Development and validation of a depression risk-predicting model for Chinese middle-aged and elderly Chronic Kidney Disease patients

Development and validation of a depression risk-predicting model for Chinese middle-aged and elderly Chronic Kidney Disease patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and validation of a depression risk-predicting model for Chinese middle-aged and elderly Chronic Kidney Disease patients Tongxin Sun, Qihui Ye, Xunliang Li, Deguang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4545265/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 A global public health issue, chronic kidney disease(CKD) may worsen more quickly if depression symptoms overlap. In this study, a nomogram model was developed and validate to predict depression in Chinese CKD patients between the ages of middle-aged and old. Methods A 7:3 random split of the 1571 participants in the China Health and Retirement Longitudinal Study was made into training and validation sets. After doing Least Absolute Shrinkage and Selection Operator(LASSO) and multivariate binary logistic regression analysis to discover determinants of depression symptoms. These predictors were used to create a nomogram, which was then evaluated for discriminative power, predictive performance, and clinical applicability using receiver operating characteristic (ROC) curves, calibration curves, Hosmer-Leme show tests, and decision curve analysis (DCA). Results The nomogram model included 10 predictors, including gender, marital status, place of residence, education level, life satisfaction. pain, sleep disorders, self-reported health, as well as comorbid chronic diseases. The Area under the curve(AUC) values of the training and validation sets were, in turn, 0.889 (95% CI: 0.869–0.908) and 0.869 (95% CI: 0.836–0.902), the values of Hosmer–Lemeshow test were p = 0.113 and p = 0.259. The calibration curves and the Hosmer-Lemeshow test results were used to verify the nomogram model's predictive capabilities. Additionally, the decision curve analysis (DCA) curves illustrated a high net clinical benefit provided by the predictive model. Conclusions We developed and validated a depression risk model for middle-aged and elderly CKD patients. Clinicians can accurately screen middle-aged and older CKD patients having depressive symptoms using the evaluation instrument, which is important for early intervention. Depression Nomogram Chronic kidney disease Prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Worldwide, chronic kidney disease (CKD) is a serious public health concern marked by functional or structural abnormalities of the kidneys lasting longer than three months. A recent cross-sectional survey conducted in China on 176,874 people revealed an 8.2% incidence of CKD [ 1 ] . A separate study revealed that the occurrence of chronic kidney disease (CKD) was 18% among those between the ages of 60 and 69, and 24% among those aged 70 to 79 [ 2 ] . In addition, middle-aged and older individuals have a higher likelihood of developing CKD due to elevated rates of hypertension, diabetes, and cardiovascular disease [ 3 ] . But middle-aged and older CKD patients' mental health is significantly impacted by the higher number of comorbidities and worse prognosis [ 4 ] ,particularly mental health disorders such as depression [ 5 ] . Depression is a prevalent mental health condition characterised by long-lasting spells of sadness or a lack of enjoyment or interest in activities [ 6 ] .In a cross-sectional research, 22% of CKD patients reported having depression [ 7 ] , a prevalence that is approximately 2–3 times higher than that of other chronic diseases [ 8 ] . A prospective observational cohort study with a 4-year follow-up carried out in Taiwan, discovered that patients exhibiting significant depression symptoms experienced a more quicker decline in their glomerular filtration rate (GFR), Moreover, the research revealed that having depressed symptoms raised the risk of CKD patients getting end-stage renal disease (ESRD) and dying [ 9 ] . Chronic illness and depression frequently coexist. This can result in adverse health outcomes and lifestyle modifications for patients, which can be a substantial strain on their families [ 10 ] . A great deal of independent research has demonstrated that sociodemographics including gender, education, and place of habitation have an impact on the onset of depression in CKD patients.Additionally, economic capacity and co-morbid cardiovascular disease are closely related to depressive symptoms [ 11 ] . Sleep problems and depression have been strongly linked in previous research [ 12 ] ; other possible contributing variables to depressed symptoms include poor self-reported health [ 13 ] , life satisfaction [ 14 ] , as well as instrumental activities of daily living (IADL) [ 15 ] . Because depression symptoms are common in the CKD population, postponing treatment adherence and deteriorating self-management of the disease can follow from delayed intervention. To minimize these adverse outcomes, we recommend routine screening for depressive symptoms, identifying middle-aged and elderly CKD patients at high risk for depression, and implementing timely early interventions. While a number of research have pinpointed depression risk variables in CKD patients, no reliable visualisation tool exists to estimate the likelihood of depression in middle-aged and elderly CKD patients. Predicting the likelihood of depression in middle-aged and elderly CKD patients is possible with the nomogram prediction model, a straightforward and practical method. It is able to score every value level of the contributing factors and add together the individual scores to get the overall score. Finally, it employs a functional transformation connection between the overall score and the likelihood of the outcome event to calculate the anticipated value of the result event for that particular person. [ 16 ] . The study's goal was to develope and validate a nomogram model that can identify the risk of sadness in middle-aged and older people with CKD. Experimental Procedures Research design and data sources This research used cross-sectional data from the 2015 China Health and Retirement Longitudinal research (CHARLS), which is a comprehensive countrywide survey of Chinese individuals aged 45 years and above [ 17 ] ; the study was initiated in 2011 as a national baseline survey, employing a sampling methodology that involved multi-stage probability sampling. 150 counties, 450 villages, and 17,000 people from around 10,000 families were included in the sample. The samples were thereafter monitored biennially, with four rounds of follow-up surveys done in 2013 (2nd), 2015 (3rd), 2018 (4th), and 2020 (5th). The surveys largely concentrated on assessing health state and functioning, gathering healthcare information, and conducting blood-based bioassays. These surveys are often regarded as very authoritative. The following were the study inclusion requirements: (1) CKD diagnosed by a doctor or professional technician; (2) aged ≥ 45 years; (3) The Complete Depression Short Form: Center for Epidemiologic Studies Depression (CES-D) score; One research exclusion criterion was missing data for the variables of greater than 20%. Eventually, the study comprised 1571 middle-aged and elderly CKD patients (Fig. 1 ). Chronic Kidney Disease The information on CKD was derived from the self-reports provided by the individuals. CKD was determined by the questionnaire " Have your doctor told you that you have chronic kidney disease (excluding tumors or cancer)?" Participants answered "yes", then they were categorized as having chronic kidney disease and the time of diagnosis was recorded [ 18 ] . Assessment of Depressive Symptoms The CESD-10 short form, a commonly used test for evaluating depressive symptoms in older adults, was employed by the CHARLS to examine participants' depressed symptoms. This instrument has been extensively validated in Chinese elderly subjects [ 19 ] . A 4-point scale from 0 (never), 1 (sometimes), 2 (frequently), and 3 (always) is used to rate the "Frequency of such thoughts and actions throughout the previous week" in the questionnaire. There is reversal scoring on the fifth and eighth items. Higher scores indicate more serious depressive symptoms; the scoring system runs from 0 to 30. Depressive symptoms were identified in this study by a CESD-10 score of 10 or higher, as indicated in the literature review [ 20 ] . The Cronbach's alpha of 0.84 indicated that the CES-D abbreviated form was highly reliable [ 21 ] . Covariates Based on a literature search and clinical features, we screened for confounders and gathered demographic data including self-reported health, age, gender, location, marital status, education level, medical insurance, retirement, family financial support, life satisfaction. Current smoking and alcohol consumption are considered healthy behaviour patterns. Health status and behavioural dimensions included whether participants socialized over the previous month. Social activities included dancing, joining groups or volunteering, attending school or training sessions, playing mahjong, helping family and friends, and more. Participants were considered to have participated in social activities if they reported engaging in one of these activities; otherwise, they were considered not to have participated in socializing. The term "Instrumental Activities of Daily Living" (IADL) encompasses a set of five tasks: financial management, prescription administration, grocery shopping, cooking, and household cleaning. Participants were classified as having an IADL difficulty if they faced challenges or could not complete any of the items in the IADL. Conversely, if participants reported no difficulties with any of the IADL items, they were deemed to have no IADL difficulties [ 22 ] . In the CHARLS questionnaire, Participants were asked, "Are you often troubled with any bodily pains?". The answer was divided into “yes” or “no”. Sleep disorders were assessed by the number of days of restless sleep in a week using the questionnaire" My sleep was restless." The responses included four choices: "Infrequently or not at all (< 1 day)", "Occasionally or to a small extent (1 − 2 days)", "Sometimes or to a moderate extent (3 − 4 days)", and "Most or all of the time (5 − 7 days)". Those who participated also provided information on the duration of their sleep. The Self-Reported Health (SRH) questionnaire contains the following query: "Please rank your health on a scale of very excellent, good, fair, bad, or very poor" The diseases-related information covariates included the number of chronic disease comorbidities, which were the following diseases participants were diagnosed with by a physician in addition to being diagnosed with CKD: high blood pressure, diabetes, heart disease, stroke, gastric or digestive disorders, arthritis, and memory-related disorders. Additionally, multimorbidity is the presence of two or more chronic diseases [ 23 ] .We divided it into three groups: one chronic disease, two chronic illnesses, and several chronic ailments. Regarding the questionnaire, please indicate whether you have any of the following impairments. This study encompassed five groups: speech issues, visual problems, hearing problems, brain damage/mental retardation, and physical impairment. Participants were categorised as having a disability if they reported any of the above options, and as not having a disability if they reported none of them. The independent variable assignments are detailed in Table 1 . Table 1 Assignment of independent variables. Variables Assignment Gender 1 = male; 0 = female Place of residence 1 = rural; 0 = city Education level 1 = below primary school; 2 = primary school; 3 = middle school; 4 = high school and higher Marital