The Prognostic Value of the Naples Prognostic Score in Depression: Association with Prevalence and Mortality

The Prognostic Value of the Naples Prognostic Score in Depression: Association with Prevalence and Mortality | 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 The Prognostic Value of the Naples Prognostic Score in Depression: Association with Prevalence and Mortality Jin Zhao, Yang Luo, Xiaofang Li, Xingfu Fan, Shiping Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6309429/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 Depressive disorder is a widespread mental health condition, distinguished by symptoms such as persistent low mood, loss of interest, diminished energy, and changes in sleep and appetite. The Naples Prognostic Score (NPS), which combines biomarkers related to inflammation and nutritional status, has been shown to have prognostic value in several diseases. This study used data from the National Health and Nutrition Examination Survey (NHANES), which was carried out between 2007 and 2018, to examine the link between NPS, depression prevalence, and mortality in people with depression. Methods The cross-sectional analysis involved 29,655 participants, with 2,688 individuals diagnosed with depression, and 2,190 participants followed for mortality outcomes. The Patient Health Questionnaire-9 (PHQ-9) was used to measure depression, and blood albumin, total cholesterol, the neutrophil-to-lymphocyte ratio (NLR), and the lymphocyte-to-monocyte ratio (LMR) were used to calculate NPS. The relationship between NPS and depression was examined using weighted logistic regression, while the relationship between NPS and mortality in depressed patients was evaluated using Cox proportional hazards models, which controlled for clinical and demographic variables. Results An increased likelihood of depression (OR = 1.32, 95% CI: 1.09–1.60, p < 0.01) and a higher risk of death from all causes (HR = 4.54, 95% CI: 2.24–9.21, p < 0.01), heart disease (HR = 8.39, 95% CI: 2.85–24.71, p < 0.01), malignant neoplasms (HR = 5.10, 95% CI: 1.21–21.41, p = 0.03), diabetes (HR = 5.66, 95% CI: 1.44–22.24, p = 0.01), and hypertension (HR = 6.68, 95% CI: 1.69–26.40, p = 0.01) were all significantly correlated with higher NPS scores. Conclusion This study highlights the clinical relevance of the NPS in predicting both the prevalence of depression and its associated mortality risk. The NPS offers a valuable tool for early risk stratification and can support the development of personalized management strategies for individuals with depression, potentially improving their long-term health outcomes. Depression inflammation nutrition mortality NHAENS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Depressive disorder is a widespread mental health condition, distinguished by symptoms such as persistent low mood, loss of interest, diminished energy, and changes in sleep and appetite[ 1 , 2 ]. According to the World Health Organization, depression ranks among the leading causes of disability globally, impacting approximately 300 million individuals[ 3 ]. This disorder exhibits a higher prevalence in women, with a notably elevated incidence among adolescents and the elderly[ 4 ]. Depression not only profoundly affects individuals' emotional and psychological well-being but also contributes to a variety of physical health issues, including sleep disturbances, weakened immune function, and cardiovascular diseases[ 5 , 6 ]. Furthermore, it significantly undermines work performance and social interactions, intensifying social and economic burdens[ 7 ]. With the rise of globalization and changes in lifestyle, the prevalence of depression has markedly increased[ 8 ]. Despite substantial strides in the treatment of depression, the identification of high-risk populations and the implementation of stratified, personalized management continue to pose critical challenges in the public health domain. Current research has provided substantial evidence that both inflammation and nutritional status are closely and profoundly linked to the incidence and prognosis of depression[ 9 , 10 ]. Studies indicate that inflammatory biomarkers, such as C-reactive protein (CRP)[ 11 ], interleukin-6 (IL-6)[ 12 ], and tumor necrosis factor-alpha (TNF-α)[ 13 ], are commonly elevated in individuals with depression. In addition to being important immune response regulators, these inflammatory markers have a significant effect on brain function via a number of processes, which helps to initiate and exacerbate depression symptoms[ 14 ]. Furthermore, malnutrition, particularly the deficiency of key nutrients such as omega-3 fatty acids[ 15 ], and vitamin B[ 16 ], has been found to be closely associated with the onset and progression of depression. Although current studies have elucidated the individual roles of inflammation or nutrition in depression, most research has focused primarily on predicting the prognosis of depression based on a single factor while overlooking the interaction between inflammation and nutrition. There exists a complex interplay between inflammation and nutrition, where both factors not only independently influence the development and progression of depression but may also exacerbate the disease through their combined effects. There is a lack of research that integrates both inflammation and nutrition to assess the prognosis of depression jointly. Therefore, we propose the development of a comprehensive predictive index that jointly considers inflammation and nutritional status to evaluate the prognosis of depression. The NPS, created by Galizia et al., combines a number of clinical indicators, such as serum albumin, total cholesterol, NLR, and LMR, to give an extensive overview of systemic inflammation and nutritional status[ 17 ]. NPS has been shown to be an independent prognostic factor for various diseases, including liver cancer[ 18 ], gastric cancer[ 19 ], triple-negative breast cancer[ 20 ], as well as non-cancer diseases like acute pulmonary embolism[ 21 ], and heart failure[ 22 ]. However, the relationship between NPS and depression has yet to be explored. In order to address this knowledge gap, a cross-sectional and cohort analysis was conducted utilizing the comprehensive data provided by the NHANES from 2007 to 2018. The objective of this study was to systematically evaluate the relationship between NPS and depression while also investigating its potential association with mortality among individuals who have suffered from depression. The goal of this study is to provide insightful information for the prognostic use of these results. Materials and Methods Study population NHANES, administered by the Centers for Disease Control and Prevention (CDC), is a comprehensive database that combines health, nutrition, and physical examination data from a representative sample of the U.S. population. This dataset is frequently used in epidemiology studies and health policy research because it offers important insights about the dietary and health status of Americans. All laboratory examinations are conducted by trained medical personnel in mobile examination centers (MECs). The data are publicly available for download on the official website ( www.cdc.gov/nchs/nhanes ). The National Center for Health Statistics (NCHS) Research Ethics Review Board approved the study protocol, and informed consent was obtained from all participants. Inclusion and exclusion criteria Our study included participants from six survey cycles conducted between 2007 and 2018 of NHANES datasets, with a total of 69,806 individuals. Based on previous research and the methodology of our study, we established the following exclusion criteria: 1) Participants with missing data for depression assessment were excluded (38,346 participants); 2) Participants missing components of the NPS, such as albumin, cholesterol, lymphocytes, monocytes, and neutrophils, were excluded (1,805 participants). Ultimately, 29,655 participants were included in the cross-sectional study. In the cohort study, individuals with missing follow-up data were excluded (498 participants), leaving 2,190 participants for mortality analysis. The study flowchart is shown in Fig. 1 . Assessment of depression According to previous studies, current depressive symptoms were assessed using the PHQ-9 [ 23 – 25 ]. The PHQ-9 is a well-validated self-report tool (Cronbach's α = 0.89) that measures depressive symptoms—such as sadness, sleep disturbances, fatigue, and difficulty concentrating—over the past two weeks. It shows moderate concordance with clinical psychiatric interviews. The questionnaire consists of nine items, each rated on a four-point Likert scale, ranging from 0 (not at all) to 3 (nearly every day), with a total score ranging from 0 to 27. A dichotomous variable was created to categorize participants into two groups: no depression (PHQ-9 score < 10) and elevated depressive symptoms (PHQ-9 score ≥ 10), based on a threshold score of 10[ 26 ]. Assessment of NPS Following the method established by Galizia et al.[ 17 ], NPS was calculated using four clinical parameters: serum albumin, total cholesterol (TC), NLR, and LMR. For each parameter, thresholds were assigned as follows: serum albumin ≥ 40 g/L, TC > 180 mg/dL, NLR ≤ 2.96, or LMR > 4.44 scored 0; levels outside these thresholds scored 1. The NPS score (0–4) was the sum of these four components (Table 1 ). According to the previous study[ 18 , 27 , 28 ], participants were stratified into three distinct groups according to their total score distribution: Group 1 (NPS score = 0), Group 2 (NPS score = 1 or 2), and Group 3 (NPS score = 3 or 4). Table 1 The standard of NPS. NPS score Points ALB(g/dL) TC(mg/dL) NLR LMR 0 ≥ 4 >180 ≤ 2.96 >4.44 1 2.96 ≤ 4.44 Assessment of mortality Deceased participants in this study were identified by linking the NHANES dataset with the National Death Index (NDI). Mortality data, including all-cause mortality as of December 31, 2019, were obtained from the 2019 Linked Mortality File (LMF). These records were matched with NHANES data and are publicly accessible online at www.cdc.gov/nchs/data-linkage/mortality.htm . Covariates Based on a comprehensive review of the existing literature, we identified several key potential covariates that may influence the outcomes, including demographic information, laboratory data, and questionnaire information. Demographic data and questionnaire information such as age, gender, race, poverty income ratio (PIR), educational level, marital status, diabetes status, smoke status, and cancer status were self-reported by the participants. Smoking status was dichotomized into two distinct categories: smokers and nonsmokers. Those who reported smoking at least 100 cigarettes during their lives were considered smokers, whereas those who reported smoking fewer than 100 cigarettes were considered nonsmokers. To represent socioeconomic position, the PIR was separated into three levels: low (< 1.3), medium (1.3 to < 3.5), and high (≥ 3.5). Diabetes and cancer status was determined based on self-reported diagnosis, with individuals classified as "yes" if they reported a prior diagnosis and "no" otherwise. Marital status was dichotomized into "married" and "unmarried" based on responses to standardized questionnaire items. Educational status was classified into three categories: less than high school, high school graduate, and more than high school. Laboratory data included albumin, ALT, AST, cholesterol, triglyceride, uric acid, globulin, fasting glucose, HbA1c, lymphocyte, monocyte, neutrophils, Hb, PLT, NLR, and LMR. BMI was stratified into three discrete groups: normal weight (BMI < 25 kg/m²), overweight (BMI ≥ 25 to < 30 kg/m²), and obese (BMI ≥ 30 kg/m²). Statical analysis To ensure the representativeness of the national population, Mobile Examination Center weights (MECs) were employed in accordance with the NHANES Analytic Guidelines, which account for the survey's sophisticated multistage probability sampling framework. Participant characteristics are summarized as the means ± standard deviations (SD) for continuous variables and as proportions for categorical variables. Group differences in continuous variables were analyzed using the analysis of Variance, while the weighted chi-square test was utilized to compare categorical variables. To address missing data, multiple imputation was performed via the "mice" package in R, and the random forest algorithm was used to ensure robust and reliable data estimation. Participants in the cross-sectional study were split into two groups: one for those who had depression and one for those who did not. The relationship between NPS and the likelihood of depression was assessed using weighted multivariable logistic regression. Using 95% confidence intervals (CIs), the outcomes were presented as odds ratios (ORs). The logistic regression models were gradually modified: Model 1 included adjustments for age, education, and marital status, while Model 2 also considered factors like age, AST, triglycerides, uric acid, fasting glucose, HbA1c, PLT, education, marriage, BMI, smoking, and diabetes. The crude model had no adjustments. In the cohort study, individuals with depression were further classified into three groups based on their NPS scores: group 1(NPS = 0), group 2 (NPS = 1 or 2), and group 3(NPS = 3 or 4). A two-sided log-rank test was used to statistically compare the cohort study analysis, which used Kaplan-Meier (KM) survival curves to examine the relationship between NPS, all-cause mortality, and specific mortality in patients with depression. The results of weighted multivariate Cox regression analysis were displayed as hazard ratios (HRs) with 95% confidence intervals (CIs) in order to further assess the association between NPS, all-cause mortality, and specific mortality among persons with depression. Model 1 was adjusted for age, gender, and race; Model 2 included additional adjustments for diabetes, cancer, BMI, education status, PLT, HbA1c, fasting glucose, Hb, triglycerides, and globulin; and the crude model included no adjustments. The Cox regression models were then stepwise adjusted. Additionally, nonlinear associations between NPS and the risk of all-cause death were methodically investigated. Additionally, we computed the AUC value and plotted the ROC curve to confirm the predictive power of NPS. R Studio (version 4.2.2) and STATA (version 15.1) were used for the statistical methods, which used a two-tailed significance criteria of P < 0.05. Results Basic characteristics of all participants The baseline characteristics of the study population are presented in Table 2 . In this study, participants were categorized into two groups based on the presence of depression: the depression group (N = 2,688, 9.06%) and the non-depression group (N = 26,967, 90.94%). Baseline characteristics were compared between the two groups, revealing that the depression group had a significantly higher mean age (48.47 ± 18.44 years) compared to the non-depression group (46.88 ± 18.89 years, P < 0.01). Several biochemical markers showed significant differences between the two groups (P < 0.01). Specifically, the depression group had significantly higher triglyceride levels (164.02 ± 129.53 mg/dL) compared to the non-depression group (150.41 ± 122.50 mg/dL, P < 0.01), lower albumin levels (4.19 ± 0.36 g/dL vs. 4.27 ± 0.35 g/dL, P < 0.01), and higher AST levels (25.74 ± 24.46 U/L vs. 25.01 ± 13.83 U/L, P = 0.02). In terms of demographics, the distribution of genders in the two groups did not differ significantly (P = 0.33). However, marital status and educational attainment showed substantial variations (P < 0.01). Both the percentage of people with less than a high school education (24.19% vs. 20.33%, P < 0.01) and the percentage of married people (52.95% vs. 38.93%, P < 0.01) were greater in the depression group. Obesity was more common in the depression group (49.01% vs. 39.35%, P < 0.01) in terms of BMI. Additionally, there was a substantially greater prevalence of diabetes (14.89% vs. 9.15%, P < 0.01) and smoking (60.37% vs. 41.84%, P < 0.01) in the depression group. These differences suggest that individuals with depression are more likely to be older, less educated, married, of lower income, obese, and smokers. All things considered, these baseline variations show that a number of biochemical, behavioral, and demographic characteristics are strongly linked to the existence of depression and help to clarify its causes and course. The main conclusion of our research is that the distribution of NPS in the depression and non-depression groups differs significantly. Specifically, the depression group has a higher proportion in NPS Group 3 (16.94% vs. 12.29%), while the proportion in NPS Group 2 is significantly lower in the depression group compared to the non-depression group (64.79% vs. 71.15%). Table 2 Characteristics of the participants. Characteristic Total Non-depression Depression P- value N = 29,655 N = 26,967(90.94%) N = 2,688(9.06%) Mean ± SD Age(years) 47.00 ± 18.86 46.88 ± 18.89 48.47 ± 18.44 < 0.01 Albumin(g/dL) 4.26 ± 0.35 4.27 ± 0.35 4.19 ± 0.36 < 0.01 ALT(U/L) 25.01 ± 19.09 24.96 ± 17.55 25.64 ± 31.87 0.10 AST(U/L) 25.07 ± 14.96 25.01 ± 13.83 25.74 ± 24.46 0.02 Cholesterol(mg/dL) 192.92 ± 41.59 192.74 ± 41.35 195.03 ± 44.18 0.01 Triglyceride(mg/dL) 151.49 ± 123.12 150.41 ± 122.50 164.02 ± 129.53 < 0.01 Uric acid(mg/dL) 5.42 ± 1.41 5.43 ± 1.41 5.31 ± 1.44 < 0.01 Globulin(g/dL) 2.83 ± 0.43 2.83 ± 0.43 2.92 ± 0.46 < 0.01 Fasting Glucose(mg/dL) 5.95 ± 1.59 5.93 ± 1.55 6.19 ± 2.00 < 0.01 HbA1c(%) 5.62 ± 0.92 5.61 ± 0.89 5.78 ± 1.15 < 0.01 Lymphocyte(1000 cells/µL) 2.14 ± 1.46 2.13 ± 1.50 2.26 ± 0.85 < 0.01 Monocyte(1000 cells/µL) 0.57 ± 0.20 0.57 ± 0.20 0.58 ± 0.20 < 0.01 Neutrophils(1000 cells/µL) 4.31 ± 1.72 4.27 ± 1.69 4.73 ± 1.92 < 0.01 Hb(g/dL) 14.22 ± 1.47 14.24 ± 1.47 14.03 ± 1.52 < 0.01 Plt(1000 cells/µL) 243.49 ± 62.23 242.63 ± 61.65 253.37 ± 67.75 < 0.01 NLR 2.20 ± 1.15 2.19 ± 1.15 2.28 ± 1.15 < 0.01 LMR 4.05 ± 1.72 4.04 ± 1.71 4.18 ± 1.83 < 0.01 Gender, n% 0.33 Male 14,653(48.90%) 13,279(48.82%) 1,374(49.87%) Female 15,002(51.10%) 13,688(51.18%) 1,314(50.13%) Race, n% 0.31 Mexican American 4,663(14.55%) 4,189(14.46%) 474(15.56%) Other Hispanic 3,130(10.08%) 2,844(10.03%) 286(10.68%) Non-Hispanic White 12,335(41.85%) 11,229(41.87%) 1,106(41.57%) Non-Hispanic Black 6,105(21.96%) 5,590(22.07%) 515(20.63%) Other Race 3,422(11.56%) 3,115(11.56%) 307(11.57%) Education, n% < 0.01 High School 15,240(56.02%) 14,026(56.57%) 1,214(49.61%) Marriage, n% < 0.01 Married 14,560(40.04%) 12,922(38.93%) 1,638(52.95%) Non-Married 15,095(59.96%) 14,045(61.07%) 1,050(47.05%) PIR, n% < 0.01 Low<1.3 8,646(19.71%) 7,355(18.15%) 1,291(37.83%) Medium1.3≥,<3.5 12,470(38.76%) 11,422(38.64%) 1,048(40.2%) High ≥ 3.5 8,539(41.53%) 8,190(43.22%) 349(21.98%) BMI(kg/m 2 ), n% < 0.01 Normal(<25kg/m 2 ) 7,689(26.6%) 7,095(26.84%) 594(23.83%) Overweight(≥ 25,<30kg/m 2 ) 9,814(33.28%) 9,103(33.81%) 711(27.15%) Obese(≥ 30kg/m 2 ) 12,152(40.12%) 10,769(39.35%) 1,383(49.01%) Diabetes, n% < 0.01 Yes 3,780(9.60%) 3,263(9.15%) 517(14.89%) NO 25,875(90.40%) 23,704(90.85%) 2,171(85.11%) Cancer, n% 0.12 Yes 2,710(10.05%) 2,422(9.97%) 288(10.97%) NO 26,945(89.95%) 24,545(90.03%) 2,400(89.03%) Smoke, n% < 0.01 Smoker 12,659(43.31%) 11,121 (41.84%) 1,538 (60.37%) Non-Smoker 16,996(56.69%) 15,846 (58.16%) 1,150 (39.63%) NPS, n% < 0.01 Group1 5,278(16.70%) 4,777(16.56%) 501(18.27%) Group2 20,447(70.65%) 18,716(71.15%) 1,731(64.79%) Group3 3,930(12.65%) 