults are always excluded from randomized controlled trials (RCTs) for cardiovascular disease, including hypertension 20 . This limited the generalizability of the results, making it difficult to accurately evaluate the safety and effectiveness of chronic disease treatments for frail individuals. Secondly, frailty is associated with reduced life expectance and lifetime morbidity. According to the results of the SHARE study, the life expectance of males was 0.1–0.8 years, and females was 0.4–5.5 years in frail individuals at age 70 21 . Thus, the duration of benefit from specific treatments may exceed life expectancy in frail individuals 22 . Finally, frailty is linked to poor adherence to antihypertensive medication 23 . Aging is the primary risk factor for the majority of chronic diseases 24 . Over increasing timescales, all people accumulate molecular and cellular damage, and these aging processes, including inflammation, genomic instability, and epigenetic changes, are highly inter-correlated 25,26 . Many of these processes are related to frailty. The prevalence of frailty among community-dwelling elderly increases with advanced age: 4% for elderly aged 65 to 69 years, 7% for elderly aged 70 to 74 years, 9% for elderly aged 75 to 79 years, 16% for elderly aged 80 to 84 years, and 26% for elderly aged 85 and over. Besides, we found that frailty has a tight correlation with cognition. Previously, it was demonstrated that changes in physical activity and disease are highly correlated with cognitive and functional outcomes in the elderly 27–29 . Cognitive function and frailty may be related by pathogenic mechanisms such as chronic inflammation and oxidative stress 30 . Reduced cognitive function can decrease the self-care ability and adherence to hypertension treatment, which further exacerbates disease progression and leads to a higher risk of frailty. Depressive symptoms were assessed using the CESD score. Depression predicts frailty due to reduced social relationships, gait speed, and physical activity 31 . Subclinical vascular disease (white matter disease) in patients with late life depression has been considered a key factor in pre-frailty 32,33 . Accumulating evidence supports a positive association between frailty and inflammatory cytokines such as IL-6, which is also elevated in depressed patients 34,35 . Mitochondrial dysfunction has been observed in several neurodegenerative diseases. Muscle biopsies obtained from depressed participants had decreased ATP production and impaired mitochondrial respiration, which is strongly associated with symptoms of frailty 36 . Furthermore, depression can adversely affect psychological status and exacerbate debilitating episodes by reducing social activities. Health status plays a direct role in frailty. The comorbidities of cardiovascular disease can exacerbate the clinical course, compromise treatment, and worsen outcomes 37,38 . The burden of cardiovascular disease is associated with increased short and long-term morbidity 39,40 . However, our study still has some limitations. First, the CHARLS data did not include a number of potential predictors in hypertension, such as metabolic syndrome and family history. Moreover, the nomogram was constructed based on the Chinese population, and its applicability to populations in other countries requires further validation by an external validation cohort. Lastly, originating from a retrospective cohort, the nomogram needs to be verified using a larger sample size and prospective set. Method 4.1 Study design The population of the study data was gathered from the CHARLS. The aim of CHARLS is to collect high-quality data about Chinese residents aged 45 and older to analyze the problem of population aging in China and promote interdisciplinary research on aging issues 41 . The multistage, stratified, probability proportional to size (PPS) sampling was done on randomly selected subjects in 28 provinces in 2011, 2013, 2015, and 2018. Eligible participants were interviewed face-to-face using computer-assisted personal interviews (CAPI). The questionnaire collected demographics, health status and functioning, old age security, and health care information. The CHARLS study was approved by the institutional review board of Peking University, and all participants gave written informed consent. For further details on this procedure, see the previously published study 41 . All the methods were carried out in accordance with relevant guidelines and regulations. For the present study, data form patients from 2013 and 2015 were extracted from the CHARLS study. We included individuals who met all of the following criteria: (1) aged ≥ 45 years; (2) patients with hypertension; (3) individuals with complete baseline information. 4.2 Data collection 4.2.1 The definition of hypertension and frailty Hypertension was defined as systolic blood pressure (SBP) ≥ 140mmHg and/or diastolic blood pressure (DBP) ≥ 90mmHg and /or taking antihypertensive medications 42 . Frailty status was measured through the frailty index (FI), a commonly used tool for assessing frailty 43,44 . FI can reflect changes in biological aging and the health trajectory of patients over time, which is constructed based on the accumulation of a range of physical, psychological, cognitive, and functional deficits in individuals. Frailty encompasses slowness, weakness, low physical activity, exhaustion, and shrinking, the details of which are below: Measurement of weakness using the self-report of “having difficulty in lifting or carrying something as heavy as 5 kg” 45 ; Slowness was considered to be present if the individuals had difficulty walking 100 meters or climbing several flights of stairs without resting; Exhaustion was assessed if a subject answered “often” or “most of the time” the question from the Center for Epidemiological Studies Depression (CES-D) Sale (Chinese version): “In the last week, I felt that everything I did was an effort” and “In the last week I could out not get going” 8 ; Low physical activity was defined if the respondent answered “No” when asked, “During a usual week, did you walk at least 10 minutes continuously?” Weight loss was measured by unintentional loss of 5 kg in the past six months or a current body mass index (BMI) < 18.5 kg/m 2 . It turns out that, in fact, weight loss is a better indicator of frailty than BMI and energy intake 46 . Patients were classified as non-frail (having 0 indicators), pre-fail (having 1–2 indicators), and frail (having three or more indicators). 4.2.2 Socio-demographic Factors Socia-demographic Factors included age, gender, marital status, education level, and insurance. Gender was male or female. Marital status was categorized as “married” if an individual lived with spouse and “unmarried” if an individual had never married or was widowed or divorced. Insurance was grouped “yes” or “no.” 4.2.3 Behavioral Factors Behavioral factors encompassed social activities, sleep activity, sleep duration, and life satisfaction. Smoking history, drinking history, and social activities were recorded as “Yes” or “No”. According to the response of, “My sleep was restless,” sleep quality was categorized into four groups. Total nighttime sleep duration was obtained from the question, “In the past month, on an average night, how many hours of actual sleep did you get at night?”. 4.2.4 Health factors Health status consisted of self-reported health, history of chronic diseases (dyslipidemia, diabetes, cancer, chronic lung disease, kidney disease, digestive disease, stroke, asthma, arthritis or rheumatism, and mental disease), vision, hearing, cognitive score, and depression. History of chronic diseases was classified as “yes” or “no” based on self-reported diagnosis. Vision, hearing, and self-reported health status were categorized as “good,” “fair,” and “poor.” Cognitive functions include attention, memory, orientation, and visuospatial skills. A total score can range from 0 to 21, where a higher score indicates better cognition. Individuals were screened for depression with the Centre for Epidemiologic Studies Depression Scale ( CES-D), which consisted of 21 items. The scores of 10 or greater signified the presence of depression. 