Immune-Metabolic Dysregulation and Suicide Risk in Adolescents with Major Depressive Disorder: A Cross- Sectional Study

Immune-Metabolic Dysregulation and Suicide Risk in Adolescents with Major Depressive Disorder: A Cross- Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Immune-Metabolic Dysregulation and Suicide Risk in Adolescents with Major Depressive Disorder: A Cross- Sectional Study Yudiao Liang, Chengyi Tan, Ming Liu, Lei Wang, Sha Zhang, Kezhi Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8124663/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jan, 2026 Read the published version in BMC Psychiatry → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Biomarkers distinguishing suicidal from non-suicidal adolescents with major depressive disorder (MDD) remain elusive. This study investigated immune-metabolic dysregulation and its predictive utility for suicide attempt (SA) risk in medication-naïve adolescents. Methods: A case-control study compared 168 non-suicidal MDD adolescents with 96 MDD+SA adolescents (recent SA). Peripheral biomarkers included immune cell ratios (neutrophil/HDL [NHR], monocyte/HDL [MHR], lymphocyte/HDL [LHR]), metabolic markers (triglycerides, HDL-C, total protein (TP)), and hematological indices. Analyses employed group comparisons (t-tests/Mann-Whitney U), Spearman correlations, binary logistic regression, and ROC analysis. Results: The groups were well-matched demographically and for clinical severity. After adjusting for covariates, the MDD+SA group exhibited significant immune-metabolic dysregulation, including granulocyte hyperactivity, elevated inflammatory ratios, profound HDL-C depletion, hypertriglyceridemia, and reduced total protein (all key findings with adjusted p < 0.05). A post-hoc False Discovery Rate analysis confirmed the robustness of the core lipid findings. Binary logistic regression, adjusted for age, sex, and BMI, identified triglycerides (adjusted OR=2.17 per mmol/L) and total protein (adjusted OR=0.93 per g/L decrease) as independent predictors of SA. A model combining triglycerides and total protein significantly outperformed individual biomarkers in ROC analysis (AUC=0.74, 95% CI: 0.68-0.80). Conclusions: Convergent lipid-protein dysregulation represents a novel pathway for adolescent SA risk, identifiable as a significant shift within the normal laboratory range. A simple two-biomarker panel shows promise for risk stratification but requires future validation. These findings highlight metabolic dysfunction as a potential target for preventive strategies, though they do not yet constitute evidence for specific clinical interventions. Major depressive disorder Adolescent suicide Immune-metabolic dysregulation Biomarkers Triglycerides Total protein Predictive model Figures Figure 1 1 Introduction Major depressive disorder (MDD) represents a critical public health burden among adolescents and young adults globally, with its association with suicidal behaviors constituting an urgent clinical and societal challenge 1 . Alarmingly, suicide is now the second leading cause of death in this age group, and up to 20% of adolescents with MDD attempt suicide within five years of diagnosis 2 , 3 . This highlights the significant unmet need for effective prevention strategies 4 , 5 . Despite these statistics, clinicians still lack objective biomarkers to distinguish suicidal from non-suicidal patients, forcing reliance on subjective interviews that have only modest predictive value 6 . This critical gap leaves a substantial unmet clinical need for reliable, biologically anchored tools that can identify adolescents at imminent risk and guide early intervention. To address this urgent gap, the present study focuses on immune-metabolic dysregulation-quantifiable alterations in lipid profiles and reduced total protein (TP)-as a peripheral blood signature capable of flagging imminent suicide attempts in drug-naïve adolescents with MDD. Because adolescence is marked by rapid neuro-immune maturation 7 , disruptions in these systems may actively potentiate suicidal behaviour. Critically, we aim to move beyond previously studied single markers or inflammatory ratios by evaluating a novel combination of readily available metabolic and nutritional biomarkers, offering clinicians a potentially more accessible and integrated laboratory tool for early risk detection 8 – 10 . Over the past decade, converging evidence has positioned peripheral immune-metabolic markers as candidate biomarkers for suicide risk in MDD. Meta-analyses of > 7,000 participants demonstrate that suicidal ideation and attempts are associated with elevated systemic inflammation, reflected by higher neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR) and platelet-to-lymphocyte ratio (PLR) 11 , 12 . Parallel lipidomic studies, however, reveal a striking developmental divergence: while adult meta-analyses consistently report associations with hypocholesterolemia (lower total cholesterol and LDL-C) 13 , large adolescent cohorts report a distinct profile characterized by hypertriglyceridemia and low HDL-C 9,10 . This apparent contradiction may be resolved by considering the unique neurodevelopmental and endocrine context of adolescence. The "high-TG, low-HDL-C" phenotype observed in youth may reflect a pathophysiology more tightly linked to the emerging insulin resistance, pronounced HPA axis reactivity, and fluctuating sex hormone levels that characterize this life stage. In contrast, the "low-cholesterol" phenotype in adults might be more indicative of long-term metabolic exhaustion, chronic malnutrition, or different inflammatory cascades. Therefore, these are likely not contradictory findings, but rather manifestations of distinct, developmentally specific pathophysiological pathways to suicidality. Furthermore, thyroid autoimmunity (elevated TSH, thyroid peroxidase antibody (TPOAb)) amplifies metabolic dysregulation and correlates with suicide attempt (SA) severity 15 , 16 . Importantly, TP—an inexpensive index of nutritional and inflammatory status—has been barely examined in youth, despite consistent reductions observed in adult suicide attempters 17 . Collectively, these findings establish immune-metabolic convergence as a promising but still fragmentary framework for adolescent suicide risk prediction. Four unresolved issues impede clinical translation. Contradictory lipid findings persist: adult meta-analyses report hypocholesterolemia 13 , whereas adolescent cohorts observe hypertriglyceridemia and low HDL-C 9,10,18 . Methodological constraints include cross-sectional designs, modest sample sizes 2 , 19 , and single-marker ROCs with AUC ≤ 0.70 11 . Protein metabolism-inexpensive yet neglected-shows consistent reductions in adult attempters 17 but remains virtually unstudied in youth. Most critically, no composite immune-metabolic panel based on a core metabolic-nutritional axis (e.g., triglycerides + total protein) has been validated for suicide risk prediction in drug-naïve adolescents, leaving clinicians without objective, practical tools. Building on the identified gaps, the primary objective of this study is to derive and internally validate a peripheral immune-metabolic signature that distinguishes adolescents with MDD who have attempted suicide from non-suicidal MDD peers. We specifically investigate whether a novel biomarker panel, focusing on the convergence of metabolic (triglycerides, HDL-C) and nutritional-inflammatory (total protein) pathways, provides superior predictive value compared to traditional inflammatory ratios. We hypothesize that adolescents with MDD + SA will exhibit higher triglycerides, lower HDL-C, and lower TP, alongside elevated inflammatory ratios compared with non-suicidal MDD controls. Furthermore, we aim to construct a multivariable biomarker panel whose discriminative accuracy surpasses that of single-marker ROC models, with a target AUC > 0.75. We will use a strict phenotypic case-control cohort of 264 drug-naive adolescents (168 MDD vs 96 MDD + SA) integrating routine clinical chemistry, lipid profiles, and ROC models to develop and internally validate predictive signatures. This approach leverages readily available clinical tests and advanced statistical techniques to create a practical and innovative risk stratification tool. These findings are expected to (i) elucidate the neuro-immuno-metabolic mechanisms underlying adolescent suicidality; (ii) equip clinicians with an objective, biologically grounded instrument for early risk detection and intervention; and (iii) inform future targeted interventions by identifying a specific, actionable metabolic-nutritional profile, thereby moving the field from associative observation towards mechanism-informed prevention. 2 Materials and Methods 2.1 Study Design and participants This was a case-control study conducted in Zigong Mental Health Center from December 2023 to April 2025. We recruited 264 medication-naive adolescents aged 11–18 years with a diagnosis of MDD, assessed by the MINI-KID instrument. Participants were confirmed to be medication-naive through detailed clinical interviews with both the adolescents and their parents/guardians, specifically inquiring about any past or current use of psychotropic medications (including antidepressants, antipsychotics, mood stabilizers, stimulants, or anxiolytics). This was further verified by reviewing all available medical records. Any individual with a history of such medication use was excluded. Participants were divided into two groups, the MDD group (control group, n = 168), adolescents who met criteria for MDD and had no lifetime history of suicide attempts. Major depressive disorder with suicide attempt group (case group, n = 96) : adolescents who met criteria for major depression and had a recent SA in the past 1 month, and had no prior lifetime attempts, identified by clinical interview and medical record review. All participants were of Han Chinese ethnicity. Exclusion criteria: Participants were excluded if they had: (1) major depression (e.g., bipolar disorder, schizophrenia, autism spectrum disorder) other than a primary diagnosis; Severe medical conditions known to affect immune/metabolic measures (e.g., active infection within 4 weeks, autoimmune disease, diabetes, severe kidney/liver disease, cancer). (3) current or past use of psychotropic medications (including antidepressants, antipsychotics, mood stabilizers) or medications known to affect immune/metabolic parameters (e.g., steroids, statins); (4) substance use disorder (excluding nicotine) in the past 6 months; (5) pregnancy or lactation. 2. 2 Ethical considerations The study protocol was approved by the institutional review board of Zigong Mental Health Center. Written informed consent was obtained from all participants and their parents or legal guardians. As this study was not a clinical trial, no clinical trial number is applicable. All procedures were performed in accordance with the ethical standards of the Declaration of Helsinki. 2.3 Clinical assessment All participants underwent a comprehensive clinical assessment, which included: demographic information such as age, gender, and ethnicity; assessment of the severity of depressive symptoms using the Zahn Self-Rating Depression Scale (SDS) 20 , 21 ; assessment of the severity of anxiety symptoms using the Zahn Self-Rating Anxiety Scale (SAS) 22 , 23 ; and strict assessment of the history and characteristics of suicidal thoughts and behaviors through structured clinical interviews and medical record reviews. 2.4 Blood sample collection and laboratory analysis Peripheral venous blood samples were obtained from all participants after an overnight fast of no less than 8 hours to minimize diurnal variation. Samples were processed within 2 hours of collection. The blood sample collection was performed during the same hospital admission, typically within 24–48 hours following the diagnostic confirmation using the MINI-KID instrument to ensure clinical state consistency. To minimize batch effects, all blood samples were processed in a single, accredited clinical laboratory. Analyses for each assay type (e.g., CBC, biochemistry, immunoassay) were performed concurrently in batches that included randomly mixed samples from both the MDD and MDD + SA groups, following standardized protocols with strict internal quality control. The following analyses were performed using standard clinical laboratory methods on a Sysmex XN-1000 blood cell analyzer (Sysmex Corporation, Kobe, Japan), a Mindray BS-2000M biochemical analyzer (Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, China), and an Antu A2000Plus chemiluminescence immunoassay analyzer (Chengdu Antu Instruments Co., Ltd., Chengdu, China). Complete blood count (CBC) and differentiation parameters were analyzed on the Sysmex XN-1000 analyzer using flow cytometry and fluorescent dye staining. Metabolic indicators (HDL-C, triglycerides (TG), total cholesterol (TC)) and liver function indexes, including TP, measured by the Biuret method), albumin (measured by the Bromocresol Green method), and globulin, were determined on the Mindray BS-2000M analyzer using standardized colorimetric assays. TG and TC were measured by the GPO-POD and CHOD-POD methods, respectively, while HDL-C and LDL-C were measured by direct homogeneous methods. Endocrine indicators (serum cortisol, free triiodothyronine (fT3), thyroid-stimulating hormone (TSH)) were quantified using the Antu A2000Plus analyzer via chemiluminescence immunoassay. The following inflammatory ratios were calculated: neutrophil-to-HDL ratio (NHR) = Neutrophil count / HDL-C, monocyte-to-HDL ratio (MHR) = Monocyte count / HDL-C, lymphocyte-to-HDL ratio (LHR) = Lymphocyte count / HDL-C, and platelet-to-HDL ratio (PHR) = Platelet count / HDL-C. All assays were conducted per the manufacturers' instructions. The analytical precision was high, with intra-assay coefficients of variation (CVs) < 3% for CBC, < 5% for lipid profiles and TP, and < 8% for endocrine markers; inter-assay CVs were < 5% for CBC, < 8% for lipid profiles and TP, and < 10% for endocrine markers. Laboratory personnel were blinded to the clinical grouping of all participants. 2.5 Statistical analysis Statistical analyses were performed using IBM SPSS Statistics version 26 (IBM Corp., Armonk, NY, USA). A two-sided significance level of α = 0.05 was applied for all tests.The normality of continuous variables was assessed using the Kolmogorov-Smirnov test. Data are presented as mean ± standard deviation (SD) for normally distributed variables, median (interquartile range, IQR) for non-normally distributed variables, and frequency (%) for categorical variables.Between-group differences in demographic and clinical characteristics were assessed using independent samples t-tests (continuous, normal), Mann-Whitney U tests (continuous, non-normal), or χ²/Fisher's exact tests (categorical), as appropriate. Between-group comparisons of biomarkers were conducted in a two-step manner. Initial unadjusted comparisons were performed using independent samples t-tests (for normally distributed data) or Mann-Whitney U tests (for non-normally distributed data). To control for potential confounding effects, these comparisons were subsequently validated using Analysis of Covariance (ANCOVA) for parametric data and Quade's non-parametric ANCOVA for non-parametric data, with age, sex, and body mass index (BMI) included as covariates in all models. The effect size for the group difference (MDD + SA vs. MDD) in these adjusted models is reported as the standardized beta coefficient (β), and the results are presented in Tables 2 and 3 . Correlation and multivariate analyses were employed to examine the relationships between biomarkers and clinical outcomes. Spearman's rank-order correlation (ρ) was used for bivariate assessments. Multiple linear regression analyses, adjusted for age, sex, and BMI, were conducted to identify biomarkers independently associated with anxiety (SAS) and depression (SDS) severity. The results are expressed as standardized beta coefficients (β) with 95% confidence intervals and are detailed in Table 4 .To identify independent predictors of a suicide attempt (MDD + SA status), binary logistic regression was performed. The model included all variables that showed significant between-group differences or were of theoretical relevance, and was adjusted for the covariates of age, sex, and BMI. The results are presented as regression coefficients (B), standard errors (SE), Wald statistics, p-values, odds ratios (OR), and 95% confidence intervals (CI) in Table 5 . The model's goodness-of-fit was evaluated using the Hosmer-Lemeshow test, and its explanatory power was quantified by Nagelkerke's R². Multicollinearity was assessed via the Variance Inflation Factor (VIF), with all values below 2.0 indicating no substantive concern.The predictive performance of significant independent biomarkers from the logistic regression was evaluated using Receiver Operating Characteristic (ROC) curve analysis, with the area under the curve (AUC) and 95% CI reported. A composite score based on the linear predictor from the binary logistic regression model was used for the combined biomarker analysis. Given the exploratory nature of this biomarker study and the biological intercorrelation among many immunological and metabolic parameters, a strict Bonferroni correction for multiple comparisons was not applied, as it would be overly conservative and increase the risk of Type II errors. Instead, the interpretation of results prioritizes effect sizes, confidence intervals, and the convergence of findings across univariate, multivariate, and predictive models to identify robust associations. Table 2 Comparison of peripheral laboratory indicators between MDD and MDD + SA groups Indicator MDD (Mean ± SD) n = 168 MDD + SA (Mean ± SD) n = 96 Statistic P value Adjusted β (Group) F (Group) Adjusted p value Neutrophil percentage (%) 53.41 ± 9.35 55.82 ± 10.12 t=-1.95 0.052 0.112 3.75 0.061 Lymphocyte percentage (%) 36.79 ± 8.66 34.22 ± 9.21 t = 2.27 0.024* -0.135 5.12 0.031* Mean corpuscular Hb conc. (g/L) 322.10 ± 10.12 324.38 ± 10.49 t=-1.73 0.084 0.098 2.41 0.102 RDW-SD (fL) 41.18 ± 3.17 41.14 ± 3.51 t = 0.10 0.921 -0.008 0.02 0.887 Platelet count (×10⁹/L) 264.27 ± 67.27 260.48 ± 63.61 t = 0.45 0.654 -0.025 0.14 0.712 Plateletcrit (%) 0.265 ± 0.057 0.268 ± 0.055 t=-0.31 0.760 0.018 0.09 0.803 Serum prealbumin (mg/L) 234.56 ± 43.14 230.10 ± 37.95 t = 0.81 0.418 -0.042 0.44 0.395 Albumin/globulin ratio 1.71 ± 0.21 1.71 ± 0.22 t=-0.33 0.745 0.015 0.06 0.769 Total cholesterol (mmol/L) 3.94 ± 0.75 3.91 ± 0.72 t = 0.29 0.775 -0.016 0.06 0.821 Free thyroxine (pmol/L) 16.17 ± 2.41 15.75 ± 2.55 t = 1.32 0.187 -0.081 1.58 0.210 Note: RDW-SD: red blood cell distribution width-standard deviation. All indicators followed a normal distribution and were initially compared using an independent-samples t-test. The Adjusted β (Group) represents the standardized beta coefficient for the 'Group' variable (MDD + SA vs. MDD) derived from Analysis of Covariance (ANCOVA) models controlling for age, sex, and body mass index (BMI). The F-statistic and corresponding Adjusted p value are for the main effect of 'Group' in these ANCOVA models. P < 0.05. Table 3 Comparison of non-normally distributed biomarkers between MDD and MDD + SA groups using Mann-Whitney U test and Quade's ANCOVA Biomarker MDD MDD + SA Z p Adjusted β (Group) Adjusted p value White blood cell count (10⁹/L) 6.17 (5.10,7.45) 6.61(5.45,8.18) –2.308 0.021* 0.138 0.028* Neutrophil count (10⁹/L) 3.13 (2.30, 4.10) 3.42(2.50, 4.50) –2.457 0.014* 0.145 0.019* Lymphocyte count (10⁹/L) 2.18 (1.70, 2.51) 2.12 (1.60,2.53) –0.175 0.861 -0.012 0.845 Monocyte count (10⁹/L) 0.42 (0.32,0.51) 0.43 (0.33,0.52) –1.549 0.121 0.068 0.135 Eosinophil count (10⁹/L) 0.11 (0.06,0.18) 0.15 (0.08, 0.22) –2.747 0.006** 0.165 0.008** Basophil count (10⁹/L) 0.02 (0.01, 0.03) 0.03 (0.02,0.05) –3.211 0.001** 0.188 0.002** Monocyte percentage (%) 6.6(5.1, 7.7) 6.6 (5.1 7.7) –0.009 0.993 0.003 0.981 Eosinophil percentage (%) 1.8 (1.0, 2.8) 2.5 (1.4, 3.5) –2.257 0.024* 0.142 0.030* Basophil percentage (%) 0.3 (0.2,0.5) 0.4 (0.3, 0.6) –2.443 0.015* 0.150 0.021* Red blood cell count (10¹²/L) 4.53(4.20,4.82) 4.39(4.10,4.66) –1.893 0.058 -0.125 0.067 Hemoglobin (g/L) 158(147, 170) 160(148,172) –0.161 0.872 0.011 0.855 Hematocrit (%) 40.1 (37.5,42.5) 38.5(36.0,40.6) –2.265 0.024* -0.140 0.032* Mean corpuscular volume (fL) 88.8(86.0,92.2) 89.7(87.0,92.2) –1.024 0.306 0.065 0.334 Mean corpuscular hemoglobin (pg) 28.5(27.4, 30.5) 29.1(28.0,30.8) –1.680 0.093 0.095 0.110 Red-cell distribution width (%) 12.7 (12.0,13.3) 12.7 (12.1, 13.2) –0.564 0.572 -0.030 0.595 Platelet distribution width (%) 15.9 (14.5,17.0) 15.8 (14.2,17.1) –0.360 0.719 -0.020 0.741 Mean platelet volume (fL) 10.15 (9.2,11.0) 10.10 (9.20,10.90) –0.520 0.603 -0.028 0.622 Total protein (g/L) 71.0 (66.0, 74.9) 69.4(65.0,72.3) –2.394 0.017* -0.142 0.023* Albumin (g/L) 45.2 (42.1, 48.5) 44.8(41.9,47.7) –0.597 0.551 -0.032 0.528 Globulin (g/L) 26.7(24.0,28.5) 25.6(22.8,28.1) –1.632 0.103 -0.085 0.118 Triglycerides (mmol/L) 0.90 (0.65,1.24) 1.13 (0.76, 1.80) –3.381 0.001** 0.204 0.001** High-density lipoprotein cholesterol (mmol/L) 1.40 (1.10,1.54) 1.27 (1.00, 1.41) –3.347 0.001** -0.180 0.002** Low-density lipoprotein cholesterol (mmol/L) 2.23 (1.70,2.61) 2.27(1.70,2.72) –0.862 0.389 0.045 0.411 Triiodothyronine (nmol/L) 1.18 (1.02,1.26) 1.21 (1.05,1.32) –0.280 0.780 0.015 0.795 Thyroxine (nmol/L) 13.3 (9.8, 18.0) 9.68 (7.5, 17.1) –1.436 0.151 -0.080 0.173 Free triiodothyronine (pmol/L) 5.51(4.90,6.44) 5.57(4.85,5.97) –0.093 0.926 0.005 0.910 Thyroid-stimulating hormone (mIU/L) 2.05 (1.40,2.95) 2.28 (1.50,3.71) –1.689 0.091 0.090 0.105 Prolactin (µg/L) 409.24(276.18,652.24) 450.56(301.25,793.8 –1.790 0.074 0.100 0.086 Cortisol (nmol/L) 11.6 (7.5, 16.0) 11.0 (7.2, 14.7) –1.034 0.301 -0.058 0.325 Testosterone (nmol/L) 0.31 (0.15, 0.52) 0.31 (0.16,0.51) –0.094 0.925 0.004 0.938 Estradiol (pmol/L) 46.4(30.5,90.3) 50.8 (32.1, 89.0) –0.172 0.863 0.010 0.849 MHR 0.29(0.20,0.38) 0.33(0.24,0.45) –3.347 0.001** 0.178 0.003** NHR 2.19 (1.50,2.84) 2.78 (1.85,3.81) –3.696 0.001** 0.195 0.001** PHR 180(130,224) 196(145,233) –2.117 0.034* 0.130 0.041* LHR 1.55 (1.10 1.90) 1.67 (1.20,2.11) –2.386 0.017* 0.140 0.022* NLR 1.45 (1.00,1.82) 1.70 (1.10,2.09) –1.844 0.065 0.105 0.078 PLR 119 (95,145) 116(85, 155) –0.22 0.826 -0.012 0.815 MLR 0.18 (0.12,0.22) 0.20 (0.14,0.26) –1.59 0.111 0.085 0.127 Note : The Adjusted β (Group) represents the standardized beta coefficient for the 'Group' variable (MDD + SA vs. MDD) derived from Quade's non-parametric ANCOVA models controlling for age, sex, and body mass index (BMI). The corresponding Adjusted p value is for the main effect of 'Group' in these models. *P < 0.05, ** P < 0.01.Abbreviations: NHR: neutrophil/high-density lipoprotein ratio; PHR: platelet/high-density lipoprotein ratio; MHR: monocyte/high-density lipoprotein ratio; LHR: lymphocyte/high-density lipoprotein ratio; NLR: Neutrophil/Lymphocyte ratio; PLR: Platelet-to-Lymphocyte ratio; MLR: Monocyte-to-Lymphocyte ratio. Table 4 Multiple Linear Regression Analysis of Biomarkers and Covariates Associated with SAS and SDS Scores Biomarker SAS Score SDS Score β (95% CI) p-value β (95% CI) p-value Age [0.08 (-0.04, 0.20)] 0.185 [0.06 (-0.06, 0.18)] 0.320 Sex [-0.10 (-0.22, 0.02)] 0.095 [-0.08 (-0.20, 0.04)] 0.180 BMI [0.05 (-0.07, 0.17)] 0.424 [0.03 (-0.09, 0.15)] 0.621 Eosinophil count [0.09 (-0.03, 0.21)] 0.135 [0.11 (-0.01, 0.23)] 0.072 Mean Corpuscular Hemoglobin [-0.15 (-0.27, -0.03)] 0.015* [-0.17 (-0.29, -0.05)] 0.006** Total Cholesterol [-0.13 (-0.25, -0.01)] 0.031* [-0.12 (-0.24, 0.00)] 0.055 HDL-C [-0.12 (-0.24, 0.00)] 0.057 [-0.16 (-0.28, -0.04)] 0.011* Cortisol [0.14 (0.02, 0.26)] 0.023* [0.06 (-0.06, 0.18)] 0.367 LHR [0.10 (-0.04, 0.24)] 0.148 [0.12 (-0.02, 0.26)] 0.084 Note: All models include all predictors listed in the table. β represents the standardized beta coefficient. CI = Confidence Interval. SAS = Self-Rating Anxiety Scale; SDS = Self-Rating Depression Scale.*P < 0.05, ** P < 0.01.Abbreviations: HDL-C: High-density lipoprotein cholesterol; LHR: Lymphocyte-to-HDL ratio. Table 5 Binary Logistic Regression Analysis of Predictors for Suicide Attempt (MDD + SA Status) Variable B SE Wald p-value OR 95% CI for OR Age 0.10 0.07 2.04 0.153 1.11 0.96, 1.27 Sex 0.40 0.32 1.56 0.211 1.49 0.80, 2.79 BMI -0.04 0.04 1.00 0.317 0.96 0.89, 1.04 Lymphocyte percentage (%) -0.035 0.043 0.66 0.416 0.97 0.89, 1.05 White blood cell count (10⁹/L) 0.155 0.205 0.57 0.450 1.17 0.78, 1.75 Hematocrit (%) -0.037 0.038 0.95 0.330 0.96 0.89, 1.04 Total protein (g/L) -0.075 0.030 6.25 0.012 0.93 0.88, 0.98 Triglycerides (mmol/L) 0.775 0.295 6.90 0.009 2.17 1.22, 3.87 High-density lipoprotein cholesterol (mmol/L) -1.240 1.080 1.32 0.251 0.29 0.03, 2.40 MHR 0.085 1.645 0.003 0.959 1.09 0.04, 27.83 NHR -0.125 0.323 0.15 0.699 0.88 0.47, 1.66 PHR -0.001 0.003 0.11 0.740 0.999 0.993, 1.005 LHR -0.048 0.605 0.006 0.937 0.95 0.29, 3.12 Constant 8.150 3.400 5.75 0.016 3471.5 - Abbreviations: SE: Standard Error; OR: Odds Ratio; CI: Confidence Interval. 3 Results 3.1 Baseline Demographic and Clinical Characteristics The study included 264 drug-naïve Han Chinese adolescents with MDD, comprising 168 without a history of suicide attempts (MDD group) and 96 with a recent suicide attempt (MDD + SA group). As summarized in Table 1 , the two groups were well-balanced across all measured demographic and clinical baseline characteristics. Demographic profiles, including gender distribution, age at enrollment (median [IQR] = 15.0 [14.0–16.0] years for both groups), and Body Mass Index (BMI), were comparable (all p > 0.05). Clinical history variables, such as family history of mental disorders, age at depression onset (median [IQR] = 14.0 [13.0–15.0] years for both groups), and lifetime prevalence of nonsuicidal self-injury (NSSI), also showed no significant differences. Furthermore, assessments of current clinical severity at enrollment revealed no significant disparities in anxiety (SAS; MDD: 71.0 [65.0–78.0], MDD + SA: 73.0 [65.5–83.0]) or depression (SDS; MDD: 60.0 [52.0-68.3], MDD + SA: 61.9 [53.0-71.1]) scores (all p > 0.05). This comprehensive baseline comparison confirms the equivalence of the groups, strengthening the validity of the subsequent biomarker analyses. Table 1 Comparison of Demographical and Clinical Characteristics Characteristic MDD (n = 168) MDD + SA (n = 96) Statistic P value Demographics Female, n (%) 137 (81.5) 85 (88.5) χ² = 2.23 0.135 Age at enrollment (years), median (IQR) 15.0 (14.0, 16.0) 15.0 (14.0, 16.0) Z = -1.147 0.252 Ethnicity (Han Chinese), n (%) 168 (100) 96 (100) - - Body Mass Index (BMI), mean ± SD 21.1 ± 3.5 20.8 ± 3.2 t = 0.72 0.472 Clinical History Family history of mental disorders, n (%) 45 (26.8) 31 (32.3) χ² = 0.95 0.330 Age at depression onset (years), median (IQR) 14.0 (13.0, 15.0) 14.0 (13.0, 15.0) Z = -0.841 0.400 Lifetime NSSI history, n (%) 68 (40.5) 46 (47.9) χ² = 1.42 0.234 Clinical Severity (at enrollment) SAS score, median (IQR) 71.0 (65.0, 78.0) 73.0 (65.5, 83.0) Z = -0.909 0.363 SDS score, median (IQR) 60.0 (52.0, 68.3) 61.9 (53.0, 71.1) Z = -1.532 0.125 Note: MDD = major depressive disorder; SA = non-suicidal self-injury; SAS = Zung Self-Rating Anxiety Scale; SDS = Zung Self-Rating Depression Scale. Comparisons were performed using χ² test or Mann–Whitney U test as appropriate. IQR = Interquartile range. 3.2 Convergent Immune-Metabolic Dysregulation in Adolescent Suicide Attempters​ Comparative biomarker analysis, adjusted for age, sex, and BMI, revealed a distinct pathophysiological signature in adolescents with major depressive disorder and a recent suicide attempt (MDD + SA) compared to non-suicidal MDD controls. Analyses of parametrically distributed data (Table 2 ) indicated that, after controlling for covariates, the MDD + SA group exhibited a significantly lower lymphocyte percentage (Adjusted β = -0.135, p = 0.031). The neutrophil percentage showed a trend toward elevation that did not reach statistical significance (Adjusted β = 0.112, p = 0.061). Analyses of non-parametrically distributed biomarkers (Table 3 ) revealed more pronounced alterations. The MDD + SA group demonstrated a state of granulocyte hyperactivity, characterized by significantly elevated counts of eosinophils (Adjusted β = 0.165, p = 0.008) and basophils (Adjusted β = 0.188, p = 0.002), alongside increased white blood cell (Adjusted β = 0.138, p = 0.028) and neutrophil counts (Adjusted β = 0.145, p = 0.019). The percentages of eosinophils and basophils were also significantly higher in the MDD + SA group (Adjusted β = 0.142, p = 0.030 and Adjusted β = 0.150, p = 0.021, respectively). Critically, the composite inflammatory ratios demonstrated robust and significant elevations in the MDD + SA group after adjustment. These included the neutrophil-to-HDL ratio (NHR) (Adjusted β = 0.195, p < 0.001), monocyte-to-HDL ratio (MHR) (Adjusted β = 0.178, p = 0.003), and lymphocyte-to-HDL ratio (LHR) (Adjusted β = 0.140, p = 0.022), collectively indicating a state of systemic inflammation coupled with impaired lipid-mediated anti-inflammatory protection.These immunological perturbations coincided with fundamental metabolic disruptions. The most consistent finding was a profound depletion of high-density lipoprotein cholesterol (HDL-C) (Adjusted β = -0.180, p = 0.002), which was accompanied by significant accumulation of triglycerides (TG) (Adjusted β = 0.204, p = 0.001). This "high-TG, low-HDL-C" lipid profile delineates a dyslipidemic state that may potentiate inflammatory processes. The significance of these core lipid alterations was further supported by a post-hoc False Discovery Rate (FDR) correction, which they withstood (FDR-adjusted p < 0.01 for both), indicating robustness against multiple testing.Concurrent hematological alterations were also observed. The MDD + SA group had significantly lower hematocrit (Adjusted β = -0.140, p = 0.032) and reduced levels of total protein (TP) (Adjusted β = -0.142, p = 0.023), with the latter showing a trend-level significance after FDR correction (FDR-adjusted p = 0.069). In contrast, endocrine homeostasis across the cortisol, gonadal, and thyroid axes remained preserved between groups (all adjusted p > 0.05). 3.3 Independent Associations of Biomarkers with Anxiety and Depressive Symptomatology Multiple linear regression analyses, which included age, sex, and body mass index (BMI) in the model, were conducted to identify biomarkers independently associated with symptom severity after accounting for these demographic and anthropometric factors. The full results of these adjusted models are presented in Table 4 . For anxiety severity (SAS score), the multivariate model revealed that lower levels of mean corpuscular hemoglobin (β = -0.152, p = 0.015) and total cholesterol (β = -0.135, p = 0.031) were significant independent predictors of higher anxiety. Conversely, higher cortisol levels (β = 0.139, p = 0.023) were independently associated with increased anxiety severity. The covariates of age, sex, and BMI themselves were not significant independent predictors of SAS scores in this model (all p > 0.05). A different pattern of associations emerged for depressive severity (SDS score). In the adjusted model, lower levels of mean corpuscular hemoglobin (β = -0.174, p = 0.006) and high-density lipoprotein cholesterol (HDL-C) (β = -0.161, p = 0.011) were significant independent predictors of more severe depressive symptoms. Similarly, age, sex, and BMI did not show significant independent associations with SDS scores (all p > 0.05). It is noteworthy that several biomarkers which demonstrated significant simple correlations in bivariate analysis (e.g., eosinophil parameters, inflammatory ratios such as LHR and MHR) did not retain their independent association in the multivariate model after adjusting for covariates and other biomarkers. This highlights the importance of evaluating independent effects within a comprehensive statistical framework. 3.4 Independent Predictors of Adolescent Suicide Risk​ A binary logistic regression analysis was performed to identify independent predictors of suicide attempt status, with the model adjusted for the covariates of age, sex, and body mass index (BMI). The full results, including the contributions of all covariates and biomarker variables, are presented in Table 5 . After adjusting for demographic and anthropometric factors, two peripheral biomarkers emerged as significant and independent predictors of suicide attempt risk. Elevated serum triglycerides (TG) demonstrated the strongest association, with each 1 mmol/L increase conferring more than a twofold increase in the odds of belonging to the MDD + SA group (Adjusted OR = 2.17, 95% CI: 1.22–3.87, p = 0.009). Conversely, reduced serum total protein (TP) was independently predictive of suicide risk, with each 1 g/L decrease associated with a 7% increase in the odds of a suicide attempt (Adjusted OR = 0.93, 95% CI: 0.88–0.98, p = 0.012).The selection of triglycerides for the final model over HDL-C was driven by the multivariate results. While both were significant in univariate analyses, only triglycerides retained strong independent predictive value in the covariate-adjusted model. In contrast, the association with HDL-C was substantially attenuated and was no longer significant (Adjusted OR = 0.29, p = 0.251), suggesting that its information regarding suicide risk in this cohort may be largely captured by the metabolic dysregulation reflected in TG levels.Notably, the inflammatory composite ratios (NHR, MHR, LHR) and other hematological parameters that were significant in bivariate analyses failed to retain independent predictive utility in the full multivariate model (all p > 0.05). The covariates of age, sex, and BMI were not significant independent predictors in the final model (all p > 0.05). The overall model demonstrated a good fit (Hosmer-Lemeshow test, p = 0.601) and explained a substantial proportion of the variance in suicide risk (Nagelkerke R² = 0.246). 3.5 Predictive Performance of Metabolic Biomarkers for Suicide Attempt Risk ROC analysis demonstrated differential predictive utility of metabolic biomarkers for distinguishing adolescents with MDD and MDD + SA from non-suicidal MDD controls (Fig. 1 ). Serum triglycerides exhibited moderate discriminative capacity (AUC = 0.627, 95% CI 0.562–0.692; p < 0.001), outperforming TP (AUC = 0.589, 95% CI 0.523–0.656; p = 0.017). A combined model integrating both biomarkers significantly enhanced predictive accuracy (AUC = 0.74, 95% CI 0.68–0.80; p < 0.001). To internally validate this combined model and address potential overfitting, we performed a 1000-iteration bootstrap validation. This analysis yielded a bias-corrected AUC of 0.73 (95% CI: 0.67–0.79), confirming the robustness of the model's performance within our dataset. Nevertheless, it is critical to emphasize that this represents an internal validation. The ultimate test of the model's generalizability and potential clinical applicability lies in its validation in an independent, prospective cohort. 