3,474(12.29%) 456(16.94%) Basic characteristics of depression individuals We performed a cohort study to examine the relationship among patients with depression between NPS, all-cause mortality, and specific mortality. The fundamental traits of depressed people are displayed in Table 3. After excluding patients with missing follow-up data, a total of 2,190 depressed patients were included in the study to examine mortality rates. The participants were divided into three groups based on their NPS scores: Group 1 (405 individuals, 18.49%), Group 2 (1,397 individuals, 63.79%), and Group 3 (388 individuals, 17.72%). The mean follow-up duration was 70.27 ± 41.74 months. During the follow-up period, 220 deaths were recorded due to all causes, 60 deaths due to heart disease, 38 deaths due to malignant neoplasms, 14 deaths due to respiratory diseases, 28 deaths due to diabetes, and 37 deaths due to hypertension as shown in Fig. 2 . There were notable variations across the groups in terms of the clinical markers and baseline characteristics. In terms of basic demographic characteristics, significant differences in age were observed between the three groups (P < 0.01). Patients in Group 1 had the oldest average age (54.40 years), while the average ages for Group 2 and Group 3 were 46.86 years and 48.35 years, respectively. Regarding gender distribution, males had a higher proportion in Group 3 (57.57%) compared to Group 2 (47.48%) and Group 1 (50.10%), with this difference reaching statistical significance (P < 0.01). In terms of racial distribution, non-Hispanic White patients represented a larger proportion in all groups, but Group 3 had a higher proportion of non-Hispanic Black and other races (P < 0.01). Additionally, in the education status, Group 1 had the lowest proportion of patients with education beyond high school (29.34%), while Group 3 had a significantly higher proportion (57.39%) of patients with education beyond high school (P < 0.01). In the analysis of lifestyle and health behaviors, BMI showed significant differences between the groups (P < 0.01). Group 3 had the highest proportion of obese patients (BMI ≥ 30 kg/m²). Regarding smoking status, 60.98% of the total sample were smokers. Although Group 3 had a higher proportion of smokers (66.47%), this difference did not reach statistical significance (P = 0.05). The prevalence of diabetes was significantly higher in Group 3 (23.64%) compared to Group 1 (12.42%), suggesting that patients with higher NPS scores may be at a higher risk of diabetes (P < 0.01). The prevalence of cancer was also notably higher in Group 3 (13.86%) compared to the other groups (P = 0.02). Significant differences were observed between the three groups in serum albumin (P < 0.01) and triglyceride levels (P < 0.01). Group 3 had the lowest serum albumin level (3.89 g/dL), indicating that patients in Group 3 may experience more severe malnutrition or metabolic issues. Triglyceride levels were significantly higher in Group 1 (202.78 mg/dL) compared to the other groups (P < 0.01). Additionally, Group 3 had higher fasting blood glucose levels than the other groups (P < 0.01), indicating that these patients may be more susceptible to anomalies in glucose metabolism.. In the mortality analysis, the all-cause mortality rate differed significantly between the groups (P < 0.01). Group 1 had the lowest all-cause mortality rate (3.98%), while Group 3 had a significantly higher rate (15.87%). The mortality rate due to heart disease was also notably higher in Group 3 (4.30%) compared to Group 1 (0.72%) (P < 0.01). Additionally, the mortality rate due to malignant neoplasms was significantly increased in Group 3 (2.57%), while Group 1 and Group 2 had lower rates (P < 0.01). There were no significant differences in respiratory disease-related mortality across the groups (P = 0.18). However, the mortality rates due to diabetes (P < 0.01) and hypertension (P < 0.01) were significantly higher in Group 3, at 2.08% and 2.63%, respectively, compared to Group 1, where these rates were 0.42% and 0.51%. Characteristic Total Group1 Group2 Group3 P -value N = 2,190 N = 405(18.49%) N = 1,397(63.79%) N = 388(17.72) Mean ± SD Follow-up times(months) 70.27 ± 41.75 76.02 ± 40.41 71.47 ± 41.59 59.96 ± 41.93 < 0.01 Age(years) 48.49 ± 18.44 54.40 ± 19.33 46.86 ± 18.39 48.35 ± 16.33 < 0.01 Albumin(g/dL) 4.18 ± 0.36 4.35 ± 0.27 4.22 ± 0.34 3.89 ± 0.36 < 0.01 ALT(U/L) 25.70 ± 33.92 25.37 ± 15.93 25.57 ± 21.87 26.49 ± 67.17 0.87 AST(U/L) 25.75 ± 26.03 25.20 ± 12.80 25.35 ± 18.72 27.76 ± 48.87 0.25 Cholesterol(mg/dL) 195.49 ± 44.92 225.87 ± 33.07 195.82 ± 44.69 162.94 ± 32.18 < 0.01 Triglyceride(mg/dL) 165.32 ± 131.65 202.78 ± 143.98 161.98 ± 136.31 138.84 ± 84.33 < 0.01 Uric acid(mg/dL) 5.30 ± 1.43 5.27 ± 1.36 5.29 ± 1.43 5.37 ± 1.51 0.53 Globulin(g/dL) 2.91 ± 0.45 2.85 ± 0.38 2.90 ± 0.44 3.00 ± 0.52 < 0.01 Fasting Glucose(mg/dL) 6.20 ± 1.99 6.18 ± 1.75 6.13 ± 1.96 6.50 ± 2.27 < 0.01 HbA1c(%) 5.78 ± 1.13 5.76 ± 1.04 5.75 ± 1.12 5.93 ± 1.23 0.01 Lymphocyte(1000 cells/µL) 2.28 ± 0.87 2.76 ± 0.74 2.27 ± 0.89 1.80 ± 0.59 < 0.01 Monocyte(1000 cells/µL) 0.59 ± 0.20 0.49 ± 0.14 0.60 ± 0.20 0.67 ± 0.23 < 0.01 Neutrophils(1000 cells/µL) 4.76 ± 1.92 4.22 ± 1.51 4.63 ± 1.87 5.77 ± 2.10 < 0.01 Hb(g/dL) 14.05 ± 1.52 14.14 ± 1.32 14.17 ± 1.48 13.51 ± 1.71 < 0.01 Plt(1000 cells/µL) 252.55 ± 66.73 258.15 ± 62.72 250.23 ± 65.18 255.20 ± 75.28 0.08 NLR 2.29 ± 1.15 1.57 ± 0.49 2.17 ± 0.93 3.45 ± 1.48 < 0.01 LMR 4.14 ± 1.79 5.76 ± 1.34 4.04 ± 1.75 2.82 ± 0.85 < 0.01 GENDER, n% < 0.01 Male 1,123(49.74%) 204(50.10%) 696(47.48%) 223(57.57%) Female 1,067(50.26%) 201(49.90%) 701(52.52%) 165(42.43%) RACE, n% < 0.01 Mexican American 382(15.19%) 90(16.91%) 231(14.06%) 61(17.51%) Other Hispanic 225(10.52%) 50(12.52%) 144(10.78%) 31(7.49%) Non-Hispanic White 911(41.75%) 154(40.91%) 586(42.83%) 171(38.69%) Non-Hispanic Black 422(20.84%) 86(24.04%) 250(18.64%) 86(25.55%) Other Race 250(11.70%) 25(5.63%) 186(13.69%) 39(10.76%) EDUCATION, n% < 0.01 High School 1,008(50.58%) 114(29.34%) 667(54.74%) 227(57.39%) Marriage, n% 0.84 Married 1,326(52.74%) 244(53.25%) 845(52.98%) 237(51.39%) Non-Married 864(47.26%) 161(46.75%) 552(47.02%) 151(48.61%) PIR, n% 0.05 Low<1.3 1,031(37.57%) 185(34.52%) 664(38.60%) 182(36.98%) Medium1.3≥,<3.5 876(40.61%) 168(45.02%) 556(40.44%) 152(36.66%) High ≥ 3.5 283(21.82%) 52(20.46%) 177(20.96%) 54(26.36%) BMI(kg/m 2 ), n% < 0.01 Normal(<25kg/m 2 ) 473(23.25%) 86(19.65%) 317(24.55%) 70(22.23%) Overweight(≥ 25,<30kg/m 2 ) 574(27.20%) 117(31.28%) 372(27.93%) 85(20.33%) Obese(≥ 30kg/m 2 ) 1,143(49.55%) 202(49.07%) 708(47.52%) 233(57.44%) Diabetes, n% < 0.01 Yes 430(15.13%) 59(12.42%) 255(13.55%) 116(23.64%) NO 1,760(84.87%) 346(87.58%) 1,142(86.45%) 272(76.36%) Smoke, n% 0.05 Smoker 1,255(60.98%) 235(59.27%) 777(59.95%) 243(66.47%) Non-Smoker 935(39.02%) 170(40.73%) 620(40.05%) 145(33.53%) Cancer, n% 0.02 Yes 244(11.18%) 45(13.54%) 145(9.77%) 54(13.86%) NO 1,946(88.82%) 360(86.46%) 1,252(90.23%) 334(86.14%) All-cause mortality, n% < 0.01 No 1,970(91.94%) 383(96.02%) 1,277(92.93%) 310(84.13%) Yes 220(8.06%) 22(3.98%) 120(7.07%) 78(15.87%) Death of heart diseases, n% < 0.01 No 2,130(97.92%) 399(99.28%) 1,363(98.14%) 368(95.7%) Yes 60(2.08%) 6(0.72%) 34(1.86%) 20(4.30%) Death of malignant neoplasms, n% < 0.01 No 2,152(98.97%) 403(99.54%) 1,377(99.23%) 372(97.43%) Yes 38(1.03%) 2(0.46%) 20(0.77%) 16(2.57%) Death of respiratory diseases, n% 0.18 No 2176(99.57%) 405(100%) 1,389(99.57%) 382(99.14%) Yes 14(0.43%) 0(0.00%) 8(0.43%) 6(0.86%) Death of diabetes, n% 0.01 No 2,162(99.14%) 402(99.58%) 1,385(99.35%) 375(97.92%) Yes 28(0.86%) 3(0.42%) 12(0.65%) 13(2.08%) Death of hypertension, n% 0.01 No 2,153(98.88%) 401(99.49%) 1,379(99.12%) 373(97.37%) Yes 37(1.12%) 4(0.51%) 18(0.88%) 15(2.63%) Tables 3. Characteristics of individuals with depression. Associations between NPS and depression prevalence Weighted logistic regression analyses were performed to investigate the relationship between NPS and depression prevalence among the 29,655 participants, as shown in Fig. 3 . Higher NPS scores were repeatedly found to be positively correlated with a greater possibility of depression. In the unadjusted model, participants in group 3 had a 25% higher likelihood of experiencing depression than did those in the reference group (group 1) (OR = 1.25, 95% CI: 1.04–1.50, P = 0.02). After adjusting for age, education status, and marriage status (Model 1), participants in Group 3 had a 44% higher likelihood of experiencing depression compared to the reference group (OR = 1.44, 95% CI: 1.19–1.73, P < 0.01). After further adjustments, the association remained statistically significant, with participants in Group 3 having a 32% higher likelihood of experiencing depression compared to the reference group (OR = 1.32, 95% CI: 1.09–1.60, P < 0.01). When compared to the reference group (group 1), group 3 continuously showed a substantial positive link with the prevalence of depression throughout all models. Although Group 2 showed a lower mortality risk in the crude model, the effect became non-significant after adjusting for confounders, while Group 3 consistently demonstrated a significantly higher odds of depression than Group 1. Associations between NPS and mortality in individuals with depression In order to investigate the connection between NPS and death in patients with depression, we created Kaplan-Meier (KM) survival curves, as illustrated in Fig. 4 . In the KM curves for all-cause mortality, heart disease mortality, malignant neoplasm mortality, diabetes mortality, and hypertension mortality, it is visually evident that the survival rate of depressed patients in Group 3 is the lowest. Log-rank tests indicated that the differences between the groups were statistically significant (P < 0.001). Figure 5 displays the findings of a weighted Cox proportional hazards regression analysis we conducted to investigate the connection between NPS and mortality in patients with depression. Based on the results of the Cox proportional hazards regression analysis, we explored the relationship between NPS and mortality due to various causes in depressed patients. The findings showed that depressed patients with higher NPS had significantly increased odds of death from all causes, heart disease, malignant neoplasms, diabetes, and hypertension, particularly in Group 3 (NPS = 3 or 4). Regarding all-cause mortality, the crude model indicated that the HR for Group 3 was 5.09 (95% CI: 2.77–9.35), which suggests that the all-cause mortality risk in Group 3 was 409% higher than that in Group 1. After adjusting for confounding factors, the model results further confirmed this finding, with Group 3 showing a 354% higher all-cause mortality risk compared to Group 1 (HR = 4.54, 95% CI: 2.24–9.21), and the result was statistically significant (P < 0.01). This indicates that depressed patients with higher NPS scores are more likely to experience all-cause mortality. In terms of heart disease mortality, Group 3 also exhibited a significantly increased risk. The crude model showed that the HR for heart disease mortality in Group 3 was 7.46 (95% CI: 2.74–20.31), indicating a 646% higher risk compared to the reference group. In the adjusted models, Group 3's risk remained significantly higher by 739% (HR = 8.39, 95% CI: 2.85–24.71). This result suggests a significant increase in heart disease-related mortality for depressed patients with higher NPS scores. In the analysis of malignant neoplasm mortality, although the result for Group 2 failed to demonstrate statistical significance (P > 0.05), Group 3's risk increased by 632% (HR = 7.32, 95% CI: 1.47–36.48), indicating that depressed patients with higher NPS scores are at greater risk of dying from cancer. After further adjustment, the risk in Group 3 remained 510% higher (HR = 5.10, 95% CI: 1.21–21.41). Regarding diabetes-related mortality, the crude model indicated that the risk for Group 3 increased by 513% (HR = 6.13, 95% CI: 1.55–24.21). The adjusted models also showed a significant increase in risk by 466% (HR = 5.66, 95% CI: 1.44–22.24, P < 0.01). These results suggest that depressed patients with higher NPS scores face a higher risk of death due to diabetes. Finally, for hypertension-related mortality, the risk for Group 3 was significantly higher in all models. The crude model showed a 661% increase in risk (HR = 7.61, 95% CI: 1.97–29.35), while Model 1 and Model 2 showed an increase of 789% (HR = 8.89, 95% CI: 2.36–33.47) and 568% (HR = 6.68, 95% CI: 1.69–26.40), respectively, all with statistical significance (P < 0.01). This indicates that depressed patients with higher NPS scores are at greater risk of mortality due to hypertension. Across all the models and different mortalities, the trend analysis (P for trend) consistently indicated a significant upward trend mortality risk with increasing NPS group levels. These findings suggest that higher NPS scores, particularly in Group 3, are strongly associated with increased mortality risk, even after comprehensive adjustment for potential confounders. Figure 5. HRs (95%CIs) of mortality according to NPS among depressed patients. Subgroup analyses To further investigate the associations between NPS and mortality in depressed individuals, subgroup analyses were performed based on diverse demographic factors as shown in Table 4 . In the subgroup analysis, due to the limited number of recorded cases for certain causes of death and the relatively small sample size for some patient characteristics, we only performed subgroup analysis for mortality from heart disease and all causes. Additionally, considering data availability and statistical power requirements, this subgroup analysis included only a selection of characteristics. Both male and female depressed patients in Group 3 had a considerably greater risk of all-cause mortality, according to the results of the subgroup analysis of all-cause mortality. Additionally, depressed patients with a high school education in Group 3 had a significantly increased risk of all-cause mortality by 3,041%, and those who were overweight had a 3,939% higher risk. Group 3 also had a significantly higher risk of all-cause death among smokers and those with a history of diabetes. The subgroup study of heart disease mortality showed that Group 3 patients, both male and female, were at significantly higher risk. There was a noteworthy 841% increase in the probability of death for individuals who were married. The chance of dying from heart disease was similarly 865% greater for smokers in Group 3. These results indicate that depressed patients with higher NPS scores show significant subgroup differences across different causes of death, especially in terms of gender, education, BMI, smoking, and diabetes. Table 4 Subgroup analyses. Stratified by Group1 Group2 Group3 All-cause mortality Gender Male Ref. 1.76(0.68–4.54)0.24 4.35(1.58–11.97)<0.01 Femal Ref. 2.72(1.11–6.67)0.03 5.05(1.88–13.57)<0.01 EDUCATION <High School Ref. 2.57(1.05–6.30)0.04 4.38(1.6-11.94)<0.01 High School Ref. 6.37(1.57–25.77)0.01 31.41(5.42-181.99)High School Ref. 0.91(0.33–2.55)0.86 2.01(0.65–6.19)0.22 Marriage Married Ref. 1.56(0.67–3.61)0.30 4.35(1.75–10.81)<0.01 Non-Married Ref. 3.10(1.08–8.93)0.04 5.70(1.74–18.65)<0.01 BMI(kg/m 2 ) Normal(<25kg/m 2 ) Ref. 2.67(0.68–10.47)0.16 4.51(1.12–18.18)0.03 Overweight(≥ 25,<30kg/m 2 ) Ref. 12.68(2.59–62.10)<0.01 40.39(6.41-254.59)<0.01 Obese(≥ 30kg/m 2 ) Ref. 1.16(0.48–2.76)0.74 3.02(1.20–7.64)0.02 Diabetes Yes Ref. 1.68(0.6–4.75)0.32 9.82(3.25–29.67)<0.01 No Ref. 2.28(0.98–5.30)0.05 3.43(1.38–8.52)0.01 Smoke Ref. Smoker Ref. 2.08(0.88–4.93)0.10 4.59(1.76–11.95)<0.01 Non-Smoker Ref. 2.5(0.92–6.81)0.07 4.88(1.91–12.50)<0.01 Cancer Yes Ref. 1.95(0.17–22.61)0.59 1.94(0.18–20.33)0.58 No Ref. 2.19(1.13–4.25)0.02 5.55(2.78–11.10)<0.01 Death of heart diseases Gender Male Ref. 2.52(0.61–10.49)0.20 7.99(1.66–38.39)0.01 Femal Ref. 4.42(1.01–19.37)0.05 9.34(1.72–50.86)0.01 Marriage Married Ref. 3.71(1.15–11.92)0.03 9.41(2.69–32.91)<0.01 Non-Married Ref. 1.03(0.23–4.56)0.97 2.75(0.33–22.77)0.35 Diabetes Yes Ref. 1.01(0.21–4.81)0.99 2.89(0.57–14.53)0.20 No Ref. 10.14(2.12–48.50)<0.01 24.43(4.71-126.69)<0.01 Smoke Smoker Ref. 4.26(1.05–17.37)0.04 9.65(2.15–43.30)<0.01 Non-Smoker Ref. 3.73(0.77–17.99)0.10 8.79(1.67–46.20)0.01 Cancer Yes Ref. 1.91(0.14–25.22)0.62 4.21(0.36–49.4)0.25 No Ref. 4.20(1.33–13.21)0.01 11.28(3.37–37.78)<0.01 ROC analysis for depression and mortality This study evaluates the predictive value of NPS for various causes of death through ROC curve analysis, comparing the classification abilities for all-cause mortality, heart disease mortality, malignant neoplasm mortality, diabetes mortality, and hypertension mortality (as shown in Fig. 6 ). The ROC curve AUC values, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for each model were used to assess its predictive power (as shown in Table 5 ). All-cause mortality: The AUC value of the NPS is 0.634, indicating moderate predictive ability. Sensitivity is 0.659, meaning it can effectively identify individuals who died from all causes, with a high PPV of 0.932, suggesting that it accurately predicts deceased individuals. Heart disease mortality: The AUC value is 0.628, showing similar performance to the all-cause mortality model, with moderate discriminatory ability. The sensitivity of 0.700 indicates that the NPS model can effectively identify individuals who died from heart disease. Malignant neoplasm mortality: The AUC value is 0.645, the highest among all causes of death, indicating the strongest predictive power. The specificity of 0.827 suggests it can effectively identify individuals who did not die from malignant neoplasms. Additionally, the PPV of 0.988 means that the model is highly accurate in predicting individuals who died from malignant neoplasms, making it suitable for clinical screening and early diagnosis. Diabetes mortality: The AUC value for diabetes mortality is 0.604, showing that the NPS model has some predictive ability in this area. Its sensitivity is 0.634, allowing it to identify individuals who died from diabetes-related causes. Although specificity is slightly lower, the model still demonstrates clinical value in predicting diabetes-related mortality, supporting early intervention for diabetes-related deaths. Hypertension mortality: The AUC value for hypertension mortality is 0.599, which is relatively low. However, the sensitivity of 0.560 indicates that the model can identify a certain proportion of individuals who died from hypertension-related causes. Despite its lower specificity, the model still provides an initial reference value for assessing the death risk of hypertension patients, particularly for screening high-risk populations. Table 5 ROC Curve Analysis for Various Causes of Mortality. Reason AUC CI Specificity Sensitivity PPV NPV All-cause mortality 0.634 0.595–0.673 0.525 0.659 0.932 0.134 Death of heart diseases 0.628 0.559–0.698 0.512 0.700 0.984 0.039 Death of malignant neoplasms 0.645 0.555–0.736 0.827 0.421 0.988 0.041 Death of diabetes 0.675 0.562–0.787 0.827 0.464 0.992 0.034 Death of hypertension 0.642 0.546–0.738 0.510 0.676 0.989 0.023 Discussion This study demonstrates for the first time the potential utility of the NPS in treating depression, specifically in determining the risk of dying from all causes along with various causes. Individuals with higher NPS scores are more likely to experience depression, according to the findings, and those who are depressed and have higher NPS scores are at significantly