Status 1 = married and living with/cohabiting with spouse; 0 = separated/widowed/divorced/never married Smoking 1 = yes; 0 = no Drinking 1 = yes; 0 = no Medical insurance 1 = yes; 0 = no Retire 1 = yes; 0 = no Financial support by parents 1 = yes; 0 = no Life satisfaction 1 = completely satisfied; 2 = very satisfied; 3 = somewhat satisfied; 4 = not very satisfied; 5 = not at all satisfied Disability 1 = yes; 0 = no Pain 1 = yes; 0 = no Social interaction 1 = yes; 0 = no IADL 1 = difficulties; 0 = no difficulties Sleep disorder 1 = < 1 days; 2 = 1 − 2days; 3 = 3 − 4days; 4 = 5 − 7days Self-reported health 1 = very poor; 2 = poor; 3 = fair; 4 = good; 5 = very good Comorbid chronic diseases 1 = one chronic condition; 2 = two chronic conditions; 3 = three or more chronic conditions Statistical analysis For continuous variables, the median and interquartile range (IQR) were used to describe them. For binary variables, rates and percentages were used to show them. We employed the Mann-Whitney U tests or t-tests to make comparisons between groups for continuous variables. We utilised Pearson's chi-square test or Fisher's exact test to evaluate the statistical significance of disparities among groups of categorical variables. In this study, missing values for all variables were less than 20% (Refer to supplementary Table S1 ). Initially, we used five multiple imputation methods for missing data and averaged them with SPSS software to improve accuracy and reduce bias in data analysis. The whole dataset was also randomly split into a training set (which made up 70% of the dataset) and a test set (which made up 30% of the dataset). The LASSO regression model was employed to determine the most suitable predictors in the training set. Afterwards, these optimal predictors were reanalyzed using multifactorial logistic regression. Ultimately, predictors with two-tailed p -values < 0.05 were chosen to create a nomogram. Furthermore, the model's discriminatory ability was evaluated by employing receiver operating characteristic (ROC) curves, calibration curves, and Hosmer-Lemeshow tests to determine the level of agreement between the projected probabilities and the actual occurrences of the model. A Hosmer-Lemeshow test result with a p -value > 0.05 indicated good of predictive agreement [ 24 ] . To figure out the nett clinical benefit of the nomogram models, decision curve analysis (DCA) is used. R version 4.3.3 and IBM SPSS Statistics 27.0 were used to do the statistical tests. Results Baseline characteristics The research included 1571 middle-aged and elderly CKD patients in all, of whom 756 (48.12%) exhibited depressive symptoms. Females had a higher prevalence of depression, with 423 cases (55.95%), compared to male patients with 333 instances (44.05%). 136 patients (17.99%) suffered from two chronic diseases, while 585 (77.38%) suffered from three or more chronic diseases. 537 (71.03%) patients experienced somatic pain. Comparing the older and middle-aged population with CKD with no depressive symptoms, the latter group had much lower SRH scores and more frequent sleep disruptions. The basic properties of the dataset are collected in Table 2 (At the bottom of the article). Table 2 Comparison of depression levels in middle-aged and elderly chronic kidney patients with different indicators. Variables Total Non-depression Depression p-value n 1571 815 756 Age(years) 62[54,68] 61[54,68] 62[54,68] 0.441 Gender < 0.001 Male 831(52.90) 498(61.10) 333(44.05) Female 740(47.10) 317(38.90) 423(55.95) Place of residence < 0.001 Rural 988(62.89) 465(57.06) 523(69.18) city 583(37.11) 350(42.94) 233(30.82) Education level < 0.001 Below primary school 694(44.18) 293(35.95) 401(53.04) Primary school 359(22.85) 189(23.19) 170(22.49) Middle school 317(20.18) 192(23.56) 125(16.53) High school and higher 201(12.79) 141(17.30) 60(7.94) Marital Status < 0.001 Married 1361(86.63) 736(90.30) 625(82.70) Separated/Widowed/Divorced/ Never married 210(13.37) 79(9.70) 131(17.30) Smoking 0.045 Yes 470(29.92) 262(32.15) 208(27.51) No 1101(70.08) 553(67.85) 548(72.49) Drinking < 0.001 Yes 567(36.09) 347(42.58) 220(29.10) No 1004(63.91) 468(57.42) 536(70.90) Medical insurance 0.314 Yes 1467(93.98) 766(93.99) 701(92.72) No 104(6.62) 49(6.01) 55(7.28) Retire < 0.001 Yes 278(17.70) 183(22.45) 95(12.57) No 1293(82.30) 632(77.55) 661(87.43) Financial support by parents 0.635 Yes 1285(81.80) 663(81.35) 622(82.28) No 286(18.20) 152(18.65) 134(17.72) Life satisfaction < 0.001 Completely satisfied 61(3.89) 7(0.86) 54(7.14) Very satisfied 151(9.61) 28(3.44) 123(16.27) Somewhat satisfied 811(51.62) 412(50.55) 399(52.78) Not very satisfied 468(29.79) 313(38.40) 155(20.52) Not at all satisfied 80(5.09) 55(7.75) 25(3.31) Disability < 0.001 Yes 195(12.41) 79(9.69) 116(15.34) No 1376(87.59) 736(90.31) 640(84.66) Pain < 0.001 Yes 759(48.31) 222(27.24) 537(71.03) No 812(51.69) 593(72.76) 219(28.97) IADL < 0.001 Difficulties 527(33.55) 153(18.77) 374(49.47) No difficulties 1044(66.45) 662(81.23) 382(50.53) Sleep disorder < 0.001 < 1 days 618(39.34) 487(59.75) 131(17.33) 1 − 2days 227(14.45) 133(16.32) 94(14.43) 3 − 4days 245(15.61) 74(9.08) 171(22.62) 5 − 7days 481(30.62) 121(14.85) 360(47.62) Self-reported health < 0.001 Very poor 61(3.88) 39(4.79) 165(21.83) poor 151(9.61) 141(17.3) 303(40.08) fair 811(51.62) 494(60.61) 254(33.60) good 468(29.79) 87(10.67) 21(2.77) very good 80(5.09) 54(6.63) 13(1.72) Comorbid chronic diseases < 0.001 1 kind 174(11.08) 139(17.05) 35(4.63) 2 kinds 342(21.77) 206(25.28) 136(17.99) 3 kinds and above 1055(67.15) 470(57.67) 585(77.38) Social interaction < 0.001 Yes 844(53.72) 477(58.53) 367(48.45) No 727(46.28) 338(41.47) 389(51.46) Sleep duration 6[5,7] 6[5,8] 5[4,7] < 0.001 We also assessed the fundamental characteristics of the training and validation groups that were randomly separated. No statistically significant differences were found across the datasets. ( p > 0.05). (Supplementary Table S2 ). Predictors of depression in middle-aged and older individuals with CKD We used 10-fold cross-validation and the LASSO regression model to find the best λ value, which was 0.031(lambda.1se). We identified a total of 10 predictors with non-zero coefficients(Fig. 2 A, B). We further performed multifactor logistic regression analysis on the selected 10 predictors to identify the final predictors and generate a nomogram(Fig. 3 ). Multifactor logistic regression further confirmed that the 10 factors mentioned above factors were independent predictors, including gender, place of residence, marital status, self-health score, life satisfaction, education level, IADL, somatic pain, sleep disorder, and number of chronic disease comorbidities. Refer to Table 3 (At the bottom of the article) for a list of these independent factors that were linked to depressive symptoms in middle-aged and older CKD patients. Table 3 Section of clinical factors by logistic regression. Variable OR (95%CI) p-value Gender Female ref ref Male 0.72(0.52,0.99) 0.040 Place of residence City ref ref Rural 1.56(1.14–2.22) 0.006 Marital Status Separated/Widowed/Divorced/Never married ref ref Married 0.46 (0.32,0.65) 0.023 Slelf-reported health Very poor ref ref Poor 0.74 (0.46,1.22) 0.238 Fair 0.28(0.17,0.45) < 0.001 Good 0.18 (0.09,0.38) < 0.001 Very good 0.23 (0.10,0.54) < 0.001 Life satisfaction Not at all satisfied ref ref Not very satisfied 1.01(0.34,3.02) 0.986 Somewhat satisfied 0.34(0.13,0.90) 0.03 Very satisfied 0.15 (0.06,0.42) < 0.001 Completely satisfied 0.15(0.05,0.46) < 0.001 Education level Below primary school ref ref Primary school 0.77(0.50–1.16) 0.210 Middle school 0.67(0.43–1.04) 0.074 High school and higher 0.45(0.26–0.77) 0.004 IADL No difficulties ref ref Difficulties 1.843(1.27–2.67) 0.001 Pain No ref ref Yes 3.29(2.36–4.60) < 0.001 Comorbid chronic diseases 1 kind ref ref 2 kinds 1.73(1.03–2.90) 0.040 3 kinds and above 1.93(1.20–3.11) 0.006 Sleep disorder < 1 day ref ref 1–2 days 2.29(1.44–3.65) < 0.001 3–4 days 8.03(4.95–13.04) < 0.001 5–7 days 7.58(5.02–11.45) < 0.001 Assessing the performance of the nomogram model in both the training and validation datasets The discriminative ability of the nomogram model was evaluated by analysing ROC curves in both the training and validation datasets. The AUC of the training set was 0.889, with a 95% Confidence Interval (CI) of 0.869–0.908. The specificity achieved a value of 0.847, while the sensitivity reached 0.800. The area under the receiver operating characteristic curve (AUC) of the validation set was 0.869, with a 95% confidence interval (CI) spanning from 0.836 to 0.902. These results show that the nomogram model exhibits strong discriminative power ( Fig. 4A, B ). There are calibration curves for the nomogram model in both the training set and the validation set shown in Fig. 5A, B . The results of the Hosmer-Lemeshow test indicated that the nomogram model had a strong fit for both the training set (χ 2 = 12.976, df = 8, p- value = 0.112) and the validation set (χ 2 = 10.08, df = 8, p- value = 0.259). The results indicated that the nomogram model effectively predicted the probability of melancholy, with a high degree of agreement between the anticipated and actual probabilities. We used decision curve analysis (DCA) to evaluate the clinical usefulness of the nomogram model. ( Fig. 6A, B ). The nomogram model exhibited superior nett benefit in comparison to the two extreme approaches (all or none) over a spectrum of individual threshold probabilities in both the training set (ranging from 0 to 0.98) and the validation set (ranging from 0 to 0.99). This facilitates early prediction and diagnosis of depression in middle-aged and elderly CKD patients, enhancing its clinical utility. Discussion As far as we know, the prediction model we created and verified is the first visualisation tool capable of forecasting the likelihood of depression in middle-aged and elderly individuals with CKD. The nomogram model can precisely ascertain the likelihood of depression risk within this specific group. The model includes 10 predictors: gender, place of residence, marital status, self-reported health, life satisfaction, education level, IADL, somatic pain, sleep disorders, and number of chronic disease comorbidities. As with other studies, ours showed that the number of depressed symptoms was significantly different between men and women, with women having far more of them than men [ 11 ] . This phenomenon may be attributed to excessive stress, genetics, hormonal fluctuations related to various aspects of reproductive function, or metabolic disorders, leading to different responses to environmental stress in males and females [ 25 , 26 ] . Furthermore, the study showed that lower educated middle-aged and elderly CKD patients were more prone to have depressed symptoms. A study on the relationship between education and mental illness showed that tertiary education level was an independent protective factor for mental illness [ 27 ] . Another study suggested that women's depressed symptoms may be lessened with more educational assistance [ 28 ] . Higher educated patients may be able to better manage their health demands because they learn more about chronic kidney disease (CKD) and have greater social support. The prevalence of depression symptoms is higher in elderly CKD patients living in rural areas than in middle-aged and elderly CKD patients living in urban areas. This result is in line with earlier studies [ 15 , 29 ] and may be attributed to inadequate patient awareness and treatment in rural regions [ 30 ] . A cross-sectional study based on the CHARLS showed that [ 31 ] people living in urban areas are less likely to develop depression. Thus, middle-aged and elderly CKD patients can benefit psychologically from metropolitan resources. Furthermore, our study revealed that having a spouse and living or cohabiting with them