4.3 Statistical methods Continuous variables were reported as means ± standard deviations, and the categorical variables were expressed as frequency (percentage). Comparisons between groups were carried out with a t-test, ANOVA, and Chi-square test. Lasso regression was performed for risk factor selection, and the independent risk factors for frailty were identified by multivariate logistic regression analysis. The identified independent risk factors were used to develop the nomogram, and the nomogram was validated in the validation cohort. The receiver operating characteristic (ROC) curves were drawn, and the area under the ROC curve (AUC) was calculated to evaluate the predictive ability of the nomogram. The calibration curve (Hosmer-Lemeshow test) was utilized to assess the predictive accuracy of the nomogram. Decision Curve Analysis (DCA) curves were performed to demonstrate the clinical validity of the nomogram by quantifying the net benefits under different threshold probabilities. All data were analyzed using R software (version 4.4.1), and P-value less than 0.05 (two-sided) was considered to be statistically significant. Conclusion In our study, we created a reliable nomogram to predict frailty in hypertensive patients based on LASSO-logistic regression analysis, incorporating age, health status, cognitive function, and CESD. The nomogram achieved good performance and applicability, which could help clinicians in the medical decision-making process. Declarations Ethics approval and consent to participate Ethics approval for the study was granted by the Ethics Review Committee of Peking University (approved number: IRB00001052–11015), and all the participants provided signed informed consent at the time of participation. Consent for publication Not applicable. Availability of data and materials The data generated and analyzed during the study are available in the CHARLS website, available in http://CHARLS.pku.edu.cn. Competing interests The authors declare that they have no competing interests Funding This study was supported by the Research Project of Shanxi Provincial Health Commission (Grant No: 2021111, GW). Authors' contributions Conceived and designed the protocol: W.G.P and W.H.R; Collected data: C.X.W and W.H.R; Analyzed data: W.G.P; Wrote the manuscript: W.G.P and C.X.W; Critically revised the manuscript: C.R.Z and S.Z.X; All authors contributed to the article and approved the submitted version. Acknowledgements The authors highly appreciate the China Health and Retirement Longitudinal Study (CHARLS) team for providing high-quality data. Data avaliability statement The data generated and analyzed during the study are available in the CHARLS website, available in http://CHARLS.pku.edu.cn. References Maleki, B. et al. 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J Clin Epidemiol 52 , 1015-1021 (1999). https://doi.org:10.1016/s0895-4356(99)00077-3 Tables Table 1 Baseline characteristics of study participants Overall Non-frailty Frailty P-value Age Gender (%) Male Female 61.8 ± 9.3 2103 (46.7) 2398 (53.3) 61.4 ± 9.2 1924 (48.5) 2043 (51.5) 64.4 ± 9.5 179 (33.5) 355 (66.5) <0.001 <0.001 Marital status (%) Unmarried Married 630 (14.0) 3871 (86.0) 523 (13.2) 3444 (86.8) 107 (20.0) 427 (80.0) <0.001 Smoking (%) 0.141 Yes No 261 (5.8) 4240 (94.2) 238 (6.0) 3729 (94.0) 23 (4.3) 511 (95.7) Social activity (%) <0.001 Yes No Sleep duration (h) Dyslipidemia (%) Diabetes (%) Cancer (%) Liver disease (%) Heart disease (%) Stroke (%) Kidney disease (%) Digestive disease (%) Mental disease (%) Arthritis or rheumatism (%) Asthma (%) Waistline (cm) Health status (%) Poor Fair Good Vision (%) Poor Fair Good Hearing (%) Poor Fair Good Depression Yes No Cognitive function 2690 (59.8) 1811 (40.2) 6.2 ± 1.9 1023 (22.7) 583 (13.0) 37 (0.8) 186 (4.1) 1049 (23.3) 196 (4.4) 374 (8.3) 977 (21.7) 49 (1.1) 1572 (34.9) 162 (3.6) 90.5 ± 11.9 1564 (34.7) 2223 (49.4) 714 (15.9) 495 (11.0) 3240 (72.0) 766 (17.0) 692 (15.4) 2281 (50.7) 1528 (33.9) 1640 (36.4) 2861 (63.6) 10.7 ± 4.1 2450 (61.8) 1517 (38.2) 6.3 ± 1.8 892 (22.5) 487 (12.3) 31 (0.8) 157 (4.0) 862 (21.7) 158 (4.0) 299 (7.5) 802 (20.2) 36 (0.9) 1299 (32.7) 128 (3.2) 90.9 ± 11.9 1206 (30.4) 2071 (52.2) 690 (17.4) 367 (9.3) 2885 (72.7) 715 (18.0) 535 (13.5) 2029 (51.1) 1403 (35.4) 1225 (30.9) 2742 (69.1) 11.0 ± 4.0 240 (44.9) 294 (55.1) 5.2 ± 2.4 131 (24.5) 96 (18.0) 6 (1.1) 29 (5.4) 187 (35.0) 38 (7.1) 75 (14.0) 175 (32.8) 13 (2.4) 273 (51.1) 34 (6.4) 87.6 ± 11.9 358 (67.0) 152 (28.5) 24 ( 4.5) 128 (24.0) 355 (66.5) 51 (9.6) 157 (29.4) 252 (47.2) 125 (23.4) 415 (77.7) 119 (22.3) 8.4 ± 4.2 <0.001 0.315 <0.001 0.571 0.136 <0.001 <0.001 <0.001 <0.001 0.003 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 Notes : Continuous variables were reported as mean and standard deviation (mean ± SD), while categorical variables were expressed as frequency (percentage). Table 2 Characteristics for training and validation cohorts Training cohort (N=3151) Validation cohort (N=1350) P-value Age 61.8 ± 9.3 61.8 ± 9.2 0.876 Gender (%) Male Female 1474 (46.8) 1677 (53.2) 629 (46.6) 721 (53.4) 0.935 Marital status (%) Unmarried Married 449 (14.2) 2702 (85.8) 181 (13.4) 1169 (86.6) 0.484 Smoking (%) 0.101 Yes No 195 (6.2) 2956 (93.8) 66 (4.9) 1284 (95.1) Social activity (%) 0.878 Yes No Sleep duration (h) Dyslipidemia (%) Diabetes (%) Cancer (%) Liver disease (%) Heart disease (%) Stroke (%) Kidney disease (%) Digestive disease (%) Mental disease (%) Arthritis or rheumatism (%) Asthma (%) Waistline (cm) Health status (%) Poor Fair Good Vision (%) Poor Fair Good Hearing (%) Poor Fair Good Depression Yes No Cognitive function 1265 (40.1) 1886 (59.9) 6.2±1.9 713 (22.6) 393 (12.5) 27 (0.9) 131 (4.2) 734 (23.3) 145 (4.6) 264 (8.4) 675 (21.4) 40 (1.3) 1106 (35.1) 110 (3.5) 90.5 ± 11.8 1112 (35.3) 1530 (48.6) 509 (16.2) 348 (11.0) 2268 (72.0) 535 (17.0) 476 (15.1) 1608 (51.0) 1067 (33.9) 1171 (37.2) 1980 (62.8) 10.6 ± 4.1 546 (40.4) 804 (59.6) 6.3±1.9 310 (23.0) 190 (14.1) 10 (0.7) 55 (4.1) 315 (23.3) 51 (3.8) 110 (8.1) 302 (22.4) 9 (0.7) 466 (34.5) 52 (3.9) 90.5 ± 12.1 452 (33.5) 693 (51.3) 205 (15.2) 147 (10.9) 972 (72.0) 231 (17.1) 216 (16.0) 673 (49.9) 461 (34.1) 469 (34.7) 881 (65.3) 10.9 ± 4.2 0.050 0.836 0.156 0.830 0.963 1.000 0.245 0.844 0.504 0.103 0.733 0.611 0.950 0.232 0.985 0.679 0.130 0.091 Table 3 Multivariate Logistic regression analysis based on the results of Lasso regression Variables β S.E OR (95%CI) P value Age 0.036 0.007 1.04 (1.02-1.05) <0.001 Health status -0.636 0.108 0.53 (0.43-0.65) <0.001 Cognitive function -0.071 0.016 0.93 (0.90-0.96) <0.001 CES-D 0.140 0.010 1.15 (1.13-1.17) <0.001 Abbreviations : β, regression coefficient; S.E., standard error; OR, odds ratio; CES-D: center for epidemiological survey, depression scale; Additional Declarations No competing interests reported. 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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-4853180","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":350833875,"identity":"6042ed1c-6948-4e6c-a212-40179cbb1b67","order_by":0,"name":"Guiping Wu","email":"","orcid":"","institution":"The Fifth Hospital of Shanxi Medical University, Shanxi Provincial People's Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Guiping","middleName":"","lastName":"Wu","suffix":""},{"id":350833876,"identity":"46c4fafa-3ffe-4320-aca6-7a2b87172187","order_by":1,"name":"Huarong Wu","email":"","orcid":"","institution":"The Fifth Hospital of Shanxi Medical University, Shanxi Provincial People's Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Huarong","middleName":"","lastName":"Wu","suffix":""},{"id":350833877,"identity":"6a3dc4bc-a590-4c54-a82a-9c71707e252c","order_by":2,"name":"Zexue Shen","email":"","orcid":"","institution":"The Fifth Hospital of Shanxi Medical University, Shanxi Provincial People's Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Zexue","middleName":"","lastName":"Shen","suffix":""},{"id":350833878,"identity":"63b6a58e-0246-4f7f-b1e2-3ec669c13f40","order_by":3,"name":"Ruizhe Chen","email":"","orcid":"","institution":"The Fifth Hospital of Shanxi Medical University, Shanxi Provincial People's Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Ruizhe","middleName":"","lastName":"Chen","suffix":""},{"id":350833879,"identity":"622f0491-7410-40f4-ad71-8f7e04074add","order_by":4,"name":"Xiaowen Che","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBAC++MNCQcS//xj5idez5kDDw98bDjALtlAtJYbiY8Pzmw4wG9wgFgdjDOSEw7z7rgjbXw8eQPDj4pthLUw8zwDajnzzNjszLMCxp4ztwlrYWPPSTjMw8acbHYjx4CZsY0ILTwM+R9AWuo3zyBWiwRHQsLBmW2HmQ0kiNViwHMg4cCHM2nMEkC/HCTKLwbsDckfEipsmPnbkzc++FFBhBYkkEB81CC0kKpjFIyCUTAKRggAAFWqRbCY6BCBAAAAAElFTkSuQmCC","orcid":"","institution":"Taiyuan Center for Disease Control and Prevention","correspondingAuthor":true,"prefix":"","firstName":"Xiaowen","middleName":"","lastName":"Che","suffix":""}],"badges":[],"createdAt":"2024-08-03 11:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4853180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4853180/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66124814,"identity":"06e64693-bfca-428a-a2c3-7a45a383353a","added_by":"auto","created_at":"2024-10-08 02:37:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":853824,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of variables based on Lasso regression. (A) The variation characteristics of the coefficient of variables; (B) the selection process of the optimum value of the parameter λ in the Lasso regression model by cross-validation method.