Diagnostic efficacy of iron metabolism-related indicators for depression in patients undergoing peritoneal dialysis

Diagnostic efficacy of iron metabolism-related indicators for depression in patients undergoing peritoneal dialysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Diagnostic efficacy of iron metabolism-related indicators for depression in patients undergoing peritoneal dialysis Chenling Liu, Jingyi Zhu, Yunfei Wang, Zhifeng Wei, Jinxiu Cheng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4529129/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 We aimed to analyse the correlation between depression and iron metabolism-related indicators and determine the efficacy of iron metabolism-related indicators in diagnosing depression in patients undergoing peritoneal dialysis (PD). This cross-sectional study included patients undergoing regular follow-up for PD between September 2022 and March 2023. Patient demographics and iron metabolism-related indicators, including serum iron (SI) and ferritin levels and transferrin saturation (TSAT), were collected and analysed. The depression status was assessed using the Patient Health Questionnaire-9. The correlation between iron metabolism-related indicators and concomitant depression was assessed using Spearman’s correlation coefficient. Binary logistic regression analysis was performed to identify independent concomitant depression risk factors. The relevant risk factors’ diagnostic efficacies were assessed using receiver operating characteristic (ROC) curve analysis. Of the 223 patients (121 [54.3%] males and 102 [45.7%] females), 88 (39.5%) had concomitant depression. SI levels (correlation coefficient [r]=-0.741, p < 0.05) and TSATs (r=-0.637, p < 0.05) were significantly correlated with depression and were identified as independent risk factors (odds ratio, 95% confidence interval [CI]: SI 0.434; 0.343–0.549; TSAT 0.782; 0.731–0.837). The ROC curve analysis revealed that SI levels and TSATs were good depression predictors (area under the curve, 95% CI: SI 0.938, 0.905–0.971; TSAT 0.876, 0.831–0.921). SI levels and TSATs were independent risk factors with high diagnostic efficacy for concomitant depression in patients undergoing PD. Thus, these patients’ psychological well-being should be simultaneously monitored. Biological sciences/Psychology Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors peritoneal dialysis depression serum iron transferrin saturation regression analysis receiver operating characteristic curve Figures Figure 1 Figure 2 1. INTRODUCTION Recently, the global prevalence of chronic kidney disease (CKD) has been on an annual increasing trend [ 1 ]. Patients with CKD experiencing a progressive decline of renal function into end-stage renal disease (ESRD) frequently require renal replacement therapy. Common forms of renal replacement therapy currently include hemodialysis, peritoneal dialysis (PD), and renal transplantation. Globally, PD has been widely adopted due to the convenience and simplicity thereof, minimal dependence on hospitals, protection of renal function, and an inconsequential effect on hemodynamics [ 2 , 3 ]. The prevalence of patients with ESRD undergoing PD in China has increased approximately 20-fold over the past 20 years [ 4 ]. PD has been shown to improve the quality of life of patients and prolong their life expectancies [ 5 , 6 ]. However, lifelong treatment and the risk of various complications are associated with major mental and economic burdens, which are highly probable factors contributing to the development of depression in these patients. Depression is associated with a significantly increased risk of mortality in patients with CKD. In particular, depression is an independent risk factor for mortality in patients on dialysis [ 7 , 8 ]. Previous studies have led to a consensus regarding the aetiology of depression correlating with diminished function of monoaminergic neurotransmitters in the brain, including 5-hydroxytryptamine (5-HT), norepinephrine, and dopamine [ 9 ]. Mlyniec et al. found a clear correlation between essential elements, such as zinc, magnesium, lithium, iron, calcium, and chromium, as well as the development of depression and anxiety [ 10 ]. Iron is an essential nutrient, a cofactor of many proteins in the body, and a major element required for normal neural development [ 11 , 12 ]. Several animal studies have shown that iron is fundamental for normal neurogenesis and the differentiation of certain brain cells and regions. Iron deficiency results in reduced dendritic arborisation of the hippocampus and striatum, thus reducing the number and complexity of connections between the neurons. Moreover, iron deficiency alters monoamine synthesis and catabolism, decreases serotonin transport protein levels, causes changes in dopamine metabolism and myelin formation, as well as decreases serotonin in the murine brain [ 13 – 16 ]. Thus, abnormalities in iron metabolism-related indicators are correlated with the development of mood and cognitive disorders [ 17 ]. In addition, the development of depression is significantly correlated with iron deficiency [ 18 ]. The accumulation of uremic toxins in patients with CKD secondary to abnormal hepcidin function and treatments for anaemia induces a chronic inflammatory state, resulting in the anomalies of many iron metabolism-related indicators to varying degrees. Therefore, it is particularly important to improve the early recognition of comorbid depressive states in patients with PD. Consequently, in the present study, we investigated the correlation between depression and serum iron (SI) metabolism-related indicators in patients undergoing PD, aiming to identify reliable clinical test parameters for the diagnosis of depression in this population. 2. METHODS 2.1 Ethical considerations This study was conducted in accordance with the principles of the World Medical Association’s Declaration of Helsinki and approved by the Institutional Review Board (IRB) of the First Affiliated Hospital of Hebei North University (IRB approval no: W20240003). Informed consent was obtained from all participants. 2.2. Study Design In this cross-sectional observational study, 223 patients undergoing PD at the Peritoneal Dialysis Center of the First Affiliated Hospital of Hebei North University between September 2022 and March 2023 were included in the analysis. The inclusion criteria were as follows: (1) age ≥ 18 years; (2) undergoing regular PD for a duration of > 6 months; (3) stable disease for 2 months and no recent negative emotional stimuli; and (4) treatment with conventional glucose-based, lactate-buffered dialysate and continuous ambulatory PD regimens. The exclusion criteria were as follows: (1) communication disorders limiting the comprehension of the questionnaire’s contents; (2) inability to express intentions accurately; (3) currently receiving psychotropic or sedative-hypnotic medications; (4) a previous personal or family history of psychiatric disorders; and (5) refusal to participate in the study. 2.3 Study flow and variables Demographic characteristics of the patients included sex, age, educational attainment, marital and employment statuses, comorbidities, the duration of PD, and the use of iron supplementations and medications for anaemia. Observational indicators included body mass index (BMI), blood urea nitrogen (BUN) levels, creatinine (Cr) levels, estimated glomerular filtration rate (eGFR), Cr clearance rate (Ccr), SI levels, serum ferritin (SF) levels, and transferrin saturation (TSAT). The depression status of the patients was assessed using the Patient Health Questionnaire (PHQ)-9 depression scale, administered by a trained member of our research group, using standardised instructions and explanations, with no other personnel present. The PHQ-9, an internationally recognised depression scale, is a reliable and valid indicator of the severity of depression [ 19 ]. Moreover, it has a high sensitivity and specificity of 88% each in the diagnosis of depression [ 20 ]. Patients were categorised into depression and non-depression cohorts based on their PHQ-9 scores. The correlations between the observational indicators and depression status were analysed. 2.4 Statistical analyses The Kolmogorov-Smirnov goodness-of-fit test was used to assess whether each test variable conformed to the normal distribution after grouping. The differences in the means of each variable between the depression and non-depression cohorts were analysed using the independent samples t-test for variables conforming to the normal distribution and the Mann-Whitney U-test for variables not conforming to the normal distribution. The Pearson’s chi-squared or Fisher exact test was used to compare count and proportion data between the two cohorts. Spearman’s correlation coefficient was used to assess the existence and strength of the correlations between depression and iron metabolism-related indicators. Binary logistic regression analysis was used to further determine whether the iron metabolism-related indicators were independent risk factors for the development of depression. The receiver operating characteristic (ROC) curve analysis was used to analyse the diagnostic efficacy of iron metabolism-related indicators for concomitant depression. Measurement data conforming to the normal distribution are expressed as means and standard deviations (means ± SDs). Measurement data not conforming to the normal distribution are expressed as medians and interquartile ranges (IQRs). Count data are expressed as n (%). The level of significance was α = 0.05, and differences with p < 0.05 were considered statistically significant. IBM SPSS Statistics 17.0 for Windows (SPSS Inc., Chicago, Illinois, USA) and GraphPad Prism ver. 9.0 (GraphPad Software, San Diego, USA) were used for all statistical analyses and data visualisation, respectively. 