protects against depression in middle-aged and elderly CKD patients. This may be explained by the increased attention and care that come with a strong marriage, which can help both partners in reducing unpleasant feelings in their life. Several published studies have investigated the increased likelihood of developing depression among individuals who are separated, divorced, widowed, or unmarried [ 31 , 32 ] , which aligns with our findings. As such, clinical staff should pay closer attention to CKD patients in rural areas, particularly those who are female, live alone, and have low levels of education. The World Health Organisation (WHO) strongly advises using self-reported health (SRH), in health interview surveys, or how a person perceives their own health [ 33 ] . A meta-analysis showed that SRH was a strong indicator of when mental illnesses will start [ 34 ] . In this work, SRH was found to be a risk indicator for middle-aged and elderly CKD patients to develop depression. The findings of this study are consistent with the confirmation by several earlier studies that a worse self-reported health condition is a strong predictor of depression [ 13 , 34 ] . For this reason, medical experts ought to stress the need of evaluating the health of CKD patients in their middle and senior years.Furthermore, Our study also found a significantly higher risk of depressive symptoms in middle-aged and elderly CKD patients with IADL disability, Indicating that individuals with restrictions on physical exercise are susceptible to experiencing depressed symptoms, which aligns with earlier research [ 35 ] , IADL assesses a person's capacity for more complex social interaction and independent living. An IADL impairment may make it more difficult for a person to carry out normal social responsibilities and reduce their chances to go shopping, cleaning, picking up medicines, cooking, or just walking. Additionally,This study found that middle-aged and elderly adults with CKD who reported being satisfied with their life had a lower probability of suffering depressive symptoms compared to those who reported being dissatisfied. The possible explanation for this is that middle-aged and elderly. CKD patients have higher levels of unhappiness with their lives. This is primarily caused by a decline in their quality of life and an increase in stress, which indirectly elevates their susceptibility to depression. A prior study [ 36 ] confirmed that depressed symptoms exhibited a substantial correlation with chronic illnesses in middle-aged and elderly adults, hence affecting their level of life satisfaction. Pain often coexists with depressive symptoms, and sometimes pain symptoms can delay the identification of depressive symptoms or even be overlooked, leading to a deterioration in the patient's depressive symptoms or worse health outcomes [ 37 – 39 ] ; in this study, middle-aged and elderly CKD patients with pain were found to be more susceptible to depressive symptoms. A recent review revealed that over 58% of CKD patients experience pain, with moderate to severe pain affecting around 49% of CKD patients [ 40 ] . These findings indicate that middle-aged and elderly individuals with chronic kidney disease (CKD) who frequently feel pain may develop significant psychological and depressed symptoms as a result of reduced quality of life and impaired physical function. This research found that sleep disturbances were a distinct and significant factor in risk for depression in middle-aged and elderly CKD patients. Approximately 84.7% of middle-aged and elderly CKD patients with depressive symptoms experienced sleep disturbances (defining the presence of poor sleep for < 1 day in less than a week as the absence of sleep disturbances [ 41 ] ). This result agrees with earlier observational research [ 25 , 42 ] . Previous studies have proposed that depression is a notable risk factor for sleep problems [ 43 , 44 ] . However, the causal association between the two has to be confirmed through future prospective cohort studies. The coexistence of depressive symptoms and sleep disorders seriously affects the quality of life of middle-aged and elderly chronic kidney disease (CKD) patients. This combination can even worsen their condition, and CKD patients with high depressive symptoms may exhibit suicidal tendencies, increasing the risk of death. Therefore, healthcare workers should screen for symptoms such as pain and sleep disorders early. Providing timely interventions may improve depressive symptoms in middle-aged and elderly CKD patients. We found that 77.38% of middle-aged and elderly depressed patients with CKD had two or more chronic diseases. A previous meta-analysis of cross-sectional studies showed that patients with multiple comorbidities were more likely to be depressed [ 45 ] . Another cross-sectional study showed [ 46 ] that Chinese older adults with multiple chronic diseases had a 1.55 times higher risk of depression than Chinese older adults without any chronic diseases. This supports our finding that having more chronic diseases makes depressive symptoms worse. After a systematic literature search, the nomogram model constructed in this study is the first visualization tool for assessing depression risk among CKD patients in middle-aged and older. The prediction model exhibited strong discriminatory capability included in the sets used for training and validation, with AUC values of 0.882 and 0.858, respectively. Excellent accuracy of the model was verified in the training and validation sets by the calibration curves and the Hosmer-Lemeshow fit test. In addition, the DCA revealed that the model demonstrates a high net clinical benefit in both the training and validation sets. These findings suggest that the nomogram model can be used to accurately identify the risk of depression in middle-aged and older CKD patients. This capacity to foresee can help medical experts spot the high-risk group of middle-aged and elderly CKD patients who exhibit depressed symptoms. Consequently, it is feasible to implement early preventative measures in order to enhance quality of life and delay the progression of illness. This study also had some shortcomings. Initially, the nomogram model was developed based on Chinese data and has only been internally validated; further external validation is required to determine its applicability to other regions or countries. Furthermore, certain data points were obtained by self-reporting by the participants, which introduces the possibility of recall bias and consequently impacts the accuracy of the responses. Moreover, causality was somewhat hampered by the cross-sectional nature of this investigation and the absence of longitudinal data. Hence, future research using longitudinal data could strengthen the model's validity. Conclusion We used data from CHARLS in China to create and test a nomogram model that can identify the chance of depressed symptoms in middle-aged and older CKD patients. This model can be utilized to evaluate the probability of depression in middle-aged and elderly CKD patients in China. Furthermore, the assessment tool can be utilized by clinical healthcare professionals to screen middle-aged and elderly CKD patients with high-risk depressive symptoms accurately. This enables the implementation of early interventions to minimize the occurrence of depression and slow down disease progression. List Of Abbreviations CKD: chronic kidney disease; LASSO: Least Absolute Shrinkage and Selection Operator; ROC: receiver operating characteristic curves; DCA: decision curve analysis; SRH: self-reported health; AUC: Area under the curve. Declarations Availability of data and materials Public access to CHARLS data is accessible via the official website ( http://charls.pku.edu.cn/en ). Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contribution TS performed data analysis and wrote the manuscript. YQ and XL provided critical revisions. DW contributed to supervised, reviewed, revised the manuscript. Acknowledgments We would like to thank the China Health and Retirement Longitudinal Study for their data and those who participated in the CHARLS for their contributions to this work. Ethics approval and consent to participate Data for this study were obtained from the China Health and Retirement Longitudinal research (CHARLS) database, the creation of which was approved by Peking University Biomedical Ethics Review Board (IRB00001052-11015). All participant data were anonymized to safeguard their privacy. Ethical approval and informed consent were not required as we had obtained access to the CHARLS database and the data used in this study were anonymized. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Wang L, Xu X, Zhang M, et al. 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Zhou T, Zhao J, Ma Y, et al. Association of cognitive impairment with the interaction between chronic kidney disease and depression: findings from NHANES 2011–2014[J]. BMC Psychiatry. 2024;24(1):312. Simões ES, a C, Miranda AS, Rocha NP, et al. Neuropsychiatric Disorders in Chronic Kidney Disease[J]. Front Pharmacol. 2019;10:932. Tsai YC, Chiu YW, Hung CC, et al. Association of symptoms of depression with progression of CKD[J]. Am J Kidney Dis. 2012;60(1):54–61. The Lancet Global H. Mental health matters[J]. Lancet Glob Health. 2020;8(11):e1352. Pu L, Zou Y, Wu SK, et al. Prevalence and associated factors of depressive symptoms among chronic kidney disease patients in China: Results from the Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE)[J]. J Psychosom Res. 2020;128:109869. Yang Q, Xiang Y, Ma G, et al. A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease[J]. Ren Fail. 2024;46(1):2317450. Jang J, Jung HS, Chae K, et al. Trajectories of self-rated health among community-dwelling individuals with depressive symptoms: A latent class growth analysis[J]. J Affect Disord. 2023;332:83–91. Park S, Lee S, Kim Y, et al. Causal Effects of Positive Affect, Life Satisfaction, Depressive Symptoms, and Neuroticism on Kidney Function: A Mendelian Randomization Study[J]. J Am Soc Nephrol. 2021;32(6):1484–96. Basu S, Maheshwari V, Sodhi B, et al. The prevalence of depression, determinants, and linkage with functional disability amongst postmenopausal women in India: Evidence from the Longitudinal Ageing Study in India[J]. Asian J Psychiatr. 2024;96:104030. Ohori Tatsuo G, Riu Hamada M, Gondo T, et al. [Nomogram as predictive model in clinical practice][J]. Gan Kagaku Ryoho. 2009;36(6):901–6. Zhao Y, Hu Y, Smith JP, et al. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS)[J]. Int J Epidemiol. 2014;43(1):61–8. Jiang B, Tang D, Dai N, et al. Association of Self-Reported Nighttime Sleep Duration with Chronic Kidney Disease: China Health and Retirement Longitudinal Study[J]. Am J Nephrol. 2023;54(7–8):249–57. Boey KW. Cross-validation of a short form of the CES-D in Chinese elderly[J]. Int J Geriatr Psychiatry. 1999;14(8):608–17. Zheng X, Wu W, Shen S. Prospective bidirectional associations between depression and chronic kidney diseases[J]. Sci Rep. 2022;12(1):10903. Chen H, Mui AC. Factorial validity of the Center for Epidemiologic Studies Depression Scale short form in older population in China[J]. Int Psychogeriatr. 2014;26(1):49–57. Liu M, Du X, Sun Y, et al. The mediating role of cognition in the relationship between sleep duration and instrumental activities of daily living disability among middle-aged and older Chinese[J]. Arch Gerontol Geriatr. 2021;94:104369. Tian Y, Zhou X, Jiang Y, et al. Bidirectional association between falls and multimorbidity in middle-aged and elderly Chinese adults: a national longitudinal study[J]. Sci Rep. 2024;14(1):9109. Yan X, Wang L, Liang C, et al. Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea[J]. Front Neurosci. 