4 Discussion This study identifies, for the first time in a medication-naïve adolescent MDD cohort, a convergent immune-metabolic signature characterized by hypertriglyceridemia, pronounced HDL-C depletion, and reduced total protein. Triglycerides and total protein served as independent predictors of suicide attempt risk beyond inflammatory composite ratios, retaining significance after adjustment for age, sex, and BMI. These findings advance the field by identifying a novel, clinically accessible biomarker combination, thereby refining the adult "low-cholesterol-suicide" hypothesis and underscoring a potential developmentally specific pathway for adolescent suicidality characterized by a metabolic-nutritional axis 9 , 10 , 24 . While the cross-sectional nature of our data precludes definitive causal inferences, the specificity and independence of the identified biomarker signature allow us to propose a plausible, mechanistic model for future validation. It remains unclear whether this metabolic state is a cause, a consequence, or a bidirectional component of suicidal behavior. Our observed "high-TG, low-HDL-C" lipid profile was remarkably consistent in adolescents with MDD + SA 14 . Mechanistically, TG accumulation activates hepatic NF-κB signaling 25 , driving peripheral inflammation and increasing blood-brain barrier permeability. Additionally, TG-induced mitochondrial dysfunction may compromise prefrontal-cingulate circuitry underlying impulse control 26 . These processes may be particularly salient during adolescence, a period marked by metabolic transition 27 . Each 1 g/L decrease in serum total protein conferred a 7.3% increase in suicide attempt risk, representing the first validation of this association in an adolescent cohort 16 . Hypoproteinemia signals chronic inflammatory malnutrition, potentially leading to reduced albumin-bound tryptophan and increased shunting down the kynurenine pathway, thereby depleting serotonin precursors and heightening impulsive aggression 28 , 29 . Although inflammatory ratios (NHR, MHR, LHR) were significantly elevated in univariate analyses, they failed to retain independent predictive value in the multivariate model. This finding can be attributed to several factors. Statistically, these ratios are derived from and thus share variance with their constituent cell counts and HDL-C levels, leading to multicollinearity which dilutes their unique contribution when all components are considered together. Biologically, the loss of significance likely reflects the dual confounding role of HDL-C itself; when severely depleted, the reduction in this central denominator shrinks both the numerator and denominator of the ratios, thereby compressing their dynamic range and discriminatory power 30 , 31 . Furthermore, the heightened immune dynamism characteristic of adolescence may render these cellular ratios more variable and less stable indicators than the core metabolic parameters (TG, TP) which they are derived from. The integration of triglycerides and total protein into existing clinical frameworks (e.g., CSSRS) leverages their routine accessibility and cost-effectiveness. While standalone predictive power remains moderate (TG AUC = 0.627; TP AUC = 0.589), their combination with clinical symptoms significantly improves sensitivity (combined AUC = 0.74) 3,15 . This aligns with broader evidence that ​​multi-modal approaches​​—incorporating inflammatory markers like neutrophil-to-lymphocyte ratio (NLR) 32 and neuroimaging biomarkers 33 —enhance suicide risk prediction. Prospective validation of the "TG + TP + clinical symptoms" model is urgently needed, particularly given the ​​neuroimmune crosstalk​​ observed in adolescent depression 34 . Our findings generate important hypotheses for future research into novel interventions. The specific metabolic and nutritional imbalances we identified suggest that interventions targeting these pathways-such as omega-3 fatty acids for dyslipidemia or nutritional support for hypoproteinemia-warrant investigation in future longitudinal intervention studies 35 . This dual action may disrupt the ​​lipid-suicidality pathway ​ ​ 10 .​ ​ Protein Optimization​​: Nutritional support (e.g., branched-chain amino acids) counteracts hypoalbuminemia-linked oxidative stress, potentially restoring neurotrophic factor synthesis (BDNF) 31 .​ ​ Adjunctive Strategies​​: Adjunctive anti-inflammatory agents (e.g., minocycline) and gut-microbiome modulation (e.g., probiotics) show promise in mitigating neuroimmune dysregulation 36 .High-risk adolescents-especially those with metabolic syndrome (MetS) or autoimmune comorbidity-should undergo ​​quarterly monitoring​​ of: Lipid profiles (TG, HDL-C, TC/HDL-C ratio) 10 , 37 Inflammatory indices (CRP, NLR) 31 , TP 14 This stratified surveillance, integrated into routine psychiatric care, enables early intervention before crisis escalation 38 . However, we must state with the utmost caution that our cross-sectional, associative data do not constitute evidence to recommend any specific treatment or intervention in current clinical practice. The imperative next step is to test whether modifying these metabolic pathways leads to a reduction in suicide risk in prospective, randomized controlled trials. Furthermore, the reviewer raised a critical point regarding the clinical interpretation of our findings. It is important to note that the median values for the majority of biomarkers, including triglycerides, HDL-C, and total protein, in both the MDD and MDD + SA groups fell within broad laboratory-defined normal ranges for adolescents. This observation is pivotal, as it suggests that the clinically relevant signal is not necessarily a value outside the pathological range, but rather a significant shift within the normal spectrum towards a less favorable metabolic and inflammatory state in adolescents who have attempted suicide. This "gradient of risk" within normative bounds highlights the potential of these biomarkers as sensitive indicators of pathophysiological processes that precede overt clinical pathology. This study exhibits several limitations that warrant cautious interpretation. First, the cross-sectional design inherently restricts causal inference, and the internal validation of our predictive model within a single cohort necessitates confirmation in an independent, prospective sample to establish generalizability 10 , 15 . Second, the modest predictive performance (AUC ≤ 0.74) underscores the need for multi-modal biomarker integration 32 , 33 . Third, regarding our immuno-metabolic measures, the reliance on composite ratios and the absence of specific inflammatory markers (e.g., CRP, IL-6) limit mechanistic insight, and the lack of body composition metrics (e.g., waist circumference) beyond BMI omits potential nuance to the metabolic profile. Finally, our study has important limitations regarding generalizability 34 . The significant overrepresentation of female participants (> 80%), while reflective of the higher known prevalence of depressive disorders and suicide attempts among adolescent females, limits the extension of our findings to male adolescents. Future studies must specifically recruit larger male cohorts to determine if the identified immune-metabolic signature is shared or distinct across genders. Furthermore, all participants were of Han Chinese ethnicity, which restricts the applicability of our results to other racial and ethnic groups. Thus, while our findings provide a robust biomarker signature within this specific demographic, their external validity awaits confirmation in more diverse, multi-ethnic populations. Future research should prioritize: (1) longitudinal cohorts tracking dynamic biomarker changes relative to suicidal behavior; (2) mechanistic dissection using advanced models to elucidate how lipid-protein dysregulation alters neural circuits; and (3) targeted trials evaluating metabolic interventions in depressed youth with dyslipidemia. (4) the inclusion of more diverse populations to assess the generalizability of these findings. In conclusion, this study provides robust evidence for an immune-metabolic signature associated with adolescent suicide risk, pointing to a potential biological pathway that warrants further investigation. Our findings advocate for integrating routine metabolic profiling into risk stratification while emphasizing that current results represent a gradient of risk within normal ranges rather than definitive diagnostic criteria. By shifting focus from subjective assessments to actionable biomarker-guided hypotheses, we unveil novel targets for preventive research. Future work must prioritize longitudinal validation and interventional trials before any clinical application can be considered. 5 Conclusions This study establishes ​​immune-metabolic dysregulation​​ as a core biological pathway in adolescent suicide risk, advocating for the integration of ​​precision psychiatry frameworks​​-incorporating routine metabolic profiling (lipids, serum proteins)-into clinical risk stratification. By shifting focus from subjective assessments to ​​actionable biomarker-guided interventions​​, we unveil novel targets for prevention (e.g., ω-3/fibrate supplementation, nutritional support). Future research must prioritize ​​longitudinal validation​​ of dynamic biomarker trajectories, ​​mechanistic dissection​​ of neuroimmune-metabolic crosstalk using advanced models, and ​​targeted trials​​ of metabolic interventions to transform reactive care into proactive, life-saving strategies. Declarations Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This project was supported by the Key Science and Technology Program of Zigong City (2023-NKY-02-11). Author Contribution Conceptualization: [Y.L.], [K.L.]; Methodology: [K.L.]; Formal Analysis: [Y.L.], [L.W.]; Investigation: [G.L.], [S.W.] (clinical assessments), [M.L.] (laboratory assays); Data Curation: [S.Z.]; Writing -Original Draft: [Y.L], [S.Z.]; Supervision, Project Administration, Funding Acquisition: [K.L.]; All authors approved the final version and agree to be accountable for all aspects of the work. Acknowledgement The authors gratefully acknowledge the contributions of: Clinical assessment teams at Zigong Mental Health Center for participant recruitment and diagnostic evaluations; Research participants and their guardians for their essential involvement in this study. Data Availability The datasets generated and analyzed during the current study are not publicly available due to patient confidentiality and ethical restrictions but are available from the corresponding author on reasonable request, subject to approval by the institutional ethics committee of Zigong Mental Health Center. References Bitsko RH, Claussen AH, Lichstein J, et al. Mental Health Surveillance Among Children - United States, 2013–2019. MMWR supplements Feb. 2022;25(2):1–42. 10.15585/mmwr.su7102a1 . Song X, Liu X, Zhou Y, Zhang X. Prevalence and correlates of suicide attempts in young patients with first-episode and drug-naïve major depressive disorder: A large cross-sectional study. Journal Affect disorders Nov 1. 2023;340:340–6. 10.1016/j.jad.2023.08.006 . Ye G, Li Z, Yue Y, et al. Suicide attempt rate and the risk factors in young, first-episode and drug-naïve Chinese Han patients with major depressive disorder. BMC psychiatry Sep. 2022;16(1):612. 10.1186/s12888-022-04254-x . Lu W, Keyes KM. Major depression with co-occurring suicidal thoughts, plans, and attempts: An increasing mental health crisis in US adolescents, 2011–2020. Psychiatry research Sep. 2023;327:115352. 10.1016/j.psychres.2023.115352 . Guo BC, Chen YJ, Huang WY, Lin MJ, Wu HP. Psychological disorders and suicide attempts in youths during the pre-COVID and post-COVID era in a Taiwan pediatric emergency department. Front Psychol. 2023;14:1281806. 10.3389/fpsyg.2023.1281806 . Ribeiro JD, Franklin JC, Fox KR, et al. Self-injurious thoughts and behaviors as risk factors for future suicide ideation, attempts, and death: a meta-analysis of longitudinal studies. Psychological medicine Jan. 2016;46(2):225–36. 10.1017/s0033291715001804 . Nusslock R, Alloy LB, Brody GH, Miller GE. Annual Research Review: Neuroimmune network model of depression: a developmental perspective. Journal child Psychol psychiatry allied disciplines Apr. 2024;65(4):538–67. 10.1111/jcpp.13961 . Kuhlman KR, Cole SW, Irwin MR, Craske MG, Fuligni AJ, Bower JE. The role of early life adversity and inflammation in stress-induced change in reward and risk processes among adolescents. Brain, behavior, and immunity . Mar. 2023;109:78–88. 10.1016/j.bbi.2023.01.004 . Ma YJ, Zhou YJ, Wang DF, et al. Association of Lipid Profile and Suicide Attempts in a Large Sample of First Episode Drug-Naive Patients With Major Depressive Disorder. Front Psychiatry. 2020;11:543632. 10.3389/fpsyt.2020.543632 . Zhao K, Zhou S, Shi X, et al. Potential metabolic monitoring indicators of suicide attempts in first episode and drug naive young patients with major depressive disorder: a cross-sectional study. BMC psychiatry Jul. 2020;28(1):387. 10.1186/s12888-020-02791-x . Daray FM, Chiapella LC, Grendas LN, et al. Peripheral blood cellular immunophenotype in suicidal ideation, suicide attempt, and suicide: a systematic review and meta-analysis. Molecular psychiatry Dec. 2024;29(12):3874–92. 10.1038/s41380-024-02587-5 . Keaton SA, Madaj ZB, Heilman P, et al. An inflammatory profile linked to increased suicide risk. Journal Affect disorders Mar 15. 2019;247:57–65. 10.1016/j.jad.2018.12.100 . Li H, Zhang X, Sun Q, Zou R, Li Z, Liu S. Association between serum lipid concentrations and attempted suicide in patients with major depressive disorder: A meta-analysis. PLoS ONE. 2020;15(12):e0243847. 10.1371/journal.pone.0243847 . Maes M, Smith R, Christophe A, Vandoolaeghe E, Van Gastel A, Neels H, Demedts P, Wauters A, Meltzer HY. Lower serum high-density lipoprotein cholesterol (HDL-C) in major depression and in depressed men with serious suicidal attempts: relationship with immune-inflammatory markers. Acta Psychiatr Scand. 1997;95(3):212–21. Feng XZ, Wang K, Li Z, et al. Association between thyroid autoimmunity and clinical characteristics in first-episode and drug-naive depressed patients with suicide attempts. General hospital psychiatry . Jul-Aug. 2023;83:156–63. 10.1016/j.genhosppsych.2023.05.008 . Zhu Q, Jiang G, Lang X, et al. Prevalence and clinical correlates of thyroid dysfunction in first-episode and drug-naïve major depressive disorder patients with metabolic syndrome. Journal Affect disorders Nov. 2023;15:341:35–41. 10.1016/j.jad.2023.08.103 . Capuzzi E, Bartoli F, Crocamo C, Malerba MR, Clerici M, Carrà G. Recent suicide attempts and serum lipid profile in subjects with mental disorders: A cross-sectional study. Psychiatry research Dec. 2018;270:611–5. 10.1016/j.psychres.2018.10.050 . Liu Z, Sun L, Sun F, et al. The abnormalities of lipid metabolism in children and adolescents with major depressive disorder and relationship with suicidal ideation and attempted suicide. Heliyon May. 2024;15(9):e30344. 10.1016/j.heliyon.2024.e30344 . Cui S, Liu Z, Liu Y, et al. Correlation Between Systemic Immune-Inflammation Index and Suicide Attempts in Children and Adolescents with First-Episode, Drug-Naïve Major Depressive Disorder During the COVID-19 Pandemic. J Inflamm Res. 2023;16:4451–60. 10.2147/jir.S433397 . Zung WW, A SELF-RATING DEPRESSION, SCALE. Archives of general psychiatry. Jan. 1965;12:63–70. 10.1001/archpsyc.1965.01720310065008 . Yesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res. 1982;17(1):37–49. 10.1016/0022-3956(82)90033-4 . Zung WW. A rating instrument for anxiety disorders. Psychosomatics Nov-Dec. 1971;12(6):371–9. 10.1016/s0033-3182(71)71479-0 . Dunstan DA, Scott N. Norms for Zung's Self-rating Anxiety Scale. BMC psychiatry Feb. 2020;28(1):90. 10.1186/s12888-019-2427-6 . Maes M, Smith R, Christophe A, et al. Lower serum high-density lipoprotein cholesterol (HDL-C) in major depression and in depressed men with serious suicidal attempts: relationship with immune-inflammatory markers. Acta psychiatrica Scandinavica Mar. 1997;95(3):212–21. 10.1111/j.1600-0447.1997.tb09622.x . Eidan AJ, Al-Harmoosh RA, Al-Amarei HM. Estimation of IL-6, INFγ, and Lipid Profile in Suicidal and Nonsuicidal Adults with Major Depressive Disorder. Journal interferon & cytokine research: official J Int Soc Interferon Cytokine Research Mar. 2019;39(3):181–9. 10.1089/jir.2018.0134 . Al-Hakeim HK, Al-Fadhel SZ, Al-Dujaili AH, Carvalho A, Sriswasdi S, Maes M. Development of a Novel Neuro-immune and Opioid-Associated Fingerprint with a Cross-Validated Ability to Identify and Authenticate Unknown Patients with Major Depression: Far Beyond Differentiation, Discrimination, and Classification. Molecular neurobiology Nov. 2019;56(11):7822–35. 10.1007/s12035-019-01647-0 . Alves-Costa S, de Souza BF, Rodrigues FA, et al. High free sugars, insulin resistance, and low socioeconomic indicators: the hubs in the complex network of non-communicable diseases in adolescents. Diabetology & metabolic syndrome Sep. 2024;28(1):235. 10.1186/s13098-024-01469-8 . Brundin L, Sellgren CM, Lim CK, et al. An enzyme in the kynurenine pathway that governs vulnerability to suicidal behavior by regulating excitotoxicity and neuroinflammation. Translational psychiatry Aug 2. 2016;6(8):e865. 10.1038/tp.2016.133 . Maes M, Vasupanrajit A, Jirakran K, Zhou B, Tunvirachaisakul C, Almulla AF. First-episode mild depression in young adults is a pre-proatherogenic condition even in the absence of subclinical metabolic syndrome: lowered lecithin-cholesterol acyltransferase as a key factor. Neuro Endocrinol letters Dec. 2024;22(7–8):475–91. Knowles EEM, Curran JE, Meikle PJ, et al. Disentangling the genetic overlap between cholesterol and suicide risk. Neuropsychopharmacology: official publication Am Coll Neuropsychopharmacology Dec. 2018;43(13):2556–63. 10.1038/s41386-018-0162-1 . Schumacher A, Muha J, Campisi SC, Bradley-Ridout G, Lee ACH, Korczak DJ. The Relationship between Neurobiological Function and Inflammation in Depressed Children and Adolescents: A Scoping Review. Neuropsychobiology. 2024;83(2):61–72. 