higher risk for death from all causes, heart disease, cancer, diabetes, and hypertension. Notably, individuals with NPS scores of 3 or 4 have a significantly higher mortality risk compared to those in the lower score groups. These findings indicate that NPS not only effectively predicts the prevalence of depression but also serves as an important prognostic indicator for the risk of death in patients with depression. As a biomarker that combines inflammation and nutritional status, the results demonstrate the potential therapeutic application value of NPS and offer a new and complete predictive evaluation tool for depression in clinical practice. Serum albumin, total cholesterol, NLR, and LMR are the four peripheral blood indicators that make up the majority of NPS. Current research widely recognizes NPS as a novel and comprehensive index reflecting systemic inflammation and nutritional status[ 29 ]. Previous studies have shown that the NPS, as a novel prognostic scoring system, has proven to be of significant value in prognostic assessments for various diseases, such as glioblastoma[ 30 ], non-small cell lung cancer[ 28 ], and metastatic colorectal cancer[ 31 ]. The study by Galizia and colleagues was the first to introduce the NPS and validate its application in cancer patients[ 17 ]. The findings of this study further extend the potential applications of NPS to depression patients. This discovery suggests that NPS is not only applicable to the prognosis of severe diseases like cancer but also serves as an important tool for early risk screening and prognostic prediction in patients with depression. The broader applicability of NPS in different clinical contexts underscores its potential as a versatile and reliable marker, facilitating earlier interventions and more accurate prognostic assessments in a wide range of conditions, including psychiatric disorders like depression. According to our research, a higher NPS score is highly correlated with higher rates of morbidity and mortality in depressed people. These rates may be directly related to the systemic inflammatory response and the nutritional status of the individual. The connection between inflammation and the etiology of depression has garnered more attention in recent years[ 32 ]. Research has demonstrated a strong correlation between the development and course of depression and chronic inflammation[ 10 ]. Several studies have found that the inflammatory response affects the balance of key neurotransmitters in the brain, such as serotonin and dopamine, which are crucial for mood regulation, by activating the immune system and altering the neurochemical environment[ 33 – 35 ]. Additionally, it is frequently seen that patients with depression have higher levels of inflammatory markers such TNF-α, IL-6, and IL-1β[ 13 , 36 ]. These factors exacerbate brain neural damage by disrupting neuroprotective mechanisms[ 37 ]. These inflammatory mediators not only affect mood and cognitive function but may also worsen depression symptoms by altering brain structures, such as causing hippocampal atrophy[ 38 ]. Researchers are also exploring the potential of anti-inflammatory treatments as a new strategy for treating depression. For example, nonsteroidal anti-inflammatory drugs (NSAIDs) and other anti-inflammatory medications have shown potential in alleviating depressive symptoms, although this approach still requires further clinical validation[ 39 ]. Furthermore, nutrition influences the onset and development of depression through multiple biological pathways. Studies have shown that metabolic dysregulation is closely associated with the onset of depression, particularly disturbances in glucose and lipid metabolism[ 40 , 41 ]. Malnutrition, in particular a lack of key vitamins (such B and D) and minerals, can cause anomalies in the neurological system's operation, which can impact behavior and mood[ 42 – 44 ]. Another key mechanism is the regulation of oxidative stress and inflammatory responses by nutritional components[ 45 ]. Oxidative stress refers to the imbalance between free radicals and antioxidants in the body, which can lead to cellular damage, particularly in the brain[ 46 , 47 ]. Many patients with depression show elevated levels of oxidative stress, suggesting that it plays an important role in the pathogenesis of depression[ 10 ]. Research indicates that diets rich in antioxidants may reduce oxidative damage, thereby offering protective effects against depression[ 48 , 49 ]. Certain nutrients can influence the production and operation of neurotransmitters (including serotonin, dopamine, and norepinephrine), which in turn regulate mood and behavior. These nutrients include omega-3 fatty acids, B vitamins, and minerals[ 15 , 16 , 50 ]. Deficiencies in these nutrients can disrupt normal neurotransmission, leading to the onset of depressive symptoms[ 51 ]. From the perspective of NPS, studies have shown that the NLR and the LMR are key markers of systemic inflammation and immune imbalance. A Chinese study involving 350 postoperative non-small cell lung cancer patients indicated that NLR and LMR are independent prognostic predictors of the risk of postoperative depression[ 52 ]. Furthermore, a study on post-stroke depression (PSD) revealed that LMR is independently associated with the development of PSD and is also linked to an increase in the severity of PSD[ 53 ]. The significance of systemic inflammatory biomarkers in the prognosis of depression is further supported by these results, which are in accordance with the baseline findings of our study. The research described above demonstrate how important inflammatory responses are to the pathophysiology of depression. The NLR reflects the balance between neutrophils and lymphocytes, serving as an indicator of the extent of systemic inflammation. Neutrophils, by releasing inflammatory mediators such as TNF-α and IL-6, can cross the blood-brain barrier and influence the central nervous system, disrupting the balance of neurotransmitters, which may ultimately trigger depressive symptoms[ 13 , 54 , 55 ]. Furthermore, these inflammatory cytokines can induce structural and functional changes in the brain, impacting emotional regulation and cognitive function. Lymphocytes, which play a crucial role in modulating excessive inflammation, act to counterbalance the inflammatory response[ 55 ]. A reduction in lymphocyte count or dysfunction in their activity can lead to immune dysregulation, contributing to the onset and progression of depression[ 56 ]. The LMR reflects the ratio between lymphocytes and monocytes, with monocytes being involved in the maintenance of chronic inflammation and the persistence of immune responses[ 57 ]. Monocytes and their differentiated macrophages play a pivotal role in chronic inflammation, and sustained inflammatory responses may influence the brain through various mechanisms, altering the synthesis, release, and receptor expression of neurotransmitters, thereby precipitating the onset of depression[ 9 , 10 ]. On the other side of NPS, serum albumin and total cholesterol levels are widely recognized as key indicators reflecting the nutritional status of the organism. Serum albumin, as a protein with free radical scavenging properties, may help explain its role in the development of depression, at least to some extent. Albumin is widely recognized as an important antioxidant, with a significant portion of the overall antioxidant capacity of serum attributed to albumin[ 58 ]. According to some studies, the pathogenesis of depression is associated with an excess of free radicals. This surplus of free radicals leads to oxidative stress, which is believed to cause oxidative damage linked to neurodegeneration and various psychiatric disorders, including depression[ 59 – 61 ]. Cholesterol is a crucial component of neuronal cell membranes, essential for maintaining the structure and function of nerve cells[ 62 ]. In addition to being essential for the transmission of brain signals, it is involved in the production and release of neurotransmitters[ 63 ]. As a part of neurotransmitter receptors, cholesterol regulates the function and density of these receptors, thereby influencing neurotransmission[ 64 , 65 ]. Additionally, cholesterol serves as a precursor for steroid hormones, such as cortisol and sex hormones, which play significant roles in mood regulation[ 66 ]. Our study revealed a strong relationship between the NPS and the prevalence and severity of depression as well as the mortality rate among depressed individuals. The NPS is a thorough instrument that accurately captures the intricate relationship between immunological response, inflammation, and malnutrition, especially in people with depression. Our results are in line with earlier studies, underscoring the crucial roles that systemic inflammation and nutritional status play in the prognosis of depression. In order to help identify high-risk depressed groups early and enable individualized treatment of depressed individuals, the NPS can be a useful tool for risk stratification and prognosis evaluation. Comprehensive interventions targeting inflammation levels and nutritional status in depression can significantly improve their prognosis. Additionally, community-based screening programs can utilize NPS for initial risk stratification, providing a scientific foundation for resource allocation and the prioritization of health interventions. However, for the various causes of death in patients with depression, the AUC for all-cause mortality is 0.634, the AUC for heart disease mortality is 0.628, the AUC for malignant neoplasms mortality is 0.645, the AUC for diabetes mortality is 0.675, and the AUC for hypertension mortality is 0.642. These results indicate that the NPS model has moderate predictive ability. While this result is promising, it also reflects that the NPS still has limitations in accurately distinguishing between individuals at risk and those not at risk. Although the NPS can provide an initial reference for risk assessment, its clinical application requires further validation and optimization to improve its predictive accuracy and broader applicability. In clinical terms, this moderate AUC value underscores the necessity of complementing the NPS with additional biomarkers or clinical assessments to improve its predictive accuracy. However, despite its moderate AUC, the NPS can still serve a role in early risk stratification, especially when combined with other screening tools. The clinical utility of the NPS in primary prevention strategies lies in its potential to prioritize further diagnostic testing for high-risk individuals, thereby optimizing resource allocation. The NPS performance could also be enhanced through future validation studies, which could explore the incorporation of more diverse datasets and consider longitudinal outcomes to refine the model's clinical applicability. Although this study has certain strengths, it also has some limitations that need further discussion and acknowledgment. Firstly, NHANES data primarily hinges upon self-reports from participants, which may result in recall bias and affect the accuracy of the data. While we employed robust statistical techniques to address missingness, we acknowledge that some degree of bias may still exist, particularly if certain variables were missing not at random. T Future research may consider about improved study designs or include external validation datasets to further reinforce the validity of our findings. Secondly, although the study has controlled for known potential confounders such as age, gender, and smoking status, there may still be unmeasured or unknown confounding factors. Furthermore, the study data is primarily based on the U.S. population, which may limit its external validity, particularly in economically underdeveloped countries or other cultural contexts. To evaluate the reliability and generalizability of the NPS across other demographic groups and healthcare systems, future research could try to reproduce our findings in a wider range of populations. Last but not the least, the cross-sectional design of NHANES limits the ability to establish causality between NPS and depression outcomes. Cross-sectional studies capture data at a single point in time, which means they cannot reveal the temporal causality between variables. Therefore, although our study shows significant associations between NPS and depression prevalence and mortality, we cannot establish whether these associations are causal. Future prospective or longitudinal studies should be carried out to gain a better understanding of the role of NPS in predicting depression risk. In conclusion, the limitations of this study suggest that future research should focus on incorporating diverse data sources, better controlling for potential confounders, and using longitudinal designs or randomized controlled trials to verify both correlations and causations. These steps will help improve the scientific validity and applicability of research conclusions. Conclusion This study establishes the significant association between the NPS and depression, revealing that higher NPS scores correlate with a greater prevalence of depression and increased mortality risk among depressed individuals. Notably, depressed individuals with NPS scores of 3 or 4 face markedly higher risks of death from multiple causes, including heart disease, cancer, and diabetes. The NPS, by reflecting systemic inflammation and nutritional status, serves as a promising tool for stratifying risk in patients with depression. Our findings underscore the potential of NPS as an early screening tool, guiding clinicians to implement more targeted interventions that could enhance the management and prognosis of individuals suffering from depression. However, further validation and optimization of NPS, alongside additional biomarkers, will be required to improve its predictive accuracy and broader clinical applicability. Abbreviations NPS Naples Prognostic Score NHANES National Health and Nutrition Examination Survey PHQ-9 Patient Health Questionnaire-9 NLR neutrophil-to-lymphocyte ratio LMR lymphocyte-to-monocyte ratio CRP C-reactive protein IL-6 interleukin-6 TNF-α tumor necrosis factor-alpha CDC Centers for Disease Control and Prevention MECs mobile examination centers NCHS National Center for Health Statistics TC total cholesterol NDI National Death Index LMF Linked Mortality File PIR poverty income ratio PPV positive predictive value NPV negative predictive value PSD post-stroke depression Declarations Human Ethics and Consent to Participate All participants provided written informed consent and study procedures were approved by the National Center for Health Statistics Research Ethics Review Board. Data availability Publicly available datasets were analyzed in this study. These data can be found on the NHANES website (https://www.cdc.gov/nchs/nhanes/index.htm). Conflicts of interest All authors declare no conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contributions statement Jin Zhao and Shiping Liu designed the experiments, and Jin Zhao, Xingfu Fan, Yang Luo, and Xiaofang Li collected and analyzed the data. Jin Zhao drafted the manuscript. Jin Zhao and Shiping Liu revised the manuscript. All authors contributed to the article and approved the submitted version. Acknowledgments We would like to thank all participants in this study. Clinical trial number: not applicable. Clinical trial number: not applicable. References Zun LS. Chapter 101 - Mood Disorders. Mood Disord. Park LT, Zarate CA. Depression in the Primary Care Setting. N Engl J Med. 2019;380:559–68. Herrman H, Kieling C, McGorry P, Horton R, Sargent J, Patel V. 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Hosoda K, Shimizu A, Kubota K, Notake T, Kitagawa N, Yoshizawa T et al. Clinical significance of the Naples prognostic score in predicting short- and long-term postoperative outcomes of patients with hepatocellular carcinoma. World J Surg. 2024;n /a n/a:wjs.12448 Xiong J, Hu H, Kang W, Liu H, Ma F, Ma S, et al. Prognostic Impact of Preoperative Naples Prognostic Score in Gastric Cancer Patients Undergoing Surgery. Front Surg. 2021;8:617744. Qiu Y, Chen Y, Shen H, Yan S, Li J, Wu W. Naples Prognostic Score: A Novel Predictor of Survival in Patients with Triple-Negative Breast Cancer. J Inflamm Res. 2024;17:5253–69. Zhu N, Lin S, Cao C. A novel prognostic prediction indicator in patients with acute pulmonary embolism: Naples prognostic score. Thromb J. 2023;21:114. Aydın SŞ, Aydemir S, Özmen M, Aksakal E, Saraç İ, Aydınyılmaz F, et al. The importance of Naples prognostic score in predicting long-term mortality in heart failure patients. Ann Med. 2025;57:2442536. Wang K, Zhao Y, Nie J, Xu H, Yu C, Wang S. Higher HEI-2015 Score Is Associated with Reduced Risk of Depression: Result from NHANES 2005–2016. Nutrients. 2021;13:348. Liu X, Liu X, Wang Y, Zeng B, Zhu B, Dai F. Association between depression and oxidative balance score: National Health and Nutrition Examination Survey (NHANES) 2005–2018. J Affect Disord. 2023;337:57–65. Zhou X, Tao X-L, Zhang L, Yang Q-K, Li Z-J, Dai L, et al. Association between cardiometabolic index and depression: National Health and Nutrition Examination Survey (NHANES) 2011–2014. J Affect Disord. 2024;351:939–47. Kroenke K, Spitzer RL, Williams JBW. The PHQ-9. J Gen Intern Med. 2001;16:606–13. Wu WW. Association of naples prognostic score and lung health: A population-based study. Respir Med. 2024;232:107751. Peng S-M, Ren J-J, Yu N, Xu J-Y, Chen G-C, Li X, et al. The prognostic value of the Naples prognostic score for patients with non-small-cell lung cancer. Sci Rep. 2022;12:5782. Zhu N, Lin S, Yu H, Liu F, Huang W, Cao C. Naples prognostic score as a novel prognostic prediction indicator in adult asthma patients: A population-based study. World Allergy Organ J. 2023;16:100825. Li J, Yang W, Yuan Y, Zuo M, Li T, Wang Z, et al. Preoperative Naples prognostic score is a reliable prognostic indicator for newly diagnosed glioblastoma patients. Front Oncol. 2022;12:775430. Miyamoto Y, Hiyoshi Y, Daitoku N, Okadome K, Sakamoto Y, Yamashita K, et al. Naples Prognostic Score Is a Useful Prognostic Marker in Patients With Metastatic Colorectal Cancer. Dis Colon Rectum. 2019;62:1485–93. Felger JC, Lotrich FE. Inflammatory Cytokines in Depression: Neurobiological Mechanisms and Therapeutic Implications. Neuroscience. 2013;246:199–229. Grace AA. Dysregulation of the dopamine system in the pathophysiology of schizophrenia and depression. Nat Rev Neurosci. 2016;17:524–32. Hassamal S. Chronic stress, neuroinflammation, and depression: an overview of pathophysiological mechanisms and emerging anti-inflammatories. Front Psychiatry. 2023;14:1130989. Mishra A, Sarangi SC, Maiti R, Sood M, Reeta K. Efficacy and Safety of Adjunctive Serotonin-Dopamine Activity Modulators in Major Depression: A Meta-Analysis of Randomized Controlled Trials. J Clin Pharmacol. 2022;62:721–32. Dowlati Y, Herrmann N, Swardfager W, Liu H, Sham L, Reim EK, et al. A meta-analysis of cytokines in major depression. Biol Psychiatry. 2010;67:446–57. Inflammatory Pathogenesis of Post-stroke Depression. Aging Dis. 2024;16:209–38. Serafini G, Costanza A, Aguglia A, Amerio A, Trabucco A, Escelsior A, et al. The Role of Inflammation in the Pathophysiology of Depression and Suicidal Behavior: Implications for Treatment. Med Clin North Am. 2023;107:1–29. Guo B, Zhang M, Hao W, Wang Y, Zhang T, Liu C. Neuroinflammation mechanisms of neuromodulation therapies for anxiety and depression. Transl Psychiatry. 