2022;16:936946. Yu X, Tian S, Wu L, et al. Construction of a depression risk prediction model for type 2 diabetes mellitus patients based on NHANES 2007–2014[J]. J Affect Disord. 2024;349:217–25. Thériault RK, Perreault ML. Hormonal regulation of circuit function: sex, systems and depression[J]. Biol Sex Differ. 2019;10(1):12. Erickson J, El-Gabalawy R, Palitsky D, et al. EDUCATIONAL ATTAINMENT AS A PROTECTIVE FACTOR FOR PSYCHIATRIC DISORDERS: FINDINGS FROM A NATIONALLY REPRESENTATIVE LONGITUDINAL STUDY[J]. Depress Anxiety. 2016;33(11):1013–22. Chlapecka A, Kagstrom A, Cermakova P. Educational attainment inequalities in depressive symptoms in more than 100,000 individuals in Europe[J]. Eur Psychiatry. 2020;63(1):e97. Srinivasan M, Reddy MM, Sarkar S, et al. Depression, Anxiety, and Stress among Rural South Indian Women-Prevalence and Correlates: A Community-Based Study[J]. J Neurosci Rural Pract. 2020;11(1):78–83. Chen S, Conwell Y, Xue J, et al. Protocol of an ongoing randomized controlled trial of care management for comorbid depression and hypertension: the Chinese Older Adult Collaborations in Health (COACH) study[J]. BMC Geriatr. 2018;18(1):124. Chen L, Chang L, Lin H, et al. Depressive disorder benefits of cities: Evidence from the China[J]. J Affect Disord. 2024;350:420–7. Marini CM, Ermer AE, Fiori KL, et al. Marital Quality, Loneliness, and Depressive Symptoms Later in Life: The Moderating Role of Own and Spousal Functional Limitations[J]. Res Hum Dev. 2020;17(4):211–34. Fan Y, He D. Self-rated health, socioeconomic status and all-cause mortality in Chinese middle-aged and elderly adults[J]. Sci Rep. 2022;12(1):9309. Chang-Quan H, Xue-Mei Z, Bi-Rong D, et al. Health status and risk for depression among the elderly: a meta-analysis of published literature[J]. Age Ageing. 2010;39(1):23–30. Tai LA, Tsai LY, Lin CH, et al. Depressive symptoms and daily living dependence in older adults with type 2 diabetes mellitus: the mediating role of positive and negative perceived stress[J]. BMC Psychiatry. 2024;24(1):14. Bai S, Wang J, Liu J, et al. Analysis of depression incidence and influence factors among middle-aged and elderly diabetic patients in China: based on CHARLS data[J]. BMC Psychiatry. 2024;24(1):146. Duan D, Yang L, Zhang M, et al. Depression and Associated Factors in Chinese Patients With Chronic Kidney Disease Without Dialysis: A Cross-Sectional Study[J]. Front Public Health. 2021;9:605651. Kroenke K, Wu J, Bair MJ, et al. Reciprocal relationship between pain and depression: a 12-month longitudinal analysis in primary care[J]. J Pain. 2011;12(9):964–73. Qiu Y, Ma Y, Huang X. Bidirectional Relationship Between Body Pain and Depressive Symptoms: A Pooled Analysis of Two National Aging Cohort Studies[J]. Front Psychiatry. 2022;13:881779. Dolati S, Tarighat F, Pashazadeh F, et al. The Role of Opioids in Pain Management in Elderly Patients with Chronic Kidney Disease: A Review Article[J]. Anesth Pain Med. 2020;10(5):e105754. Zhou Y, Ni Y, Jones M, et al. Sleep Behaviors and Progression of Multimorbidity in Middle-Aged and Older Adults: A Prospective Cohort Study From China[J]. J Gerontol Biol Sci Med Sci. 2023;78(10):1871–80. Li W, Ruan W, Peng Y, et al. Associations of socioeconomic status and sleep disorder with depression among US adults[J]. J Affect Disord. 2021;295:21–7. Chung TC, Chung CH, Peng HJ, et al. An analysis of whether sleep disorder will result in postpartum depression[J]. Oncotarget. 2018;9(38):25304–14. Yazıcı R, Güney İ. Prevalence and related factors of poor sleep quality in patients with pre-dialysis chronic kidney disease[J]. Int J Artif Organs. 2022;45(11):905–10. Triolo F, Harber-Aschan L, Belvederi Murri M, et al. The complex interplay between depression and multimorbidity in late life: risks and pathways[J]. Mech Ageing Dev. 2020;192:111383. Liu H, Zhou Z, Fan X, et al. Association Between Multiple Chronic Conditions and Depressive Symptoms Among Older Adults in China: Evidence From the China Health and Retirement Longitudinal Study (CHARLS)[J]. Int J Public Health. 2023;68:1605572. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4545265","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325804080,"identity":"bb0e9f1a-c040-4882-b780-785e16cbc4b9","order_by":0,"name":"Tongxin Sun","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tongxin","middleName":"","lastName":"Sun","suffix":""},{"id":325804081,"identity":"3e271f2b-7b9b-4e75-a3cb-db84776762b6","order_by":1,"name":"Qihui Ye","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qihui","middleName":"","lastName":"Ye","suffix":""},{"id":325804082,"identity":"1a84570b-8796-4143-8c33-a00125520113","order_by":2,"name":"Xunliang Li","email":"","orcid":"","institution":"the Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xunliang","middleName":"","lastName":"Li","suffix":""},{"id":325804083,"identity":"e16f7365-14e4-4a31-ba52-231efa2233c1","order_by":3,"name":"Deguang Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYLACHgjF+ICxAcwwIFoLswHJWtgkiNJicPzs4RdvKmoT+2e3X6v4uWNbYgN78zYJhpo7uLWcyUuznHPmeOKMO2fKbvaeuZ3YwHOsTILh2DOcWswO5JgZ87YdS2y4kZN2g7cNqEUixwzowsO4tZx/A9Ty71jifKCWwr8gLfJvCGi5kWP8mLehJnHDjfRjzBBbePBrsb/xxoxxzrEDxhtv5DBLy7bdNm7jSSu2SDiGW4tkf47xhzc1dbLzbqQ//Pi27bZsP/vhjTc+1ODWwgCKDgYGkAIeSHSwgYgEfBqAkf6BgaEOSLM/wK9uFIyCUTAKRiwAAAnzYj9Xf/OjAAAAAElFTkSuQmCC","orcid":"","institution":"Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Deguang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-07 09:49:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4545265/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4545265/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60615445,"identity":"f79c1ead-a5f1-4344-82c0-629d75901c24","added_by":"auto","created_at":"2024-07-18 20:12:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103912,"visible":true,"origin":"","legend":"\u003cp\u003eParticipants Selection Flowchart.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4545265/v1/c8c3b99a865c95fa7ab1b019.png"},{"id":60615447,"identity":"1d40c007-7c8b-45df-a934-844bf1e54909","added_by":"auto","created_at":"2024-07-18 20:12:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Regularization path for LASSO coefficients, generating a distribution of coefficients based on a logarithmic (lambda) sequence with nonzero coefficients generated by the optimal lambda.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e We selected the optimal parameter (lambda-1se) in the LASSO model through 10-fold cross-validation, which resulted in 10 non-zero coefficients corresponding to the vertical dashed line on the right.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4545265/v1/221e91cd2aa3ffd2c91b1918.png"},{"id":60616408,"identity":"6e5b18dc-2049-443d-a911-0c26a5029039","added_by":"auto","created_at":"2024-07-18 20:20:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":120574,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram model estimates the risk of depression in middle-aged and elderly patients with CKD.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4545265/v1/943cf733be4e19fab595b4e6.png"},{"id":60615448,"identity":"7bd01465-9752-4c9f-a1a1-69f21159a635","added_by":"auto","created_at":"2024-07-18 20:12:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67464,"visible":true,"origin":"","legend":"\u003cp\u003eDiscrimination of the nomogram prediction model for depression in middle-aged and elderly CKD patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eThe ROC curve of the training set.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB \u003c/strong\u003eThe ROC curve of the validation set.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4545265/v1/aa6d4e271a9ad6a52e23484a.png"},{"id":60615451,"identity":"0f925c63-9acd-467c-ad78-4d687ed6d136","added_by":"auto","created_at":"2024-07-18 20:12:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":82621,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves of the nomogram prediction models for depression in middle-aged and elderly CKD patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Calibration curve of nomogram prediction in the training group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB \u003c/strong\u003eCalibration curve predicted by the nomograms in the validation group.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4545265/v1/df70c95853fdd25f89e19118.png"},{"id":60615450,"identity":"d06e1285-a430-45b8-b735-84dc3c9b6cc6","added_by":"auto","created_at":"2024-07-18 20:12:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":58762,"visible":true,"origin":"","legend":"\u003cp\u003eThe DCA of the nomogram prediction models for depression in middle-aged and elderly CKD patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA \u0026nbsp;\u003c/strong\u003eDCA of the nomogram in the training set.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB \u0026nbsp;\u003c/strong\u003eThe DCA of the nomogram in the validation set.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4545265/v1/dabc637c2b426b23535ca05f.png"},{"id":62084537,"identity":"20889423-1be2-493b-b900-699d0a0accbf","added_by":"auto","created_at":"2024-08-09 06:32:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1317948,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4545265/v1/af553ba6-8533-4915-9bea-6fa637eb8a03.pdf"},{"id":60615446,"identity":"d8fde9cf-106f-4e99-a5c4-3e5da1778fb8","added_by":"auto","created_at":"2024-07-18 20:12:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21409,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-4545265/v1/51de01cbf366be2d0aa31601.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a depression risk-predicting model for Chinese middle-aged and elderly Chronic Kidney Disease patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWorldwide, chronic kidney disease (CKD) is a serious public health concern marked by functional or structural abnormalities of the kidneys lasting longer than three months. A recent cross-sectional survey conducted in China on 176,874 people revealed an 8.2% incidence of CKD\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. A separate study revealed that the occurrence of chronic kidney disease (CKD) was 18% among those between the ages of 60 and 69, and 24% among those aged 70 to 79\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In addition, middle-aged and older individuals have a higher likelihood of developing CKD due to elevated rates of hypertension, diabetes, and cardiovascular disease\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. But middle-aged and older CKD patients' mental health is significantly impacted by the higher number of comorbidities and worse prognosis\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e,particularly mental health disorders such as depression\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDepression is a prevalent mental health condition characterised by long-lasting spells of sadness or a lack of enjoyment or interest in activities\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.In a cross-sectional research, 22% of CKD patients reported having depression\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, a prevalence that is approximately 2\u0026ndash;3 times higher than that of other chronic diseases\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. A prospective observational cohort study with a 4-year follow-up carried out in Taiwan, discovered that patients exhibiting significant depression symptoms experienced a more quicker decline in their glomerular filtration rate (GFR), Moreover, the research revealed that having depressed symptoms raised the risk of CKD patients getting end-stage renal disease (ESRD) and dying\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Chronic illness and depression frequently coexist. This can result in adverse health outcomes and lifestyle modifications for patients, which can be a substantial strain on their families\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA great deal of independent research has demonstrated that sociodemographics including gender, education, and place of habitation have an impact on the onset of depression in CKD patients.Additionally, economic capacity and co-morbid cardiovascular disease are closely related to depressive symptoms\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Sleep problems and depression have been strongly linked in previous research\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e; other possible contributing variables to depressed symptoms include poor self-reported health\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, life satisfaction\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, as well as instrumental activities of daily living (IADL)\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Because depression symptoms are common in the CKD population, postponing treatment adherence and deteriorating self-management of the disease can follow from delayed intervention. To minimize these adverse outcomes, we recommend routine screening for depressive symptoms, identifying middle-aged and elderly CKD patients at high risk for depression, and implementing timely early interventions.