10.1159/000538060 . Puangsri P, Ninla-Aesong P. Potential usefulness of complete blood count parameters and inflammatory ratios as simple biomarkers of depression and suicide risk in drug-naive, adolescents with major depressive disorder. Psychiatry research Nov. 2021;305:114216. 10.1016/j.psychres.2021.114216 . Bajaj S, Blair KS, Dobbertin M, et al. Machine learning based identification of structural brain alterations underlying suicide risk in adolescents. Discover mental health Feb. 2023;13(1):6. 10.1007/s44192-023-00033-6 . Kaufman EA, Crowell SE, Coleman J, Puzia ME, Gray DD, Strayer DL. Electroencephalographic and cardiovascular markers of vulnerability within families of suicidal adolescents: A pilot study. Biological psychology Jul. 2018;136:46–56. 10.1016/j.biopsycho.2018.05.007 . Crews FT, Coleman LG Jr., Macht VA, Vetreno RP, Alcohol. HMGB1, and Innate Immune Signaling in the Brain. Alcohol research: Curr reviews. 2024;44(1):04. 10.35946/arcr.v44.1.04 . Deleemans JM, Chleilat F, Reimer RA, et al. The chemo-gut study: investigating the long-term effects of chemotherapy on gut microbiota, metabolic, immune, psychological and cognitive parameters in young adult Cancer survivors; study protocol. BMC cancer Dec. 2019;23(1):1243. 10.1186/s12885-019-6473-8 . Sun N, Liu Z, Sun L, et al. Higher levels of total cholesterol/high-density lipoprotein cholesterol ratios are associated with an increased risk of suicidal behavior in children and adolescents with depressive disorders. Front Psychiatry. 2025;16:1557451. 10.3389/fpsyt.2025.1557451 . Hashimoto O, Kuniishi H, Nakatake Y, Yamada M, Wada K, Sekiguchi M. Early life stress from allergic dermatitis causes depressive-like behaviors in adolescent male mice through neuroinflammatory priming. Brain, behavior, and immunity . Nov. 2020;90:319–31. 10.1016/j.bbi.2020.09.013 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Jan, 2026 Read the published version in BMC Psychiatry → Version 1 posted Editorial decision: Revision requested 08 Dec, 2025 Reviews received at journal 06 Dec, 2025 Reviews received at journal 06 Dec, 2025 Reviewers agreed at journal 05 Dec, 2025 Reviewers agreed at journal 03 Dec, 2025 Reviewers invited by journal 03 Dec, 2025 Editor invited by journal 25 Nov, 2025 Editor assigned by journal 24 Nov, 2025 Submission checks completed at journal 24 Nov, 2025 First submitted to journal 15 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8124663","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554820251,"identity":"28231200-fdab-4aec-84f6-b7ba0a2da9ba","order_by":0,"name":"Yudiao Liang","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yudiao","middleName":"","lastName":"Liang","suffix":""},{"id":554820252,"identity":"692eadbd-0e93-4eff-af94-0715d1cfd88b","order_by":1,"name":"Chengyi Tan","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chengyi","middleName":"","lastName":"Tan","suffix":""},{"id":554820253,"identity":"2a845a8d-4aa8-4dba-99e0-6e1c2204e6ec","order_by":2,"name":"Ming Liu","email":"","orcid":"","institution":"Zigong Hospital Affiliated to Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Liu","suffix":""},{"id":554820254,"identity":"1562813d-51b7-4435-ad9c-9026e6f7b4be","order_by":3,"name":"Lei Wang","email":"","orcid":"","institution":"Zigong Hospital Affiliated to Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""},{"id":554820255,"identity":"3349458e-c92a-4250-945c-2bacaff2c028","order_by":4,"name":"Sha Zhang","email":"","orcid":"","institution":"Zigong Hospital Affiliated to Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sha","middleName":"","lastName":"Zhang","suffix":""},{"id":554820256,"identity":"57e5638d-8dfa-421d-a08d-1292c3dc54ac","order_by":5,"name":"Kezhi Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBACCWYQWSDBwM/MfPgBCVoMJBgk29nSDIjTAiaBag3O8yhIEKVFsp338MsfBhaJmw/zAHXW2EQT1CLNzJdmIWEgkbjtMO+BBwzH0nIbCGmRY+YxMzAAa+FLMGBsOEyklgSgls3NPAYSRGmRZuYxfnAAqGUDM7FaJJt5zBgbDCSMZxwGBnICMX6ROH/G+OOPijrZ/v7Dhx98qLEhrAUI2BDRkUCEchBg/kCkwlEwCkbBKBipAAAMPzbOXN/sJwAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":true,"prefix":"","firstName":"Kezhi","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-11-16 01:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8124663/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8124663/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12888-025-07751-x","type":"published","date":"2026-01-24T15:58:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":97688993,"identity":"639ff30e-abf2-4f3e-a982-564ad02825e2","added_by":"auto","created_at":"2025-12-08 10:42:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174825,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8124663/v1/9893cfe9fe2b97570387ef22.docx"},{"id":97688990,"identity":"af765d62-a608-46cc-ace5-79d4b6d27368","added_by":"auto","created_at":"2025-12-08 10:42:03","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8307,"visible":true,"origin":"","legend":"","description":"","filename":"8e7406c7323849febfa55c6372b8dc7d.json","url":"https://assets-eu.researchsquare.com/files/rs-8124663/v1/5c9f662b67de2840783dec3c.json"},{"id":97688992,"identity":"5aac7548-643e-4b8b-a3a4-17ddad0e6874","added_by":"auto","created_at":"2025-12-08 10:42:03","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":173196,"visible":true,"origin":"","legend":"","description":"","filename":"8e7406c7323849febfa55c6372b8dc7d1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8124663/v1/f4d0dd6755559bb94a6c1878.xml"},{"id":97688991,"identity":"dc4c3423-7e60-454c-8bb2-c340328148f6","added_by":"auto","created_at":"2025-12-08 10:42:03","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113836,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8124663/v1/b301359be32291637a6b1a63.jpeg"},{"id":97688996,"identity":"a7029f04-a937-47cf-8ec6-1035f19c3bfc","added_by":"auto","created_at":"2025-12-08 10:42:03","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":168836,"visible":true,"origin":"","legend":"","description":"","filename":"8e7406c7323849febfa55c6372b8dc7d1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8124663/v1/8ef6b41b466a2b5d7ebc346b.xml"},{"id":97893852,"identity":"2bed281b-1549-47fa-8107-f00710e5d9b0","added_by":"auto","created_at":"2025-12-10 15:31:22","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":176689,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8124663/v1/fa9edd40ee5665a2f0acde17.html"},{"id":97688995,"identity":"50ac4e9d-68d6-4d4c-82de-1e023ce064d8","added_by":"auto","created_at":"2025-12-08 10:42:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25281,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curves Comparing Predictive Utility of Metabolic Biomarkers for Suicide Attempt Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curves demonstrate the discriminative accuracy of serum total protein (TP), triglycerides (TG), and their combined model for distinguishing adolescents with major depressive disorder and recent suicide attempts (MDD+SA) from non-suicidal MDD controls. The combined model (solid green line) represents the linear predictor derived from a binary logistic regression model incorporating both TP and TG as continuous predictors. This combined model significantly outperformed individual biomarkers, achieving an AUC of 0.74 (95% CI: 0.68-0.80; p\u0026lt;0.001). Triglycerides alone (dash-dotted red line) yielded an AUC of 0.627 (95% CI: 0.562-0.692; p\u0026lt;0.001), while TP (dotted blue line) showed modest predictive value (AUC=0.589, 95% CI: 0.523-0.656; p=0.017). The diagonal grey line represents chance performance (AUC=0.50).\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8124663/v1/d9e3ce07d9f3d70ce8a1a967.png"},{"id":101152061,"identity":"c418af17-a3be-4f97-b291-f92615a9c65f","added_by":"auto","created_at":"2026-01-26 16:09:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1299938,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8124663/v1/fe36c150-a245-49f0-bb60-26bdaa7f2439.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Immune-Metabolic Dysregulation and Suicide Risk in Adolescents with Major Depressive Disorder: A Cross- Sectional Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) represents a critical public health burden among adolescents and young adults globally, with its association with suicidal behaviors constituting an urgent clinical and societal challenge\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Alarmingly, suicide is now the second leading cause of death in this age group, and up to 20% of adolescents with MDD attempt suicide within five years of diagnosis \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This highlights the significant unmet need for effective prevention strategies\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Despite these statistics, clinicians still lack objective biomarkers to distinguish suicidal from non-suicidal patients, forcing reliance on subjective interviews that have only modest predictive value\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This critical gap leaves a substantial unmet clinical need for reliable, biologically anchored tools that can identify adolescents at imminent risk and guide early intervention.\u003c/p\u003e\u003cp\u003eTo address this urgent gap, the present study focuses on immune-metabolic dysregulation-quantifiable alterations in lipid profiles and reduced total protein (TP)-as a peripheral blood signature capable of flagging imminent suicide attempts in drug-na\u0026iuml;ve adolescents with MDD. Because adolescence is marked by rapid neuro-immune maturation\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, disruptions in these systems may actively potentiate suicidal behaviour. Critically, we aim to move beyond previously studied single markers or inflammatory ratios by evaluating a novel combination of readily available metabolic and nutritional biomarkers, offering clinicians a potentially more accessible and integrated laboratory tool for early risk detection\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOver the past decade, converging evidence has positioned peripheral immune-metabolic markers as candidate biomarkers for suicide risk in MDD. Meta-analyses of \u0026gt;\u0026thinsp;7,000 participants demonstrate that suicidal ideation and attempts are associated with elevated systemic inflammation, reflected by higher neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR) and platelet-to-lymphocyte ratio (PLR) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Parallel lipidomic studies, however, reveal a striking developmental divergence: while adult meta-analyses consistently report associations with hypocholesterolemia (lower total cholesterol and LDL-C) \u003csup\u003e13\u003c/sup\u003e, large adolescent cohorts report a distinct profile characterized by hypertriglyceridemia and low HDL-C\u003csup\u003e9,10\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis apparent contradiction may be resolved by considering the unique neurodevelopmental and endocrine context of adolescence. The \"high-TG, low-HDL-C\" phenotype observed in youth may reflect a pathophysiology more tightly linked to the emerging insulin resistance, pronounced HPA axis reactivity, and fluctuating sex hormone levels that characterize this life stage. In contrast, the \"low-cholesterol\" phenotype in adults might be more indicative of long-term metabolic exhaustion, chronic malnutrition, or different inflammatory cascades. Therefore, these are likely not contradictory findings, but rather manifestations of distinct, developmentally specific pathophysiological pathways to suicidality.\u003c/p\u003e\u003cp\u003eFurthermore, thyroid autoimmunity (elevated TSH, thyroid peroxidase antibody (TPOAb)) amplifies metabolic dysregulation and correlates with suicide attempt (SA) severity\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Importantly, TP\u0026mdash;an inexpensive index of nutritional and inflammatory status\u0026mdash;has been barely examined in youth, despite consistent reductions observed in adult suicide attempters\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Collectively, these findings establish immune-metabolic convergence as a promising but still fragmentary framework for adolescent suicide risk prediction.\u003c/p\u003e\u003cp\u003eFour unresolved issues impede clinical translation. Contradictory lipid findings persist: adult meta-analyses report hypocholesterolemia \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, whereas adolescent cohorts observe hypertriglyceridemia and low HDL-C \u003csup\u003e9,10,18\u003c/sup\u003e. Methodological constraints include cross-sectional designs, modest sample sizes\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, and single-marker ROCs with AUC\u0026thinsp;\u0026le;\u0026thinsp;0.70\u003csup\u003e11\u003c/sup\u003e. Protein metabolism-inexpensive yet neglected-shows consistent reductions in adult attempters\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e but remains virtually unstudied in youth. Most critically, no composite immune-metabolic panel based on a core metabolic-nutritional axis (e.g., triglycerides\u0026thinsp;+\u0026thinsp;total protein) has been validated for suicide risk prediction in drug-na\u0026iuml;ve adolescents, leaving clinicians without objective, practical tools.\u003c/p\u003e\u003cp\u003eBuilding on the identified gaps, the primary objective of this study is to derive and internally validate a peripheral immune-metabolic signature that distinguishes adolescents with MDD who have attempted suicide from non-suicidal MDD peers. We specifically investigate whether a novel biomarker panel, focusing on the convergence of metabolic (triglycerides, HDL-C) and nutritional-inflammatory (total protein) pathways, provides superior predictive value compared to traditional inflammatory ratios. We hypothesize that adolescents with MDD\u0026thinsp;+\u0026thinsp;SA will exhibit higher triglycerides, lower HDL-C, and lower TP, alongside elevated inflammatory ratios compared with non-suicidal MDD controls. Furthermore, we aim to construct a multivariable biomarker panel whose discriminative accuracy surpasses that of single-marker ROC models, with a target AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.75.\u003c/p\u003e\u003cp\u003eWe will use a strict phenotypic case-control cohort of 264 drug-naive adolescents (168 MDD vs 96 MDD\u0026thinsp;+\u0026thinsp;SA) integrating routine clinical chemistry, lipid profiles, and ROC models to develop and internally validate predictive signatures. This approach leverages readily available clinical tests and advanced statistical techniques to create a practical and innovative risk stratification tool. These findings are expected to (i) elucidate the neuro-immuno-metabolic mechanisms underlying adolescent suicidality; (ii) equip clinicians with an objective, biologically grounded instrument for early risk detection and intervention; and (iii) inform future targeted interventions by identifying a specific, actionable metabolic-nutritional profile, thereby moving the field from associative observation towards mechanism-informed prevention.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and participants\u003c/h2\u003e\u003cp\u003eThis was a case-control study conducted in Zigong Mental Health Center from December 2023 to April 2025. We recruited 264 medication-naive adolescents aged 11\u0026ndash;18 years with a diagnosis of MDD, assessed by the MINI-KID instrument. Participants were confirmed to be medication-naive through detailed clinical interviews with both the adolescents and their parents/guardians, specifically inquiring about any past or current use of psychotropic medications (including antidepressants, antipsychotics, mood stabilizers, stimulants, or anxiolytics). This was further verified by reviewing all available medical records. Any individual with a history of such medication use was excluded. Participants were divided into two groups, the MDD group (control group, n\u0026thinsp;=\u0026thinsp;168), adolescents who met criteria for MDD and had no lifetime history of suicide attempts. Major depressive disorder with suicide attempt group (case group, n\u0026thinsp;=\u0026thinsp;96) : adolescents who met criteria for major depression and had a recent SA in the past 1 month, and had no prior lifetime attempts, identified by clinical interview and medical record review. All participants were of Han Chinese ethnicity. Exclusion criteria: Participants were excluded if they had: (1) major depression (e.g., bipolar disorder, schizophrenia, autism spectrum disorder) other than a primary diagnosis; Severe medical conditions known to affect immune/metabolic measures (e.g., active infection within 4 weeks, autoimmune disease, diabetes, severe kidney/liver disease, cancer). (3) current or past use of psychotropic medications (including antidepressants, antipsychotics, mood stabilizers) or medications known to affect immune/metabolic parameters (e.g., steroids, statins); (4) substance use disorder (excluding nicotine) in the past 6 months; (5) pregnancy or lactation.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e2. 2 Ethical considerations\u003c/h3\u003e\n\u003cp\u003eThe study protocol was approved by the institutional review board of Zigong Mental Health Center. Written informed consent was obtained from all participants and their parents or legal guardians. As this study was not a clinical trial, no clinical trial number is applicable. All procedures were performed in accordance with the ethical standards of the Declaration of Helsinki.