3. RESULTS 3.1 Demographic characteristics of patients Of the 223 patients undergoing regular PD included in the study, 121 (54.3%) were male, and 102 (45.7%) were female. Based on the depression status, the depression and non-depression cohorts comprised 88 (39.5%) and 135 (60.5%) patients, respectively. Table 1 depicts the demographic characteristics of each cohort. Table 1. Demographic characteristics of the study participants Variable Total Depression cohort Non-depression cohort t/Z/X 2 p Participants, n (%) 223 88 (39.5) 135 (60.5) Gender 5.789 0.016 Male, n (%) 121 (54.3) 39 (44.3) 82 (60.7) Female, n (%) 102 (45.7) 49 (55.7) 53 (39.3) Age (years) 54.87 ± 12.56 55.01 ± 1.25 54.78 ± 1.13 -0.122 0.901 Duration of PD (months) 36 (18，60) 36 (17，60) Educational attainment 7.043 0.071 Elementary school, n (%) 65 (29.1) 33 (37.5) 32 (23.7) Middle school, n (%) 83 (37.2) 32 (36.4) 51 (37.8) High school, n (%) 52 (23.3) 18 (20.5) 34 (25.2) University, n (%) 23 (10.3) 5 (5.7) 18 (13.3) Marital status 0.020 0.888 Married, n (%) 184 (82.5) 73 (83.0) 111 (82.2) Unmarried, n (%) 39 (17.5) 15 (17.0) 24 (17.8) Employment status 2.930 0.402 Unemployed, n (%) 16 (7.2) 9 (10.2) 7 (5.2) Farming, n (%) 123 (55.2) 50 (56.8) 73 (54.1) Labor, n (%) 50 (22.4) 18 (20.5) 32 (23.7) Retired, n (%) 34 (15.2) 11 (12.5) 23 (17.0) Comorbidities 4.124 0.227 1, n (%) 134 (60.1) 46 (52.3) 88 (65.2) 2, n (%) 81 (36.3) 38 (43.2) 43 (31.9) 3, n (%) 6 (2.7) 3 (3.4) 3 (2.2) 4, n (%) 2 (10.9) 1 (1.1) 1 (0.7) Iron supplementation use 0.984 0.321 Yes, n (%) 108 (48.4) 39 (44.3) 69 (51.1) No, n (%) 115 (51.5) 49 (55.7) 66 (48.9) Laboratory test parameters BMI 20.82 ± 2.04 20.30 (19.33，22.45) 20.10 (19.30, 22.30) BUN (mmol/L) 17.56 ± 5.49 16.53 ± 0.52 18.23 ± 0.49 -1.935 0.053 Cr (µmmol/L) 849.92 ± 391.91 853.23 ± 50.62 847.77 ± 28.29 -0.737 0.461 Ccr (mL/min) 7.99 ± 3.79 7.82 ± 0.39 8.11 ± 0.33 -0.665 0.506 eGFR (mL/min) 8.61 ± 4.37 8.40 ± 0.46 8.74 ± 0.38 -0.244 0.807 SI (umol/L) 10.39 ± 5.13 6.44 ± 0.19 12.97 ± 0.43 -11.05 0.000 SF (ng/mL) 74.8 (40.2，132.4) 117.17 ± 15.19 100.73 ± 7.05 -0.636 0.525 TSAT (%) 22.05 ± 11.76 13.87 ± 0.57 27.38 ± 1.01 -9.492 0.000 Abbreviations: Quantitative data are expressed as the mean ± SD if normally distributed or as the median ( interquartile range ) if non-normally distributed. BMI= body mass index; BUN= blood urea nitrogen; Cr= creatinine; Ccr= creatinine clearance; eGFR= estimated glomerular filtration rate; SI:= serum iron;SF= serum ferritin; SI= serum iron; TSAT= transferrin saturation. Comorbidities: hypertension, diabetes, cardiovascular disease, or cerebrovascular disease; 1= any of the afore-mentioned; 2= any of the two afore-mentioned; 3= any of the three afore-mentioned; 4= all of the four afore-mentioned. * Statistical significance was set at p < 0.05. The Kolmogorov-Smirnov goodness-of-fit test revealed that age, the duration of PD, SI levels, SF levels, TSATs, BUN levels, Cr levels, eGFRs, Ccr, and BMIs did not conform to the normal distribution after the formation of cohorts of patients by the depression status. Thereafter, the Mann-Whitney U test was used to compare these variables to determine whether the differences between the two cohorts were statistically significant. The results indicated that the differences in the SI levels (Z = -11.047, p < 0.05) and TSATs (Z = -9.429, p < 0.05) between the depression and non-depression cohorts were statistically significant. The mean SI and TSAT values between the two cohorts were compared (Figure 1). Figure 1. Comparative analysis of the mean serum iron (SI) and transferrin saturation (TSAT) values between the depression and non-depression cohorts. Abbreviations: TSAT= transferrin saturation，SI= serum iron; Statistical significance was set at p < 0.05. The comparison between the counts was performed using the chi-squared test after values were assigned to the variables in each cohort, and the statistical results showed that the theoretical frequencies of the observed variables, such as sex, educational attainment, marital and employment statuses, as well as iron supplementation use, were > 5. Therefore, Pearson's chi-squared test was used to compare the proportional differences in the variables between the two cohorts. After values were assigned to the comorbidities, the theoretical frequencies of 50% of the combinations were < 5. Thus, Fisher's exact test was used to compare the proportional differences in the comorbidities between the two cohorts. Tables 2 and 3 present the detailed results and value assignments, respectively. Table 2. Spearman correlation analyses for each variable are depicted PHQ-9 depression score Age SI SF TSAT Ccr eGFR BUN Cr Duration of PD (months) BMI PHQ-9 depression score 1.000 Age 0.008 1.000 SI -0.741 ** -0.016 1.000 SF -0.043 0.066 0.169 * 1.000 TSAT -0.637 ** 0.023 0.887 ** 0.326 ** 1.000 Ccr -0.045 0.068 0.010 -0.038 -0.011 1.000 eGFR -0.016 0.095 -0.012 -0.051 -0.034 0.933 ** 1.000 BUN -0.130 -0.129 0.123 0.118 0.141 * -0.276 ** -0.359 ** 1.000 Cr -0.049 -0.230 ** 0.073 0.042 0.077 -0.735 ** -0.794 ** 0.529 ** 1.000 Duration of PD (months) 0.040 -0.096 -0.056 0.009 -0.096 -0.311 ** -0.299 ** -0.014 0.194 ** 1.000 BMI 0.007 -0.087 -0.035 -0.019 0.032 -0.171 * -0.202 ** 0.136 * 0.338 ** 0.001 1.000 Abbreviations: BMI= body mass index; BUN= blood urea nitrogen; Ccr= creatinine clearance Cr= creatinine; eGFR= estimated glomerular filtration rate; PD= peritoneal dialysis; PHQ-9= Patient Health Questionnaire-9; SF= serum ferritin; SI= serum iron; TSAT= transferrin saturation. Each value in the table is a correlation coefficient (R S between the continuous variable and concomitant depression in study participants on PD. Test efficiency P<0.05, * means P<0.05, ** means P<0.01. Table 3. Chi-square test results of counting data grouped by depressive state Category χ 2 p Contingency coefficient Gender 5.789 0.016 0.159 Educational attainment 7.043 0.071 0.175 Marital status 0.020 0.888 0.009 Employment status 2.930 0.402 0.114 Comorbidities 4.124 0.227 0.128 Iron supplementation use 0.984 0.321 0.066 Statistical significance was set at p < 0.05. 3.2 Correlation analysis Spearman correlation coefficient was used for correlation analysis of continuous measurement data. The correlation between observations and depressive status was assessed using the chi-squared test of column associations for continuous data. The findings revealed that SI levels (correlation coefficient = -0.741, p < 0.05) and TSATs (correlation coefficient = -0.637, p < 0.05) were statistically significantly correlated with depression. Contingency coefficient analysis showed that the correlation with sex was statistically significant (p = 0.016). Further analysis yielded a contingency coefficient of 0.159, thus making sex a negligible factor. The results of Spearman's correlation coefficient are shown in Table 2. The results of the chi-squared test and column contact are shown in Table 3. The chi-squared test assignment is shown in Table 4. Table 4. Count data assignment table Observational indicator Value assignment Gender 1= male 2= female Educational attainment 1= elementary school 2= middle school 3= high school 4= university and above Marital status 1= unmarried 2= married Employment status 1= unemployed 2= farming 3= labor 4= retired Comorbidities (hypertension, diabetes, cardiovascular disease, or cerebrovascular disease) 1= any of the aside-mentioned 2= any two of the aside-mentioned 3= any three of the aside-mentioned 4= all four of the aside-mentioned Iron supplementation use 1= yes 2= no 3.3 Binary logistic regression First, univariate logistic regression was used to analyse each observational indicator individually and to determine whether it was an independent risk factor for concomitant depression (Table 5). Table 5. Screening of variables by univariate logistic regression analysis is shown Observational indicator β Wald p OR 95% CI of OR SI -0.841 50.223 0.000 0.431 0.342–0.544 SF 0.001 1.153 0.283 1.001 0.999–1.004 TSAT -0.223 53.065 0.000 0.800 0.754–0.850 Cr -0.020 0.292 0.589 0.980 0.910–1.055 eGFR -0.19 0.332 0.564 0.981 0.921–1.046 BUN -0.059 5.017 0.025 0.942 0.894–0.993 BMI 0.028 0.175 0.676 1.028 0.902–1.172 Duration of PD 0.002 0.173 0.677 1.002 0.993–1.011 Abbreviations: β= beta coefficient; BMI= body mass index; BUN= blood urea nitrogen; Ccr= creatinine clearance; CI= confidence interval; Cr= creatinine; eGFR= estimated glomerular filtration rate; OR= Odds ratio; PD= peritoneal dialysis; SF= serum ferritin; SI= serum iron; TSAT= transferrin saturation; Wald= The results of the Wald test of SPSS. Statistical significance was set at p < 0.05. Due to the significant collinearity between SI levels and TSATs, these two variables were excluded. A multivariate binary logistic regression analysis of the clinical factors influencing concomitant depression in patients undergoing PD was performed after the control for age, the duration of PD, BMI, Cr level, eGFR, BUN level, Ccr, and SF level. The Hosmer–Lemeshow chi-squared statistic of the model constructed after the removal of SI levels was 7.356 (p = 0.499), revealing that the model had a good fit. The overall predictive efficacy of the model was 80.7%, and the predictive efficacy for concomitant depression in patients undergoing PD was 77.3%. TSATs (odds ratio [OR] = 0.782; 95% confidence interval [CI]: 0.731–0.837) were found to be an independent risk factor. The results of the analyses are detailed in Table 6. In the multivariate binary logistic regression analysis after the removal of TSATs, the Hosmer–Lemeshow chi-squared statistic of the model was 10.131 (p = 0.256), revealing that the model had a good fit. The overall predictive efficacy of the model was 87.4%, and the predictive efficacy for concomitant depression in patients undergoing PD was 85.2%. SI levels (OR = 0.434; 95% CI: 0.343–0.549) were found to be an independent risk factor. The results of the analyses are detailed in Table 7. The results of the binary logistic regression analysis showed that SI levels and TSATs were independent risk factors for concomitant depression in patients undergoing PD after the control for variables, including age, the duration of PD, BMI, Cr levels, eEGFR, BUN levels, Ccr, and SF levels. Table 6. Multivariate logistic regression analysis is depicted with the serum iron removed Parameter β Standard error Wald P OR 95% CI of OR Lower limit Upper limit Age 0.007 0.016 0.178 0.673 1.007 0.976 1.039 SF 0.006 0.002 7.617 0.006 1.006 1.002 1.011 TSAT -0.246 0.034 51.313 0.000 0.782 0.731 0.837 Ccr 0.023 0.162 0.020 0.887 1.023 0.746 1.404 eGFR -0.085 0.142 0.360 0.549 0.919 0.696 1.213 BUN -0.079 0.042 3.567 0.059 0.924 0.852 1.003 Cr 0.000 0.001 0.007 0.931 1.000 0.998 1.002 Duration of PD (months) -0.006 0.007 0.857 0.355 0.994 0.981 1.007 BMI 0.061 0.095 0.416 0.519 1.063 0.883 1.280 Constant 4.101 2.459 2.781 0.095 60.417 Abbreviations: β= beta coefficient; OR= Odds ratio; BMI= body mass index; BUN= blood urea nitrogen; Ccr= creatinine clearance; CI= confidence interval; Cr= creatinine; eGFR= estimated glomerular filtration rate; PD= peritoneal dialysis; SF= serum ferritin; TSAT= transferrin saturation; Wald= The results of the Wald test of SPSS. Statistical significance was set at p < 0.05.s Table 7. Multivariate logistic regression analysis is depicted with the transferrin saturation removed β Standard error Wald P OR 95% CI of OR Lower limit Upper limit Age 0.003 0.018 0.036 0.849 1.003 0.969 1.039 SF 0.002 0.002 0.935 0.334 1.002 0.998 1.007 Ccr -0.008 0.193 0.002 0.965 0.992 0.679 1.448 eGFR -0.030 0.169 0.031 0.860 0.971 0.698 1.351 BUN -0.079 0.046 3.028 0.082 0.924 0.845 1.010 Cr 0.001 0.001 0.235 0.628 1.001 0.998 1.003 Duration of PD (months) -0.002 0.009 0.045 0.833 0.998 0.981 1.015 BMI -0.036 0.105 0.121 0.728 0.964 0.786 1.184 SI -0.835 0.120 48.026 0.000 0.434 0.343 0.549 Constant 8.594 2.853 9.071 0.003 5396.637 Abbreviations: β= beta coefficient; BMI= body mass index; BUN= blood urea nitrogen; Ccr= creatinine clearance; CI= confidence interval; Cr= creatinine; eGFR= estimated glomerular filtration rate; PD= peritoneal dialysis; SF= serum ferritin; SI= serum iron; Wald= The results of the Wald test of SPSS. Statistical significance was set at p < 0.05. 