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4853180/v1/7ecc7eaa747d5d2a8a06710e.jpg"},{"id":66124819,"identity":"b166732d-6309-4180-9486-34b1aeff375b","added_by":"auto","created_at":"2024-10-08 02:37:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":208075,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram used to predict frailty in patients with chronic liver disease.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4853180/v1/b22fa418704d29209c2ed69e.jpg"},{"id":66124815,"identity":"4e25020d-15d5-4f59-8233-83d3a5088e2b","added_by":"auto","created_at":"2024-10-08 02:37:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":656165,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Receiver operating characteristic (ROC) curve for the training set; (B) Receiver operating characteristic (ROC) curve for the validation set.\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4853180/v1/11058645721a78c246502b6f.jpg"},{"id":66124816,"identity":"18a7f716-8ec4-4c6c-9374-a906871ef976","added_by":"auto","created_at":"2024-10-08 02:37:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":663516,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Calibration curve for the training set; (B) Calibration curve for the validation set.\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4853180/v1/89613bff74f0ea70dd931093.jpg"},{"id":66125987,"identity":"375d1733-a4ba-4501-965a-fcede234fbe6","added_by":"auto","created_at":"2024-10-08 02:45:58","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":774732,"visible":true,"origin":"","legend":"\u003cp\u003e(A)\u003cstrong\u003e \u003c/strong\u003eDecision curve analysis (DCA) for the training set; (B) Decision curve analysis (DCA) for the training set.\u003c/p\u003e","description":"","filename":"Figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4853180/v1/969d0c59226c9cdfa7de3fc8.jpg"},{"id":72392774,"identity":"f742ec17-b140-43b5-96be-d94405ea85ec","added_by":"auto","created_at":"2024-12-26 11:47:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3924438,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4853180/v1/28e2f2c9-6899-413e-880f-676c9b08bb52.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eConstruction and validation of a nomogram to predict frailty in Chinese patients with hypertension: evidence from the China Health and Retirement Longitudinal Study (CHARLS)\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension is the main risk factor for inducing cardiovascular diseases (CVDs), which are the most common cause of death from non-communicable diseases\u003csup\u003e1,2\u003c/sup\u003e. Hypertension has emerged as the leading cause of death, accounting for 9.4\u0026nbsp;million deaths each year\u003csup\u003e3\u003c/sup\u003e. In China, high systolic BP (SBP) was the top risk factor for both the number of deaths and the percentage of the disability-adjusted life years (DALYs)\u003csup\u003e4\u003c/sup\u003e. Although 46.9% of hypertensive patients were aware of their condition and 40.7% were taking prescription antihypertensive medications, only 15.3% achieved blood control\u003csup\u003e5\u003c/sup\u003e. Hypertension results in substantial disability and places a heavy burden on the healthcare system.\u003c/p\u003e \u003cp\u003eThe pathophysiology of hypertension is shaped by a combination of environmental, genetic, anatomical, neurologic, endocrine, humoral, and hemodynamic factors\u003csup\u003e6\u003c/sup\u003e. In addition, psychosocial factors, including occupational stress, socioeconomic stress, anxiety, and depression, are possible enhancers and triggers of hypertension. Higher levels of psychosocial stress occur in those with hypertension compared to those with normal blood pressure\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDefined as a state of decreased physiological reserve and increased vulnerability to possible intrinsic or extrinsic stressors, frailty involves several domains: physical, psychological, social, and others, which amplifies the risk of adverse health outcomes\u003csup\u003e8\u0026ndash;10\u003c/sup\u003e. Moreover, frailty has been proven to be associated with cardiovascular mortality and morbidity, and multiple mechanisms (physical inactivity, subclinical vascular and cardiac alterations, oxidative stress, deoxyribonucleic acid damage and telomere length shortening inflammatory markers, endocrine dysregulation, accelerated cellular senescence, and epigenetic modifications) contribute to the association between frailty and symptomatic cardiovascular disease\u003csup\u003e11\u0026ndash;13\u003c/sup\u003e. Recognition of frailty status can help physicians determine surgical risk and prognosis\u003csup\u003e14\u003c/sup\u003e. However, there is a lack of a prediction model for frailty in patients with hypertension. Since frailty is reversible, early intervention can improve patient prognosis. Therefore, we established a nomogram to identify those at risk for frailty in hypertension, providing implications for health interventions and community services that can slow the progression of frailty and improve the quality of life in the elderly population.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Participant characteristics\u003c/h2\u003e\n \u003cp\u003eIn 2013 and 2015, 4725 and 5193 patients with hypertension were recruited, removing duplicates for a total of 4501 patients from the China Health and Retirement Longitudinal Study (CHARLS). The mean age of the 4501 enrolled adults with hypertension was 61.8 years (SD: \u0026plusmn; 9.3), with 2103 (46.7%) male and 2398 (53.5%) female. For health factors, there were 583 (13.0%) patients with diabetes and 374 (8.3%) with kidney disease. In addition, the mean cognition score was 10.7 (SD: \u0026plusmn; 4.1), and 1640 (36.4%) of the participants had depression (Table \u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe prevalence of frailty in hypertension was 11.9% (534/4501). Most variables were significantly different when compared between older hypertension patients with frailty and non-frailty (Table \u003cspan\u003e1\u003c/span\u003e). Baseline data showed that compared to the non-frailty patients, the frailty patients mainly comprised women (66.5% VS. 51.5%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and patients of a higher average age (64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5 VS. 61.