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Clinical assessment\u003c/h2\u003e\u003cp\u003eAll participants underwent a comprehensive clinical assessment, which included: demographic information such as age, gender, and ethnicity; assessment of the severity of depressive symptoms using the Zahn Self-Rating Depression Scale (SDS) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e; assessment of the severity of anxiety symptoms using the Zahn Self-Rating Anxiety Scale (SAS) \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e; and strict assessment of the history and characteristics of suicidal thoughts and behaviors through structured clinical interviews and medical record reviews.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Blood sample collection and laboratory analysis\u003c/h2\u003e\u003cp\u003ePeripheral venous blood samples were obtained from all participants after an overnight fast of no less than 8 hours to minimize diurnal variation. Samples were processed within 2 hours of collection. The blood sample collection was performed during the same hospital admission, typically within 24\u0026ndash;48 hours following the diagnostic confirmation using the MINI-KID instrument to ensure clinical state consistency. To minimize batch effects, all blood samples were processed in a single, accredited clinical laboratory. Analyses for each assay type (e.g., CBC, biochemistry, immunoassay) were performed concurrently in batches that included randomly mixed samples from both the MDD and MDD\u0026thinsp;+\u0026thinsp;SA groups, following standardized protocols with strict internal quality control. The following analyses were performed using standard clinical laboratory methods on a Sysmex XN-1000 blood cell analyzer (Sysmex Corporation, Kobe, Japan), a Mindray BS-2000M biochemical analyzer (Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, China), and an Antu A2000Plus chemiluminescence immunoassay analyzer (Chengdu Antu Instruments Co., Ltd., Chengdu, China). Complete blood count (CBC) and differentiation parameters were analyzed on the Sysmex XN-1000 analyzer using flow cytometry and fluorescent dye staining. Metabolic indicators (HDL-C, triglycerides (TG), total cholesterol (TC)) and liver function indexes, including TP, measured by the Biuret method), albumin (measured by the Bromocresol Green method), and globulin, were determined on the Mindray BS-2000M analyzer using standardized colorimetric assays. TG and TC were measured by the GPO-POD and CHOD-POD methods, respectively, while HDL-C and LDL-C were measured by direct homogeneous methods. Endocrine indicators (serum cortisol, free triiodothyronine (fT3), thyroid-stimulating hormone (TSH)) were quantified using the Antu A2000Plus analyzer via chemiluminescence immunoassay. The following inflammatory ratios were calculated: neutrophil-to-HDL ratio (NHR)\u0026thinsp;=\u0026thinsp;Neutrophil count / HDL-C, monocyte-to-HDL ratio (MHR)\u0026thinsp;=\u0026thinsp;Monocyte count / HDL-C, lymphocyte-to-HDL ratio (LHR)\u0026thinsp;=\u0026thinsp;Lymphocyte count / HDL-C, and platelet-to-HDL ratio (PHR)\u0026thinsp;=\u0026thinsp;Platelet count / HDL-C. All assays were conducted per the manufacturers' instructions. The analytical precision was high, with intra-assay coefficients of variation (CVs)\u0026thinsp;\u0026lt;\u0026thinsp;3% for CBC, \u0026lt;\u0026thinsp;5% for lipid profiles and TP, and \u0026lt;\u0026thinsp;8% for endocrine markers; inter-assay CVs were \u0026lt;\u0026thinsp;5% for CBC, \u0026lt;\u0026thinsp;8% for lipid profiles and TP, and \u0026lt;\u0026thinsp;10% for endocrine markers. Laboratory personnel were blinded to the clinical grouping of all participants.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics version 26 (IBM Corp., Armonk, NY, USA). A two-sided significance level of α\u0026thinsp;=\u0026thinsp;0.05 was applied for all tests.The normality of continuous variables was assessed using the Kolmogorov-Smirnov test. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for normally distributed variables, median (interquartile range, IQR) for non-normally distributed variables, and frequency (%) for categorical variables.Between-group differences in demographic and clinical characteristics were assessed using independent samples t-tests (continuous, normal), Mann-Whitney U tests (continuous, non-normal), or χ\u0026sup2;/Fisher's exact tests (categorical), as appropriate. Between-group comparisons of biomarkers were conducted in a two-step manner. Initial unadjusted comparisons were performed using independent samples t-tests (for normally distributed data) or Mann-Whitney U tests (for non-normally distributed data). To control for potential confounding effects, these comparisons were subsequently validated using Analysis of Covariance (ANCOVA) for parametric data and Quade's non-parametric ANCOVA for non-parametric data, with age, sex, and body mass index (BMI) included as covariates in all models. The effect size for the group difference (MDD\u0026thinsp;+\u0026thinsp;SA vs. MDD) in these adjusted models is reported as the standardized beta coefficient (β), and the results are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Correlation and multivariate analyses were employed to examine the relationships between biomarkers and clinical outcomes. Spearman's rank-order correlation (ρ) was used for bivariate assessments. Multiple linear regression analyses, adjusted for age, sex, and BMI, were conducted to identify biomarkers independently associated with anxiety (SAS) and depression (SDS) severity. The results are expressed as standardized beta coefficients (β) with 95% confidence intervals and are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e.To identify independent predictors of a suicide attempt (MDD\u0026thinsp;+\u0026thinsp;SA status), binary logistic regression was performed. The model included all variables that showed significant between-group differences or were of theoretical relevance, and was adjusted for the covariates of age, sex, and BMI. The results are presented as regression coefficients (B), standard errors (SE), Wald statistics, p-values, odds ratios (OR), and 95% confidence intervals (CI) in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The model's goodness-of-fit was evaluated using the Hosmer-Lemeshow test, and its explanatory power was quantified by Nagelkerke's R\u0026sup2;. Multicollinearity was assessed via the Variance Inflation Factor (VIF), with all values below 2.0 indicating no substantive concern.The predictive performance of significant independent biomarkers from the logistic regression was evaluated using Receiver Operating Characteristic (ROC) curve analysis, with the area under the curve (AUC) and 95% CI reported. A composite score based on the linear predictor from the binary logistic regression model was used for the combined biomarker analysis. Given the exploratory nature of this biomarker study and the biological intercorrelation among many immunological and metabolic parameters, a strict Bonferroni correction for multiple comparisons was not applied, as it would be overly conservative and increase the risk of Type II errors. Instead, the interpretation of results prioritizes effect sizes, confidence intervals, and the convergence of findings across univariate, multivariate, and predictive models to identify robust associations.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of peripheral laboratory indicators between MDD and MDD\u0026thinsp;+\u0026thinsp;SA groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMDD (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;168\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMDD\u0026thinsp;+\u0026thinsp;SA (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) n\u0026thinsp;=\u0026thinsp;96\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAdjusted β (Group)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF (Group)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAdjusted p value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil percentage (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e53.41\u0026thinsp;\u0026plusmn;\u0026thinsp;9.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e55.82\u0026thinsp;\u0026plusmn;\u0026thinsp;10.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et=-1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte percentage (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e36.79\u0026thinsp;\u0026plusmn;\u0026thinsp;8.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e34.22\u0026thinsp;\u0026plusmn;\u0026thinsp;9.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;2.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.024*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.031*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean corpuscular Hb conc. (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e322.10\u0026thinsp;\u0026plusmn;\u0026thinsp;10.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e324.38\u0026thinsp;\u0026plusmn;\u0026thinsp;10.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et=-1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW-SD (fL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e41.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e41.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count (\u0026times;10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e264.27\u0026thinsp;\u0026plusmn;\u0026thinsp;67.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e260.48\u0026thinsp;\u0026plusmn;\u0026thinsp;63.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlateletcrit (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.265\u0026thinsp;\u0026plusmn;\u0026thinsp;0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.268\u0026thinsp;\u0026plusmn;\u0026thinsp;0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et=-0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.803\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum prealbumin (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e234.56\u0026thinsp;\u0026plusmn;\u0026thinsp;43.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e230.10\u0026thinsp;\u0026plusmn;\u0026thinsp;37.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin/globulin ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et=-0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFree thyroxine (pmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e16.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e15.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: RDW-SD: red blood cell distribution width-standard deviation. All indicators followed a normal distribution and were initially compared using an independent-samples t-test. The Adjusted β (Group) represents the standardized beta coefficient for the 'Group' variable (MDD\u0026thinsp;+\u0026thinsp;SA vs. MDD) derived from Analysis of Covariance (ANCOVA) models controlling for age, sex, and body mass index (BMI). The F-statistic and corresponding Adjusted p value are for the main effect of 'Group' in these ANCOVA models. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of non-normally distributed biomarkers between MDD and MDD\u0026thinsp;+\u0026thinsp;SA groups using Mann-Whitney U test and Quade's ANCOVA\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiomarker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMDD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMDD\u0026thinsp;+\u0026thinsp;SA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAdjusted β (Group)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAdjusted p value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite blood cell count (10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.17 (5.10,7.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.61(5.45,8.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;2.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.021*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.028*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil count (10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.13 (2.30, 4.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.42(2.50, 4.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;2.457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.014*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.019*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte count (10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.18 (1.70, 2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.12 (1.60,2.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyte count (10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42 (0.32,0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.43 (0.33,0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;1.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEosinophil count (10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11 (0.06,0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.15 (0.08, 0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;2.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.006**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.008**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasophil count (10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02 (0.01, 0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03 (0.02,0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;3.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyte percentage (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.6(5.1, 7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.6 (5.1 7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEosinophil percentage (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.8 (1.0, 2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5 (1.4, 3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;2.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.024*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.030*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasophil percentage (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3 (0.2,0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4 (0.3, 0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;2.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.015*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.021*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed blood cell count (10\u0026sup1;\u0026sup2;/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.53(4.20,4.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.39(4.10,4.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;1.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e158(147, 170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160(148,172)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematocrit (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.1 (37.5,42.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.5(36.0,40.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;2.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.024*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.032*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean corpuscular volume (fL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88.8(86.0,92.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.7(87.0,92.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;1.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean corpuscular hemoglobin (pg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.5(27.4, 30.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.1(28.0,30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;1.680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed-cell distribution width (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.7 (12.0,13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.7 (12.1, 13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet distribution width (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.9 (14.5,17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.8 (14.2,17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.741\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean platelet volume (fL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.15 (9.2,11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.10 (9.20,10.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.622\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal protein (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.0 (66.0, 74.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.4(65.0,72.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;2.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.017*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.023*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.2 (42.1, 48.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.8(41.9,47.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.528\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlobulin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.7(24.0,28.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.6(22.8,28.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;1.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.90 (0.65,1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.13 (0.76, 1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;3.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-density lipoprotein cholesterol (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.40 (1.10,1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.27 (1.00, 1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;3.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-density lipoprotein cholesterol (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.23 (1.70,2.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.27(1.70,2.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriiodothyronine (nmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.18 (1.02,1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.21 (1.05,1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.795\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThyroxine (nmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.3 (9.8, 18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.68 (7.5, 17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;1.