\u003c/p\u003e \u003cp\u003eWhile a number of research have pinpointed depression risk variables in CKD patients, no reliable visualisation tool exists to estimate the likelihood of depression in middle-aged and elderly CKD patients. Predicting the likelihood of depression in middle-aged and elderly CKD patients is possible with the nomogram prediction model, a straightforward and practical method. It is able to score every value level of the contributing factors and add together the individual scores to get the overall score. Finally, it employs a functional transformation connection between the overall score and the likelihood of the outcome event to calculate the anticipated value of the result event for that particular person.\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe study's goal was to develope and validate a nomogram model that can identify the risk of sadness in middle-aged and older people with CKD.\u003c/p\u003e"},{"header":"Experimental Procedures","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch design and data sources\u003c/h2\u003e \u003cp\u003eThis research used cross-sectional data from the 2015 China Health and Retirement Longitudinal research (CHARLS), which is a comprehensive countrywide survey of Chinese individuals aged 45 years and above\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e; the study was initiated in 2011 as a national baseline survey, employing a sampling methodology that involved multi-stage probability sampling. 150 counties, 450 villages, and 17,000 people from around 10,000 families were included in the sample. The samples were thereafter monitored biennially, with four rounds of follow-up surveys done in 2013 (2nd), 2015 (3rd), 2018 (4th), and 2020 (5th). The surveys largely concentrated on assessing health state and functioning, gathering healthcare information, and conducting blood-based bioassays. These surveys are often regarded as very authoritative.\u003c/p\u003e \u003cp\u003eThe following were the study inclusion requirements: (1) CKD diagnosed by a doctor or professional technician; (2) aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years; (3) The Complete Depression Short Form: Center for Epidemiologic Studies Depression (CES-D) score; One research exclusion criterion was missing data for the variables of greater than 20%. Eventually, the study comprised 1571 middle-aged and elderly CKD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eChronic Kidney Disease\u003c/h2\u003e \u003cp\u003eThe information on CKD was derived from the self-reports provided by the individuals. CKD was determined by the questionnaire \" Have your doctor told you that you have chronic kidney disease (excluding tumors or cancer)?\" Participants answered \"yes\", then they were categorized as having chronic kidney disease and the time of diagnosis was recorded\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of Depressive Symptoms\u003c/h2\u003e \u003cp\u003eThe CESD-10 short form, a commonly used test for evaluating depressive symptoms in older adults, was employed by the CHARLS to examine participants' depressed symptoms. This instrument has been extensively validated in Chinese elderly subjects \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. A 4-point scale from 0 (never), 1 (sometimes), 2 (frequently), and 3 (always) is used to rate the \"Frequency of such thoughts and actions throughout the previous week\" in the questionnaire. There is reversal scoring on the fifth and eighth items. Higher scores indicate more serious depressive symptoms; the scoring system runs from 0 to 30. Depressive symptoms were identified in this study by a CESD-10 score of 10 or higher, as indicated in the literature review\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The Cronbach's alpha of 0.84 indicated that the CES-D abbreviated form was highly reliable\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eBased on a literature search and clinical features, we screened for confounders and gathered demographic data including self-reported health, age, gender, location, marital status, education level, medical insurance, retirement, family financial support, life satisfaction. Current smoking and alcohol consumption are considered healthy behaviour patterns.\u003c/p\u003e \u003cp\u003eHealth status and behavioural dimensions included whether participants socialized over the previous month. Social activities included dancing, joining groups or volunteering, attending school or training sessions, playing mahjong, helping family and friends, and more. Participants were considered to have participated in social activities if they reported engaging in one of these activities; otherwise, they were considered not to have participated in socializing.\u003c/p\u003e \u003cp\u003eThe term \"Instrumental Activities of Daily Living\" (IADL) encompasses a set of five tasks: financial management, prescription administration, grocery shopping, cooking, and household cleaning. Participants were classified as having an IADL difficulty if they faced challenges or could not complete any of the items in the IADL. Conversely, if participants reported no difficulties with any of the IADL items, they were deemed to have no IADL difficulties\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the CHARLS questionnaire, Participants were asked, \"Are you often troubled with any bodily pains?\". The answer was divided into \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no\u0026rdquo;. Sleep disorders were assessed by the number of days of restless sleep in a week using the questionnaire\" My sleep was restless.\" The responses included four choices: \"Infrequently or not at all (\u0026lt;\u0026thinsp;1 day)\", \"Occasionally or to a small extent (1\u0026thinsp;\u0026minus;\u0026thinsp;2 days)\", \"Sometimes or to a moderate extent (3\u0026thinsp;\u0026minus;\u0026thinsp;4 days)\", and \"Most or all of the time (5\u0026thinsp;\u0026minus;\u0026thinsp;7 days)\". Those who participated also provided information on the duration of their sleep.\u003c/p\u003e \u003cp\u003eThe Self-Reported Health (SRH) questionnaire contains the following query: \"Please rank your health on a scale of very excellent, good, fair, bad, or very poor\" The diseases-related information covariates included the number of chronic disease comorbidities, which were the following diseases participants were diagnosed with by a physician in addition to being diagnosed with CKD: high blood pressure, diabetes, heart disease, stroke, gastric or digestive disorders, arthritis, and memory-related disorders. Additionally, multimorbidity is the presence of two or more chronic diseases\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.We divided it into three groups: one chronic disease, two chronic illnesses, and several chronic ailments.\u003c/p\u003e \u003cp\u003eRegarding the questionnaire, please indicate whether you have any of the following impairments. This study encompassed five groups: speech issues, visual problems, hearing problems, brain damage/mental retardation, and physical impairment. Participants were categorised as having a disability if they reported any of the above options, and as not having a disability if they reported none of them. The independent variable assignments are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssignment of independent variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssignment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;male; 0\u0026thinsp;=\u0026thinsp;female\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;rural; 0\u0026thinsp;=\u0026thinsp;city\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;below primary school;\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;primary school;\u003c/p\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;middle school;\u003c/p\u003e \u003cp\u003e4\u0026thinsp;=\u0026thinsp;high school and higher\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;married and living with/cohabiting with spouse;\u003c/p\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;separated/widowed/divorced/never married\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;yes; 0\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;yes; 0\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;yes; 0\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;yes; 0\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial support by parents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;yes; 0\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;completely satisfied;\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;very satisfied;\u003c/p\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;somewhat satisfied;\u003c/p\u003e \u003cp\u003e4\u0026thinsp;=\u0026thinsp;not very satisfied;\u003c/p\u003e \u003cp\u003e5\u0026thinsp;=\u0026thinsp;not at all satisfied\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;yes; 0\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;yes; 0\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;yes; 0\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;difficulties; 0\u0026thinsp;=\u0026thinsp;no difficulties\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;1 days;\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;1\u0026thinsp;\u0026minus;\u0026thinsp;2days;\u003c/p\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;3\u0026thinsp;\u0026minus;\u0026thinsp;4days;\u003c/p\u003e \u003cp\u003e4\u0026thinsp;=\u0026thinsp;5\u0026thinsp;\u0026minus;\u0026thinsp;7days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;very poor;\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;poor;\u003c/p\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;fair;\u003c/p\u003e \u003cp\u003e4\u0026thinsp;=\u0026thinsp;good;\u003c/p\u003e \u003cp\u003e5\u0026thinsp;=\u0026thinsp;very good\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbid chronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;one chronic condition;\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;two chronic conditions;\u003c/p\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;three or more chronic conditions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor continuous variables, the median and interquartile range (IQR) were used to describe them. For binary variables, rates and percentages were used to show them. We employed the Mann-Whitney U tests or t-tests to make comparisons between groups for continuous variables. We utilised Pearson's chi-square test or Fisher's exact test to evaluate the statistical significance of disparities among groups of categorical variables.