2023;13:5. Mehta D, Raison CL, Woolwine BJ, Haroon E, Binder EB, Miller AH, et al. Transcriptional signatures related to glucose and lipid metabolism predict treatment response to the tumor necrosis factor antagonist infliximab in patients with treatment-resistant depression. Brain Behav Immun. 2013;31:205–15. Wang A, Wan X, Zhuang P, Jia W, Ao Y, Liu X, et al. High fried food consumption impacts anxiety and depression due to lipid metabolism disturbance and neuroinflammation. Proc Natl Acad Sci. 2023;120:e2221097120. Borges-Vieira JG, Cardoso CKS. Efficacy of B-vitamins and vitamin D therapy in improving depressive and anxiety disorders: a systematic review of randomized controlled trials. Nutr Neurosci. 2023;26:187–207. Okereke OI, Reynolds CF, Mischoulon D, Chang G, Vyas CM, Cook NR, et al. Effect of Long-term Vitamin D3 Supplementation vs Placebo on Risk of Depression or Clinically Relevant Depressive Symptoms and on Change in Mood Scores: A Randomized Clinical Trial. JAMA. 2020;324:471. Casseb GAS, Kaster MP, Rodrigues ALS. Potential Role of Vitamin D for the Management of Depression and Anxiety. CNS Drugs. 2019;33:619–37. Kris-Etherton PM, Petersen KS, Hibbeln JR, Hurley D, Kolick V, Peoples S, et al. Nutrition and behavioral health disorders: depression and anxiety. Nutr Rev. 2021;79:247–60. Bhatt S, Nagappa AN, Patil CR. Role of oxidative stress in depression. Drug Discov Today. 2020;25:1270–6. Vaváková M, Ďuračková Z, Trebatická J. Markers of Oxidative Stress and Neuroprogression in Depression Disorder. Oxid Med Cell Longev. 2015;2015:1–12. Rasmus P, Kozłowska E. Antioxidant and Anti-Inflammatory Effects of Carotenoids in Mood Disorders: An Overview. Antioxidants. 2023;12:676. Xu Q, Qian X, Sun F, Liu H, Dou Z, Zhang J. Independent and joint associations of dietary antioxidant intake with risk of post-stroke depression and all-cause mortality. J Affect Disord. 2023;322:84–90. Wang J, Um P, Dickerman BA, Liu J, Zinc. Magnesium, Selenium and Depression: A Review of the Evidence, Potential Mechanisms and Implications. Nutrients. 2018;10:584. Hoepner C, McIntyre R, Papakostas G. Impact of Supplementation and Nutritional Interventions on Pathogenic Processes of Mood Disorders: A Review of the Evidence. Nutrients. 2021;13:767. Wu X, Dai L, Guo H, Peng C, Zhang P, Mo L, et al. Constructing a Multivariate Predictive Model for Postoperative 90-Day Depression Risk in Non-Small Cell Lung Cancer Based on Preoperative Peripheral Blood NLR, LMR, and PLR. Discov Med. 2025;37:348. Chong L, Han L, Liu R, Ma G, Ren H. Association of Lymphocyte-to-Monocyte Ratio with Poststroke Depression in Patients with Acute Ischemic Stroke. Med Sci Monit. 2021;27:e930076. Ma K, Zhang H, Baloch Z. Pathogenetic and Therapeutic Applications of Tumor Necrosis Factor-? (TNF-?) in Major Depressive Disorder: A Systematic Review. Int J Mol Sci. 2016;17:733. Troubat R, Barone P, Leman S, Desmidt T, Cressant A, Atanasova B, et al. Neuroinflammation and depression: A review. Eur J Neurosci. 2021;53:151–71. Dai J, Lin X-T, Shen L-L, Zhang X-W, Ding Z-W, Wang J, et al. Immune indicators and depression in adolescents: Associations with monocytes, lymphocytes, and direct bilirubin. World J Psychiatry. 2025;15:101818. Wang L, Gao J, Liu B, Fu Y, Yao Z, Guo S, et al. The association between lymphocyte-to-monocyte ratio and all-cause mortality in obese hypertensive patients with diabetes and without diabetes: results from the cohort study of NHANES 2001–2018. Front Endocrinol. 2024;15:1387272. Bourdon E, Blache D. The importance of proteins in defense against oxidation. Antioxid Redox Signal. 2001;3:293–311. Maes M, Galecki P, Chang YS, Berk M. A review on the oxidative and nitrosative stress (O&NS) pathways in major depression and their possible contribution to the (neuro)degenerative processes in that illness. Prog Neuropsychopharmacol Biol Psychiatry. 2011;35:676–92. Bajpai A. Oxidative stress and major depression. J Clin Diagn Res. 2014;8:CC04–07. Liu T, Zhong S, Liao X, Chen J, He T, Lai S, et al. A Meta-Analysis of Oxidative Stress Markers in Depression. PLoS ONE. 2015;10:e0138904. Shin KC, Ali Moussa HY, Park Y. Cholesterol imbalance and neurotransmission defects in neurodegeneration. Exp Mol Med. 2024;56:1685–90. A.m P Mr, K. A.l Z. Brain cholesterol metabolism and its defects: linkage to neurodegenerative diseases and synaptic dysfunction. Acta Naturae Англоязычная Версия. 2016;8(1):58–73. Cheon SY. Impaired Cholesterol Metabolism, Neurons, and Neuropsychiatric Disorders. Exp Neurobiol. 2023;32:57–67. Chen S-J, Cho R-L, Yeh SH-H, Tsai M-C, Chuang Y-P, Lien C-F, et al. Pitavastatin attenuates hypercholesterolemia-induced decline in serotonin transporter availability. Lipids Health Dis. 2024;23:250. Fiacco S, Walther A, Ehlert U. Steroid secretion in healthy aging. Psychoneuroendocrinology. 2019;105:64–78. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6309429","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452087498,"identity":"09b2732d-f3b3-464a-a9ca-5f403be21611","order_by":0,"name":"Jin Zhao","email":"","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Zhao","suffix":""},{"id":452087500,"identity":"f84104d0-a37e-49ac-be3c-7495b2412cfc","order_by":1,"name":"Yang Luo","email":"","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical 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Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACPmYQyfNPjp+Z+fADorSwgbXIHDCWbGdLMyBOC5i0OZBocJ5HQYI4Lew8xp8Lcu4kGB/mYTBgqLGJJsJhPGbSM848yzM7zHvgAcOxtNwGYrQw8/YwF5sd5kswYGw4TJQW48+8/5gTNzfzGEgQq8VAmofncOIGZuK1sJVJz+BJM5Y4DAzkBGL8ws9/ePPnAh4bOf7+w4cffKixIawFBJjhrARilKNqGQWjYBSMglGADQAA47807PBL/soAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":true,"prefix":"","firstName":"Shiping","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-03-26 06:53:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6309429/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6309429/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82349694,"identity":"8a1e18a4-830a-4047-b205-f03b78ed746a","added_by":"auto","created_at":"2025-05-09 10:47:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":681709,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant selection for this study.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6309429/v1/678af9c612f22dafe19b33f6.png"},{"id":82349697,"identity":"2da907f6-a8a7-413f-b354-eb0aeda5f81c","added_by":"auto","created_at":"2025-05-09 10:47:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1603305,"visible":true,"origin":"","legend":"\u003cp\u003eThe mortality distribution of individuals with depression\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6309429/v1/968ac79d7c7e1551038d1777.png"},{"id":82349696,"identity":"60e4bfae-afe7-48ed-8f5f-2584b67b6538","added_by":"auto","created_at":"2025-05-09 10:47:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":517492,"visible":true,"origin":"","legend":"\u003cp\u003eORs (95%CIs) of the prevalence of depression according to the NPS\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6309429/v1/816aab1d18e96333379b7d44.png"},{"id":82349705,"identity":"9a01ce41-c929-49ba-9a00-7a3d9e637fff","added_by":"auto","created_at":"2025-05-09 10:47:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4543622,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curve of different mortality among depressed individuals.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6309429/v1/5ed5bf5d710bf9f4b59933b3.png"},{"id":82349708,"identity":"da792cfe-81b8-4581-866c-573a7c11e593","added_by":"auto","created_at":"2025-05-09 10:47:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2554662,"visible":true,"origin":"","legend":"\u003cp\u003eHRs (95%CIs) of mortality according to NPS among depressed patients.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6309429/v1/57c60d32d184ab43dcb42401.png"},{"id":82351423,"identity":"1cbd7622-cec2-401a-8ed0-238b2f3b10b2","added_by":"auto","created_at":"2025-05-09 10:55:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1895699,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve Analysis for Various Causes of Mortality.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6309429/v1/633293c095a3b353dbe8c668.png"},{"id":88395756,"identity":"1bb3cd83-6813-4a02-8d07-158895a9a1e6","added_by":"auto","created_at":"2025-08-06 06:02:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14464019,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6309429/v1/ceeb4315-e0fd-4bff-beb2-44489800b1c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Prognostic Value of the Naples Prognostic Score in Depression: Association with Prevalence and Mortality","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDepressive disorder is a widespread mental health condition, distinguished by symptoms such as persistent low mood, loss of interest, diminished energy, and changes in sleep and appetite[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the World Health Organization, depression ranks among the leading causes of disability globally, impacting approximately 300\u0026nbsp;million individuals[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This disorder exhibits a higher prevalence in women, with a notably elevated incidence among adolescents and the elderly[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Depression not only profoundly affects individuals' emotional and psychological well-being but also contributes to a variety of physical health issues, including sleep disturbances, weakened immune function, and cardiovascular diseases[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, it significantly undermines work performance and social interactions, intensifying social and economic burdens[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. With the rise of globalization and changes in lifestyle, the prevalence of depression has markedly increased[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite substantial strides in the treatment of depression, the identification of high-risk populations and the implementation of stratified, personalized management continue to pose critical challenges in the public health domain.\u003c/p\u003e \u003cp\u003eCurrent research has provided substantial evidence that both inflammation and nutritional status are closely and profoundly linked to the incidence and prognosis of depression[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Studies indicate that inflammatory biomarkers, such as C-reactive protein (CRP)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], interleukin-6 (IL-6)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and tumor necrosis factor-alpha (TNF-α)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], are commonly elevated in individuals with depression. In addition to being important immune response regulators, these inflammatory markers have a significant effect on brain function via a number of processes, which helps to initiate and exacerbate depression symptoms[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, malnutrition, particularly the deficiency of key nutrients such as omega-3 fatty acids[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and vitamin B[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], has been found to be closely associated with the onset and progression of depression.\u003c/p\u003e \u003cp\u003eAlthough current studies have elucidated the individual roles of inflammation or nutrition in depression, most research has focused primarily on predicting the prognosis of depression based on a single factor while overlooking the interaction between inflammation and nutrition. There exists a complex interplay between inflammation and nutrition, where both factors not only independently influence the development and progression of depression but may also exacerbate the disease through their combined effects. There is a lack of research that integrates both inflammation and nutrition to assess the prognosis of depression jointly. Therefore, we propose the development of a comprehensive predictive index that jointly considers inflammation and nutritional status to evaluate the prognosis of depression.\u003c/p\u003e \u003cp\u003eThe NPS, created by Galizia et al., combines a number of clinical indicators, such as serum albumin, total cholesterol, NLR, and LMR, to give an extensive overview of systemic inflammation and nutritional status[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. NPS has been shown to be an independent prognostic factor for various diseases, including liver cancer[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], gastric cancer[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], triple-negative breast cancer[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], as well as non-cancer diseases like acute pulmonary embolism[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and heart failure[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, the relationship between NPS and depression has yet to be explored.\u003c/p\u003e \u003cp\u003eIn order to address this knowledge gap, a cross-sectional and cohort analysis was conducted utilizing the comprehensive data provided by the NHANES from 2007 to 2018. The objective of this study was to systematically evaluate the relationship between NPS and depression while also investigating its potential association with mortality among individuals who have suffered from depression. The goal of this study is to provide insightful information for the prognostic use of these results.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eNHANES, administered by the Centers for Disease Control and Prevention (CDC), is a comprehensive database that combines health, nutrition, and physical examination data from a representative sample of the U.S. population. This dataset is frequently used in epidemiology studies and health policy research because it offers important insights about the dietary and health status of Americans. All laboratory examinations are conducted by trained medical personnel in mobile examination centers (MECs). The data are publicly available for download on the official website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cdc.gov/nchs/nhanes\u003c/span\u003e\u003c/span\u003e). The National Center for Health Statistics (NCHS) Research Ethics Review Board approved the study protocol, and informed consent was obtained from all participants.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eOur study included participants from six survey cycles conducted between 2007 and 2018 of NHANES datasets, with a total of 69,806 individuals. Based on previous research and the methodology of our study, we established the following exclusion criteria: 1) Participants with missing data for depression assessment were excluded (38,346 participants); 2) Participants missing components of the NPS, such as albumin, cholesterol, lymphocytes, monocytes, and neutrophils, were excluded (1,805 participants). Ultimately, 29,655 participants were included in the cross-sectional study. In the cohort study, individuals with missing follow-up data were excluded (498 participants), leaving 2,190 participants for mortality analysis. The study flowchart is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003eAssessment of depression\u003c/h3\u003e\n\u003cp\u003eAccording to previous studies, current depressive symptoms were assessed using the PHQ-9 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. The PHQ-9 is a well-validated self-report tool (Cronbach\u0026apos;s \u0026alpha;\u0026thinsp;=\u0026thinsp;0.89) that measures depressive symptoms\u0026mdash;such as sadness, sleep disturbances, fatigue, and difficulty concentrating\u0026mdash;over the past two weeks. It shows moderate concordance with clinical psychiatric interviews. The questionnaire consists of nine items, each rated on a four-point Likert scale, ranging from 0 (not at all) to 3 (nearly every day), with a total score ranging from 0 to 27. A dichotomous variable was created to categorize participants into two groups: no depression (PHQ-9 score\u0026thinsp;\u0026lt;\u0026thinsp;10) and elevated depressive symptoms (PHQ-9 score\u0026thinsp;\u0026ge;\u0026thinsp;10), based on a threshold score of 10[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAssessment of NPS\u003c/h3\u003e\n\u003cp\u003eFollowing the method established by Galizia et al.[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e], NPS was calculated using four clinical parameters: serum albumin, total cholesterol (TC), NLR, and LMR. For each parameter, thresholds were assigned as follows: serum albumin\u0026thinsp;\u0026ge;\u0026thinsp;40 g/L, TC\u0026thinsp;\u0026gt;\u0026thinsp;180 mg/dL, NLR\u0026thinsp;\u0026le;\u0026thinsp;2.96, or LMR\u0026thinsp;\u0026gt;\u0026thinsp;4.44 scored 0; levels outside these thresholds scored 1. The NPS score (0\u0026ndash;4) was the sum of these four components (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). According to the previous study[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e], participants were stratified into three distinct groups according to their total score distribution: Group 1 (NPS score\u0026thinsp;=\u0026thinsp;0), Group 2 (NPS score\u0026thinsp;=\u0026thinsp;1 or 2), and Group 3 (NPS score\u0026thinsp;=\u0026thinsp;3 or 4).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe standard of NPS.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eNPS score\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoints\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB(g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssessment of mortality\u003c/h3\u003e\n\u003cp\u003eDeceased participants in this study were identified by linking the NHANES dataset with the National Death Index (NDI). Mortality data, including all-cause mortality as of December 31, 2019, were obtained from the 2019 Linked Mortality File (LMF). These records were matched with NHANES data and are publicly accessible online at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cdc.gov/nchs/data-linkage/mortality.htm\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eCovariates\u003c/h2\u003e\n \u003cp\u003eBased on a comprehensive review of the existing literature, we identified several key potential covariates that may influence the outcomes, including demographic information, laboratory data, and questionnaire information. Demographic data and questionnaire information such as age, gender, race, poverty income ratio (PIR), educational level, marital status, diabetes status, smoke status, and cancer status were self-reported by the participants. Smoking status was dichotomized into two distinct categories: smokers and nonsmokers. Those who reported smoking at least 100 cigarettes during their lives were considered smokers, whereas those who reported smoking fewer than 100 cigarettes were considered nonsmokers. To represent socioeconomic position, the PIR was separated into three levels: low (\u0026lt;\u0026thinsp;1.3), medium (1.3 to \u0026lt;\u0026thinsp;3.5), and high (\u0026ge;\u0026thinsp;3.5). Diabetes and cancer status was determined based on self-reported diagnosis, with individuals classified as \u0026quot;yes\u0026quot; if they reported a prior diagnosis and \u0026quot;no\u0026quot; otherwise. Marital status was dichotomized into \u0026quot;married\u0026quot; and \u0026quot;unmarried\u0026quot; based on responses to standardized questionnaire items. Educational status was classified into three categories: less than high school, high school graduate, and more than high school. Laboratory data included albumin, ALT, AST, cholesterol, triglyceride, uric acid, globulin, fasting glucose, HbA1c, lymphocyte, monocyte, neutrophils, Hb, PLT, NLR, and LMR. BMI was stratified into three discrete groups: normal weight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u0026sup2;), overweight (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 to \u0026lt;\u0026thinsp;30 kg/m\u0026sup2;), and obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStatical analysis\u003c/h3\u003e\n\u003cp\u003eTo ensure the representativeness of the national population, Mobile Examination Center weights (MECs) were employed in accordance with the NHANES Analytic Guidelines, which account for the survey\u0026apos;s sophisticated multistage probability sampling framework. Participant characteristics are summarized as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD) for continuous variables and as proportions for categorical variables. Group differences in continuous variables were analyzed using the analysis of Variance, while the weighted chi-square test was utilized to compare categorical variables. To address missing data, multiple imputation was performed via the \u0026quot;mice\u0026quot; package in R, and the random forest algorithm was used to ensure robust and reliable data estimation.