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). It was noteworthy that patients with frailty had more chronic conditions, along with poor cognition score (8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 VS. 11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a significant rate of depression (22.3% VS. 69.1%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eAll patients were completely randomized in a ratio of 7:3 into a training cohort (N\u0026thinsp;=\u0026thinsp;3151) and a validation cohort (N\u0026thinsp;=\u0026thinsp;1350). There was no significant difference among the following variables between the training cohort and validation cohort (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the random grouping was rational (Table \u003cspan\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e2.2 Prediction model built based on Lasso-logistic regression\u003c/h3\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.2.1 Independent risk factors for hypertension patients with frailty\u003c/h2\u003e\n \u003cp\u003eThe training cohort was used to establish a prognostic model, and the validation cohort was used for validation purposes. The parameters were firstly screened by a LASSO regression analysis, and the variation characteristics of the coefficient of these variables were shown in Fig. \u003cspan\u003e1\u003c/span\u003e. The model performance was excellent with the \u0026lambda; of the standard error (0.02293). Variables based on LASSO regression were further analyzed by multivariate logistic regression to screen independent risk factors for frailty, including age (OR: 1.04, 95%CI: 1.02\u0026ndash;1.05, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), health status (OR: 0.53, 95%CI: 0.43\u0026ndash;0.65, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cognitive function (OR: 0.93, 95%CI: 0.43\u0026ndash;0.96, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the Center for Epidemiological Studies Depression (CES-D, OR: 1.15, 95%CI: 1.13\u0026ndash;1.17, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table \u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.2.2 Construct the nomogram\u003c/h2\u003e\n \u003cp\u003eA nomogram was developed based on the independent factors of the LASSO-logistic regression (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). The AUC of the predictive model was 0.830 (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e). The calibration curve showed the agreement between the predicted and actual results (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e). Additionally, the clinical validity of the nomogram was evaluated by the decision curve analysis (DCA). From the DCA curve, the nomogram could acquire the net benefit in most of the reasonable threshold probabilities (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.2.3 Validate the nomogram\u003c/h2\u003e\n \u003cp\u003eIn order to verify the robustness of the nomogram, an internal validation cohort was performed. The AUC in the validation set was 0.854 (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e). Good linearity and clinical applicability of the nomogram were found in the calibration and DCA curves (Figs.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e and \u003cspan\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eFrailty represents a global health issue with high prevalence and adverse health effects\u003csup\u003e15,16\u003c/sup\u003e. In our study, the probability of frailty in hypertensive patients was 11.9%. For patients with hypertension, accurate and prompt diagnosis of frailty is important because it allows for effective interventions and treatments\u003csup\u003e17\u003c/sup\u003e. Therefore, we established a reliable model to identify the risk of frailty in hypertensive patients and assist in its management to help clinicians and patients share decisions.\u003c/p\u003e \u003cp\u003eIn our research, we utilized LASSO regression to screen variables and adjust for complexity. Compared to the univariate regression analysis, Lasso regression can minimize the multicollinearity in variables. The nomogram contains four risk factors, including age, health status, cognitive function, and CES-D. The use of the nomogram is that the corresponding predicted values are first transformed into the corresponding nomogram scores. The scores are summed, and the probability of a hypertensive patient being frail is derived from the realistic values at the intersection points. The AUCs and calibration curves suggested that the nomogram had good discrimination and accuracy in both the training and validation cohorts, while the DCA curves further showed that the nomogram conferred high clinical net benefits.\u003c/p\u003e \u003cp\u003eAt present, most of the studies on frailty have been done on the Western population; few have been done on the Chinese or oriental populations. Recently, standardized definitions of frailty have been proposed, which were characterized by unintentional weight loss, self-reported fatigue, weakness, slower walking speed, and reduced physical activity\u003csup\u003e8,18\u003c/sup\u003e. Due to the aging and increasingly complex nature of cardiovascular patients, there is growing awareness of frailty in cardiovascular medicine\u003csup\u003e14,19\u003c/sup\u003e. The selection of hypertension treatment might also be influenced by frailty. Frail older adults are always excluded from randomized controlled trials (RCTs) for cardiovascular disease, including hypertension\u003csup\u003e20\u003c/sup\u003e. This limited the generalizability of the results, making it difficult to accurately evaluate the safety and effectiveness of chronic disease treatments for frail individuals. Secondly, frailty is associated with reduced life expectance and lifetime morbidity. According to the results of the SHARE study, the life expectance of males was 0.1\u0026ndash;0.8 years, and females was 0.4\u0026ndash;5.5 years in frail individuals at age 70\u003csup\u003e21\u003c/sup\u003e. Thus, the duration of benefit from specific treatments may exceed life expectancy in frail individuals\u003csup\u003e22\u003c/sup\u003e. Finally, frailty is linked to poor adherence to antihypertensive medication\u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAging is the primary risk factor for the majority of chronic diseases\u003csup\u003e24\u003c/sup\u003e. Over increasing timescales, all people accumulate molecular and cellular damage, and these aging processes, including inflammation, genomic instability, and epigenetic changes, are highly inter-correlated\u003csup\u003e25,26\u003c/sup\u003e. Many of these processes are related to frailty. The prevalence of frailty among community-dwelling elderly increases with advanced age: 4% for elderly aged 65 to 69 years, 7% for elderly aged 70 to 74 years, 9% for elderly aged 75 to 79 years, 16% for elderly aged 80 to 84 years, and 26% for elderly aged 85 and over. Besides, we found that frailty has a tight correlation with cognition. Previously, it was demonstrated that changes in physical activity and disease are highly correlated with cognitive and functional outcomes in the elderly\u003csup\u003e27\u0026ndash;29\u003c/sup\u003e. Cognitive function and frailty may be related by pathogenic mechanisms such as chronic inflammation and oxidative stress\u003csup\u003e30\u003c/sup\u003e. Reduced cognitive function can decrease the self-care ability and adherence to hypertension treatment, which further exacerbates disease progression and leads to a higher risk of frailty. Depressive symptoms were assessed using the CESD score. Depression predicts frailty due to reduced social relationships, gait speed, and physical activity\u003csup\u003e31\u003c/sup\u003e. Subclinical vascular disease (white matter disease) in patients with late life depression has been considered a key factor in pre-frailty\u003csup\u003e32,33\u003c/sup\u003e. Accumulating evidence supports a positive association between frailty and inflammatory cytokines such as IL-6, which is also elevated in depressed patients\u003csup\u003e34,35\u003c/sup\u003e. Mitochondrial dysfunction has been observed in several neurodegenerative diseases. Muscle biopsies obtained from depressed participants had decreased ATP production and impaired mitochondrial respiration, which is strongly associated with symptoms of frailty\u003csup\u003e36\u003c/sup\u003e. Furthermore, depression can adversely affect psychological status and exacerbate debilitating episodes by reducing social activities. Health status plays a direct role in frailty. The comorbidities of cardiovascular disease can exacerbate the clinical course, compromise treatment, and worsen outcomes\u003csup\u003e37,38\u003c/sup\u003e. The burden of cardiovascular disease is associated with increased short and long-term morbidity\u003csup\u003e39,40\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, our study still has some limitations. First, the CHARLS data did not include a number of potential predictors in hypertension, such as metabolic syndrome and family history. Moreover, the nomogram was constructed based on the Chinese population, and its applicability to populations in other countries requires further validation by an external validation cohort. Lastly, originating from a retrospective cohort, the nomogram needs to be verified using a larger sample size and prospective set.