\u003c/p\u003e \u003cp\u003eIn this study, missing values for all variables were less than 20% (Refer to supplementary \u003cb\u003eTable S1\u003c/b\u003e). Initially, we used five multiple imputation methods for missing data and averaged them with SPSS software to improve accuracy and reduce bias in data analysis. The whole dataset was also randomly split into a training set (which made up 70% of the dataset) and a test set (which made up 30% of the dataset). The LASSO regression model was employed to determine the most suitable predictors in the training set. Afterwards, these optimal predictors were reanalyzed using multifactorial logistic regression. Ultimately, predictors with two-tailed \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were chosen to create a nomogram.\u003c/p\u003e \u003cp\u003eFurthermore, the model's discriminatory ability was evaluated by employing receiver operating characteristic (ROC) curves, calibration curves, and Hosmer-Lemeshow tests to determine the level of agreement between the projected probabilities and the actual occurrences of the model. A Hosmer-Lemeshow test result with a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicated good of predictive agreement\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. To figure out the nett clinical benefit of the nomogram models, decision curve analysis (DCA) is used.\u003c/p\u003e \u003cp\u003eR version 4.3.3 and IBM SPSS Statistics 27.0 were used to do the statistical tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe research included 1571 middle-aged and elderly CKD patients in all, of whom 756 (48.12%) exhibited depressive symptoms. Females had a higher prevalence of depression, with 423 cases (55.95%), compared to male patients with 333 instances (44.05%). 136 patients (17.99%) suffered from two chronic diseases, while 585 (77.38%) suffered from three or more chronic diseases. 537 (71.03%) patients experienced somatic pain. Comparing the older and middle-aged population with CKD with no depressive symptoms, the latter group had much lower SRH scores and more frequent sleep disruptions. The basic properties of the dataset are collected in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(At the bottom of the article).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of depression levels in middle-aged and elderly chronic kidney patients with different indicators.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-depression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62[54,68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61[54,68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62[54,68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e831(52.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e498(61.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e333(44.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e740(47.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e317(38.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e423(55.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e988(62.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e465(57.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e523(69.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e583(37.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350(42.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233(30.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow primary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e694(44.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293(35.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e401(53.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e359(22.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189(23.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170(22.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317(20.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192(23.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125(16.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school and higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201(12.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141(17.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60(7.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1361(86.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e736(90.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e625(82.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated/Widowed/Divorced/ Never married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210(13.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79(9.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131(17.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e470(29.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262(32.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e208(27.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1101(70.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e553(67.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e548(72.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e567(36.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e347(42.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220(29.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1004(63.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e468(57.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e536(70.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1467(93.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e766(93.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e701(92.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104(6.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(6.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55(7.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e278(17.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183(22.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95(12.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1293(82.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e632(77.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e661(87.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial support by parents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1285(81.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e663(81.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e622(82.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286(18.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152(18.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134(17.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompletely satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61(3.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54(7.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151(9.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123(16.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomewhat satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e811(51.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e412(50.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e399(52.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot very satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e468(29.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e313(38.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155(20.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot at all satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80(5.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55(7.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(3.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195(12.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79(9.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116(15.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1376(87.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e736(90.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e640(84.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e759(48.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222(27.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e537(71.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e812(51.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e593(72.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e219(28.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifficulties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e527(33.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153(18.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e374(49.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo difficulties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1044(66.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e662(81.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e382(50.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e618(39.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e487(59.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131(17.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;2days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227(14.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133(16.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94(14.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026thinsp;\u0026minus;\u0026thinsp;4days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245(15.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74(9.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171(22.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026thinsp;\u0026minus;\u0026thinsp;7days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e481(30.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121(14.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e360(47.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61(3.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(4.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165(21.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151(9.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141(17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e303(40.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e811(51.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e494(60.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e254(33.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e468(29.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87(10.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003every good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80(5.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54(6.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbid chronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 kind\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174(11.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139(17.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 kinds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e342(21.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206(25.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136(17.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 kinds and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1055(67.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e470(57.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e585(77.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e844(53.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e477(58.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e367(48.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e727(46.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e338(41.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e389(51.