Lower insulin resistance in Chinese patients with severe major depressive disorder: associations with the inflammatory response

Lower insulin resistance in Chinese patients with severe major depressive disorder: associations with the inflammatory response | 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 Lower insulin resistance in Chinese patients with severe major depressive disorder: associations with the inflammatory response Yueyang Luo, Mengqi Niu, Tangcong Chen, Jing Li, Abbas F. Almulla, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8943225/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Major depressive disorder (MDD) is widely acknowledged as stemming from the dysregulation of neuroimmune, metabolic, and oxidative stress (NIMETOX) pathways. The objective of this study was to examine insulin metabolism in Chinese patients with MDD and to examine the relationship between insulin resistance and the acute phase protein (APP) response, as shown by lower albumin and transferrin. Methods: This investigation utilized a cross-sectional case-control approach, enrolling 125 inpatients with MDD and 40 healthy controls. Results: Patients with MDD exhibited markedly reduced levels of fasting plasma glucose, insulin, and insulin resistance, and heightened insulin sensitivity, in comparison to healthy controls. The significance of these alterations persisted after controlling for metabolic syndrome, body mass index (BMI), and age, but was nullified with adjustment for both negative APPs. We determined that elevated BMI, albumin, transferrin, and age levels accounted for 41.4% of the variance in insulin resistance. Insulin resistance was significantly and inversely associated with weight loss. We found that 27.7% of the variance in overall depression severity was accounted for by adverse childhood experiences (positive correlation) and insulin resistance (negative correlation). Conclusions: This work demonstrates that Chinese MDD patients display increased insulin sensitivity and reduced insulin resistance, with these alterations being associated with a modest smoldering inflammatory response. MDD is characterized by a hormetic response that enhances insulin efficacy, hence optimizing glucose consumption to sustain normal organ function. It is incorrect to claim that MDD is intrinsically associated with increased insulin resistance. inflammation major depressive disorder neuroimmune biomarkers metabolic syndrome insulin resistance Introduction Major depressive disorder (MDD) is increasingly recognized as a result of dysregulation of neuroimmune, metabolic, and oxidative stress (NIMETOX) pathways (Maes, Almulla, et al., 2025 a; Maes, Jirakran, et al., 2025b ). Recent studies indicate that MDD is significantly linked to persistent low-grade inflammation, insulin resistance (IR), and cardiometabolic disorders, such as metabolic syndrome (MetS), type 2 diabetes mellitus (T2DM), and cardiovascular disease (Ma et al., 2025 ; Maes, Almulla, et al., 2025 a; Maes, Niu, et al., 2025c ). Within this immunometabolic framework, activation of M1 macrophages and elevated levels of proinflammatory cytokines, such as interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF)-α, might impair insulin signaling, triggering peripheral and central insulin resistance that leads to hyperglycemia and compensatory hyperinsulinemia (Maes, Almulla, et al., 2025 a; Maes, Jirakran, et al., 2025b ). This inflammatory response triggers an acute-phase reaction in the liver, reducing negative acute-phase proteins (APPs) levels, particularly albumin and transferrin (Maes, 1993 ). Although scientific research often suggests a bidirectional association between insulin resistance and MDD, the findings vary across studies (Fanelli et al., 2025 ). Some reviews indicate significantly elevated fasting insulin levels and HOMA-IR indices in MDD patients (Fernandes et al., 2022 ), whereas other studies did not find increased insulin resistance indices in MDD compared to controls (de Melo et al., 2017 ; Maes, Jirakran, et al., 2025b ; Morelli et al., 2021 ). A meta-analysis showed that during the acute phase of depression, patients had significantly elevated insulin levels, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) indices, and insulin variability (Fernandes et al., 2022 ). In contrast, these markers showed no significant changes during remission or following antidepressant treatment (Fernandes et al., 2022 ). However, the latter analysis has a key methodological limitation: the strategy for controlling confounding factors relied solely on matching sex, age, and BMI during the study design phase, without further adjusting for MetS, overweight or obesity, in the statistical models. The inclusion of people with MetS or high BMI values in the analyses may have caused bias in metabolic data, resulting in biased findings (Maes et al., 2024 ; Maes, Jirakran, et al., 2025b ). Nevertheless, the associations between insulin resistance and MDD go far beyond the possible differences between MDD and healthy controls. Thus, increased insulin resistance in MDD contributes to increased oxidative and nitrosative stress, and together with enhanced activation of cytokine networks, leads to increased severity of the affective and physio-somatic symptoms in MDD (Maes, Jirakran, et al., 