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Study design\u003c/h2\u003e \u003cp\u003eThe population of the study data was gathered from the CHARLS. The aim of CHARLS is to collect high-quality data about Chinese residents aged 45 and older to analyze the problem of population aging in China and promote interdisciplinary research on aging issues\u003csup\u003e41\u003c/sup\u003e. The multistage, stratified, probability proportional to size (PPS) sampling was done on randomly selected subjects in 28 provinces in 2011, 2013, 2015, and 2018. Eligible participants were interviewed face-to-face using computer-assisted personal interviews (CAPI). The questionnaire collected demographics, health status and functioning, old age security, and health care information. The CHARLS study was approved by the institutional review board of Peking University, and all participants gave written informed consent. For further details on this procedure, see the previously published study\u003csup\u003e41\u003c/sup\u003e. All the methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003cp\u003eFor the present study, data form patients from 2013 and 2015 were extracted from the CHARLS study. We included individuals who met all of the following criteria: (1) aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years; (2) patients with hypertension; (3) individuals with complete baseline information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Data collection\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 The definition of hypertension and frailty\u003c/h2\u003e \u003cp\u003eHypertension was defined as systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;140mmHg and/or diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;90mmHg and /or taking antihypertensive medications\u003csup\u003e42\u003c/sup\u003e. Frailty status was measured through the frailty index (FI), a commonly used tool for assessing frailty\u003csup\u003e43,44\u003c/sup\u003e. FI can reflect changes in biological aging and the health trajectory of patients over time, which is constructed based on the accumulation of a range of physical, psychological, cognitive, and functional deficits in individuals. Frailty encompasses slowness, weakness, low physical activity, exhaustion, and shrinking, the details of which are below:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMeasurement of weakness using the self-report of \u0026ldquo;having difficulty in lifting or carrying something as heavy as 5 kg\u0026rdquo;\u003csup\u003e45\u003c/sup\u003e;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSlowness was considered to be present if the individuals had difficulty walking 100 meters or climbing several flights of stairs without resting;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExhaustion was assessed if a subject answered \u0026ldquo;often\u0026rdquo; or \u0026ldquo;most of the time\u0026rdquo; the question from the Center for Epidemiological Studies Depression (CES-D) Sale (Chinese version): \u0026ldquo;In the last week, I felt that everything I did was an effort\u0026rdquo; and \u0026ldquo;In the last week I could out not get going\u0026rdquo;\u003csup\u003e8\u003c/sup\u003e;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLow physical activity was defined if the respondent answered \u0026ldquo;No\u0026rdquo; when asked, \u0026ldquo;During a usual week, did you walk at least 10 minutes continuously?\u0026rdquo;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWeight loss was measured by unintentional loss of 5 kg in the past six months or a current body mass index (BMI)\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e. It turns out that, in fact, weight loss is a better indicator of frailty than BMI and energy intake\u003csup\u003e46\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003ePatients were classified as non-frail (having 0 indicators), pre-fail (having 1\u0026ndash;2 indicators), and frail (having three or more indicators).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2.2 Socio-demographic Factors\u003c/h2\u003e \u003cp\u003eSocia-demographic Factors included age, gender, marital status, education level, and insurance. Gender was male or female. Marital status was categorized as \u0026ldquo;married\u0026rdquo; if an individual lived with spouse and \u0026ldquo;unmarried\u0026rdquo; if an individual had never married or was widowed or divorced. Insurance was grouped \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no.\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2.3 Behavioral Factors\u003c/h2\u003e \u003cp\u003eBehavioral factors encompassed social activities, sleep activity, sleep duration, and life satisfaction. Smoking history, drinking history, and social activities were recorded as \u0026ldquo;Yes\u0026rdquo; or \u0026ldquo;No\u0026rdquo;. According to the response of, \u0026ldquo;My sleep was restless,\u0026rdquo; sleep quality was categorized into four groups. Total nighttime sleep duration was obtained from the question, \u0026ldquo;In the past month, on an average night, how many hours of actual sleep did you get at night?\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2.4 Health factors\u003c/h2\u003e \u003cp\u003eHealth status consisted of self-reported health, history of chronic diseases (dyslipidemia, diabetes, cancer, chronic lung disease, kidney disease, digestive disease, stroke, asthma, arthritis or rheumatism, and mental disease), vision, hearing, cognitive score, and depression. History of chronic diseases was classified as \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no\u0026rdquo; based on self-reported diagnosis. Vision, hearing, and self-reported health status were categorized as \u0026ldquo;good,\u0026rdquo; \u0026ldquo;fair,\u0026rdquo; and \u0026ldquo;poor.\u0026rdquo; Cognitive functions include attention, memory, orientation, and visuospatial skills. A total score can range from 0 to 21, where a higher score indicates better cognition. Individuals were screened for depression with the Centre for Epidemiologic Studies Depression Scale ( CES-D), which consisted of 21 items. The scores of 10 or greater signified the presence of depression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Statistical methods\u003c/h2\u003e \u003cp\u003eContinuous variables were reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations, and the categorical variables were expressed as frequency (percentage). Comparisons between groups were carried out with a t-test, ANOVA, and Chi-square test. Lasso regression was performed for risk factor selection, and the independent risk factors for frailty were identified by multivariate logistic regression analysis. The identified independent risk factors were used to develop the nomogram, and the nomogram was validated in the validation cohort. The receiver operating characteristic (ROC) curves were drawn, and the area under the ROC curve (AUC) was calculated to evaluate the predictive ability of the nomogram. The calibration curve (Hosmer-Lemeshow test) was utilized to assess the predictive accuracy of the nomogram. Decision Curve Analysis (DCA) curves were performed to demonstrate the clinical validity of the nomogram by quantifying the net benefits under different threshold probabilities.\u003c/p\u003e \u003cp\u003eAll data were analyzed using R software (version 4.4.1), and P-value less than 0.05 (two-sided) was considered to be statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn our study, we created a reliable nomogram to predict frailty in hypertensive patients based on LASSO-logistic regression analysis, incorporating age, health status, cognitive function, and CESD. The nomogram achieved good performance and applicability, which could help clinicians in the medical decision-making process.