3.4 Diagnostic performance The diagnostic efficacy of SI levels for concomitant depression in patients undergoing PD was determined using ROC curve analyses. The results revealed that the SI levels (area under the curve = 0.938, 95% CI: 0.905–0.971; cutoff value = 8.95; sensitivity = 84.4%; and specificity = 97.7%) and TSATs (area under the curve = 0.876, 95% CI: 0.831–0.921; sensitivity = 79.3%; specificity = 85.2%) were good predictors of depression (Figure 2). 4. DISCUSSION In the present study, we found that the prevalence of depression among patients undergoing regular PD at the Peritoneal Dialysis Center of our hospital was 39.5%. The results of correlation analyses revealed that depression in patients undergoing PD was statistically significantly negatively correlated with SI levels and TSATs. Finally, the ROC curve analysis demonstrated that SI levels and TSATs had high diagnostic efficacy for concomitant depression in patients undergoing PD. A study by Okan et al. on the correlation of anxiety and depression with metabolites in patients with fibromyalgia revealed higher incidence rates of depression and anxiety in patients with iron deficiency [21]. A German study concluded that iron deficiency was significantly correlated with depression in patients with type 1 diabetes mellitus (p = 0.043 and p = 0.049 for SI and SF levels, respectively) [18]. A German cross-sectional study involving phenotypically healthy individuals analysed the correlation of iron metabolism-related indicators with depression and found a significant correlation between SI levels and depression, which was more pronounced in men [22]. Findings of a study by Oliveira et al. on the correlation of depressive symptoms in patients with multiple sclerosis revealed that lower iron levels were associated with the presence of depressive symptoms [23], consistent with the results of the present study. Based on the underlying molecular mechanisms, the probable aetiology of iron deficiency in patients with concomitant depression is that iron ions serve as cofactors for key enzymes in the tryptophan metabolic pathway. Moreover, iron deficiency inhibits the activity of these enzymes and blocks the production of 5-HT. In particular, 5-HT is synthesised from tryptophan via the methoxyindole and kynurenine metabolic pathways and is believed to play a major role in the treatment of depression and sleep disorders. Indoleamine and tryptophan 2,3-dioxygenases are the rate-limiting enzymes in these metabolic pathways [24], and iron is a key cofactor for both [25]. Thus, insufficient free iron results in decreased 5-HT levels in the body, which in turn can cause depression [9]. These studies provide a theory for the mechanism of depression induced by reduced SI levels from a molecular perspective. No correlations were observed between SF, Cr, and BUN levels, eGFRs, Ccrs, the duration of PD, or sex and depression in our patients undergoing PD. However, a study from Spain regarding women without a previous history of depression assessed the correlation between depressive symptoms and iron metabolism-related indicators at 48 hours, as well as 8 and 32 weeks postpartum. Findings thereof demonstrated a strong correlation between SF levels and postpartum depression (OR = 3.73, 95% CI: 1.84–7.56, p = 0.0001, SF cutoff value = 7.26 μg/L), and SF levels were found to have a high specificity and sensitivity for predicting postpartum depression [26]. Most previous studies have shown a correlation between depression and SF levels; however, no such correlation was observed in the present study. Two potential causes may lead to differences in the results. First, differences in the study populations may exist. In the previous Spanish study, the maternal and fetal metabolic demands for iron evidently increased during pregnancy. Thus, the compensatory mechanisms of the body mobilised the iron reserves and replenished SF subsequent to SI deficiency to maintain normal SI levels in the body. Second, in our study, the metabolic status of patients undergoing PD, as well as the use of iron supplementations and other medications to treat anaemia, may have contributed to these differences. The strengths of the present study include its single-centre, cross-sectional design and relatively large sample size. Statistical analyses demonstrated a significant correlation between SI levels and TSATs with concomitant depression in patients undergoing PD. Furthermore, SI levels and TSATs were found to have high diagnostic efficacy for concomitant depression in patients undergoing PD, which enables early identification of depression by nephrologists. However, the study had the following limitations. Although statistical analyses revealed significant correlations between SI levels and TSATs with concomitant depression in patients undergoing PD, the physiological basis of SI levels and TSATs in patients with depression has not been fully researched and demonstrated. Thus, future prospective clinical trials are required to confirm these conclusions. The prevalence of patients undergoing PD with depression at our Peritoneal Dialysis Center was 39.5%, which highlights the potential influence of psychological factors on the quality of life and disease outcomes. Furthermore, the psychological well-being of patients should be simultaneously monitored with their physical health. Regarding the correlation between concomitant depression and iron metabolism-related indicators in patients undergoing PD, the results of correlation analysis revealed that there were statistically significant differences in SI and TSAT values between the depression and non-depression cohorts. Furthermore, SI levels and TSATs had high diagnostic efficacy for concomitant depression in these patients. Consequently, these laboratory indicators can provide objective reference values for the diagnosis of concomitant depression, enable early identification of depression by nephrologists, and provide a new theoretical basis for the importance of iron supplementation in patients undergoing PD. In conclusion, SI levels and TSATs were found to be independent risk factors with high diagnostic efficacy for concomitant depression in patients undergoing PD. Abbreviations Declarations Acknowledgements: Chenling Liuwrote the manuscript. Shengjun Liu reviewed and edited the manuscript. Author contributions: Chenling Liu,the first author, participated in article writing, data collection and data analysis. Jingyi Zhu,the second author, participated in data collection and analysis. Yunfei Wang, the third author, participated in data collection and analysis. Zhifeng Wei, the fourth author, participated in the data collection Jinxiu Cheng, the fifth author, participated in the data collection. Xin Jin, the sixth author, participated in data collection. Shengjun Liu, the corresponding author, participated in article topic selection, article revision, data analysis, etc. Data availability statement: The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests: There is no conflict of interest among all authors of this article. 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Adhami, V. M., Husain, R., Husain, R., & Seth P. K. Influence of iron deficiency and lead treatment on behavior and cerebellar and hippocampal polyamine levels in neonatal rats. Neurochem. Res. 21, 915–922 (1996). Bianco, L. E., Unger, E. L., Earley, C. J., & Beard, J. L. Iron deficiency alters the day-night variation in monoamine levels in mice. Chronobiol. Int. 26, 447–463 (2009). Beard, J. L., & Connor, J. R. Iron status and neural functioning. Annu. Rev. Nutr. 23, 41–58 (2003). Lomagno, K. A., et al. Increasing iron and zinc in pre-menopausal women and its effects on mood and cognition: a systematic review. Nutrients 6, 5117–5141 (2014). Levey, A. S., Bosch, J. P., Lewis, J. B., Greene, T., Rogers, N., & Roth, D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann. Intern. Med. 130, 461–470 (1999). Chen, W. B., Pan, X. L., Kang, X. X., & Wan, X. H. Seventh editor of diagnostics . 78–81 (Peking, People’s Medical Publishing House, 2008). Gilbody, S., Richards, D., Brealey, S., & Hewitt, C. Screening for depression in medical settings with the Patient Health Questionnaire (PHQ): a diagnostic meta-analysis[J]. J. Gen. Intern. Med. 22, 1596–1602 (2007). Kroenke, K., Spitzer, R. L,, & Williams, J. B. The PHQ-9: validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606–613 (2001). Bergis, D., Tessmer, L., & Badenhoop, K. Iron deficiency in long standing type 1 diabetes mellitus and its association with depression and impaired quality of life. Diabetes Res. Clin. Pract. 151, 74–81 (2019). Okan, S., Caglıyan Turk, A., Sıvgın, H., Ozsoy, F., & Okan F. Association of ferritin levels with depression, anxiety, sleep quality, and physical functioning in patients with fibromyalgia syndrome: a cross-sectional study. Croat. Med. J. 60, 515–520 (2019). Baune, B. T., Eckardstein, A., & Berger, K. Lack of association between iron metabolism and depressive mood in an elderly general population. Int. Psychogeriatr. 18, 437–444 (2006). Oliveira, S. R., et al. Insulin resistance, atherogenicity, and iron metabolism in multiple sclerosis with and without depression: Associations with inflammatory and oxidative stress biomarkers and uric acid. Psychiatry Res. 250, 113–120 (2017). Kałuźna-Czaplińska, J., Gatarek, P., Chirumbolo, S., Chartrand, M. S., & Bjørklund, G. How important is tryptophan in human health?. Crit. Rev. Food Sci. Nutr. 59, 72–88 (2019). Patrick, R. P., & Ames, B. N. Vitamin D and the omega-3 fatty acids control serotonin synthesis and action, part 2: relevance for ADHD, bipolar disorder, schizophrenia, and impulsive behavior. FASEB J. 29, 2207–2222 (2015). Albacar, G., et al. An association between plasma ferritin concentrations measured 48 h after delivery and postpartum depression. J. Affect. Disord. 131, 136–142 (2011). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4529129","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":316917975,"identity":"e930ac55-225a-412b-83d9-a1c966071123","order_by":0,"name":"Chenling Liu","email":"","orcid":"","institution":"Graduate School of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Chenling","middleName":"","lastName":"Liu","suffix":""},{"id":316917976,"identity":"eb3ad367-8b06-4843-ba44-83c6ba2664c8","order_by":1,"name":"Jingyi Zhu","email":"","orcid":"","institution":"Graduate School of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Jingyi","middleName":"","lastName":"Zhu","suffix":""},{"id":316917981,"identity":"aef5e457-fa0e-41ad-a8ea-dc57cd239412","order_by":2,"name":"Yunfei Wang","email":"","orcid":"","institution":"Fourth Hospital of Cangzhou City","correspondingAuthor":false,"prefix":"","firstName":"Yunfei","middleName":"","lastName":"Wang","suffix":""},{"id":316917982,"identity":"657a1c6f-e0c7-47b9-a29b-622b0f11b8f5","order_by":3,"name":"Zhifeng Wei","email":"","orcid":"","institution":"First Affiliated Hospital of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Zhifeng","middleName":"","lastName":"Wei","suffix":""},{"id":316917987,"identity":"f0a31068-a1b1-43cc-b250-f18ade1c94dd","order_by":4,"name":"Jinxiu Cheng","email":"","orcid":"","institution":"First Affiliated Hospital of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Jinxiu","middleName":"","lastName":"Cheng","suffix":""},{"id":316917993,"identity":"05b3a3c8-5993-4045-9d5d-0eacd67813f0","order_by":5,"name":"Xin Jin","email":"","orcid":"","institution":"First Affiliated Hospital of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Jin","suffix":""},{"id":316917996,"identity":"f36199d6-a597-4cff-9b7f-3712e4351f09","order_by":6,"name":"Shengjun Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACNvnzjx8k/LCRs+9vP/ggoaKGsBY+CR42g489acYGEmeSDR6cOUZYi5wED4PkDLbDiQYMCWaSD1uYiXCYdO8BYx6ewwnmDAfSKhIb2Bj427sT8GuROZfwmMciPc+yufHYjcQdMgwSZ85uwK+FIcEAaIt1McOBA2k3Es+wMRhI5BLWIs3DxpzYcCDBrCCxjZkILRI5BkDvOyduAGphIE4Lz7E0cCBLzjiTLJFw5hgPQb/ItzcfBkclP3/7wY8/Kmrk+Nt78WvBADykKR8Fo2AUjIJRgBUAAPeETCm2TrdQAAAAAElFTkSuQmCC","orcid":"","institution":"First Affiliated Hospital of Hebei North University","correspondingAuthor":true,"prefix":"","firstName":"Shengjun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-06-04 15:21:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4529129/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4529129/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59137553,"identity":"5896064d-f394-4dab-8a3f-988447cd0bb6","added_by":"auto","created_at":"2024-06-26 19:00:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41904,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative analysis of the mean serum iron (SI) and transferrin saturation (TSAT) values between the depression and non-depression cohorts.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4529129/v1/2e3a6b018eedb80891117395.jpg"},{"id":59137554,"identity":"0149c6bb-1bd1-4112-9e48-df5e8d405d97","added_by":"auto","created_at":"2024-06-26 19:00:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic performance of the transferrin saturation (TSAT) and serum iron (SI)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4529129/v1/dc04b189d81066739f8fe059.jpg"},{"id":59743290,"identity":"f2d68e35-dd74-41ab-bd9e-5154948e3dbe","added_by":"auto","created_at":"2024-07-05 15:59:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":901009,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4529129/v1/1a7c66d3-743a-4a25-aacc-255e934df62e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic efficacy of iron metabolism-related indicators for depression in patients undergoing peritoneal dialysis","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eRecently, the global prevalence of chronic kidney disease (CKD) has been on an annual increasing trend [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Patients with CKD experiencing a progressive decline of renal function into end-stage renal disease (ESRD) frequently require renal replacement therapy. Common forms of renal replacement therapy currently include hemodialysis, peritoneal dialysis (PD), and renal transplantation. Globally, PD has been widely adopted due to the convenience and simplicity thereof, minimal dependence on hospitals, protection of renal function, and an inconsequential effect on hemodynamics [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The prevalence of patients with ESRD undergoing PD in China has increased approximately 20-fold over the past 20 years [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. PD has been shown to improve the quality of life of patients and prolong their life expectancies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, lifelong treatment and the risk of various complications are associated with major mental and economic burdens, which are highly probable factors contributing to the development of depression in these patients. Depression is associated with a significantly increased risk of mortality in patients with CKD. In particular, depression is an independent risk factor for mortality in patients on dialysis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have led to a consensus regarding the aetiology of depression correlating with diminished function of monoaminergic neurotransmitters in the brain, including 5-hydroxytryptamine (5-HT), norepinephrine, and dopamine [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Mlyniec et al. found a clear correlation between essential elements, such as zinc, magnesium, lithium, iron, calcium, and chromium, as well as the development of depression and anxiety [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Iron is an essential nutrient, a cofactor of many proteins in the body, and a major element required for normal neural development [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Several animal studies have shown that iron is fundamental for normal neurogenesis and the differentiation of certain brain cells and regions. Iron deficiency results in reduced dendritic arborisation of the hippocampus and striatum, thus reducing the number and complexity of connections between the neurons. Moreover, iron deficiency alters monoamine synthesis and catabolism, decreases serotonin transport protein levels, causes changes in dopamine metabolism and myelin formation, as well as decreases serotonin in the murine brain [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Thus, abnormalities in iron metabolism-related indicators are correlated with the development of mood and cognitive disorders [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In addition, the development of depression is significantly correlated with iron deficiency [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The accumulation of uremic toxins in patients with CKD secondary to abnormal hepcidin function and treatments for anaemia induces a chronic inflammatory state, resulting in the anomalies of many iron metabolism-related indicators to varying degrees.\u003c/p\u003e \u003cp\u003eTherefore, it is particularly important to improve the early recognition of comorbid depressive states in patients with PD. Consequently, in the present study, we investigated the correlation between depression and serum iron (SI) metabolism-related indicators in patients undergoing PD, aiming to identify reliable clinical test parameters for the diagnosis of depression in this population.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Ethical considerations\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the principles of the World Medical Association\u0026rsquo;s Declaration of Helsinki and approved by the Institutional Review Board (IRB) of the First Affiliated Hospital of Hebei North University (IRB approval no: W20240003). Informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study Design\u003c/h2\u003e \u003cp\u003eIn this cross-sectional observational study, 223 patients undergoing PD at the Peritoneal Dialysis Center of the First Affiliated Hospital of Hebei North University between September 2022 and March 2023 were included in the analysis. The inclusion criteria were as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) undergoing regular PD for a duration of \u0026gt;\u0026thinsp;6 months; (3) stable disease for 2 months and no recent negative emotional stimuli; and (4) treatment with conventional glucose-based, lactate-buffered dialysate and continuous ambulatory PD regimens.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were as follows: (1) communication disorders limiting the comprehension of the questionnaire\u0026rsquo;s contents; (2) inability to express intentions accurately; (3) currently receiving psychotropic or sedative-hypnotic medications; (4) a previous personal or family history of psychiatric disorders; and (5) refusal to participate in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Study flow and variables\u003c/h2\u003e \u003cp\u003eDemographic characteristics of the patients included sex, age, educational attainment, marital and employment statuses, comorbidities, the duration of PD, and the use of iron supplementations and medications for anaemia. Observational indicators included body mass index (BMI), blood urea nitrogen (BUN) levels, creatinine (Cr) levels, estimated glomerular filtration rate (eGFR), Cr clearance rate (Ccr), SI levels, serum ferritin (SF) levels, and transferrin saturation (TSAT). The depression status of the patients was assessed using the Patient Health Questionnaire (PHQ)-9 depression scale, administered by a trained member of our research group, using standardised instructions and explanations, with no other personnel present. The PHQ-9, an internationally recognised depression scale, is a reliable and valid indicator of the severity of depression [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, it has a high sensitivity and specificity of 88% each in the diagnosis of depression [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Patients were categorised into depression and non-depression cohorts based on their PHQ-9 scores. The correlations between the observational indicators and depression status were analysed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analyses\u003c/h2\u003e \u003cp\u003eThe Kolmogorov-Smirnov goodness-of-fit test was used to assess whether each test variable conformed to the normal distribution after grouping. The differences in the means of each variable between the depression and non-depression cohorts were analysed using the independent samples t-test for variables conforming to the normal distribution and the Mann-Whitney U-test for variables not conforming to the normal distribution. The Pearson\u0026rsquo;s chi-squared or Fisher exact test was used to compare count and proportion data between the two cohorts.