\u003c/p\u003e\n\u003cp\u003eParticipants in the cross-sectional study were split into two groups: one for those who had depression and one for those who did not. The relationship between NPS and the likelihood of depression was assessed using weighted multivariable logistic regression. Using 95% confidence intervals (CIs), the outcomes were presented as odds ratios (ORs). The logistic regression models were gradually modified: Model 1 included adjustments for age, education, and marital status, while Model 2 also considered factors like age, AST, triglycerides, uric acid, fasting glucose, HbA1c, PLT, education, marriage, BMI, smoking, and diabetes. The crude model had no adjustments.\u003c/p\u003e\n\u003cp\u003eIn the cohort study, individuals with depression were further classified into three groups based on their NPS scores: group 1(NPS\u0026thinsp;=\u0026thinsp;0), group 2 (NPS\u0026thinsp;=\u0026thinsp;1 or 2), and group 3(NPS\u0026thinsp;=\u0026thinsp;3 or 4). A two-sided log-rank test was used to statistically compare the cohort study analysis, which used Kaplan-Meier (KM) survival curves to examine the relationship between NPS, all-cause mortality, and specific mortality in patients with depression. The results of weighted multivariate Cox regression analysis were displayed as hazard ratios (HRs) with 95% confidence intervals (CIs) in order to further assess the association between NPS, all-cause mortality, and specific mortality among persons with depression. Model 1 was adjusted for age, gender, and race; Model 2 included additional adjustments for diabetes, cancer, BMI, education status, PLT, HbA1c, fasting glucose, Hb, triglycerides, and globulin; and the crude model included no adjustments. The Cox regression models were then stepwise adjusted. Additionally, nonlinear associations between NPS and the risk of all-cause death were methodically investigated.\u003c/p\u003e\n\u003cp\u003eAdditionally, we computed the AUC value and plotted the ROC curve to confirm the predictive power of NPS. R Studio (version 4.2.2) and STATA (version 15.1) were used for the statistical methods, which used a two-tailed significance criteria of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBasic characteristics of all participants\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the study population are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In this study, participants were categorized into two groups based on the presence of depression: the depression group (N\u0026thinsp;=\u0026thinsp;2,688, 9.06%) and the non-depression group (N\u0026thinsp;=\u0026thinsp;26,967, 90.94%). Baseline characteristics were compared between the two groups, revealing that the depression group had a significantly higher mean age (48.47\u0026thinsp;\u0026plusmn;\u0026thinsp;18.44 years) compared to the non-depression group (46.88\u0026thinsp;\u0026plusmn;\u0026thinsp;18.89 years, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Several biochemical markers showed significant differences between the two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Specifically, the depression group had significantly higher triglyceride levels (164.02\u0026thinsp;\u0026plusmn;\u0026thinsp;129.53 mg/dL) compared to the non-depression group (150.41\u0026thinsp;\u0026plusmn;\u0026thinsp;122.50 mg/dL, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), lower albumin levels (4.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36 g/dL vs. 4.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35 g/dL, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and higher AST levels (25.74\u0026thinsp;\u0026plusmn;\u0026thinsp;24.46 U/L vs. 25.01\u0026thinsp;\u0026plusmn;\u0026thinsp;13.83 U/L, P\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e \u003cp\u003eIn terms of demographics, the distribution of genders in the two groups did not differ significantly (P\u0026thinsp;=\u0026thinsp;0.33). However, marital status and educational attainment showed substantial variations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Both the percentage of people with less than a high school education (24.19% vs. 20.33%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and the percentage of married people (52.95% vs. 38.93%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were greater in the depression group. Obesity was more common in the depression group (49.01% vs. 39.35%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in terms of BMI. Additionally, there was a substantially greater prevalence of diabetes (14.89% vs. 9.15%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and smoking (60.37% vs. 41.84%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in the depression group.\u003c/p\u003e \u003cp\u003eThese differences suggest that individuals with depression are more likely to be older, less educated, married, of lower income, obese, and smokers. All things considered, these baseline variations show that a number of biochemical, behavioral, and demographic characteristics are strongly linked to the existence of depression and help to clarify its causes and course.\u003c/p\u003e \u003cp\u003eThe main conclusion of our research is that the distribution of NPS in the depression and non-depression groups differs significantly. Specifically, the depression group has a higher proportion in NPS Group 3 (16.94% vs. 12.29%), while the proportion in NPS Group 2 is significantly lower in the depression group compared to the non-depression group (64.79% vs. 71.15%).\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\u003eCharacteristics of the participants.\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\u003eCharacteristic\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\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;29,655\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;26,967(90.94%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,688(9.06%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.00\u0026thinsp;\u0026plusmn;\u0026thinsp;18.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.88\u0026thinsp;\u0026plusmn;\u0026thinsp;18.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.47\u0026thinsp;\u0026plusmn;\u0026thinsp;18.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin(g/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALT(U/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.01\u0026thinsp;\u0026plusmn;\u0026thinsp;19.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.96\u0026thinsp;\u0026plusmn;\u0026thinsp;17.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.64\u0026thinsp;\u0026plusmn;\u0026thinsp;31.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST(U/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.07\u0026thinsp;\u0026plusmn;\u0026thinsp;14.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.01\u0026thinsp;\u0026plusmn;\u0026thinsp;13.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.74\u0026thinsp;\u0026plusmn;\u0026thinsp;24.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCholesterol(mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192.92\u0026thinsp;\u0026plusmn;\u0026thinsp;41.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192.74\u0026thinsp;\u0026plusmn;\u0026thinsp;41.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195.03\u0026thinsp;\u0026plusmn;\u0026thinsp;44.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglyceride(mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151.49\u0026thinsp;\u0026plusmn;\u0026thinsp;123.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150.41\u0026thinsp;\u0026plusmn;\u0026thinsp;122.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164.02\u0026thinsp;\u0026plusmn;\u0026thinsp;129.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUric acid(mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobulin(g/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFasting Glucose(mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymphocyte(1000 cells/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonocyte(1000 cells/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutrophils(1000 cells/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHb(g/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlt(1000 cells/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e243.49\u0026thinsp;\u0026plusmn;\u0026thinsp;62.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e242.63\u0026thinsp;\u0026plusmn;\u0026thinsp;61.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e253.37\u0026thinsp;\u0026plusmn;\u0026thinsp;67.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLMR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender, n%\u003c/b\u003e\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\u003cem\u003e0.33\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,653(48.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,279(48.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,374(49.87%)\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\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,002(51.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,688(51.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,314(50.13%)\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\u003e\u003cb\u003eRace, n%\u003c/b\u003e\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\u003cem\u003e0.31\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMexican American\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,663(14.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,189(14.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e474(15.56%)\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\u003e\u003cb\u003eOther Hispanic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,130(10.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,844(10.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e286(10.68%)\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\u003e\u003cb\u003eNon-Hispanic White\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,335(41.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,229(41.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,106(41.57%)\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\u003e\u003cb\u003eNon-Hispanic Black\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,105(21.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,590(22.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e515(20.63%)\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\u003e\u003cb\u003eOther Race\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,422(11.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,115(11.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e307(11.57%)\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\u003e\u003cb\u003eEducation, n%\u003c/b\u003e\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\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;High School\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,189(20.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,436(20.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e753(24.19%)\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\u003e\u003cb\u003eHigh School\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,226(23.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,505(23.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e721(26.20%)\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\u003e\u003cb\u003e\u0026gt;High School\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,240(56.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,026(56.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,214(49.61%)\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\u003e\u003cb\u003eMarriage, n%\u003c/b\u003e\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\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarried\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,560(40.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,922(38.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,638(52.95%)\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\u003e\u003cb\u003eNon-Married\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,095(59.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,045(61.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,050(47.05%)\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\u003e\u003cb\u003ePIR, n%\u003c/b\u003e\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\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow\u0026lt;1.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,646(19.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,355(18.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,291(37.83%)\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\u003e\u003cb\u003eMedium1.3\u0026ge;,\u0026lt;3.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,470(38.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,422(38.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,048(40.2%)\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\u003e\u003cb\u003eHigh\u0026thinsp;\u0026ge;\u0026thinsp;3.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,539(41.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,190(43.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e349(21.98%)\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\u003e\u003cb\u003eBMI(kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), n%\u003c/b\u003e\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\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormal(\u0026lt;25kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,689(26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,095(26.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e594(23.83%)\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\u003e\u003cb\u003eOverweight(\u0026ge;\u0026thinsp;25,\u0026lt;30kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,814(33.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,103(33.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e711(27.15%)\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\u003e\u003cb\u003eObese(\u0026ge;\u0026thinsp;30kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,152(40.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,769(39.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,383(49.01%)\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\u003e\u003cb\u003eDiabetes, n%\u003c/b\u003e\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\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,780(9.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,263(9.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e517(14.89%)\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\u003e\u003cb\u003eNO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25,875(90.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23,704(90.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,171(85.11%)\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\u003e\u003cb\u003eCancer, n%\u003c/b\u003e\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\u003cem\u003e0.12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,710(10.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,422(9.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e288(10.97%)\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\u003e\u003cb\u003eNO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26,945(89.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24,545(90.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,400(89.03%)\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\u003e\u003cb\u003eSmoke, n%\u003c/b\u003e\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\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,659(43.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,121 (41.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,538 (60.37%)\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\u003e\u003cb\u003eNon-Smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,996(56.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,846 (58.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,150 (39.63%)\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\u003e\u003cb\u003eNPS, n%\u003c/b\u003e\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\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGroup1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,278(16.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,777(16.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e501(18.27%)\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\u003e\u003cb\u003eGroup2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,447(70.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,716(71.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,731(64.79%)\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\u003e\u003cb\u003eGroup3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,930(12.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,474(12.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e456(16.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBasic characteristics of depression individuals\u003c/h2\u003e \u003cp\u003eWe performed a cohort study to examine the relationship among patients with depression between NPS, all-cause mortality, and specific mortality. The fundamental traits of depressed people are displayed in Table\u0026nbsp;3.