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval for the study was granted by the Ethics Review Committee of Peking University (approved number: IRB00001052\u0026ndash;11015), and all the participants provided signed informed consent at the time of participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated and analyzed during the study are available in the CHARLS website, available in http://CHARLS.pku.edu.cn.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Research Project of Shanxi Provincial Health Commission (Grant No: 2021111, GW).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceived and designed the protocol: W.G.P and W.H.R; Collected data: C.X.W and W.H.R; Analyzed data: W.G.P; Wrote the manuscript: W.G.P and C.X.W; Critically revised the manuscript: C.R.Z and S.Z.X; All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors highly appreciate the China Health and Retirement Longitudinal Study (CHARLS) team for providing high-quality data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData avaliability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated and analyzed during the study are available in the CHARLS website, available in http://CHARLS.pku.edu.cn.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMaleki, B.\u003cem\u003e et al.\u003c/em\u003e MicroRNAs and exosomes: Cardiac stem cells in heart diseases. \u003cem\u003ePathol Res Pract\u003c/em\u003e \u003cstrong\u003e229\u003c/strong\u003e, 153701 (2022). https://doi.org:10.1016/j.prp.2021.153701\u003c/li\u003e\n\u003cli\u003eMills, K. 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Frailty and cognitive decline. \u003cem\u003eTransl Res\u003c/em\u003e \u003cstrong\u003e221\u003c/strong\u003e, 58-64 (2020). https://doi.org:10.1016/j.trsl.2020.01.002\u003c/li\u003e\n\u003cli\u003eHajek, A.\u003cem\u003e et al.\u003c/em\u003e Predictors of Frailty in Old Age - Results of a Longitudinal Study. \u003cem\u003eJ Nutr Health Aging\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 952-957 (2016). https://doi.org:10.1007/s12603-015-0634-5\u003c/li\u003e\n\u003cli\u003eSoysal, P.\u003cem\u003e et al.\u003c/em\u003e Relationship between depression and frailty in older adults: A systematic review and meta-analysis. \u003cem\u003eAgeing Res Rev\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 78-87 (2017). https://doi.org:10.1016/j.arr.2017.03.005\u003c/li\u003e\n\u003cli\u003eNewberg, A. R., Davydow, D. S. \u0026amp; Lee, H. B. Cerebrovascular disease basis of depression: post-stroke depression and vascular depression. \u003cem\u003eInt Rev Psychiatry\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 433-441 (2006). https://doi.org:10.1080/09540260600935447\u003c/li\u003e\n\u003cli\u003eSoysal, P.\u003cem\u003e et al.\u003c/em\u003e Inflammation and frailty in the elderly: A systematic review and meta-analysis. \u003cem\u003eAgeing Res Rev\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 1-8 (2016). https://doi.org:10.1016/j.arr.2016.08.006\u003c/li\u003e\n\u003cli\u003eVaughan, L., Corbin, A. L. \u0026amp; Goveas, J. S. Depression and frailty in later life: a systematic review. \u003cem\u003eClin Interv Aging\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 1947-1958 (2015). https://doi.org:10.2147/cia.S69632\u003c/li\u003e\n\u003cli\u003eBrown, P. J.\u003cem\u003e et al.\u003c/em\u003e The Depressed Frail Phenotype: The Clinical Manifestation of Increased Biological Aging. \u003cem\u003eAm J Geriatr Psychiatry\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 1084-1094 (2016). https://doi.org:10.1016/j.jagp.2016.06.005\u003c/li\u003e\n\u003cli\u003eAdams, K. F., Jr.\u003cem\u003e et al.\u003c/em\u003e Characteristics and outcomes of patients hospitalized for heart failure in the United States: rationale, design, and preliminary observations from the first 100,000 cases in the Acute Decompensated Heart Failure National Registry (ADHERE). \u003cem\u003eAm Heart J\u003c/em\u003e \u003cstrong\u003e149\u003c/strong\u003e, 209-216 (2005). https://doi.org:10.1016/j.ahj.2004.08.005\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Connor, C. M.\u003cem\u003e et al.\u003c/em\u003e Predictors of mortality after discharge in patients hospitalized with heart failure: an analysis from the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF). \u003cem\u003eAm Heart J\u003c/em\u003e \u003cstrong\u003e156\u003c/strong\u003e, 662-673 (2008). https://doi.org:10.1016/j.ahj.2008.04.030\u003c/li\u003e\n\u003cli\u003eSchmidt, M., Ulrichsen, S. P., Pedersen, L., B\u0026oslash;tker, H. E. \u0026amp; S\u0026oslash;rensen, H. T. Thirty-year trends in heart failure hospitalization and mortality rates and the prognostic impact of co-morbidity: a Danish nationwide cohort study. \u003cem\u003eEur J Heart Fail\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 490-499 (2016). https://doi.org:10.1002/ejhf.486\u003c/li\u003e\n\u003cli\u003ePalazzuoli, A., Ruocco, G. \u0026amp; Gronda, E. Noncardiac comorbidity clustering in heart failure: an overlooked aspect with potential therapeutic door. \u003cem\u003eHeart Fail Rev\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 767-778 (2022). https://doi.org:10.1007/s10741-020-09972-6\u003c/li\u003e\n\u003cli\u003eZhao, Y., Hu, Y., Smith, J. P., Strauss, J. \u0026amp; Yang, G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). \u003cem\u003eInt J Epidemiol\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 61-68 (2014). https://doi.org:10.1093/ije/dys203\u003c/li\u003e\n\u003cli\u003eChinese Guidelines for Prevention and Treatment of Hypertension-A report of the Revision Committee of Chinese Guidelines for Prevention and Treatment of Hypertension. \u003cem\u003eJ Geriatr Cardiol\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 182-241 (2019). https://doi.org:10.11909/j.issn.1671-5411.2019.03.014\u003c/li\u003e\n\u003cli\u003eRockwood, K.\u003cem\u003e et al.\u003c/em\u003e A global clinical measure of fitness and frailty in elderly people. \u003cem\u003eCmaj\u003c/em\u003e \u003cstrong\u003e173\u003c/strong\u003e, 489-495 (2005). https://doi.org:10.1503/cmaj.050051\u003c/li\u003e\n\u003cli\u003eCoca Payeras, A., Williams, B. \u0026amp; Mancia, G. Response to \u0026apos;Comment on 2018 ESC/ESH Guidelines for the management of arterial hypertension\u0026apos;. \u003cem\u003eEur Heart J\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 2093 (2019). https://doi.org:10.1093/eurheartj/ehz219\u003c/li\u003e\n\u003cli\u003eTheou, O.\u003cem\u003e et al.\u003c/em\u003e Modifications to the frailty phenotype criteria: Systematic review of the current literature and investigation of 262 frailty phenotypes in the Survey of Health, Ageing, and Retirement in Europe. \u003cem\u003eAgeing Res Rev\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 78-94 (2015). https://doi.org:10.1016/j.arr.2015.04.001\u003c/li\u003e\n\u003cli\u003eChin, A. P. M. J., Dekker, J. M., Feskens, E. J., Schouten, E. G. \u0026amp; Kromhout, D. How to select a frail elderly population? A comparison of three working definitions. \u003cem\u003eJ Clin Epidemiol\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 1015-1021 (1999). https://doi.org:10.1016/s0895-4356(99)00077-3\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Baseline characteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.741410488245933%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.998191681735985%\" valign=\"top\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eNon-frailty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eFrailty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.741410488245933%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003eGender (%)\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.998191681735985%\" valign=\"top\"\u003e\n \u003cp\u003e61.8 \u0026plusmn; 9.3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2103 (46.7)\u003c/p\u003e\n \u003cp\u003e2398 (53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003e61.4 \u0026plusmn; 9.2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1924 (48.5)\u003c/p\u003e\n \u003cp\u003e2043 (51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003e64.4 \u0026plusmn; 9.5\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e179 (33.5)\u003c/p\u003e\n \u003cp\u003e355 (66.