\u003c/p\u003e \u003cp\u003eSpearman\u0026rsquo;s correlation coefficient was used to assess the existence and strength of the correlations between depression and iron metabolism-related indicators. Binary logistic regression analysis was used to further determine whether the iron metabolism-related indicators were independent risk factors for the development of depression. The receiver operating characteristic (ROC) curve analysis was used to analyse the diagnostic efficacy of iron metabolism-related indicators for concomitant depression. Measurement data conforming to the normal distribution are expressed as means and standard deviations (means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs). Measurement data not conforming to the normal distribution are expressed as medians and interquartile ranges (IQRs). Count data are expressed as n (%). The level of significance was α\u0026thinsp;=\u0026thinsp;0.05, and differences with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. IBM SPSS Statistics 17.0 for Windows (SPSS Inc., Chicago, Illinois, USA) and GraphPad Prism ver. 9.0 (GraphPad Software, San Diego, USA) were used for all statistical analyses and data visualisation, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e\u003cstrong\u003e3.1 Demographic characteristics of patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 223 patients undergoing regular PD included in the study, 121 (54.3%) were male, and 102 (45.7%) were female. Based on the depression status, the depression and non-depression cohorts comprised 88 (39.5%) and 135 (60.5%) patients, respectively. Table 1 depicts the demographic characteristics of each cohort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Demographic characteristics of the study participants\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"645\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003eDepression cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eNon-depression cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003et/Z/X\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eParticipants, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e88\u0026nbsp;(39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e135\u0026nbsp;(60.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e5.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eMale, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e121\u0026nbsp;(54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e39\u0026nbsp;(44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e82\u0026nbsp;(60.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eFemale, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e102\u0026nbsp;(45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e49\u0026nbsp;(55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e53\u0026nbsp;(39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e54.87 \u0026plusmn; 12.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e55.01 \u0026plusmn; 1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e54.78 \u0026plusmn; 1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eDuration of PD\u0026nbsp;(months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e36\u0026nbsp;(18，60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e36\u0026nbsp;(17，60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational attainment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e7.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eElementary school, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e65\u0026nbsp;(29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e33\u0026nbsp;(37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026nbsp;(23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle school, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e83\u0026nbsp;(37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026nbsp;(36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e51\u0026nbsp;(37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eHigh school, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e52\u0026nbsp;(23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e18\u0026nbsp;(20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e34\u0026nbsp;(25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e23\u0026nbsp;(10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e5\u0026nbsp;(5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e18\u0026nbsp;(13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eMarried, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e184\u0026nbsp;(82.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e73\u0026nbsp;(83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e111\u0026nbsp;(82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eUnmarried, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e39\u0026nbsp;(17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e15\u0026nbsp;(17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e24\u0026nbsp;(17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e2.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eUnemployed, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e16\u0026nbsp;(7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e9\u0026nbsp;(10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e7\u0026nbsp;(5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eFarming, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e123\u0026nbsp;(55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e50\u0026nbsp;(56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e73\u0026nbsp;(54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eLabor, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e50\u0026nbsp;(22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e18\u0026nbsp;(20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e32\u0026nbsp;(23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eRetired, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e34\u0026nbsp;(15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e11\u0026nbsp;(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e23\u0026nbsp;(17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e4.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e1, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e134\u0026nbsp;(60.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e46\u0026nbsp;(52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e88\u0026nbsp;(65.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e2, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e81\u0026nbsp;(36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e38\u0026nbsp;(43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e43\u0026nbsp;(31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e3, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e6\u0026nbsp;(2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026nbsp;(3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026nbsp;(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e4, n\u0026nbsp;(%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e2\u0026nbsp;(10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp;(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp;(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIron supplementation use\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eYes, n\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e108\u0026nbsp;(48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e39\u0026nbsp;(44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e69\u0026nbsp;(51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eNo, n\u0026nbsp;(%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e115\u0026nbsp;(51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e49\u0026nbsp;(55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e66\u0026nbsp;(48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory test parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e20.82 \u0026plusmn; 2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e20.30\u0026nbsp;(19.33，22.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e20.10\u0026nbsp;(19.30, 22.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eBUN\u0026nbsp;(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e17.56 \u0026plusmn; 5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e16.53 \u0026plusmn; 0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e18.23 \u0026plusmn; 0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e-1.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eCr\u0026nbsp;(\u0026micro;mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e849.92 \u0026plusmn; 391.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e853.23 \u0026plusmn; 50.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e847.77 \u0026plusmn; 28.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eCcr\u0026nbsp;(mL/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e7.99 \u0026plusmn; 3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e7.82 \u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e8.11 \u0026plusmn; 0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eeGFR\u0026nbsp;(mL/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e8.61 \u0026plusmn; 4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e8.40 \u0026plusmn; 0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e8.74 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eSI\u0026nbsp;(umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e10.39 \u0026plusmn; 5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e6.44 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e12.97 \u0026plusmn; 0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e-11.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eSF\u0026nbsp;(ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e74.8\u0026nbsp;(40.2，132.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e117.17 \u0026plusmn; 15.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e100.73 \u0026plusmn; 7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e-0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003eTSAT\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e22.05 \u0026plusmn; 11.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"top\"\u003e\n \u003cp\u003e13.87 \u0026plusmn; 0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.099378881987576%\" valign=\"top\"\u003e\n \u003cp\u003e27.38 \u0026plusmn; 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.900621118012422%\" valign=\"top\"\u003e\n \u003cp\u003e-9.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations:\u0026nbsp;\u003cem\u003eQuantitative data are expressed as the mean \u0026plusmn; SD if normally distributed or as the median\u0026nbsp;\u003c/em\u003e\u003cem\u003e(\u003c/em\u003e\u003cem\u003einterquartile range\u003c/em\u003e\u003cem\u003e)\u003c/em\u003e\u003cem\u003e\u0026nbsp;if non-normally distributed.\u003c/em\u003e BMI= body mass index; BUN= blood urea nitrogen; Cr= creatinine; Ccr= creatinine clearance; eGFR= estimated glomerular filtration rate; SI:=\u0026nbsp;serum iron;SF= serum ferritin; SI= serum iron; TSAT= transferrin saturation. Comorbidities: hypertension, diabetes, cardiovascular disease, or cerebrovascular disease; 1= any of the afore-mentioned; 2= any of the two afore-mentioned; 3= any of the three afore-mentioned; 4= all of the four afore-mentioned.