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6[5,7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6[5,8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5[4,7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe also assessed the fundamental characteristics of the training and validation groups that were randomly separated. No statistically significant differences were found across the datasets. (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). (Supplementary \u003cb\u003eTable S2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of depression in middle-aged and older individuals with CKD\u003c/h2\u003e \u003cp\u003eWe used 10-fold cross-validation and the LASSO regression model to find the best λ value, which was 0.031(lambda.1se). We identified a total of 10 predictors with non-zero coefficients(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). We further performed multifactor logistic regression analysis on the selected 10 predictors to identify the final predictors and generate a nomogram(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Multifactor logistic regression further confirmed that the 10 factors mentioned above factors were independent predictors, including gender, place of residence, marital status, self-health score, life satisfaction, education level, IADL, somatic pain, sleep disorder, and number of chronic disease comorbidities. Refer to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(At the bottom of the article) for a list of these independent factors that were linked to depressive symptoms in middle-aged and older CKD patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSection of clinical factors by logistic regression.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72(0.52,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.56(1.14\u0026ndash;2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated/Widowed/Divorced/Never married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46 (0.32,0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlelf-reported health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74 (0.46,1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28(0.17,0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18 (0.09,0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23 (0.10,0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot at all satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot very satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01(0.34,3.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomewhat satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34(0.13,0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15 (0.06,0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompletely satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15(0.05,0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow primary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77(0.50\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67(0.43\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school and higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45(0.26\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo difficulties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifficulties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.843(1.27\u0026ndash;2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.29(2.36\u0026ndash;4.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbid chronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 kind\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 kinds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.73(1.03\u0026ndash;2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 kinds and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.93(1.20\u0026ndash;3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.29(1.44\u0026ndash;3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.03(4.95\u0026ndash;13.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;7 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.58(5.02\u0026ndash;11.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssessing the performance of the nomogram model in both the training and validation datasets\u003c/h2\u003e \u003cp\u003eThe discriminative ability of the nomogram model was evaluated by analysing ROC curves in both the training and validation datasets. The AUC of the training set was 0.889, with a 95% Confidence Interval (CI) of 0.869\u0026ndash;0.908. The specificity achieved a value of 0.847, while the sensitivity reached 0.800. The area under the receiver operating characteristic curve (AUC) of the validation set was 0.869, with a 95% confidence interval (CI) spanning from 0.836 to 0.902. These results show that the nomogram model exhibits strong discriminative power (\u003cb\u003eFig.\u0026nbsp;4A, B\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThere are calibration curves for the nomogram model in both the training set and the validation set shown in \u003cb\u003eFig.\u0026nbsp;5A, B\u003c/b\u003e. The results of the Hosmer-Lemeshow test indicated that the nomogram model had a strong fit for both the training set (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;12.976, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, \u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;=\u0026thinsp;0.112) and the validation set (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.08, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, \u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;=\u0026thinsp;0.259). The results indicated that the nomogram model effectively predicted the probability of melancholy, with a high degree of agreement between the anticipated and actual probabilities.\u003c/p\u003e \u003cp\u003eWe used decision curve analysis (DCA) to evaluate the clinical usefulness of the nomogram model. (\u003cb\u003eFig.\u0026nbsp;6A, B\u003c/b\u003e). The nomogram model exhibited superior nett benefit in comparison to the two extreme approaches (all or none) over a spectrum of individual threshold probabilities in both the training set (ranging from 0 to 0.98) and the validation set (ranging from 0 to 0.99). This facilitates early prediction and diagnosis of depression in middle-aged and elderly CKD patients, enhancing its clinical utility.\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eAs far as we know, the prediction model we created and verified is the first visualisation tool capable of forecasting the likelihood of depression in middle-aged and elderly individuals with CKD. The nomogram model can precisely ascertain the likelihood of depression risk within this specific group. The model includes 10 predictors: gender, place of residence, marital status, self-reported health, life satisfaction, education level, IADL, somatic pain, sleep disorders, and number of chronic disease comorbidities.\u003c/p\u003e \u003cp\u003eAs with other studies, ours showed that the number of depressed symptoms was significantly different between men and women, with women having far more of them than men\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. This phenomenon may be attributed to excessive stress, genetics, hormonal fluctuations related to various aspects of reproductive function, or metabolic disorders, leading to different responses to environmental stress in males and females\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the study showed that lower educated middle-aged and elderly CKD patients were more prone to have depressed symptoms. A study on the relationship between education and mental illness showed that tertiary education level was an independent protective factor for mental illness\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Another study suggested that women's depressed symptoms may be lessened with more educational assistance\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Higher educated patients may be able to better manage their health demands because they learn more about chronic kidney disease (CKD) and have greater social support.\u003c/p\u003e \u003cp\u003eThe prevalence of depression symptoms is higher in elderly CKD patients living in rural areas than in middle-aged and elderly CKD patients living in urban areas. This result is in line with earlier studies\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003eand may be attributed to inadequate patient awareness and treatment in rural regions\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. A cross-sectional study based on the CHARLS showed that\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003epeople living in urban areas are less likely to develop depression. Thus, middle-aged and elderly CKD patients can benefit psychologically from metropolitan resources. Furthermore, our study revealed that having a spouse and living or cohabiting with them protects against depression in middle-aged and elderly CKD patients. This may be explained by the increased attention and care that come with a strong marriage, which can help both partners in reducing unpleasant feelings in their life. Several published studies have investigated the increased likelihood of developing depression among individuals who are separated, divorced, widowed, or unmarried\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, which aligns with our findings. As such, clinical staff should pay closer attention to CKD patients in rural areas, particularly those who are female, live alone, and have low levels of education.\u003c/p\u003e \u003cp\u003eThe World Health Organisation (WHO) strongly advises using self-reported health (SRH), in health interview surveys, or how a person perceives their own health\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. A meta-analysis showed that SRH was a strong indicator of when mental illnesses will start\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. In this work, SRH was found to be a risk indicator for middle-aged and elderly CKD patients to develop depression. The findings of this study are consistent with the confirmation by several earlier studies that a worse self-reported health condition is a strong predictor of depression\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. For this reason, medical experts ought to stress the need of evaluating the health of CKD patients in their middle and senior years.Furthermore, Our study also found a significantly higher risk of depressive symptoms in middle-aged and elderly CKD patients with IADL disability, Indicating that individuals with restrictions on physical exercise are susceptible to experiencing depressed symptoms, which aligns with earlier research\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, IADL assesses a person's capacity for more complex social interaction and independent living. An IADL impairment may make it more difficult for a person to carry out normal social responsibilities and reduce their chances to go shopping, cleaning, picking up medicines, cooking, or just walking. Additionally,This study found that middle-aged and elderly adults with CKD who reported being satisfied with their life had a lower probability of suffering depressive symptoms compared to those who reported being dissatisfied. The possible explanation for this is that middle-aged and elderly. CKD patients have higher levels of unhappiness with their lives. This is primarily caused by a decline in their quality of life and an increase in stress, which indirectly elevates their susceptibility to depression. A prior study\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003econfirmed that depressed symptoms exhibited a substantial correlation with chronic illnesses in middle-aged and elderly adults, hence affecting their level of life satisfaction.