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.741410488245933%\" valign=\"top\"\u003e\n \u003cp\u003eMarital status (%)\u003c/p\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.998191681735985%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e630 (14.0)\u003c/p\u003e\n \u003cp\u003e3871 (86.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e523 (13.2)\u003c/p\u003e\n \u003cp\u003e3444 (86.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e107 (20.0)\u003c/p\u003e\n \u003cp\u003e427 (80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.741410488245933%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.998191681735985%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.741410488245933%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.998191681735985%\" valign=\"top\"\u003e\n \u003cp\u003e261 (5.8)\u003c/p\u003e\n \u003cp\u003e4240 (94.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003e238 (6.0)\u003c/p\u003e\n \u003cp\u003e3729 (94.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003e23 (4.3)\u003c/p\u003e\n \u003cp\u003e511 (95.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.741410488245933%\" valign=\"top\"\u003e\n \u003cp\u003eSocial activity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.998191681735985%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.741410488245933%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eSleep duration (h)\u003c/p\u003e\n \u003cp\u003eDyslipidemia (%)\u003c/p\u003e\n \u003cp\u003eDiabetes (%)\u003c/p\u003e\n \u003cp\u003eCancer (%)\u003c/p\u003e\n \u003cp\u003eLiver disease (%)\u003c/p\u003e\n \u003cp\u003eHeart disease (%)\u003c/p\u003e\n \u003cp\u003eStroke (%)\u003c/p\u003e\n \u003cp\u003eKidney disease (%)\u003c/p\u003e\n \u003cp\u003eDigestive disease (%)\u003c/p\u003e\n \u003cp\u003eMental disease (%)\u003c/p\u003e\n \u003cp\u003eArthritis or rheumatism (%)\u003c/p\u003e\n \u003cp\u003eAsthma (%)\u003c/p\u003e\n \u003cp\u003eWaistline (cm)\u003c/p\u003e\n \u003cp\u003eHealth status\u0026nbsp;(%)\u003c/p\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003cp\u003eFair\u003c/p\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003cp\u003eVision (%)\u003c/p\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003cp\u003eFair\u003c/p\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003cp\u003eHearing (%)\u003c/p\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003cp\u003eFair\u003c/p\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eCognitive function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.998191681735985%\" valign=\"top\"\u003e\n \u003cp\u003e2690 (59.8)\u003c/p\u003e\n \u003cp\u003e1811 (40.2)\u003c/p\u003e\n \u003cp\u003e6.2 \u0026plusmn; 1.9\u003c/p\u003e\n \u003cp\u003e1023 (22.7)\u003c/p\u003e\n \u003cp\u003e583 (13.0)\u003c/p\u003e\n \u003cp\u003e37 (0.8)\u003c/p\u003e\n \u003cp\u003e186 (4.1)\u003c/p\u003e\n \u003cp\u003e1049 (23.3)\u003c/p\u003e\n \u003cp\u003e196 (4.4)\u003c/p\u003e\n \u003cp\u003e374 (8.3)\u003c/p\u003e\n \u003cp\u003e977 (21.7)\u003c/p\u003e\n \u003cp\u003e49 (1.1)\u003c/p\u003e\n \u003cp\u003e1572 (34.9)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e162 (3.6)\u003c/p\u003e\n \u003cp\u003e90.5 \u0026plusmn; 11.9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1564 (34.7)\u003c/p\u003e\n \u003cp\u003e2223 (49.4)\u003c/p\u003e\n \u003cp\u003e714 (15.9)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e495 (11.0)\u003c/p\u003e\n \u003cp\u003e3240 (72.0)\u003c/p\u003e\n \u003cp\u003e766 (17.0)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e692 (15.4)\u003c/p\u003e\n \u003cp\u003e2281 (50.7)\u003c/p\u003e\n \u003cp\u003e1528 (33.9)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1640 (36.4)\u003c/p\u003e\n \u003cp\u003e2861 (63.6)\u003c/p\u003e\n \u003cp\u003e10.7 \u0026plusmn; 4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003e2450 (61.8)\u003c/p\u003e\n \u003cp\u003e1517 (38.2)\u003c/p\u003e\n \u003cp\u003e6.3 \u0026plusmn; 1.8\u003c/p\u003e\n \u003cp\u003e892 (22.5)\u003c/p\u003e\n \u003cp\u003e487 (12.3)\u003c/p\u003e\n \u003cp\u003e31 (0.8)\u003c/p\u003e\n \u003cp\u003e157 (4.0)\u003c/p\u003e\n \u003cp\u003e862 (21.7)\u003c/p\u003e\n \u003cp\u003e158 (4.0)\u003c/p\u003e\n \u003cp\u003e299 (7.5)\u003c/p\u003e\n \u003cp\u003e802 (20.2)\u003c/p\u003e\n \u003cp\u003e36 (0.9)\u003c/p\u003e\n \u003cp\u003e1299 (32.7)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e128 (3.2)\u003c/p\u003e\n \u003cp\u003e90.9 \u0026plusmn; 11.9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1206 (30.4)\u003c/p\u003e\n \u003cp\u003e2071 (52.2)\u003c/p\u003e\n \u003cp\u003e690 (17.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e367 (9.3)\u003c/p\u003e\n \u003cp\u003e2885 (72.7)\u003c/p\u003e\n \u003cp\u003e715 (18.0)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e535 (13.5)\u003c/p\u003e\n \u003cp\u003e2029 (51.1)\u003c/p\u003e\n \u003cp\u003e1403 (35.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1225 (30.9)\u003c/p\u003e\n \u003cp\u003e2742 (69.1)\u003c/p\u003e\n \u003cp\u003e11.0 \u0026plusmn; 4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003e240 (44.9)\u003c/p\u003e\n \u003cp\u003e294 (55.1)\u003c/p\u003e\n \u003cp\u003e5.2 \u0026plusmn; 2.4\u003c/p\u003e\n \u003cp\u003e131 (24.5)\u003c/p\u003e\n \u003cp\u003e96 (18.0)\u003c/p\u003e\n \u003cp\u003e6 (1.1)\u003c/p\u003e\n \u003cp\u003e29 (5.4)\u003c/p\u003e\n \u003cp\u003e187 (35.0)\u003c/p\u003e\n \u003cp\u003e38 (7.1)\u003c/p\u003e\n \u003cp\u003e75 (14.0)\u003c/p\u003e\n \u003cp\u003e175 (32.8)\u003c/p\u003e\n \u003cp\u003e13 (2.4)\u003c/p\u003e\n \u003cp\u003e273 (51.1)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e34 (6.4)\u003c/p\u003e\n \u003cp\u003e87.6 \u0026plusmn; 11.9\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e358 (67.0)\u003c/p\u003e\n \u003cp\u003e152 (28.5)\u003c/p\u003e\n \u003cp\u003e24 ( 4.5)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e128 (24.0)\u003c/p\u003e\n \u003cp\u003e355 (66.5)\u003c/p\u003e\n \u003cp\u003e51 (9.6)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e157 (29.4)\u003c/p\u003e\n \u003cp\u003e252 (47.2)\u003c/p\u003e\n \u003cp\u003e125 (23.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e415 (77.7)\u003c/p\u003e\n \u003cp\u003e119 (22.3)\u003c/p\u003e\n \u003cp\u003e8.4 \u0026plusmn; 4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e:\u0026nbsp;Continuous variables were reported as mean and standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), while categorical variables were\u0026nbsp;expressed as frequency\u0026nbsp;(percentage).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Characteristics for training and validation cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.11392405063291%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" valign=\"top\"\u003e\n \u003cp\u003eTraining cohort\u003c/p\u003e\n \u003cp\u003e(N=3151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003eValidation cohort\u003c/p\u003e\n \u003cp\u003e(N=1350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.11392405063291%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" valign=\"top\"\u003e\n \u003cp\u003e61.8 \u0026plusmn; 9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e61.8 \u0026plusmn; 9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.11392405063291%\" valign=\"top\"\u003e\n \u003cp\u003eGender (%)\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1474 (46.8)\u003c/p\u003e\n \u003cp\u003e1677 (53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e629 (46.6)\u003c/p\u003e\n \u003cp\u003e721 (53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.11392405063291%\" valign=\"top\"\u003e\n \u003cp\u003eMarital status (%)\u003c/p\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e449 (14.2)\u003c/p\u003e\n \u003cp\u003e2702 (85.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e181 (13.4)\u003c/p\u003e\n \u003cp\u003e1169 (86.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.11392405063291%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.11392405063291%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" valign=\"top\"\u003e\n \u003cp\u003e195 (6.2)\u003c/p\u003e\n \u003cp\u003e2956 (93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e66 (4.9)\u003c/p\u003e\n \u003cp\u003e1284 (95.