\u003c/p\u003e\n\u003cp\u003e*\u0026nbsp;Statistical significance was set at p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eThe Kolmogorov-Smirnov goodness-of-fit test revealed that age, the duration of PD, SI levels, SF levels, TSATs, BUN levels, Cr levels, eGFRs, Ccr, and BMIs did not conform to the normal distribution after the formation of cohorts of patients by the depression status. Thereafter, the Mann-Whitney U test was used to compare these variables to determine whether the differences between the two cohorts were statistically significant. The results indicated that the differences in the SI levels (Z = -11.047, p \u0026lt; 0.05) and TSATs (Z = -9.429, p \u0026lt; 0.05) between the depression and non-depression cohorts were statistically significant. The mean SI and TSAT values between the two cohorts were compared (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1. Comparative analysis of the mean serum iron (SI) and transferrin saturation (TSAT) values between the depression and non-depression cohorts.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: TSAT= transferrin saturation，SI= serum iron;\u003cem\u003e\u0026nbsp;Statistical significance was set at p \u0026lt; 0.05.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe comparison between the counts was performed using the chi-squared test after values were assigned to the variables in each cohort, and the statistical results showed that the theoretical frequencies of the observed variables, such as sex, educational attainment, marital and employment statuses, as well as iron supplementation use, were \u0026gt; 5. Therefore, Pearson\u0026apos;s chi-squared test was used to compare the proportional differences in the variables between the two cohorts. After values were assigned to the comorbidities, the theoretical frequencies of 50% of the combinations were \u0026lt; 5. Thus, Fisher\u0026apos;s exact test was used to compare the proportional differences in the comorbidities between the two cohorts. Tables 2 and 3 present the detailed results and value assignments, respectively.\u003c/p\u003e\n\u003cp\u003eTable 2. Spearman correlation analyses for each variable are depicted\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"106%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.053763440860216%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.129032258064516%\" colspan=\"2\"\u003e\n \u003cp\u003ePHQ-9 depression score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003eSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003eSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003eTSAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003eCcr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.526881720430108%\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\"\u003e\n \u003cp\u003eDuration of PD (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.451612903225806%\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.21276595744681%\" colspan=\"2\"\u003e\n \u003cp\u003ePHQ-9 depression score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.21276595744681%\" colspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.21276595744681%\" colspan=\"2\"\u003e\n \u003cp\u003eSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e-0.741\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.21276595744681%\" colspan=\"2\"\u003e\n \u003cp\u003eSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.169\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.21276595744681%\" colspan=\"2\"\u003e\n \u003cp\u003eTSAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e-0.637\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.887\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.326\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.21276595744681%\" colspan=\"2\"\u003e\n \u003cp\u003eCcr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.21276595744681%\" colspan=\"2\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e0.933\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.21276595744681%\" colspan=\"2\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e-0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.141\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-0.276\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-0.359\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.21276595744681%\" colspan=\"2\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e-0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.230\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-0.735\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-0.794\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.529\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.21276595744681%\" colspan=\"2\"\u003e\n \u003cp\u003eDuration of PD (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-0.311\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-0.299\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.194\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.21276595744681%\" colspan=\"2\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-0.171\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e-0.202\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.136\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.338\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: BMI= body mass index; BUN= blood urea nitrogen; Ccr= creatinine clearance Cr= creatinine; eGFR= estimated glomerular filtration rate; PD= peritoneal dialysis; PHQ-9= Patient Health Questionnaire-9; SF= serum ferritin; SI= serum iron; TSAT= transferrin saturation.\u003c/p\u003e\n\u003cp\u003eEach value in the table is a correlation coefficient (R\u003csub\u003eS\u003c/sub\u003e between the continuous variable and concomitant depression in study participants on PD.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Test efficiency P\u0026lt;0.05, * means P\u0026lt;0.05, ** means P\u0026lt;0.01.\u003c/p\u003e\n\u003cp\u003eTable 3. Chi-square test results of counting data grouped by depressive state\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eContingency coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e5.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eEducational attainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e7.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eEmployment status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e2.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e4.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eIron supplementation use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eStatistical significance was set at p \u0026lt; 0.05.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Correlation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpearman correlation coefficient was used for correlation analysis of continuous measurement data. The correlation between observations and depressive status was assessed using the chi-squared test of column associations for continuous data. The findings revealed that SI levels (correlation coefficient = -0.741, p \u0026lt; 0.05) and TSATs (correlation coefficient = -0.637, p \u0026lt; 0.05) were statistically significantly correlated with depression. Contingency coefficient analysis showed that the correlation with sex was statistically significant (p = 0.016). Further analysis yielded a contingency coefficient of 0.159, thus making sex a negligible factor. The results of Spearman\u0026apos;s correlation coefficient are shown in Table 2. The results of the chi-squared test and column contact are shown in Table 3. The chi-squared test assignment is shown in Table 4.\u003c/p\u003e\n\u003cp\u003eTable 4. Count data assignment table\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"103%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eObservational indicator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue assignment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e1= male\u003c/p\u003e\n \u003cp\u003e2= female\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eEducational attainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e1= elementary school\u003c/p\u003e\n \u003cp\u003e2= middle school\u003c/p\u003e\n \u003cp\u003e3= high school\u003c/p\u003e\n \u003cp\u003e4= university and above\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e1= unmarried\u003c/p\u003e\n \u003cp\u003e2= married\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eEmployment status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e1= unemployed\u003c/p\u003e\n \u003cp\u003e2= farming\u003c/p\u003e\n \u003cp\u003e3= labor\u003c/p\u003e\n \u003cp\u003e4= retired\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003cp\u003e(hypertension, diabetes, cardiovascular disease, or cerebrovascular disease)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e1= any of the aside-mentioned\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2= any two of the aside-mentioned\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3= any three of the aside-mentioned\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4= all four of the aside-mentioned\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eIron supplementation use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e1= yes\u003c/p\u003e\n \u003cp\u003e2= no\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Binary logistic regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, univariate logistic regression was used to analyse each observational indicator individually and to determine whether it was an independent risk factor for concomitant depression (Table 5).\u003c/p\u003e\n\u003cp\u003eTable 5. Screening of variables by univariate logistic regression analysis is shown\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eObservational indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e95% CI of OR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e-0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e50.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.342\u0026ndash;0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e1.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.999\u0026ndash;1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eTSAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e-0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e53.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.754\u0026ndash;0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.910\u0026ndash;1.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.921\u0026ndash;1.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e-0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e5.