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFree triiodothyronine (pmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.51(4.90,6.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.57(4.85,5.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.910\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThyroid-stimulating hormone (mIU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.05 (1.40,2.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.28 (1.50,3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;1.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProlactin (\u0026micro;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e409.24(276.18,652.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e450.56(301.25,793.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;1.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCortisol (nmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.6 (7.5, 16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.0 (7.2, 14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;1.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTestosterone (nmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.31 (0.15, 0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.31 (0.16,0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstradiol (pmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.4(30.5,90.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.8 (32.1, 89.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29(0.20,0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33(0.24,0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;3.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.003**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.19 (1.50,2.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.78 (1.85,3.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;3.696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180(130,224)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e196(145,233)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;2.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.034*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.041*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.55 (1.10 1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.67 (1.20,2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;2.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.017*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.022*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.45 (1.00,1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.70 (1.10,2.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;1.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119 (95,145)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116(85, 155)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.18 (0.12,0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.20 (0.14,0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: The Adjusted β (Group) represents the standardized beta coefficient for the 'Group' variable (MDD\u0026thinsp;+\u0026thinsp;SA vs. MDD) derived from Quade's non-parametric ANCOVA models controlling for age, sex, and body mass index (BMI). The corresponding Adjusted p value is for the main effect of 'Group' in these models. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01.Abbreviations: NHR: neutrophil/high-density lipoprotein ratio; PHR: platelet/high-density lipoprotein ratio; MHR: monocyte/high-density lipoprotein ratio; LHR: lymphocyte/high-density lipoprotein ratio; NLR: Neutrophil/Lymphocyte ratio; PLR: Platelet-to-Lymphocyte ratio; MLR: Monocyte-to-Lymphocyte ratio.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple Linear Regression Analysis of Biomarkers and Covariates Associated with SAS and SDS Scores\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiomarker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSAS Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSDS Score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.08 (-0.04, 0.20)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.06 (-0.06, 0.18)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.320\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[-0.10 (-0.22, 0.02)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[-0.08 (-0.20, 0.04)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.180\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.05 (-0.07, 0.17)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.03 (-0.09, 0.15)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEosinophil count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.09 (-0.03, 0.21)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.11 (-0.01, 0.23)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean Corpuscular Hemoglobin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[-0.15 (-0.27, -0.03)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.015*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[-0.17 (-0.29, -0.05)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Cholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[-0.13 (-0.25, -0.01)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.031*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[-0.12 (-0.24, 0.00)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[-0.12 (-0.24, 0.00)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[-0.16 (-0.28, -0.04)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.011*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCortisol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.14 (0.02, 0.26)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.023*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.06 (-0.06, 0.18)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.10 (-0.04, 0.24)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.12 (-0.02, 0.26)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: All models include all predictors listed in the table. β represents the standardized beta coefficient. CI\u0026thinsp;=\u0026thinsp;Confidence Interval. SAS\u0026thinsp;=\u0026thinsp;Self-Rating Anxiety Scale; SDS\u0026thinsp;=\u0026thinsp;Self-Rating Depression Scale.*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01.Abbreviations: HDL-C: High-density lipoprotein cholesterol; LHR: Lymphocyte-to-HDL ratio.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBinary Logistic Regression Analysis of Predictors for Suicide Attempt (MDD\u0026thinsp;+\u0026thinsp;SA Status)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI for OR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.96, 1.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.80, 2.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.89, 1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte percentage (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.89, 1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite blood cell count (10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.78, 1.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematocrit (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.89, 1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal protein (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.88, 0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.22, 3.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-density lipoprotein cholesterol (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03, 2.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.04, 27.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.47, 1.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.993, 1.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.29, 3.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3471.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: SE: Standard Error; OR: Odds Ratio; CI: Confidence Interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline Demographic and Clinical Characteristics\u003c/h2\u003e\u003cp\u003eThe study included 264 drug-na\u0026iuml;ve Han Chinese adolescents with MDD, comprising 168 without a history of suicide attempts (MDD group) and 96 with a recent suicide attempt (MDD\u0026thinsp;+\u0026thinsp;SA group). As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the two groups were well-balanced across all measured demographic and clinical baseline characteristics. Demographic profiles, including gender distribution, age at enrollment (median [IQR]\u0026thinsp;=\u0026thinsp;15.0 [14.0\u0026ndash;16.0] years for both groups), and Body Mass Index (BMI), were comparable (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Clinical history variables, such as family history of mental disorders, age at depression onset (median [IQR]\u0026thinsp;=\u0026thinsp;14.0 [13.0\u0026ndash;15.0] years for both groups), and lifetime prevalence of nonsuicidal self-injury (NSSI), also showed no significant differences. Furthermore, assessments of current clinical severity at enrollment revealed no significant disparities in anxiety (SAS; MDD: 71.0 [65.0\u0026ndash;78.0], MDD\u0026thinsp;+\u0026thinsp;SA: 73.0 [65.5\u0026ndash;83.0]) or depression (SDS; MDD: 60.0 [52.0-68.3], MDD\u0026thinsp;+\u0026thinsp;SA: 61.9 [53.0-71.1]) scores (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This comprehensive baseline comparison confirms the equivalence of the groups, strengthening the validity of the subsequent biomarker analyses.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Demographical and Clinical Characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMDD (n\u0026thinsp;=\u0026thinsp;168)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMDD\u0026thinsp;+\u0026thinsp;SA (n\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137 (81.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85 (88.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2; = 2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at enrollment (years), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.0 (14.0, 16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.0 (14.0, 16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ = -1.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity (Han Chinese), n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody Mass Index (BMI), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.472\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily history of mental disorders, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (26.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (32.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2; = 0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at depression onset (years), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.0 (13.0, 15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.0 (13.0, 15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ = -0.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLifetime NSSI history, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (40.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (47.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2; = 1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical Severity (at enrollment)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAS score, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.0 (65.0, 78.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.0 (65.5, 83.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ = -0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDS score, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.0 (52.0, 68.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.9 (53.0, 71.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ = -1.532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: MDD\u0026thinsp;=\u0026thinsp;major depressive disorder; SA\u0026thinsp;=\u0026thinsp;non-suicidal self-injury; SAS\u0026thinsp;=\u0026thinsp;Zung Self-Rating Anxiety Scale; SDS\u0026thinsp;=\u0026thinsp;Zung Self-Rating Depression Scale. Comparisons were performed using χ\u0026sup2; test or Mann\u0026ndash;Whitney U test as appropriate. IQR\u0026thinsp;=\u0026thinsp;Interquartile range.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Convergent Immune-Metabolic Dysregulation in Adolescent Suicide Attempters​\u003c/h2\u003e\u003cp\u003eComparative biomarker analysis, adjusted for age, sex, and BMI, revealed a distinct pathophysiological signature in adolescents with major depressive disorder and a recent suicide attempt (MDD\u0026thinsp;+\u0026thinsp;SA) compared to non-suicidal MDD controls. Analyses of parametrically distributed data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e) indicated that, after controlling for covariates, the MDD\u0026thinsp;+\u0026thinsp;SA group exhibited a significantly lower lymphocyte percentage (Adjusted β = -0.135, p\u0026thinsp;=\u0026thinsp;0.031). The neutrophil percentage showed a trend toward elevation that did not reach statistical significance (Adjusted β\u0026thinsp;=\u0026thinsp;0.112, p\u0026thinsp;=\u0026thinsp;0.061). Analyses of non-parametrically distributed biomarkers (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed more pronounced alterations. The MDD\u0026thinsp;+\u0026thinsp;SA group demonstrated a state of granulocyte hyperactivity, characterized by significantly elevated counts of eosinophils (Adjusted β\u0026thinsp;=\u0026thinsp;0.165, p\u0026thinsp;=\u0026thinsp;0.008) and basophils (Adjusted β\u0026thinsp;=\u0026thinsp;0.188, p\u0026thinsp;=\u0026thinsp;0.002), alongside increased white blood cell (Adjusted β\u0026thinsp;=\u0026thinsp;0.138, p\u0026thinsp;=\u0026thinsp;0.028) and neutrophil counts (Adjusted β\u0026thinsp;=\u0026thinsp;0.145, p\u0026thinsp;=\u0026thinsp;0.019). The percentages of eosinophils and basophils were also significantly higher in the MDD\u0026thinsp;+\u0026thinsp;SA group (Adjusted β\u0026thinsp;=\u0026thinsp;0.142, p\u0026thinsp;=\u0026thinsp;0.030 and Adjusted β\u0026thinsp;=\u0026thinsp;0.150, p\u0026thinsp;=\u0026thinsp;0.021, respectively).\u003c/p\u003e\u003cp\u003eCritically, the composite inflammatory ratios demonstrated robust and significant elevations in the MDD\u0026thinsp;+\u0026thinsp;SA group after adjustment. These included the neutrophil-to-HDL ratio (NHR) (Adjusted β\u0026thinsp;=\u0026thinsp;0.195, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), monocyte-to-HDL ratio (MHR) (Adjusted β\u0026thinsp;=\u0026thinsp;0.178, p\u0026thinsp;=\u0026thinsp;0.003), and lymphocyte-to-HDL ratio (LHR) (Adjusted β\u0026thinsp;=\u0026thinsp;0.140, p\u0026thinsp;=\u0026thinsp;0.022), collectively indicating a state of systemic inflammation coupled with impaired lipid-mediated anti-inflammatory protection.These immunological perturbations coincided with fundamental metabolic disruptions. The most consistent finding was a profound depletion of high-density lipoprotein cholesterol (HDL-C) (Adjusted β = -0.180, p\u0026thinsp;=\u0026thinsp;0.002), which was accompanied by significant accumulation of triglycerides (TG) (Adjusted β\u0026thinsp;=\u0026thinsp;0.204, p\u0026thinsp;=\u0026thinsp;0.001). This \"high-TG, low-HDL-C\" lipid profile delineates a dyslipidemic state that may potentiate inflammatory processes. The significance of these core lipid alterations was further supported by a post-hoc False Discovery Rate (FDR) correction, which they withstood (FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for both), indicating robustness against multiple testing.Concurrent hematological alterations were also observed. The MDD\u0026thinsp;+\u0026thinsp;SA group had significantly lower hematocrit (Adjusted β = -0.140, p\u0026thinsp;=\u0026thinsp;0.032) and reduced levels of total protein (TP) (Adjusted β = -0.142, p\u0026thinsp;=\u0026thinsp;0.023), with the latter showing a trend-level significance after FDR correction (FDR-adjusted p\u0026thinsp;=\u0026thinsp;0.069). In contrast, endocrine homeostasis across the cortisol, gonadal, and thyroid axes remained preserved between groups (all adjusted p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Independent Associations of Biomarkers with Anxiety and Depressive Symptomatology\u003c/h2\u003e\u003cp\u003eMultiple linear regression analyses, which included age, sex, and body mass index (BMI) in the model, were conducted to identify biomarkers independently associated with symptom severity after accounting for these demographic and anthropometric factors. The full results of these adjusted models are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. For anxiety severity (SAS score), the multivariate model revealed that lower levels of mean corpuscular hemoglobin (β = -0.152, p\u0026thinsp;=\u0026thinsp;0.015) and total cholesterol (β = -0.135, p\u0026thinsp;=\u0026thinsp;0.031) were significant independent predictors of higher anxiety. Conversely, higher cortisol levels (β\u0026thinsp;=\u0026thinsp;0.139, p\u0026thinsp;=\u0026thinsp;0.023) were independently associated with increased anxiety severity. The covariates of age, sex, and BMI themselves were not significant independent predictors of SAS scores in this model (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). A different pattern of associations emerged for depressive severity (SDS score). In the adjusted model, lower levels of mean corpuscular hemoglobin (β = -0.174, p\u0026thinsp;=\u0026thinsp;0.006) and high-density lipoprotein cholesterol (HDL-C) (β = -0.161, p\u0026thinsp;=\u0026thinsp;0.011) were significant independent predictors of more severe depressive symptoms. Similarly, age, sex, and BMI did not show significant independent associations with SDS scores (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). It is noteworthy that several biomarkers which demonstrated significant simple correlations in bivariate analysis (e.g., eosinophil parameters, inflammatory ratios such as LHR and MHR) did not retain their independent association in the multivariate model after adjusting for covariates and other biomarkers. This highlights the importance of evaluating independent effects within a comprehensive statistical framework.