\u003c/p\u003e \u003cp\u003eAfter excluding patients with missing follow-up data, a total of 2,190 depressed patients were included in the study to examine mortality rates. The participants were divided into three groups based on their NPS scores: Group 1 (405 individuals, 18.49%), Group 2 (1,397 individuals, 63.79%), and Group 3 (388 individuals, 17.72%). The mean follow-up duration was 70.27\u0026thinsp;\u0026plusmn;\u0026thinsp;41.74 months. During the follow-up period, 220 deaths were recorded due to all causes, 60 deaths due to heart disease, 38 deaths due to malignant neoplasms, 14 deaths due to respiratory diseases, 28 deaths due to diabetes, and 37 deaths due to hypertension as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. There were notable variations across the groups in terms of the clinical markers and baseline characteristics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn terms of basic demographic characteristics, significant differences in age were observed between the three groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Patients in Group 1 had the oldest average age (54.40 years), while the average ages for Group 2 and Group 3 were 46.86 years and 48.35 years, respectively. Regarding gender distribution, males had a higher proportion in Group 3 (57.57%) compared to Group 2 (47.48%) and Group 1 (50.10%), with this difference reaching statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In terms of racial distribution, non-Hispanic White patients represented a larger proportion in all groups, but Group 3 had a higher proportion of non-Hispanic Black and other races (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, in the education status, Group 1 had the lowest proportion of patients with education beyond high school (29.34%), while Group 3 had a significantly higher proportion (57.39%) of patients with education beyond high school (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eIn the analysis of lifestyle and health behaviors, BMI showed significant differences between the groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Group 3 had the highest proportion of obese patients (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;). Regarding smoking status, 60.98% of the total sample were smokers. Although Group 3 had a higher proportion of smokers (66.47%), this difference did not reach statistical significance (P\u0026thinsp;=\u0026thinsp;0.05). The prevalence of diabetes was significantly higher in Group 3 (23.64%) compared to Group 1 (12.42%), suggesting that patients with higher NPS scores may be at a higher risk of diabetes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The prevalence of cancer was also notably higher in Group 3 (13.86%) compared to the other groups (P\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e \u003cp\u003eSignificant differences were observed between the three groups in serum albumin (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and triglyceride levels (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Group 3 had the lowest serum albumin level (3.89 g/dL), indicating that patients in Group 3 may experience more severe malnutrition or metabolic issues. Triglyceride levels were significantly higher in Group 1 (202.78 mg/dL) compared to the other groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, Group 3 had higher fasting blood glucose levels than the other groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that these patients may be more susceptible to anomalies in glucose metabolism..\u003c/p\u003e \u003cp\u003eIn the mortality analysis, the all-cause mortality rate differed significantly between the groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Group 1 had the lowest all-cause mortality rate (3.98%), while Group 3 had a significantly higher rate (15.87%). The mortality rate due to heart disease was also notably higher in Group 3 (4.30%) compared to Group 1 (0.72%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, the mortality rate due to malignant neoplasms was significantly increased in Group 3 (2.57%), while Group 1 and Group 2 had lower rates (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). There were no significant differences in respiratory disease-related mortality across the groups (P\u0026thinsp;=\u0026thinsp;0.18). However, the mortality rates due to diabetes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and hypertension (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were significantly higher in Group 3, at 2.08% and 2.63%, respectively, compared to Group 1, where these rates were 0.42% and 0.51%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\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\u003eGroup1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,190\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;405(18.49%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,397(63.79%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;388(17.72)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFollow-up times(months)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.27\u0026thinsp;\u0026plusmn;\u0026thinsp;41.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.02\u0026thinsp;\u0026plusmn;\u0026thinsp;40.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.47\u0026thinsp;\u0026plusmn;\u0026thinsp;41.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.96\u0026thinsp;\u0026plusmn;\u0026thinsp;41.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.49\u0026thinsp;\u0026plusmn;\u0026thinsp;18.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.40\u0026thinsp;\u0026plusmn;\u0026thinsp;19.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.86\u0026thinsp;\u0026plusmn;\u0026thinsp;18.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.35\u0026thinsp;\u0026plusmn;\u0026thinsp;16.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin(g/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALT(U/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.70\u0026thinsp;\u0026plusmn;\u0026thinsp;33.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.37\u0026thinsp;\u0026plusmn;\u0026thinsp;15.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.57\u0026thinsp;\u0026plusmn;\u0026thinsp;21.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.49\u0026thinsp;\u0026plusmn;\u0026thinsp;67.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.87\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST(U/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.75\u0026thinsp;\u0026plusmn;\u0026thinsp;26.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.20\u0026thinsp;\u0026plusmn;\u0026thinsp;12.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.35\u0026thinsp;\u0026plusmn;\u0026thinsp;18.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.76\u0026thinsp;\u0026plusmn;\u0026thinsp;48.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCholesterol(mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195.49\u0026thinsp;\u0026plusmn;\u0026thinsp;44.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225.87\u0026thinsp;\u0026plusmn;\u0026thinsp;33.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195.82\u0026thinsp;\u0026plusmn;\u0026thinsp;44.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162.94\u0026thinsp;\u0026plusmn;\u0026thinsp;32.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglyceride(mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165.32\u0026thinsp;\u0026plusmn;\u0026thinsp;131.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202.78\u0026thinsp;\u0026plusmn;\u0026thinsp;143.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161.98\u0026thinsp;\u0026plusmn;\u0026thinsp;136.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138.84\u0026thinsp;\u0026plusmn;\u0026thinsp;84.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUric acid(mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.53\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobulin(g/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFasting Glucose(mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymphocyte(1000 cells/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonocyte(1000 cells/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutrophils(1000 cells/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHb(g/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlt(1000 cells/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252.55\u0026thinsp;\u0026plusmn;\u0026thinsp;66.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258.15\u0026thinsp;\u0026plusmn;\u0026thinsp;62.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250.23\u0026thinsp;\u0026plusmn;\u0026thinsp;65.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e255.20\u0026thinsp;\u0026plusmn;\u0026thinsp;75.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.08\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLMR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGENDER, n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,123(49.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204(50.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e696(47.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e223(57.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,067(50.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201(49.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e701(52.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e165(42.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRACE, n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMexican American\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e382(15.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90(16.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e231(14.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61(17.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther Hispanic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225(10.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(12.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144(10.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(7.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Hispanic White\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e911(41.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154(40.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e586(42.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e171(38.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Hispanic Black\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e422(20.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86(24.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250(18.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86(25.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther Race\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250(11.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(5.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186(13.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39(10.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEDUCATION, n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;High School\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e611(24.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171(39.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e330(18.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110(29.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh School\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e571(25.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120(31.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400(26.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51(13.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;High School\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,008(50.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114(29.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e667(54.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227(57.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarriage, n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.84\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarried\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,326(52.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244(53.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e845(52.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e237(51.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Married\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e864(47.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161(46.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e552(47.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e151(48.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIR, n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.05\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow\u0026lt;1.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,031(37.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185(34.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e664(38.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e182(36.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedium1.3\u0026ge;,\u0026lt;3.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e876(40.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168(45.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e556(40.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152(36.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh\u0026thinsp;\u0026ge;\u0026thinsp;3.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283(21.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52(20.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e177(20.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54(26.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormal(\u0026lt;25kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e473(23.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86(19.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e317(24.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70(22.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverweight(\u0026ge;\u0026thinsp;25,\u0026lt;30kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e574(27.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117(31.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e372(27.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85(20.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObese(\u0026ge;\u0026thinsp;30kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,143(49.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202(49.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e708(47.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e233(57.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes, n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430(15.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(12.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e255(13.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116(23.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,760(84.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e346(87.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,142(86.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e272(76.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoke, n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.05\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,255(60.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235(59.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e777(59.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e243(66.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e935(39.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170(40.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e620(40.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145(33.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer, n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244(11.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(13.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145(9.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54(13.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,946(88.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360(86.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,252(90.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e334(86.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-cause mortality, n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,970(91.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e383(96.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,277(92.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e310(84.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220(8.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(3.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120(7.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78(15.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath of heart diseases, n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,130(97.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e399(99.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,363(98.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e368(95.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(2.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(0.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(1.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20(4.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath of malignant neoplasms, n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,152(98.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e403(99.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,377(99.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e372(97.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(1.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(0.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(0.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16(2.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath of respiratory diseases, n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.18\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2176(99.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e405(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,389(99.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e382(99.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(0.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(0.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(0.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath of diabetes, n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,162(99.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e402(99.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,385(99.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e375(97.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(0.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(0.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(0.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13(2.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath of hypertension, n%\u003c/b\u003e\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,153(98.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e401(99.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,379(99.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e373(97.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(1.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(0.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(0.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15(2.