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.894\u0026ndash;0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e1.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.902\u0026ndash;1.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.93121693121693%\" valign=\"top\"\u003e\n \u003cp\u003eDuration of PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.57848324514991%\" valign=\"top\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e0.993\u0026ndash;1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: \u0026beta;= beta coefficient; BMI= body mass index; BUN= blood urea nitrogen; Ccr= creatinine clearance; CI= confidence interval; Cr= creatinine; eGFR= estimated glomerular filtration rate; OR= Odds ratio; PD= peritoneal dialysis; SF= serum ferritin; SI= serum iron; TSAT= transferrin saturation; Wald= The results of the Wald test of SPSS. \u003cem\u003eStatistical significance was set at p \u0026lt; 0.05.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDue to the significant collinearity between SI levels and TSATs, these two variables were excluded. A multivariate binary logistic regression analysis of the clinical factors influencing concomitant depression in patients undergoing PD was performed after the control for age, the duration of PD, BMI, Cr level, eGFR, BUN level, Ccr, and SF level. The Hosmer\u0026ndash;Lemeshow chi-squared statistic of the model constructed after the removal of SI levels was 7.356 (p = 0.499), revealing that the model had a good fit. The overall predictive efficacy of the model was 80.7%, and the predictive efficacy for concomitant depression in patients undergoing PD was 77.3%. TSATs (odds ratio [OR] = 0.782; 95% confidence interval [CI]: 0.731\u0026ndash;0.837) were found to be an independent risk factor. The results of the analyses are detailed in Table 6. \u0026nbsp;In the multivariate binary logistic regression analysis after the removal of TSATs, the Hosmer\u0026ndash;Lemeshow chi-squared statistic of the model was 10.131 (p = 0.256), revealing that the model had a good fit. The overall predictive efficacy of the model was 87.4%, and the predictive efficacy for concomitant depression in patients undergoing PD was 85.2%. SI levels (OR = 0.434; 95% CI: 0.343\u0026ndash;0.549) were found to be an independent risk factor. The results of the analyses are detailed in Table 7. The results of the binary logistic regression analysis showed that SI levels and TSATs were independent risk factors for concomitant depression in patients undergoing PD after the control for variables, including age, the duration of PD, BMI, Cr levels, eEGFR, BUN levels, Ccr, and SF levels.\u003c/p\u003e\n\u003cp\u003eTable 6. Multivariate logistic regression analysis is depicted with the serum iron removed\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.05792163543441%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.38330494037479%\" colspan=\"2\"\u003e\n \u003cp\u003e95% CI of\u0026nbsp;OR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.32046332046332%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.355212355212355%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.32046332046332%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.463320463320464%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.776061776061775%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eLower limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.478764478764479%\"\u003e\n \u003cp\u003eUpper limit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.05792163543441%\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.606473594548552%\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.776831345826235%\"\u003e\n \u003cp\u003e1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003eSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e7.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.05792163543441%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.606473594548552%\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.776831345826235%\"\u003e\n \u003cp\u003e1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003eTSAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e-0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e51.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.05792163543441%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.606473594548552%\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.776831345826235%\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003eCcr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.05792163543441%\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp\u003e1.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.606473594548552%\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.776831345826235%\"\u003e\n \u003cp\u003e1.404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e-0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.05792163543441%\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.606473594548552%\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.776831345826235%\"\u003e\n \u003cp\u003e1.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e-0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e3.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.05792163543441%\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.606473594548552%\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.776831345826235%\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.05792163543441%\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.606473594548552%\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.776831345826235%\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003eDuration of PD (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.05792163543441%\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.606473594548552%\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.776831345826235%\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.05792163543441%\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp\u003e1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.606473594548552%\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.776831345826235%\"\u003e\n \u003cp\u003e1.280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e4.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.90289608177172%\"\u003e\n \u003cp\u003e2.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.754684838160136%\"\u003e\n \u003cp\u003e2.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.05792163543441%\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.391822827938672%\"\u003e\n \u003cp\u003e60.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.606473594548552%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.776831345826235%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: \u0026beta;=\u0026nbsp;beta coefficient;\u0026nbsp;OR= Odds ratio;\u0026nbsp;BMI= body mass index; BUN= blood urea nitrogen; Ccr= creatinine clearance; CI= confidence interval; Cr= creatinine; eGFR= estimated glomerular filtration rate; PD= peritoneal dialysis; SF= serum ferritin; TSAT= transferrin saturation; Wald= The results of the Wald test of SPSS.\u003cem\u003e\u0026nbsp;Statistical significance was set at p \u0026lt; 0.05.s\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 7. Multivariate logistic regression analysis is depicted with the transferrin saturation removed\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"559\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.0556552962298%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.6983842010772%\" colspan=\"2\"\u003e\n \u003cp\u003e95% CI of\u0026nbsp;OR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.34826883910387%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.441955193482688%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.441955193482688%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.441955193482688%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.441955193482688%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.441955193482688%\"\u003e\n \u003cp\u003eLower limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.441955193482688%\"\u003e\n \u003cp\u003eUpper limit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.0556552962298%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.0556552962298%\"\u003e\n \u003cp\u003eSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.0556552962298%\"\u003e\n \u003cp\u003eCcr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e1.448\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.0556552962298%\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e1.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.0556552962298%\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e-0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e3.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e1.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.0556552962298%\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.0556552962298%\"\u003e\n \u003cp\u003eDuration of PD (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.0556552962298%\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e1.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.0556552962298%\"\u003e\n \u003cp\u003eSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e-0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e48.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.0556552962298%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e8.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e2.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e9.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\n \u003cp\u003e5396.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.8491921005386%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: \u0026beta;=\u0026nbsp;beta coefficient; BMI= body mass index; BUN= blood urea nitrogen; Ccr= creatinine clearance; CI= confidence interval; Cr= creatinine; eGFR= estimated glomerular filtration rate; PD= peritoneal dialysis; SF= serum ferritin; SI= serum iron; Wald= The results of the Wald test of SPSS.\u003cem\u003e\u0026nbsp;Statistical significance was set at p \u0026lt; 0.05.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Diagnostic performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnostic efficacy of SI levels for concomitant depression in patients undergoing PD was determined using ROC curve analyses. The results revealed that the SI levels (area under the curve = 0.938, 95% CI: 0.905\u0026ndash;0.971; cutoff value = 8.95; sensitivity = 84.4%; and specificity = 97.7%) and TSATs (area under the curve = 0.876, 95% CI: 0.831\u0026ndash;0.921; sensitivity = 79.3%; specificity = 85.2%) were good predictors of depression (Figure 2).