\u003c/p\u003e \u003cp\u003ePain often coexists with depressive symptoms, and sometimes pain symptoms can delay the identification of depressive symptoms or even be overlooked, leading to a deterioration in the patient's depressive symptoms or worse health outcomes\u003csup\u003e[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e; in this study, middle-aged and elderly CKD patients with pain were found to be more susceptible to depressive symptoms. A recent review revealed that over 58% of CKD patients experience pain, with moderate to severe pain affecting around 49% of CKD patients\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. These findings indicate that middle-aged and elderly individuals with chronic kidney disease (CKD) who frequently feel pain may develop significant psychological and depressed symptoms as a result of reduced quality of life and impaired physical function.\u003c/p\u003e \u003cp\u003eThis research found that sleep disturbances were a distinct and significant factor in risk for depression in middle-aged and elderly CKD patients. Approximately 84.7% of middle-aged and elderly CKD patients with depressive symptoms experienced sleep disturbances (defining the presence of poor sleep for \u0026lt;\u0026thinsp;1 day in less than a week as the absence of sleep disturbances\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e). This result agrees with earlier observational research\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Previous studies have proposed that depression is a notable risk factor for sleep problems\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. However, the causal association between the two has to be confirmed through future prospective cohort studies. The coexistence of depressive symptoms and sleep disorders seriously affects the quality of life of middle-aged and elderly chronic kidney disease (CKD) patients. This combination can even worsen their condition, and CKD patients with high depressive symptoms may exhibit suicidal tendencies, increasing the risk of death. Therefore, healthcare workers should screen for symptoms such as pain and sleep disorders early. Providing timely interventions may improve depressive symptoms in middle-aged and elderly CKD patients.\u003c/p\u003e \u003cp\u003eWe found that 77.38% of middle-aged and elderly depressed patients with CKD had two or more chronic diseases. A previous meta-analysis of cross-sectional studies showed that patients with multiple comorbidities were more likely to be depressed\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Another cross-sectional study showed\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e that Chinese older adults with multiple chronic diseases had a 1.55 times higher risk of depression than Chinese older adults without any chronic diseases. This supports our finding that having more chronic diseases makes depressive symptoms worse.\u003c/p\u003e \u003cp\u003eAfter a systematic literature search, the nomogram model constructed in this study is the first visualization tool for assessing depression risk among CKD patients in middle-aged and older. The prediction model exhibited strong discriminatory capability included in the sets used for training and validation, with AUC values of 0.882 and 0.858, respectively. Excellent accuracy of the model was verified in the training and validation sets by the calibration curves and the Hosmer-Lemeshow fit test. In addition, the DCA revealed that the model demonstrates a high net clinical benefit in both the training and validation sets. These findings suggest that the nomogram model can be used to accurately identify the risk of depression in middle-aged and older CKD patients. This capacity to foresee can help medical experts spot the high-risk group of middle-aged and elderly CKD patients who exhibit depressed symptoms. Consequently, it is feasible to implement early preventative measures in order to enhance quality of life and delay the progression of illness.\u003c/p\u003e \u003cp\u003eThis study also had some shortcomings. Initially, the nomogram model was developed based on Chinese data and has only been internally validated; further external validation is required to determine its applicability to other regions or countries. Furthermore, certain data points were obtained by self-reporting by the participants, which introduces the possibility of recall bias and consequently impacts the accuracy of the responses. Moreover, causality was somewhat hampered by the cross-sectional nature of this investigation and the absence of longitudinal data. Hence, future research using longitudinal data could strengthen the model's validity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe used data from CHARLS in China to create and test a nomogram model that can identify the chance of depressed symptoms in middle-aged and older CKD patients. This model can be utilized to evaluate the probability of depression in middle-aged and elderly CKD patients in China. Furthermore, the assessment tool can be utilized by clinical healthcare professionals to screen middle-aged and elderly CKD patients with high-risk depressive symptoms accurately. This enables the implementation of early interventions to minimize the occurrence of depression and slow down disease progression.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cp\u003eCKD: chronic kidney disease; LASSO: Least Absolute Shrinkage and Selection Operator; ROC: receiver operating characteristic curves; DCA: decision curve analysis; SRH: self-reported health; AUC: Area under the curve.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublic access to CHARLS data is accessible via the official website\u0026nbsp;(\u003cu\u003ehttp://charls.pku.edu.cn/en\u003c/u\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTS performed data analysis and wrote the manuscript.\u003c/p\u003e\n\u003cp\u003eYQ and XL provided critical revisions.\u003c/p\u003e\n\u003cp\u003eDW contributed to supervised, reviewed, revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the China Health and Retirement Longitudinal Study for their data and those who participated in the CHARLS for their contributions to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData for this study were obtained from the China Health and Retirement Longitudinal research (CHARLS) database, the creation of which was approved by Peking University Biomedical Ethics Review Board (IRB00001052-11015). All participant data were anonymized to safeguard their privacy. Ethical approval and informed consent were not required as we had obtained access to the CHARLS database and the data used in this study were anonymized.\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\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang L, Xu X, Zhang M, et al. Prevalence of Chronic Kidney Disease in China: Results From the Sixth China Chronic Disease and Risk Factor Surveillance[J]. JAMA Intern Med. 2023;183(4):298\u0026ndash;310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Wang F, Wang L, et al. Prevalence of chronic kidney disease in China: a cross-sectional survey[J]. 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EDUCATIONAL ATTAINMENT AS A PROTECTIVE FACTOR FOR PSYCHIATRIC DISORDERS: FINDINGS FROM A NATIONALLY REPRESENTATIVE LONGITUDINAL STUDY[J]. Depress Anxiety. 2016;33(11):1013\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChlapecka A, Kagstrom A, Cermakova P. Educational attainment inequalities in depressive symptoms in more than 100,000 individuals in Europe[J]. Eur Psychiatry. 2020;63(1):e97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrinivasan M, Reddy MM, Sarkar S, et al. Depression, Anxiety, and Stress among Rural South Indian Women-Prevalence and Correlates: A Community-Based Study[J]. J Neurosci Rural Pract. 2020;11(1):78\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Conwell Y, Xue J, et al. Protocol of an ongoing randomized controlled trial of care management for comorbid depression and hypertension: the Chinese Older Adult Collaborations in Health (COACH) study[J]. 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Age Ageing. 2010;39(1):23\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTai LA, Tsai LY, Lin CH, et al. Depressive symptoms and daily living dependence in older adults with type 2 diabetes mellitus: the mediating role of positive and negative perceived stress[J]. BMC Psychiatry. 2024;24(1):14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai S, Wang J, Liu J, et al. Analysis of depression incidence and influence factors among middle-aged and elderly diabetic patients in China: based on CHARLS data[J]. BMC Psychiatry. 2024;24(1):146.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuan D, Yang L, Zhang M, et al. Depression and Associated Factors in Chinese Patients With Chronic Kidney Disease Without Dialysis: A Cross-Sectional Study[J]. Front Public Health. 2021;9:605651.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroenke K, Wu J, Bair MJ, et al. 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Int J Public Health. 2023;68:1605572.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Depression, Nomogram, Chronic kidney disease, Prediction model","lastPublishedDoi":"10.21203/rs.3.rs-4545265/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4545265/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eA global public health issue, chronic kidney disease(CKD) may worsen more quickly if depression symptoms overlap. In this study, a nomogram model was developed and validate to predict depression in Chinese CKD patients between the ages of middle-aged and old.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA 7:3 random split of the 1571 participants in the China Health and Retirement Longitudinal Study was made into training and validation sets. After doing Least Absolute Shrinkage and Selection Operator(LASSO) and multivariate binary logistic regression analysis to discover determinants of depression symptoms. These predictors were used to create a nomogram, which was then evaluated for discriminative power, predictive performance, and clinical applicability using receiver operating characteristic (ROC) curves, calibration curves, Hosmer-Leme show tests, and decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe nomogram model included 10 predictors, including gender, marital status, place of residence, education level, life satisfaction. pain, sleep disorders, self-reported health, as well as comorbid chronic diseases. The Area under the curve(AUC) values of the training and validation sets were, in turn, 0.889 (95% CI: 0.869\u0026ndash;0.908) and 0.869 (95% CI: 0.836\u0026ndash;0.902), the values of Hosmer\u0026ndash;Lemeshow test were \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.113 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.259. The calibration curves and the Hosmer-Lemeshow test results were used to verify the nomogram model's predictive capabilities. Additionally, the decision curve analysis (DCA) curves illustrated a high net clinical benefit provided by the predictive model.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe developed and validated a depression risk model for middle-aged and elderly CKD patients. Clinicians can accurately screen middle-aged and older CKD patients having depressive symptoms using the evaluation instrument, which is important for early intervention.\u003c/p\u003e","manuscriptTitle":"Development and validation of a depression risk-predicting model for Chinese middle-aged and elderly Chronic Kidney Disease patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 20:12:14","doi":"10.21203/rs.3.rs-4545265/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":"dde20179-37d2-48b4-9dcf-93d08e6b83c9","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-09T06:24:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-18 20:12:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4545265","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4545265","identity":"rs-4545265","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}