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTables\u0026nbsp;3. Characteristics of individuals with depression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between NPS and depression prevalence\u003c/h2\u003e \u003cp\u003eWeighted logistic regression analyses were performed to investigate the relationship between NPS and depression prevalence among the 29,655 participants, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Higher NPS scores were repeatedly found to be positively correlated with a greater possibility of depression. In the unadjusted model, participants in group 3 had a 25% higher likelihood of experiencing depression than did those in the reference group (group 1) (OR\u0026thinsp;=\u0026thinsp;1.25, 95% CI: 1.04\u0026ndash;1.50, P\u0026thinsp;=\u0026thinsp;0.02). After adjusting for age, education status, and marriage status (Model 1), participants in Group 3 had a 44% higher likelihood of experiencing depression compared to the reference group (OR\u0026thinsp;=\u0026thinsp;1.44, 95% CI: 1.19\u0026ndash;1.73, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). After further adjustments, the association remained statistically significant, with participants in Group 3 having a 32% higher likelihood of experiencing depression compared to the reference group (OR\u0026thinsp;=\u0026thinsp;1.32, 95% CI: 1.09\u0026ndash;1.60, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). When compared to the reference group (group 1), group 3 continuously showed a substantial positive link with the prevalence of depression throughout all models. Although Group 2 showed a lower mortality risk in the crude model, the effect became non-significant after adjusting for confounders, while Group 3 consistently demonstrated a significantly higher odds of depression than Group 1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between NPS and mortality in individuals with depression\u003c/h2\u003e \u003cp\u003eIn order to investigate the connection between NPS and death in patients with depression, we created Kaplan-Meier (KM) survival curves, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In the KM curves for all-cause mortality, heart disease mortality, malignant neoplasm mortality, diabetes mortality, and hypertension mortality, it is visually evident that the survival rate of depressed patients in Group 3 is the lowest. Log-rank tests indicated that the differences between the groups were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure 5 displays the findings of a weighted Cox proportional hazards regression analysis we conducted to investigate the connection between NPS and mortality in patients with depression. Based on the results of the Cox proportional hazards regression analysis, we explored the relationship between NPS and mortality due to various causes in depressed patients. The findings showed that depressed patients with higher NPS had significantly increased odds of death from all causes, heart disease, malignant neoplasms, diabetes, and hypertension, particularly in Group 3 (NPS\u0026thinsp;=\u0026thinsp;3 or 4).\u003c/p\u003e \u003cp\u003eRegarding all-cause mortality, the crude model indicated that the HR for Group 3 was 5.09 (95% CI: 2.77\u0026ndash;9.35), which suggests that the all-cause mortality risk in Group 3 was 409% higher than that in Group 1. After adjusting for confounding factors, the model results further confirmed this finding, with Group 3 showing a 354% higher all-cause mortality risk compared to Group 1 (HR\u0026thinsp;=\u0026thinsp;4.54, 95% CI: 2.24\u0026ndash;9.21), and the result was statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This indicates that depressed patients with higher NPS scores are more likely to experience all-cause mortality.\u003c/p\u003e \u003cp\u003eIn terms of heart disease mortality, Group 3 also exhibited a significantly increased risk. The crude model showed that the HR for heart disease mortality in Group 3 was 7.46 (95% CI: 2.74\u0026ndash;20.31), indicating a 646% higher risk compared to the reference group. In the adjusted models, Group 3's risk remained significantly higher by 739% (HR\u0026thinsp;=\u0026thinsp;8.39, 95% CI: 2.85\u0026ndash;24.71). This result suggests a significant increase in heart disease-related mortality for depressed patients with higher NPS scores.\u003c/p\u003e \u003cp\u003eIn the analysis of malignant neoplasm mortality, although the result for Group 2 failed to demonstrate statistical significance (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), Group 3's risk increased by 632% (HR\u0026thinsp;=\u0026thinsp;7.32, 95% CI: 1.47\u0026ndash;36.48), indicating that depressed patients with higher NPS scores are at greater risk of dying from cancer. After further adjustment, the risk in Group 3 remained 510% higher (HR\u0026thinsp;=\u0026thinsp;5.10, 95% CI: 1.21\u0026ndash;21.41).\u003c/p\u003e \u003cp\u003eRegarding diabetes-related mortality, the crude model indicated that the risk for Group 3 increased by 513% (HR\u0026thinsp;=\u0026thinsp;6.13, 95% CI: 1.55\u0026ndash;24.21). The adjusted models also showed a significant increase in risk by 466% (HR\u0026thinsp;=\u0026thinsp;5.66, 95% CI: 1.44\u0026ndash;22.24, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These results suggest that depressed patients with higher NPS scores face a higher risk of death due to diabetes.\u003c/p\u003e \u003cp\u003eFinally, for hypertension-related mortality, the risk for Group 3 was significantly higher in all models. The crude model showed a 661% increase in risk (HR\u0026thinsp;=\u0026thinsp;7.61, 95% CI: 1.97\u0026ndash;29.35), while Model 1 and Model 2 showed an increase of 789% (HR\u0026thinsp;=\u0026thinsp;8.89, 95% CI: 2.36\u0026ndash;33.47) and 568% (HR\u0026thinsp;=\u0026thinsp;6.68, 95% CI: 1.69\u0026ndash;26.40), respectively, all with statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This indicates that depressed patients with higher NPS scores are at greater risk of mortality due to hypertension.\u003c/p\u003e \u003cp\u003eAcross all the models and different mortalities, the trend analysis (P for trend) consistently indicated a significant upward trend mortality risk with increasing NPS group levels. These findings suggest that higher NPS scores, particularly in Group 3, are strongly associated with increased mortality risk, even after comprehensive adjustment for potential confounders.\u003c/p\u003e \u003cp\u003eFigure 5. HRs (95%CIs) of mortality according to NPS among depressed patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\u003e \u003cp\u003eTo further investigate the associations between NPS and mortality in depressed individuals, subgroup analyses were performed based on diverse demographic factors as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In the subgroup analysis, due to the limited number of recorded cases for certain causes of death and the relatively small sample size for some patient characteristics, we only performed subgroup analysis for mortality from heart disease and all causes. Additionally, considering data availability and statistical power requirements, this subgroup analysis included only a selection of characteristics.\u003c/p\u003e \u003cp\u003eBoth male and female depressed patients in Group 3 had a considerably greater risk of all-cause mortality, according to the results of the subgroup analysis of all-cause mortality. Additionally, depressed patients with a high school education in Group 3 had a significantly increased risk of all-cause mortality by 3,041%, and those who were overweight had a 3,939% higher risk. Group 3 also had a significantly higher risk of all-cause death among smokers and those with a history of diabetes.\u003c/p\u003e \u003cp\u003eThe subgroup study of heart disease mortality showed that Group 3 patients, both male and female, were at significantly higher risk. There was a noteworthy 841% increase in the probability of death for individuals who were married. The chance of dying from heart disease was similarly 865% greater for smokers in Group 3.\u003c/p\u003e \u003cp\u003eThese results indicate that depressed patients with higher NPS scores show significant subgroup differences across different causes of death, especially in terms of gender, education, BMI, smoking, and diabetes.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup analyses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStratified by\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.76(0.68\u0026ndash;4.54)0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.35(1.58\u0026ndash;11.97)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.72(1.11\u0026ndash;6.67)0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.05(1.88\u0026ndash;13.57)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEDUCATION\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;High School\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.57(1.05\u0026ndash;6.30)0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.38(1.6-11.94)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh School\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.37(1.57\u0026ndash;25.77)0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.41(5.42-181.99)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;High School\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91(0.33\u0026ndash;2.55)0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.01(0.65\u0026ndash;6.19)0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarriage\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarried\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.56(0.67\u0026ndash;3.61)0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.35(1.75\u0026ndash;10.81)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Married\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.10(1.08\u0026ndash;8.93)0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.70(1.74\u0026ndash;18.65)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormal(\u0026lt;25kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.67(0.68\u0026ndash;10.47)0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.51(1.12\u0026ndash;18.18)0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverweight(\u0026ge;\u0026thinsp;25,\u0026lt;30kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.68(2.59\u0026ndash;62.10)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.39(6.41-254.59)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObese(\u0026ge;\u0026thinsp;30kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16(0.48\u0026ndash;2.76)0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.02(1.20\u0026ndash;7.64)0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.68(0.6\u0026ndash;4.75)0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.82(3.25\u0026ndash;29.67)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.28(0.98\u0026ndash;5.30)0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.43(1.38\u0026ndash;8.52)0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoke\u003c/b\u003e\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.08(0.88\u0026ndash;4.93)0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.59(1.76\u0026ndash;11.95)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.5(0.92\u0026ndash;6.81)0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.88(1.91\u0026ndash;12.50)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.95(0.17\u0026ndash;22.61)0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.94(0.18\u0026ndash;20.33)0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.19(1.13\u0026ndash;4.25)0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.55(2.78\u0026ndash;11.10)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath of heart diseases\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.52(0.61\u0026ndash;10.49)0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.99(1.66\u0026ndash;38.39)0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.42(1.01\u0026ndash;19.37)0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.34(1.72\u0026ndash;50.86)0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarriage\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarried\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.71(1.15\u0026ndash;11.92)0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.41(2.69\u0026ndash;32.91)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Married\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03(0.23\u0026ndash;4.56)0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.75(0.33\u0026ndash;22.77)0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01(0.21\u0026ndash;4.81)0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.89(0.57\u0026ndash;14.53)0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.14(2.12\u0026ndash;48.50)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.43(4.71-126.69)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoke\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.26(1.05\u0026ndash;17.37)0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.65(2.15\u0026ndash;43.30)\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.73(0.77\u0026ndash;17.99)0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.79(1.67\u0026ndash;46.20)0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.91(0.14\u0026ndash;25.22)0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.21(0.36\u0026ndash;49.4)0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.20(1.33\u0026ndash;13.21)0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.28(3.37\u0026ndash;37.78)\u0026lt;0.01\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=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eROC analysis for depression and mortality\u003c/h2\u003e \u003cp\u003eThis study evaluates the predictive value of NPS for various causes of death through ROC curve analysis, comparing the classification abilities for all-cause mortality, heart disease mortality, malignant neoplasm mortality, diabetes mortality, and hypertension mortality (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The ROC curve AUC values, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for each model were used to assess its predictive power (as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll-cause mortality: The AUC value of the NPS is 0.634, indicating moderate predictive ability. Sensitivity is 0.659, meaning it can effectively identify individuals who died from all causes, with a high PPV of 0.932, suggesting that it accurately predicts deceased individuals.\u003c/p\u003e \u003cp\u003eHeart disease mortality: The AUC value is 0.628, showing similar performance to the all-cause mortality model, with moderate discriminatory ability. The sensitivity of 0.700 indicates that the NPS model can effectively identify individuals who died from heart disease.\u003c/p\u003e \u003cp\u003eMalignant neoplasm mortality: The AUC value is 0.645, the highest among all causes of death, indicating the strongest predictive power. The specificity of 0.827 suggests it can effectively identify individuals who did not die from malignant neoplasms. Additionally, the PPV of 0.988 means that the model is highly accurate in predicting individuals who died from malignant neoplasms, making it suitable for clinical screening and early diagnosis.\u003c/p\u003e \u003cp\u003eDiabetes mortality: The AUC value for diabetes mortality is 0.604, showing that the NPS model has some predictive ability in this area. Its sensitivity is 0.634, allowing it to identify individuals who died from diabetes-related causes. Although specificity is slightly lower, the model still demonstrates clinical value in predicting diabetes-related mortality, supporting early intervention for diabetes-related deaths.\u003c/p\u003e \u003cp\u003eHypertension mortality: The AUC value for hypertension mortality is 0.599, which is relatively low. However, the sensitivity of 0.560 indicates that the model can identify a certain proportion of individuals who died from hypertension-related causes. Despite its lower specificity, the model still provides an initial reference value for assessing the death risk of hypertension patients, particularly for screening high-risk populations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC Curve Analysis for Various Causes of Mortality.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-cause mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.595\u0026ndash;0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath of heart diseases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.559\u0026ndash;0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath of malignant neoplasms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.555\u0026ndash;0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath of diabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.562\u0026ndash;0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath of hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.546\u0026ndash;0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.023\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\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates for the first time the potential utility of the NPS in treating depression, specifically in determining the risk of dying from all causes along with various causes. Individuals with higher NPS scores are more likely to experience depression, according to the findings, and those who are depressed and have higher NPS scores are at significantly higher risk for death from all causes, heart disease, cancer, diabetes, and hypertension. Notably, individuals with NPS scores of 3 or 4 have a significantly higher mortality risk compared to those in the lower score groups. These findings indicate that NPS not only effectively predicts the prevalence of depression but also serves as an important prognostic indicator for the risk of death in patients with depression. As a biomarker that combines inflammation and nutritional status, the results demonstrate the potential therapeutic application value of NPS and offer a new and complete predictive evaluation tool for depression in clinical practice.\u003c/p\u003e \u003cp\u003eSerum albumin, total cholesterol, NLR, and LMR are the four peripheral blood indicators that make up the majority of NPS. Current research widely recognizes NPS as a novel and comprehensive index reflecting systemic inflammation and nutritional status[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Previous studies have shown that the NPS, as a novel prognostic scoring system, has proven to be of significant value in prognostic assessments for various diseases, such as glioblastoma[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], non-small cell lung cancer[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and metastatic colorectal cancer[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The study by Galizia and colleagues was the first to introduce the NPS and validate its application in cancer patients[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The findings of this study further extend the potential applications of NPS to depression patients. This discovery suggests that NPS is not only applicable to the prognosis of severe diseases like cancer but also serves as an important tool for early risk screening and prognostic prediction in patients with depression. The broader applicability of NPS in different clinical contexts underscores its potential as a versatile and reliable marker, facilitating earlier interventions and more accurate prognostic assessments in a wide range of conditions, including psychiatric disorders like depression.