\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eIn the present study, we found that the prevalence of depression among patients undergoing regular PD at the Peritoneal Dialysis Center of our hospital was 39.5%. The results of correlation analyses revealed that depression in patients undergoing PD was statistically significantly negatively correlated with SI levels and TSATs. Finally, the ROC curve analysis demonstrated that SI levels and TSATs had high diagnostic efficacy for concomitant depression in patients undergoing PD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA study by Okan et al. on the correlation of anxiety and depression with metabolites in patients with fibromyalgia revealed higher incidence rates of depression and anxiety in patients with iron deficiency\u0026nbsp;[21]. A German study concluded that iron deficiency was significantly correlated with depression in patients with type 1 diabetes mellitus (p = 0.043 and p = 0.049 for SI and SF levels, respectively)\u0026nbsp;[18]. A German cross-sectional study involving phenotypically healthy individuals analysed the correlation of iron metabolism-related indicators with depression and found a significant correlation between SI levels and depression, which was more pronounced in men\u0026nbsp;[22]. Findings of a study by Oliveira et al. on the correlation of depressive symptoms in patients with multiple sclerosis revealed that lower iron levels were associated with the presence of depressive symptoms\u0026nbsp;[23], consistent with the results of the present study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Based on the underlying molecular mechanisms, the probable aetiology of iron deficiency in patients with concomitant depression is that iron ions serve as cofactors for key enzymes in the tryptophan metabolic pathway. Moreover, iron deficiency inhibits the activity of these enzymes and blocks the production of 5-HT. In particular, 5-HT is synthesised from tryptophan via the methoxyindole and kynurenine metabolic pathways and is believed to play a major role in the treatment of depression and sleep disorders. Indoleamine and tryptophan 2,3-dioxygenases are the rate-limiting enzymes in these metabolic pathways\u0026nbsp;[24], and iron is a key cofactor for both\u0026nbsp;[25]. Thus, insufficient free iron results in decreased 5-HT levels in the body, which in turn can cause depression\u0026nbsp;[9]. These studies provide a theory for the mechanism of depression induced by reduced SI levels from a molecular perspective.\u003c/p\u003e\n\u003cp\u003eNo correlations were observed between SF, Cr, and BUN levels, eGFRs, Ccrs, the duration of PD, or\u0026nbsp;sex\u0026nbsp;and depression in our patients undergoing PD. However, a study from Spain regarding women without a previous history of depression assessed the correlation between depressive symptoms and iron metabolism-related indicators at 48 hours, as well as 8 and 32 weeks postpartum. Findings thereof demonstrated a strong correlation between SF levels and postpartum depression (OR\u0026nbsp;= 3.73, 95% CI: 1.84\u0026ndash;7.56, p = 0.0001, SF cutoff value = 7.26 \u0026mu;g/L), and SF levels were found to have a high specificity and sensitivity for predicting postpartum depression\u0026nbsp;[26]. Most previous studies have shown a correlation between depression and SF levels; however, no such correlation was observed in the present study. Two potential causes may lead to differences in the results. First, differences in the study populations may exist. In the previous Spanish study, the maternal and fetal metabolic demands for iron evidently increased during pregnancy. Thus, the compensatory mechanisms of the body mobilised the iron reserves and replenished SF subsequent to SI deficiency to maintain normal SI levels in the body. Second, in our study, the metabolic status of patients undergoing PD, as well as the use of iron supplementations and other medications to treat anaemia, may have contributed to these differences.\u003c/p\u003e\n\u003cp\u003eThe strengths of the present study include its single-centre, cross-sectional design and relatively large sample size. Statistical analyses demonstrated a significant correlation between SI levels and TSATs with concomitant depression in patients undergoing PD. Furthermore, SI levels and TSATs were found to have high diagnostic efficacy for concomitant depression in patients undergoing PD, which enables early identification of depression by nephrologists.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, the study had the following limitations. Although statistical analyses revealed significant correlations between SI levels and TSATs with concomitant depression in patients undergoing PD, the physiological basis of SI levels and TSATs in patients with depression has not been fully researched and demonstrated. Thus, future prospective clinical trials are required to confirm these conclusions.\u003c/p\u003e\n\u003cp\u003eThe prevalence of patients undergoing PD with depression at our Peritoneal Dialysis Center was 39.5%, which highlights the potential influence of psychological factors on the quality of life and disease outcomes. Furthermore, the psychological well-being of patients should be simultaneously monitored with their physical health.\u003c/p\u003e\n\u003cp\u003eRegarding the correlation between concomitant depression and iron metabolism-related indicators in patients undergoing PD, the results of correlation analysis revealed that there were statistically significant differences in SI and TSAT values between the depression and non-depression cohorts. Furthermore, SI levels and TSATs had high diagnostic efficacy for concomitant depression in these patients. Consequently, these laboratory indicators can provide objective reference values for the diagnosis of concomitant depression, enable early identification of depression by nephrologists, and provide a new theoretical basis for the importance of iron supplementation in patients undergoing PD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion,\u0026nbsp;SI levels and TSATs were found to be independent risk factors with high diagnostic efficacy for concomitant depression in patients undergoing PD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChenling Liuwrote the manuscript. Shengjun Liu reviewed and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChenling Liu,the first author,\u0026nbsp;participated in article writing, data collection and data analysis.\u003c/p\u003e\n\u003cp\u003eJingyi Zhu,the second author, participated in data collection and analysis.\u003c/p\u003e\n\u003cp\u003eYunfei Wang,\u0026nbsp;the third author, participated in data collection and analysis.\u003c/p\u003e\n\u003cp\u003eZhifeng Wei, the fourth author, participated in the data collection\u003c/p\u003e\n\u003cp\u003eJinxiu Cheng, the fifth author, participated in the data collection.\u003c/p\u003e\n\u003cp\u003eXin Jin,\u0026nbsp;the sixth author, participated in data collection.\u003c/p\u003e\n\u003cp\u003eShengjun Liu, the\u0026nbsp;corresponding author,\u0026nbsp;participated in article topic selection, article revision, data analysis, etc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThere is no conflict of interest among all authors of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD Chronic Kidney Disease Collaboration. 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FASEB J. 29, 2207\u0026ndash;2222 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbacar, G., et al. An association between plasma ferritin concentrations measured 48 h after delivery and postpartum depression. J. Affect. Disord. 131, 136\u0026ndash;142 (2011).\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":"peritoneal dialysis, depression, serum iron, transferrin saturation, regression analysis, receiver operating characteristic curve","lastPublishedDoi":"10.21203/rs.3.rs-4529129/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4529129/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe aimed to analyse the correlation between depression and iron metabolism-related indicators and determine the efficacy of iron metabolism-related indicators in diagnosing depression in patients undergoing peritoneal dialysis (PD). This cross-sectional study included patients undergoing regular follow-up for PD between September 2022 and March 2023. Patient demographics and iron metabolism-related indicators, including serum iron (SI) and ferritin levels and transferrin saturation (TSAT), were collected and analysed. The depression status was assessed using the Patient Health Questionnaire-9. The correlation between iron metabolism-related indicators and concomitant depression was assessed using Spearman\u0026rsquo;s correlation coefficient. Binary logistic regression analysis was performed to identify independent concomitant depression risk factors. The relevant risk factors\u0026rsquo; diagnostic efficacies were assessed using receiver operating characteristic (ROC) curve analysis. Of the 223 patients (121 [54.3%] males and 102 [45.7%] females), 88 (39.5%) had concomitant depression. SI levels (correlation coefficient [r]=-0.741, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and TSATs (r=-0.637, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were significantly correlated with depression and were identified as independent risk factors (odds ratio, 95% confidence interval [CI]: SI 0.434; 0.343\u0026ndash;0.549; TSAT 0.782; 0.731\u0026ndash;0.837). The ROC curve analysis revealed that SI levels and TSATs were good depression predictors (area under the curve, 95% CI: SI 0.938, 0.905\u0026ndash;0.971; TSAT 0.876, 0.831\u0026ndash;0.921). SI levels and TSATs were independent risk factors with high diagnostic efficacy for concomitant depression in patients undergoing PD. Thus, these patients\u0026rsquo; psychological well-being should be simultaneously monitored.\u003c/p\u003e","manuscriptTitle":"Diagnostic efficacy of iron metabolism-related indicators for depression in patients undergoing peritoneal dialysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-26 19:00:09","doi":"10.21203/rs.3.rs-4529129/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":"c932663f-90e7-401a-a642-4a6324213ca3","owner":[],"postedDate":"June 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33512436,"name":"Biological sciences/Psychology"},{"id":33512437,"name":"Health sciences/Diseases"},{"id":33512438,"name":"Health sciences/Medical research"},{"id":33512439,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-07-05T15:51:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-26 19:00:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4529129","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4529129","identity":"rs-4529129","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}