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.11392405063291%\" valign=\"top\"\u003e\n \u003cp\u003eSocial activity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.11392405063291%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eSleep duration (h)\u003c/p\u003e\n \u003cp\u003eDyslipidemia (%)\u003c/p\u003e\n \u003cp\u003eDiabetes (%)\u003c/p\u003e\n \u003cp\u003eCancer (%)\u003c/p\u003e\n \u003cp\u003eLiver disease (%)\u003c/p\u003e\n \u003cp\u003eHeart disease (%)\u003c/p\u003e\n \u003cp\u003eStroke (%)\u003c/p\u003e\n \u003cp\u003eKidney disease (%)\u003c/p\u003e\n \u003cp\u003eDigestive disease (%)\u003c/p\u003e\n \u003cp\u003eMental disease (%)\u003c/p\u003e\n \u003cp\u003eArthritis or rheumatism (%)\u003c/p\u003e\n \u003cp\u003eAsthma (%)\u003c/p\u003e\n \u003cp\u003eWaistline (cm)\u003c/p\u003e\n \u003cp\u003eHealth status\u0026nbsp;(%)\u003c/p\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003cp\u003eFair\u003c/p\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003cp\u003eVision (%)\u003c/p\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003cp\u003eFair\u003c/p\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003cp\u003eHearing (%)\u003c/p\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003cp\u003eFair\u003c/p\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eCognitive function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.678119349005424%\" valign=\"top\"\u003e\n \u003cp\u003e1265 (40.1)\u003c/p\u003e\n \u003cp\u003e1886 (59.9)\u003c/p\u003e\n \u003cp\u003e6.2\u0026plusmn;1.9\u003c/p\u003e\n \u003cp\u003e713 (22.6)\u003c/p\u003e\n \u003cp\u003e393 (12.5)\u003c/p\u003e\n \u003cp\u003e27 (0.9)\u003c/p\u003e\n \u003cp\u003e131 (4.2)\u003c/p\u003e\n \u003cp\u003e734 (23.3)\u003c/p\u003e\n \u003cp\u003e145 (4.6)\u003c/p\u003e\n \u003cp\u003e264 (8.4)\u003c/p\u003e\n \u003cp\u003e675 (21.4)\u003c/p\u003e\n \u003cp\u003e40 (1.3)\u003c/p\u003e\n \u003cp\u003e1106 (35.1)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e110 (3.5)\u003c/p\u003e\n \u003cp\u003e90.5 \u0026plusmn; 11.8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1112 (35.3)\u003c/p\u003e\n \u003cp\u003e1530 (48.6)\u003c/p\u003e\n \u003cp\u003e509 (16.2)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e348 (11.0)\u003c/p\u003e\n \u003cp\u003e2268 (72.0)\u003c/p\u003e\n \u003cp\u003e535 (17.0)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e476 (15.1)\u003c/p\u003e\n \u003cp\u003e1608 (51.0)\u003c/p\u003e\n \u003cp\u003e1067 (33.9)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1171 (37.2)\u003c/p\u003e\n \u003cp\u003e1980 (62.8)\u003c/p\u003e\n \u003cp\u003e10.6 \u0026plusmn; 4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e546 (40.4)\u003c/p\u003e\n \u003cp\u003e804 (59.6)\u003c/p\u003e\n \u003cp\u003e6.3\u0026plusmn;1.9\u003c/p\u003e\n \u003cp\u003e310 (23.0)\u003c/p\u003e\n \u003cp\u003e190 (14.1)\u003c/p\u003e\n \u003cp\u003e10 (0.7)\u003c/p\u003e\n \u003cp\u003e55 (4.1)\u003c/p\u003e\n \u003cp\u003e315 (23.3)\u003c/p\u003e\n \u003cp\u003e51 (3.8)\u003c/p\u003e\n \u003cp\u003e110 (8.1)\u003c/p\u003e\n \u003cp\u003e302 (22.4)\u003c/p\u003e\n \u003cp\u003e9 (0.7)\u003c/p\u003e\n \u003cp\u003e466 (34.5)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e52 (3.9)\u003c/p\u003e\n \u003cp\u003e90.5 \u0026plusmn; 12.1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e452 (33.5)\u003c/p\u003e\n \u003cp\u003e693 (51.3)\u003c/p\u003e\n \u003cp\u003e205 (15.2)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e147 (10.9)\u003c/p\u003e\n \u003cp\u003e972 (72.0)\u003c/p\u003e\n \u003cp\u003e231 (17.1)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e216 (16.0)\u003c/p\u003e\n \u003cp\u003e673 (49.9)\u003c/p\u003e\n \u003cp\u003e461 (34.1)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e469 (34.7)\u003c/p\u003e\n \u003cp\u003e881 (65.3)\u003c/p\u003e\n \u003cp\u003e10.9 \u0026plusmn; 4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Multivariate Logistic regression analysis based on the results of Lasso regression\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.061371841155236%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.826714801444044%\" valign=\"top\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.24548736462094%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.061371841155236%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.826714801444044%\" valign=\"top\"\u003e\n \u003cp\u003e1.04 (1.02-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.24548736462094%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.061371841155236%\" valign=\"top\"\u003e\n \u003cp\u003eHealth status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e-0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.826714801444044%\" valign=\"top\"\u003e\n \u003cp\u003e0.53 (0.43-0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.24548736462094%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.061371841155236%\" valign=\"top\"\u003e\n \u003cp\u003eCognitive function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e-0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.826714801444044%\" valign=\"top\"\u003e\n \u003cp\u003e0.93 (0.90-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.24548736462094%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.061371841155236%\" valign=\"top\"\u003e\n \u003cp\u003eCES-D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.14801444043321%\" valign=\"top\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.826714801444044%\" valign=\"top\"\u003e\n \u003cp\u003e1.15 (1.13-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.24548736462094%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: \u0026beta;, regression coefficient; S.E., standard error; OR, odds ratio; CES-D: center for epidemiological survey, depression scale;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"hypertension, frailty, nomogram, Lasso-logistic regression","lastPublishedDoi":"10.21203/rs.3.rs-4853180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4853180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFrailty is common in patients with hypertension and predisposes patients to poor postoperative outcomes. However, there is a lack of a prediction model for frailty in patients with hypertension. Therefore, we established a nomogram to identify those at risk for frailty in hypertension, providing implications for health interventions and community services.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe patients diagnosed with hypertension were collected from the the China Health and Retirement Longitudinal Study (CHARLS) database and were randomly divided into a training cohort and a validation group at a ratio of 7:3. The independent risk factors of frailty were determined by LASSO regression and multivariable logistic regression and a nomogram predict model for frailty was established. The performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe content of the nomogram includes age, health status, cognitive function, and the Center for Epidemiological Studies Depression (CES-D). The AUCs of the training cohort and validation cohort (0.830 and 0.854) suggested good discrimination of the nomogram. Calibration curves and DCA curves proved accuracy and clinical applicability.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe nomogram we created for the frailty in hypertensive patients had good performance and applicability, which could help clinicians in the medical decision-making process.\u003c/p\u003e","manuscriptTitle":"Construction and validation of a nomogram to predict frailty in Chinese patients with hypertension: evidence from the China Health and Retirement Longitudinal Study (CHARLS)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 02:37:53","doi":"10.21203/rs.3.rs-4853180/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3acc24c5-32e5-4cf0-a2af-63b5d495c9d5","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-26T11:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-08 02:37:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4853180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4853180","identity":"rs-4853180","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}