\u003c/p\u003e \u003cp\u003eAccording to our research, a higher NPS score is highly correlated with higher rates of morbidity and mortality in depressed people. These rates may be directly related to the systemic inflammatory response and the nutritional status of the individual. The connection between inflammation and the etiology of depression has garnered more attention in recent years[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Research has demonstrated a strong correlation between the development and course of depression and chronic inflammation[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Several studies have found that the inflammatory response affects the balance of key neurotransmitters in the brain, such as serotonin and dopamine, which are crucial for mood regulation, by activating the immune system and altering the neurochemical environment[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, it is frequently seen that patients with depression have higher levels of inflammatory markers such TNF-α, IL-6, and IL-1β[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These factors exacerbate brain neural damage by disrupting neuroprotective mechanisms[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These inflammatory mediators not only affect mood and cognitive function but may also worsen depression symptoms by altering brain structures, such as causing hippocampal atrophy[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Researchers are also exploring the potential of anti-inflammatory treatments as a new strategy for treating depression. For example, nonsteroidal anti-inflammatory drugs (NSAIDs) and other anti-inflammatory medications have shown potential in alleviating depressive symptoms, although this approach still requires further clinical validation[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, nutrition influences the onset and development of depression through multiple biological pathways. Studies have shown that metabolic dysregulation is closely associated with the onset of depression, particularly disturbances in glucose and lipid metabolism[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Malnutrition, in particular a lack of key vitamins (such B and D) and minerals, can cause anomalies in the neurological system's operation, which can impact behavior and mood[\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Another key mechanism is the regulation of oxidative stress and inflammatory responses by nutritional components[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Oxidative stress refers to the imbalance between free radicals and antioxidants in the body, which can lead to cellular damage, particularly in the brain[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Many patients with depression show elevated levels of oxidative stress, suggesting that it plays an important role in the pathogenesis of depression[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Research indicates that diets rich in antioxidants may reduce oxidative damage, thereby offering protective effects against depression[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Certain nutrients can influence the production and operation of neurotransmitters (including serotonin, dopamine, and norepinephrine), which in turn regulate mood and behavior. These nutrients include omega-3 fatty acids, B vitamins, and minerals[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Deficiencies in these nutrients can disrupt normal neurotransmission, leading to the onset of depressive symptoms[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom the perspective of NPS, studies have shown that the NLR and the LMR are key markers of systemic inflammation and immune imbalance. A Chinese study involving 350 postoperative non-small cell lung cancer patients indicated that NLR and LMR are independent prognostic predictors of the risk of postoperative depression[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Furthermore, a study on post-stroke depression (PSD) revealed that LMR is independently associated with the development of PSD and is also linked to an increase in the severity of PSD[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The significance of systemic inflammatory biomarkers in the prognosis of depression is further supported by these results, which are in accordance with the baseline findings of our study.\u003c/p\u003e \u003cp\u003eThe research described above demonstrate how important inflammatory responses are to the pathophysiology of depression. The NLR reflects the balance between neutrophils and lymphocytes, serving as an indicator of the extent of systemic inflammation. Neutrophils, by releasing inflammatory mediators such as TNF-α and IL-6, can cross the blood-brain barrier and influence the central nervous system, disrupting the balance of neurotransmitters, which may ultimately trigger depressive symptoms[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Furthermore, these inflammatory cytokines can induce structural and functional changes in the brain, impacting emotional regulation and cognitive function. Lymphocytes, which play a crucial role in modulating excessive inflammation, act to counterbalance the inflammatory response[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. A reduction in lymphocyte count or dysfunction in their activity can lead to immune dysregulation, contributing to the onset and progression of depression[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The LMR reflects the ratio between lymphocytes and monocytes, with monocytes being involved in the maintenance of chronic inflammation and the persistence of immune responses[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Monocytes and their differentiated macrophages play a pivotal role in chronic inflammation, and sustained inflammatory responses may influence the brain through various mechanisms, altering the synthesis, release, and receptor expression of neurotransmitters, thereby precipitating the onset of depression[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other side of NPS, serum albumin and total cholesterol levels are widely recognized as key indicators reflecting the nutritional status of the organism. Serum albumin, as a protein with free radical scavenging properties, may help explain its role in the development of depression, at least to some extent. Albumin is widely recognized as an important antioxidant, with a significant portion of the overall antioxidant capacity of serum attributed to albumin[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. According to some studies, the pathogenesis of depression is associated with an excess of free radicals. This surplus of free radicals leads to oxidative stress, which is believed to cause oxidative damage linked to neurodegeneration and various psychiatric disorders, including depression[\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Cholesterol is a crucial component of neuronal cell membranes, essential for maintaining the structure and function of nerve cells[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In addition to being essential for the transmission of brain signals, it is involved in the production and release of neurotransmitters[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. As a part of neurotransmitter receptors, cholesterol regulates the function and density of these receptors, thereby influencing neurotransmission[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Additionally, cholesterol serves as a precursor for steroid hormones, such as cortisol and sex hormones, which play significant roles in mood regulation[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study revealed a strong relationship between the NPS and the prevalence and severity of depression as well as the mortality rate among depressed individuals. The NPS is a thorough instrument that accurately captures the intricate relationship between immunological response, inflammation, and malnutrition, especially in people with depression. Our results are in line with earlier studies, underscoring the crucial roles that systemic inflammation and nutritional status play in the prognosis of depression.\u003c/p\u003e \u003cp\u003eIn order to help identify high-risk depressed groups early and enable individualized treatment of depressed individuals, the NPS can be a useful tool for risk stratification and prognosis evaluation. Comprehensive interventions targeting inflammation levels and nutritional status in depression can significantly improve their prognosis. Additionally, community-based screening programs can utilize NPS for initial risk stratification, providing a scientific foundation for resource allocation and the prioritization of health interventions.\u003c/p\u003e \u003cp\u003eHowever, for the various causes of death in patients with depression, the AUC for all-cause mortality is 0.634, the AUC for heart disease mortality is 0.628, the AUC for malignant neoplasms mortality is 0.645, the AUC for diabetes mortality is 0.675, and the AUC for hypertension mortality is 0.642. These results indicate that the NPS model has moderate predictive ability. While this result is promising, it also reflects that the NPS still has limitations in accurately distinguishing between individuals at risk and those not at risk. Although the NPS can provide an initial reference for risk assessment, its clinical application requires further validation and optimization to improve its predictive accuracy and broader applicability. In clinical terms, this moderate AUC value underscores the necessity of complementing the NPS with additional biomarkers or clinical assessments to improve its predictive accuracy. However, despite its moderate AUC, the NPS can still serve a role in early risk stratification, especially when combined with other screening tools. The clinical utility of the NPS in primary prevention strategies lies in its potential to prioritize further diagnostic testing for high-risk individuals, thereby optimizing resource allocation. The NPS performance could also be enhanced through future validation studies, which could explore the incorporation of more diverse datasets and consider longitudinal outcomes to refine the model's clinical applicability.\u003c/p\u003e \u003cp\u003eAlthough this study has certain strengths, it also has some limitations that need further discussion and acknowledgment. Firstly, NHANES data primarily hinges upon self-reports from participants, which may result in recall bias and affect the accuracy of the data. While we employed robust statistical techniques to address missingness, we acknowledge that some degree of bias may still exist, particularly if certain variables were missing not at random. T Future research may consider about improved study designs or include external validation datasets to further reinforce the validity of our findings. Secondly, although the study has controlled for known potential confounders such as age, gender, and smoking status, there may still be unmeasured or unknown confounding factors. Furthermore, the study data is primarily based on the U.S. population, which may limit its external validity, particularly in economically underdeveloped countries or other cultural contexts. To evaluate the reliability and generalizability of the NPS across other demographic groups and healthcare systems, future research could try to reproduce our findings in a wider range of populations. Last but not the least, the cross-sectional design of NHANES limits the ability to establish causality between NPS and depression outcomes. Cross-sectional studies capture data at a single point in time, which means they cannot reveal the temporal causality between variables. Therefore, although our study shows significant associations between NPS and depression prevalence and mortality, we cannot establish whether these associations are causal. Future prospective or longitudinal studies should be carried out to gain a better understanding of the role of NPS in predicting depression risk. In conclusion, the limitations of this study suggest that future research should focus on incorporating diverse data sources, better controlling for potential confounders, and using longitudinal designs or randomized controlled trials to verify both correlations and causations. These steps will help improve the scientific validity and applicability of research conclusions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study establishes the significant association between the NPS and depression, revealing that higher NPS scores correlate with a greater prevalence of depression and increased mortality risk among depressed individuals. Notably, depressed individuals with NPS scores of 3 or 4 face markedly higher risks of death from multiple causes, including heart disease, cancer, and diabetes. The NPS, by reflecting systemic inflammation and nutritional status, serves as a promising tool for stratifying risk in patients with depression. Our findings underscore the potential of NPS as an early screening tool, guiding clinicians to implement more targeted interventions that could enhance the management and prognosis of individuals suffering from depression. However, further validation and optimization of NPS, alongside additional biomarkers, will be required to improve its predictive accuracy and broader clinical applicability.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNaples Prognostic Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePHQ-9\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePatient Health Questionnaire-9\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elymphocyte-to-monocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL-6\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterleukin-6\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNF-α\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor necrosis factor-alpha\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCenters for Disease Control and Prevention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMECs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emobile examination centers\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Center for Health Statistics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Death Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinked Mortality File\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epoverty income ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epositive predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enegative predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epost-stroke depression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent and study procedures were approved by the National Center for Health Statistics Research Ethics Review Board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. These data can be found on the NHANES website (https://www.cdc.gov/nchs/nhanes/index.htm).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflicts of interest with respect to the research, authorship, and/or publication of this article.\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’ contributions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJin Zhao and Shiping Liu designed the experiments, and Jin Zhao, Xingfu Fan, Yang Luo, and Xiaofang Li collected and analyzed the data. Jin Zhao drafted the manuscript. Jin Zhao and Shiping Liu revised the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all participants in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: not applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: not applicable.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZun LS. Chapter 101 - Mood Disorders. Mood Disord.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark LT, Zarate CA. Depression in the Primary Care Setting. N Engl J Med. 2019;380:559\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerrman H, Kieling C, McGorry P, Horton R, Sargent J, Patel V. Reducing the global burden of depression: a Lancet-World Psychiatric Association Commission. 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Lipids Health Dis. 2024;23:250.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiacco S, Walther A, Ehlert U. Steroid secretion in healthy aging. Psychoneuroendocrinology. 2019;105:64\u0026ndash;78.\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, inflammation, nutrition, mortality, NHAENS","lastPublishedDoi":"10.21203/rs.3.rs-6309429/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6309429/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDepressive disorder is a widespread mental health condition, distinguished by symptoms such as persistent low mood, loss of interest, diminished energy, and changes in sleep and appetite. The Naples Prognostic Score (NPS), which combines biomarkers related to inflammation and nutritional status, has been shown to have prognostic value in several diseases. This study used data from the National Health and Nutrition Examination Survey (NHANES), which was carried out between 2007 and 2018, to examine the link between NPS, depression prevalence, and mortality in people with depression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe cross-sectional analysis involved 29,655 participants, with 2,688 individuals diagnosed with depression, and 2,190 participants followed for mortality outcomes. The Patient Health Questionnaire-9 (PHQ-9) was used to measure depression, and blood albumin, total cholesterol, the neutrophil-to-lymphocyte ratio (NLR), and the lymphocyte-to-monocyte ratio (LMR) were used to calculate NPS. The relationship between NPS and depression was examined using weighted logistic regression, while the relationship between NPS and mortality in depressed patients was evaluated using Cox proportional hazards models, which controlled for clinical and demographic variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAn increased likelihood of depression (OR\u0026thinsp;=\u0026thinsp;1.32, 95% CI: 1.09\u0026ndash;1.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and a higher risk of death from all causes (HR\u0026thinsp;=\u0026thinsp;4.54, 95% CI: 2.24\u0026ndash;9.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), heart disease (HR\u0026thinsp;=\u0026thinsp;8.39, 95% CI: 2.85\u0026ndash;24.71, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), malignant neoplasms (HR\u0026thinsp;=\u0026thinsp;5.10, 95% CI: 1.21\u0026ndash;21.41, p\u0026thinsp;=\u0026thinsp;0.03), diabetes (HR\u0026thinsp;=\u0026thinsp;5.66, 95% CI: 1.44\u0026ndash;22.24, p\u0026thinsp;=\u0026thinsp;0.01), and hypertension (HR\u0026thinsp;=\u0026thinsp;6.68, 95% CI: 1.69\u0026ndash;26.40, p\u0026thinsp;=\u0026thinsp;0.01) were all significantly correlated with higher NPS scores.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study highlights the clinical relevance of the NPS in predicting both the prevalence of depression and its associated mortality risk. The NPS offers a valuable tool for early risk stratification and can support the development of personalized management strategies for individuals with depression, potentially improving their long-term health outcomes.\u003c/p\u003e","manuscriptTitle":"The Prognostic Value of the Naples Prognostic Score in Depression: Association with Prevalence and Mortality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 10:47:52","doi":"10.21203/rs.3.rs-6309429/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":"322b78eb-8c48-400e-a118-9bce5041657f","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-06T05:54:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 10:47:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6309429","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6309429","identity":"rs-6309429","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}