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Independent Predictors of Adolescent Suicide Risk​\u003c/h2\u003e\u003cp\u003eA binary logistic regression analysis was performed to identify independent predictors of suicide attempt status, with the model adjusted for the covariates of age, sex, and body mass index (BMI). The full results, including the contributions of all covariates and biomarker variables, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. After adjusting for demographic and anthropometric factors, two peripheral biomarkers emerged as significant and independent predictors of suicide attempt risk. Elevated serum triglycerides (TG) demonstrated the strongest association, with each 1 mmol/L increase conferring more than a twofold increase in the odds of belonging to the MDD\u0026thinsp;+\u0026thinsp;SA group (Adjusted OR\u0026thinsp;=\u0026thinsp;2.17, 95% CI: 1.22\u0026ndash;3.87, p\u0026thinsp;=\u0026thinsp;0.009). Conversely, reduced serum total protein (TP) was independently predictive of suicide risk, with each 1 g/L decrease associated with a 7% increase in the odds of a suicide attempt (Adjusted OR\u0026thinsp;=\u0026thinsp;0.93, 95% CI: 0.88\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.012).The selection of triglycerides for the final model over HDL-C was driven by the multivariate results. While both were significant in univariate analyses, only triglycerides retained strong independent predictive value in the covariate-adjusted model. In contrast, the association with HDL-C was substantially attenuated and was no longer significant (Adjusted OR\u0026thinsp;=\u0026thinsp;0.29, p\u0026thinsp;=\u0026thinsp;0.251), suggesting that its information regarding suicide risk in this cohort may be largely captured by the metabolic dysregulation reflected in TG levels.Notably, the inflammatory composite ratios (NHR, MHR, LHR) and other hematological parameters that were significant in bivariate analyses failed to retain independent predictive utility in the full multivariate model (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The covariates of age, sex, and BMI were not significant independent predictors in the final model (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The overall model demonstrated a good fit (Hosmer-Lemeshow test, p\u0026thinsp;=\u0026thinsp;0.601) and explained a substantial proportion of the variance in suicide risk (Nagelkerke R\u0026sup2; = 0.246).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Predictive Performance of Metabolic Biomarkers for Suicide Attempt Risk\u003c/h2\u003e\u003cp\u003eROC analysis demonstrated differential predictive utility of metabolic biomarkers for distinguishing adolescents with MDD and MDD\u0026thinsp;+\u0026thinsp;SA from non-suicidal MDD controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Serum triglycerides exhibited moderate discriminative capacity (AUC\u0026thinsp;=\u0026thinsp;0.627, 95% CI 0.562\u0026ndash;0.692; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), outperforming TP (AUC\u0026thinsp;=\u0026thinsp;0.589, 95% CI 0.523\u0026ndash;0.656; p\u0026thinsp;=\u0026thinsp;0.017). A combined model integrating both biomarkers significantly enhanced predictive accuracy (AUC\u0026thinsp;=\u0026thinsp;0.74, 95% CI 0.68\u0026ndash;0.80; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). To internally validate this combined model and address potential overfitting, we performed a 1000-iteration bootstrap validation. This analysis yielded a bias-corrected AUC of 0.73 (95% CI: 0.67\u0026ndash;0.79), confirming the robustness of the model's performance within our dataset. Nevertheless, it is critical to emphasize that this represents an internal validation. The ultimate test of the model's generalizability and potential clinical applicability lies in its validation in an independent, prospective cohort.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study identifies, for the first time in a medication-na\u0026iuml;ve adolescent MDD cohort, a convergent immune-metabolic signature characterized by hypertriglyceridemia, pronounced HDL-C depletion, and reduced total protein. Triglycerides and total protein served as independent predictors of suicide attempt risk beyond inflammatory composite ratios, retaining significance after adjustment for age, sex, and BMI. These findings advance the field by identifying a novel, clinically accessible biomarker combination, thereby refining the adult \"low-cholesterol-suicide\" hypothesis and underscoring a potential developmentally specific pathway for adolescent suicidality characterized by a metabolic-nutritional axis\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile the cross-sectional nature of our data precludes definitive causal inferences, the specificity and independence of the identified biomarker signature allow us to propose a plausible, mechanistic model for future validation. It remains unclear whether this metabolic state is a cause, a consequence, or a bidirectional component of suicidal behavior.\u003c/p\u003e\u003cp\u003eOur observed \"high-TG, low-HDL-C\" lipid profile was remarkably consistent in adolescents with MDD\u0026thinsp;+\u0026thinsp;SA\u003csup\u003e14\u003c/sup\u003e. Mechanistically, TG accumulation activates hepatic NF-κB signaling\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, driving peripheral inflammation and increasing blood-brain barrier permeability. Additionally, TG-induced mitochondrial dysfunction may compromise prefrontal-cingulate circuitry underlying impulse control\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. These processes may be particularly salient during adolescence, a period marked by metabolic transition\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Each 1 g/L decrease in serum total protein conferred a 7.3% increase in suicide attempt risk, representing the first validation of this association in an adolescent cohort\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Hypoproteinemia signals chronic inflammatory malnutrition, potentially leading to reduced albumin-bound tryptophan and increased shunting down the kynurenine pathway, thereby depleting serotonin precursors and heightening impulsive aggression\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough inflammatory ratios (NHR, MHR, LHR) were significantly elevated in univariate analyses, they failed to retain independent predictive value in the multivariate model. This finding can be attributed to several factors. Statistically, these ratios are derived from and thus share variance with their constituent cell counts and HDL-C levels, leading to multicollinearity which dilutes their unique contribution when all components are considered together. Biologically, the loss of significance likely reflects the dual confounding role of HDL-C itself; when severely depleted, the reduction in this central denominator shrinks both the numerator and denominator of the ratios, thereby compressing their dynamic range and discriminatory power\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Furthermore, the heightened immune dynamism characteristic of adolescence may render these cellular ratios more variable and less stable indicators than the core metabolic parameters (TG, TP) which they are derived from.\u003c/p\u003e\u003cp\u003eThe integration of triglycerides and total protein into existing clinical frameworks (e.g., CSSRS) leverages their routine accessibility and cost-effectiveness. While standalone predictive power remains moderate (TG AUC\u0026thinsp;=\u0026thinsp;0.627; TP AUC\u0026thinsp;=\u0026thinsp;0.589), their combination with clinical symptoms significantly improves sensitivity (combined AUC\u0026thinsp;=\u0026thinsp;0.74)\u003csup\u003e3,15\u003c/sup\u003e. This aligns with broader evidence that ​​multi-modal approaches​​\u0026mdash;incorporating inflammatory markers like neutrophil-to-lymphocyte ratio (NLR) \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and neuroimaging biomarkers\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u0026mdash;enhance suicide risk prediction. Prospective validation of the \"TG\u0026thinsp;+\u0026thinsp;TP\u0026thinsp;+\u0026thinsp;clinical symptoms\" model is urgently needed, particularly given the ​​neuroimmune crosstalk​​ observed in adolescent depression\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur findings generate important hypotheses for future research into novel interventions. The specific metabolic and nutritional imbalances we identified suggest that interventions targeting these pathways-such as omega-3 fatty acids for dyslipidemia or nutritional support for hypoproteinemia-warrant investigation in future longitudinal intervention studies\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This dual action may disrupt the ​​lipid-suicidality pathway\u003cb\u003e​\u003c/b\u003e​\u003csup\u003e10\u003c/sup\u003e.​\u003cb\u003e​\u003c/b\u003eProtein Optimization​​: Nutritional support (e.g., branched-chain amino acids) counteracts hypoalbuminemia-linked oxidative stress, potentially restoring neurotrophic factor synthesis (BDNF) \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.​\u003cb\u003e​\u003c/b\u003eAdjunctive Strategies​​: Adjunctive anti-inflammatory agents (e.g., minocycline) and gut-microbiome modulation (e.g., probiotics) show promise in mitigating neuroimmune dysregulation\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.High-risk adolescents-especially those with metabolic syndrome (MetS) or autoimmune comorbidity-should undergo ​​quarterly monitoring​​ of: Lipid profiles (TG, HDL-C, TC/HDL-C ratio) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003eInflammatory indices (CRP, NLR)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, TP\u003csup\u003e14\u003c/sup\u003eThis stratified surveillance, integrated into routine psychiatric care, enables early intervention before crisis escalation\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. However, we must state with the utmost caution that our cross-sectional, associative data do not constitute evidence to recommend any specific treatment or intervention in current clinical practice. The imperative next step is to test whether modifying these metabolic pathways leads to a reduction in suicide risk in prospective, randomized controlled trials.\u003c/p\u003e\u003cp\u003eFurthermore, the reviewer raised a critical point regarding the clinical interpretation of our findings. It is important to note that the median values for the majority of biomarkers, including triglycerides, HDL-C, and total protein, in both the MDD and MDD\u0026thinsp;+\u0026thinsp;SA groups fell within broad laboratory-defined normal ranges for adolescents. This observation is pivotal, as it suggests that the clinically relevant signal is not necessarily a value outside the pathological range, but rather a significant shift within the normal spectrum towards a less favorable metabolic and inflammatory state in adolescents who have attempted suicide. This \"gradient of risk\" within normative bounds highlights the potential of these biomarkers as sensitive indicators of pathophysiological processes that precede overt clinical pathology.\u003c/p\u003e\u003cp\u003eThis study exhibits several limitations that warrant cautious interpretation. First, the cross-sectional design inherently restricts causal inference, and the internal validation of our predictive model within a single cohort necessitates confirmation in an independent, prospective sample to establish generalizability\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Second, the modest predictive performance (AUC\u0026thinsp;\u0026le;\u0026thinsp;0.74) underscores the need for multi-modal biomarker integration\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Third, regarding our immuno-metabolic measures, the reliance on composite ratios and the absence of specific inflammatory markers (e.g., CRP, IL-6) limit mechanistic insight, and the lack of body composition metrics (e.g., waist circumference) beyond BMI omits potential nuance to the metabolic profile. Finally, our study has important limitations regarding generalizability\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The significant overrepresentation of female participants (\u0026gt;\u0026thinsp;80%), while reflective of the higher known prevalence of depressive disorders and suicide attempts among adolescent females, limits the extension of our findings to male adolescents. Future studies must specifically recruit larger male cohorts to determine if the identified immune-metabolic signature is shared or distinct across genders. Furthermore, all participants were of Han Chinese ethnicity, which restricts the applicability of our results to other racial and ethnic groups. Thus, while our findings provide a robust biomarker signature within this specific demographic, their external validity awaits confirmation in more diverse, multi-ethnic populations.\u003c/p\u003e\u003cp\u003eFuture research should prioritize: (1) longitudinal cohorts tracking dynamic biomarker changes relative to suicidal behavior; (2) mechanistic dissection using advanced models to elucidate how lipid-protein dysregulation alters neural circuits; and (3) targeted trials evaluating metabolic interventions in depressed youth with dyslipidemia. (4) the inclusion of more diverse populations to assess the generalizability of these findings.\u003c/p\u003e\u003cp\u003eIn conclusion, this study provides robust evidence for an immune-metabolic signature associated with adolescent suicide risk, pointing to a potential biological pathway that warrants further investigation. Our findings advocate for integrating routine metabolic profiling into risk stratification while emphasizing that current results represent a gradient of risk within normal ranges rather than definitive diagnostic criteria. By shifting focus from subjective assessments to actionable biomarker-guided hypotheses, we unveil novel targets for preventive research. Future work must prioritize longitudinal validation and interventional trials before any clinical application can be considered.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis study establishes ​​immune-metabolic dysregulation​​ as a core biological pathway in adolescent suicide risk, advocating for the integration of ​​precision psychiatry frameworks​​-incorporating routine metabolic profiling (lipids, serum proteins)-into clinical risk stratification. By shifting focus from subjective assessments to ​​actionable biomarker-guided interventions​​, we unveil novel targets for prevention (e.g., ω-3/fibrate supplementation, nutritional support). Future research must prioritize ​​longitudinal validation​​ of dynamic biomarker trajectories, ​​mechanistic dissection​​ of neuroimmune-metabolic crosstalk using advanced models, and ​​targeted trials​​ of metabolic interventions to transform reactive care into proactive, life-saving strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest Statement\u003c/h2\u003e\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis project was supported by the Key Science and Technology Program of Zigong City (2023-NKY-02-11).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: [Y.L.], [K.L.]; Methodology: [K.L.]; Formal Analysis: [Y.L.], [L.W.]; Investigation: [G.L.], [S.W.] (clinical assessments), [M.L.] (laboratory assays); Data Curation: [S.Z.]; Writing -Original Draft: [Y.L], [S.Z.]; Supervision, Project Administration, Funding Acquisition: [K.L.]; All authors approved the final version and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge the contributions of: Clinical assessment teams at Zigong Mental Health Center for participant recruitment and diagnostic evaluations; Research participants and their guardians for their essential involvement in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to patient confidentiality and ethical restrictions but are available from the corresponding author on reasonable request, subject to approval by the institutional ethics committee of Zigong Mental Health Center.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBitsko RH, Claussen AH, Lichstein J, et al. Mental Health Surveillance Among Children - United States, 2013\u0026ndash;2019. MMWR supplements Feb. 2022;25(2):1\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15585/mmwr.su7102a1\u003c/span\u003e\u003cspan address=\"10.15585/mmwr.su7102a1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong X, Liu X, Zhou Y, Zhang X. Prevalence and correlates of suicide attempts in young patients with first-episode and drug-na\u0026iuml;ve major depressive disorder: A large cross-sectional study. Journal Affect disorders Nov 1. 2023;340:340\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2023.08.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2023.08.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYe G, Li Z, Yue Y, et al. Suicide attempt rate and the risk factors in young, first-episode and drug-na\u0026iuml;ve Chinese Han patients with major depressive disorder. BMC psychiatry Sep. 2022;16(1):612. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12888-022-04254-x\u003c/span\u003e\u003cspan address=\"10.1186/s12888-022-04254-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu W, Keyes KM. Major depression with co-occurring suicidal thoughts, plans, and attempts: An increasing mental health crisis in US adolescents, 2011\u0026ndash;2020. Psychiatry research Sep. 2023;327:115352. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.psychres.2023.115352\u003c/span\u003e\u003cspan address=\"10.1016/j.psychres.2023.115352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo BC, Chen YJ, Huang WY, Lin MJ, Wu HP. Psychological disorders and suicide attempts in youths during the pre-COVID and post-COVID era in a Taiwan pediatric emergency department. Front Psychol. 2023;14:1281806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyg.2023.1281806\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2023.1281806\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRibeiro JD, Franklin JC, Fox KR, et al. Self-injurious thoughts and behaviors as risk factors for future suicide ideation, attempts, and death: a meta-analysis of longitudinal studies. Psychological medicine Jan. 2016;46(2):225\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/s0033291715001804\u003c/span\u003e\u003cspan address=\"10.1017/s0033291715001804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNusslock R, Alloy LB, Brody GH, Miller GE. Annual Research Review: Neuroimmune network model of depression: a developmental perspective. Journal child Psychol psychiatry allied disciplines Apr. 2024;65(4):538\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jcpp.13961\u003c/span\u003e\u003cspan address=\"10.1111/jcpp.13961\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuhlman KR, Cole SW, Irwin MR, Craske MG, Fuligni AJ, Bower JE. The role of early life adversity and inflammation in stress-induced change in reward and risk processes among adolescents. \u003cem\u003eBrain, behavior, and immunity\u003c/em\u003e. Mar. 2023;109:78\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbi.2023.01.004\u003c/span\u003e\u003cspan address=\"10.1016/j.bbi.2023.01.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa YJ, Zhou YJ, Wang DF, et al. Association of Lipid Profile and Suicide Attempts in a Large Sample of First Episode Drug-Naive Patients With Major Depressive Disorder. Front Psychiatry. 2020;11:543632. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyt.2020.543632\u003c/span\u003e\u003cspan address=\"10.3389/fpsyt.2020.543632\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao K, Zhou S, Shi X, et al. Potential metabolic monitoring indicators of suicide attempts in first episode and drug naive young patients with major depressive disorder: a cross-sectional study. BMC psychiatry Jul. 2020;28(1):387. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12888-020-02791-x\u003c/span\u003e\u003cspan address=\"10.1186/s12888-020-02791-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDaray FM, Chiapella LC, Grendas LN, et al. Peripheral blood cellular immunophenotype in suicidal ideation, suicide attempt, and suicide: a systematic review and meta-analysis. Molecular psychiatry Dec. 2024;29(12):3874\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41380-024-02587-5\u003c/span\u003e\u003cspan address=\"10.1038/s41380-024-02587-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeaton SA, Madaj ZB, Heilman P, et al. An inflammatory profile linked to increased suicide risk. Journal Affect disorders Mar 15. 2019;247:57\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2018.12.100\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2018.12.100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi H, Zhang X, Sun Q, Zou R, Li Z, Liu S. Association between serum lipid concentrations and attempted suicide in patients with major depressive disorder: A meta-analysis. PLoS ONE. 2020;15(12):e0243847. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0243847\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0243847\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaes M, Smith R, Christophe A, Vandoolaeghe E, Van Gastel A, Neels H, Demedts P, Wauters A, Meltzer HY. Lower serum high-density lipoprotein cholesterol (HDL-C) in major depression and in depressed men with serious suicidal attempts: relationship with immune-inflammatory markers. Acta Psychiatr Scand. 1997;95(3):212\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeng XZ, Wang K, Li Z, et al. Association between thyroid autoimmunity and clinical characteristics in first-episode and drug-naive depressed patients with suicide attempts. \u003cem\u003eGeneral hospital psychiatry\u003c/em\u003e. Jul-Aug. 2023;83:156\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.genhosppsych.2023.05.008\u003c/span\u003e\u003cspan address=\"10.1016/j.genhosppsych.2023.05.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu Q, Jiang G, Lang X, et al. Prevalence and clinical correlates of thyroid dysfunction in first-episode and drug-na\u0026iuml;ve major depressive disorder patients with metabolic syndrome. Journal Affect disorders Nov. 2023;15:341:35\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2023.08.103\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2023.08.103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCapuzzi E, Bartoli F, Crocamo C, Malerba MR, Clerici M, Carr\u0026agrave; G. Recent suicide attempts and serum lipid profile in subjects with mental disorders: A cross-sectional study. Psychiatry research Dec. 2018;270:611\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.psychres.2018.10.050\u003c/span\u003e\u003cspan address=\"10.1016/j.psychres.2018.10.050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z, Sun L, Sun F, et al. The abnormalities of lipid metabolism in children and adolescents with major depressive disorder and relationship with suicidal ideation and attempted suicide. Heliyon May. 2024;15(9):e30344. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2024.e30344\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e30344\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCui S, Liu Z, Liu Y, et al. Correlation Between Systemic Immune-Inflammation Index and Suicide Attempts in Children and Adolescents with First-Episode, Drug-Na\u0026iuml;ve Major Depressive Disorder During the COVID-19 Pandemic. J Inflamm Res. 2023;16:4451\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/jir.S433397\u003c/span\u003e\u003cspan address=\"10.2147/jir.S433397\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZung WW, A SELF-RATING DEPRESSION, SCALE. Archives of general psychiatry. Jan. 1965;12:63\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/archpsyc.1965.01720310065008\u003c/span\u003e\u003cspan address=\"10.1001/archpsyc.1965.01720310065008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res. 1982;17(1):37\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0022-3956(82)90033-4\u003c/span\u003e\u003cspan address=\"10.1016/0022-3956(82)90033-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZung WW. A rating instrument for anxiety disorders. Psychosomatics Nov-Dec. 1971;12(6):371\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0033-3182(71)71479-0\u003c/span\u003e\u003cspan address=\"10.1016/s0033-3182(71)71479-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDunstan DA, Scott N. Norms for Zung's Self-rating Anxiety Scale. BMC psychiatry Feb. 2020;28(1):90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12888-019-2427-6\u003c/span\u003e\u003cspan address=\"10.1186/s12888-019-2427-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaes M, Smith R, Christophe A, et al. Lower serum high-density lipoprotein cholesterol (HDL-C) in major depression and in depressed men with serious suicidal attempts: relationship with immune-inflammatory markers. Acta psychiatrica Scandinavica Mar. 1997;95(3):212\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1600-0447.1997.tb09622.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1600-0447.1997.tb09622.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEidan AJ, Al-Harmoosh RA, Al-Amarei HM. Estimation of IL-6, INFγ, and Lipid Profile in Suicidal and Nonsuicidal Adults with Major Depressive Disorder. Journal interferon \u0026amp; cytokine research: official J Int Soc Interferon Cytokine Research Mar. 2019;39(3):181\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/jir.2018.0134\u003c/span\u003e\u003cspan address=\"10.1089/jir.2018.0134\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl-Hakeim HK, Al-Fadhel SZ, Al-Dujaili AH, Carvalho A, Sriswasdi S, Maes M. Development of a Novel Neuro-immune and Opioid-Associated Fingerprint with a Cross-Validated Ability to Identify and Authenticate Unknown Patients with Major Depression: Far Beyond Differentiation, Discrimination, and Classification. Molecular neurobiology Nov. 2019;56(11):7822\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12035-019-01647-0\u003c/span\u003e\u003cspan address=\"10.1007/s12035-019-01647-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlves-Costa S, de Souza BF, Rodrigues FA, et al. High free sugars, insulin resistance, and low socioeconomic indicators: the hubs in the complex network of non-communicable diseases in adolescents. Diabetology \u0026amp; metabolic syndrome Sep. 2024;28(1):235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13098-024-01469-8\u003c/span\u003e\u003cspan address=\"10.1186/s13098-024-01469-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrundin L, Sellgren CM, Lim CK, et al. An enzyme in the kynurenine pathway that governs vulnerability to suicidal behavior by regulating excitotoxicity and neuroinflammation. Translational psychiatry Aug 2. 2016;6(8):e865. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/tp.2016.133\u003c/span\u003e\u003cspan address=\"10.1038/tp.2016.133\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaes M, Vasupanrajit A, Jirakran K, Zhou B, Tunvirachaisakul C, Almulla AF. First-episode mild depression in young adults is a pre-proatherogenic condition even in the absence of subclinical metabolic syndrome: lowered lecithin-cholesterol acyltransferase as a key factor. Neuro Endocrinol letters Dec. 2024;22(7\u0026ndash;8):475\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKnowles EEM, Curran JE, Meikle PJ, et al. Disentangling the genetic overlap between cholesterol and suicide risk. Neuropsychopharmacology: official publication Am Coll Neuropsychopharmacology Dec. 2018;43(13):2556\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41386-018-0162-1\u003c/span\u003e\u003cspan address=\"10.1038/s41386-018-0162-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchumacher A, Muha J, Campisi SC, Bradley-Ridout G, Lee ACH, Korczak DJ. The Relationship between Neurobiological Function and Inflammation in Depressed Children and Adolescents: A Scoping Review. Neuropsychobiology. 2024;83(2):61\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000538060\u003c/span\u003e\u003cspan address=\"10.1159/000538060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePuangsri P, Ninla-Aesong P. Potential usefulness of complete blood count parameters and inflammatory ratios as simple biomarkers of depression and suicide risk in drug-naive, adolescents with major depressive disorder. Psychiatry research Nov. 2021;305:114216. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.psychres.2021.114216\u003c/span\u003e\u003cspan address=\"10.1016/j.psychres.2021.114216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBajaj S, Blair KS, Dobbertin M, et al. Machine learning based identification of structural brain alterations underlying suicide risk in adolescents. Discover mental health Feb. 2023;13(1):6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s44192-023-00033-6\u003c/span\u003e\u003cspan address=\"10.1007/s44192-023-00033-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaufman EA, Crowell SE, Coleman J, Puzia ME, Gray DD, Strayer DL. Electroencephalographic and cardiovascular markers of vulnerability within families of suicidal adolescents: A pilot study. Biological psychology Jul. 2018;136:46\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.biopsycho.2018.05.007\u003c/span\u003e\u003cspan address=\"10.1016/j.biopsycho.2018.05.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrews FT, Coleman LG Jr., Macht VA, Vetreno RP, Alcohol. HMGB1, and Innate Immune Signaling in the Brain. Alcohol research: Curr reviews. 2024;44(1):04. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.35946/arcr.v44.1.04\u003c/span\u003e\u003cspan address=\"10.35946/arcr.v44.1.04\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeleemans JM, Chleilat F, Reimer RA, et al. The chemo-gut study: investigating the long-term effects of chemotherapy on gut microbiota, metabolic, immune, psychological and cognitive parameters in young adult Cancer survivors; study protocol. BMC cancer Dec. 2019;23(1):1243. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12885-019-6473-8\u003c/span\u003e\u003cspan address=\"10.1186/s12885-019-6473-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun N, Liu Z, Sun L, et al. Higher levels of total cholesterol/high-density lipoprotein cholesterol ratios are associated with an increased risk of suicidal behavior in children and adolescents with depressive disorders. Front Psychiatry. 2025;16:1557451. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyt.2025.1557451\u003c/span\u003e\u003cspan address=\"10.3389/fpsyt.2025.1557451\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHashimoto O, Kuniishi H, Nakatake Y, Yamada M, Wada K, Sekiguchi M. Early life stress from allergic dermatitis causes depressive-like behaviors in adolescent male mice through neuroinflammatory priming. \u003cem\u003eBrain, behavior, and immunity\u003c/em\u003e. Nov. 2020;90:319\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbi.2020.09.013\u003c/span\u003e\u003cspan address=\"10.1016/j.bbi.2020.09.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Major depressive disorder, Adolescent suicide, Immune-metabolic dysregulation, Biomarkers, Triglycerides, Total protein, Predictive model","lastPublishedDoi":"10.21203/rs.3.rs-8124663/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8124663/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eBiomarkers distinguishing suicidal from non-suicidal adolescents with major depressive disorder (MDD) remain elusive. This study investigated immune-metabolic dysregulation and its predictive utility for suicide attempt (SA) risk in medication-naïve adolescents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA case-control study compared 168 non-suicidal MDD adolescents with 96 MDD+SA adolescents (recent SA). Peripheral biomarkers included immune cell ratios (neutrophil/HDL [NHR], monocyte/HDL [MHR], lymphocyte/HDL [LHR]), metabolic markers (triglycerides, HDL-C, total protein (TP)), and hematological indices. Analyses employed group comparisons (t-tests/Mann-Whitney U), Spearman correlations, binary logistic regression, and ROC analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The groups were well-matched demographically and for clinical severity. After adjusting for covariates, the MDD+SA group exhibited significant immune-metabolic dysregulation, including granulocyte hyperactivity, elevated inflammatory ratios, profound HDL-C depletion, hypertriglyceridemia, and reduced total protein (all key findings with adjusted p \u0026lt; 0.05). A post-hoc False Discovery Rate analysis confirmed the robustness of the core lipid findings. Binary logistic regression, adjusted for age, sex, and BMI, identified triglycerides (adjusted OR=2.17 per mmol/L) and total protein (adjusted OR=0.93 per g/L decrease) as independent predictors of SA. A model combining triglycerides and total protein significantly outperformed individual biomarkers in ROC analysis (AUC=0.74, 95% CI: 0.68-0.80).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eConvergent lipid-protein dysregulation represents a novel pathway for adolescent SA risk, identifiable as a significant shift within the normal laboratory range. A simple two-biomarker panel shows promise for risk stratification but requires future validation. These findings highlight metabolic dysfunction as a potential target for preventive strategies, though they do not yet constitute evidence for specific clinical interventions.\u003c/p\u003e","manuscriptTitle":"Immune-Metabolic Dysregulation and Suicide Risk in Adolescents with Major Depressive Disorder: A Cross- Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 10:41:58","doi":"10.21203/rs.3.rs-8124663/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-08T10:06:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-06T12:35:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-06T11:27:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148713323398129979336763728279285554726","date":"2025-12-05T23:20:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16340381354431932147750120729734129598","date":"2025-12-03T16:59:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-03T16:40:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-25T09:53:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-24T10:17:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-24T10:12:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2025-11-16T01:47:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33923359-1e9e-4c9f-bb20-7d95488d69d8","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:05:48+00:00","versionOfRecord":{"articleIdentity":"rs-8124663","link":"https://doi.org/10.1186/s12888-025-07751-x","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2026-01-24 15:58:55","publishedOnDateReadable":"January 24th, 2026"},"versionCreatedAt":"2025-12-08 10:41:58","video":"","vorDoi":"10.1186/s12888-025-07751-x","vorDoiUrl":"https://doi.org/10.1186/s12888-025-07751-x","workflowStages":[]},"version":"v1","identity":"rs-8124663","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8124663","identity":"rs-8124663","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}