2025b ; Morelli et al., 2021 ). Such changes may be explained by intertwined causal relationships between insulin resistance, oxidative stress and activated cytokine networks (Maes, Jirakran, et al., 2025b ). The inconsistent association between insulin resistance and MDD limits its reliability as a robust biomarker of MDD. In contrast, the negative APP response with low albumin and transferrin levels demonstrates great consistency in their association with MDD (Maes et al., 1991 ; Wainberg et al., 2021 ). These two negative APPs are closely linked to immune activation, adverse childhood experiences (ACEs), and the severity of somatic and affective symptoms (Almulla et al., 2025 ; Maes, 1993 ). While alterations in negative APPs (albumin and transferrin) have consistently been linked to MDD and immune activation, their interactions with insulin resistance in MDD patients remain unclear. In addition, research specifically examining the relationship between insulin resistance and inflammation in Chinese MDD patients remains scarce. Thus, this study aimed to investigate the insulin resistance status in Chinese MDD patients and further elucidate the association between insulin resistance and the negative APP response, as reflected by the negative APPs albumin and transferrin. We hypothesize that changes in insulin resistance are associated with the severity of MDD and that these changes are associated with the negative APP response. Methods Participants This research utilized a cross-sectional case-control approach and included 165 participants, comprising 125 people diagnosed with MDD and 40 healthy controls. Recruitment occurred at the Psychiatric Center of Sichuan Provincial People’s Hospital in Chengdu, China. The eligibility criteria for participants with MDD were: (1) a validated diagnosis of MDD as per the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5); (2) a score exceeding 18 on the 21-item Hamilton Depression Rating Scale (HAMD-21) (Hamilton, 1960 ); (3) an age range of 18 to 65 years, irrespective of gender; and (4) the capacity to furnish written informed consent, with guardian consent when necessary. Healthy controls were matched to MDD patients according to sex, age, body mass index (BMI), and educational attainment, and were recruited among hospital workers and their connections. The exclusion criteria for both MDD patients and healthy controls included: (1) pregnancy or lactation; (2) a history of acute infections or recent surgeries within the last three months; (3) severe allergic reactions in the preceding month; (4) treatment with immunosuppressive or immunomodulatory agents, including glucocorticoids; (5) use of therapeutic-dose antioxidants or omega-3 supplements in the last three months; (6) frequent or chronic analgesic use; (7) serious medical illnesses such as autoimmune disorders, systemic lupus erythematosus, rheumatoid arthritis, type 1 diabetes, inflammatory bowel disease, psoriasis, chronic obstructive pulmonary disease, or malignancy; (8) neurological disorders including epilepsy, stroke, brain tumors, Parkinson’s disease, Alzheimer’s disease, or multiple sclerosis; (9) personality or developmental disorders (e.g., borderline, antisocial, or severe intellectual disability); (10) other major psychiatric diagnoses, such as bipolar disorder, schizophrenia, schizoaffective disorder, psycho-organic conditions, or substance-use disorders (except nicotine dependence). Furthermore, healthy controls were excluded if they had a history of MDD or dysthymia, any DSM-IV anxiety disorder, or a family history of mood disorders, suicide, or substance use disorders other than nicotine dependence. Clinical Assessments All participants underwent a semi-structured interview conducted by a trained physician to collect demographic and clinical information, including age, sex, education, medical and psychiatric history, and family history. Psychiatric diagnoses were assessed using the Mini International Neuropsychiatric Interview (MINI) (Sheehan et al., 1998 ), which screens for both current and lifetime psychiatric disorders according to the DSM-IV and ICD-10 criteria. On the same day, a single evaluator assessed depression, anxiety, and somatic symptoms. Depression severity was measured using the total score on the 21-item Hamilton Depression Rating Scale (HAMD) (Hamilton, 1960 ), and anxiety severity was assessed using the Hamilton Anxiety Rating Scale (HAMA) (Hamilton, 1959 ). Self-reported anxiety was evaluated using the state version of the State-Trait Anxiety Inventory (STAI) (Spielberger et al., 1971 ). Fibromyalgia and Chronic Fatigue Syndrome (CFS) symptoms were quantified using the 12-item Fibro Fatigue Scale (FF) (Zachrisson et al., 2002 ). To minimize the influence of overlapping somatic items across scales, pure scores were derived for each affective domain. Pure HAMD and pure HAMA were computed by selecting items specific to depression, anxiety, and subjective anxiety, while excluding confounding somatic items. Pure HAMD was calculated as the sum of the individual items related to depression, guilt, suicidal ideation, and loss of interest, excluding any somatic items. The pure HAMA score is the sum of items that assess anxiety, tension, fear, and anxious behavior. Pure FF score is calculated by summing the z-scores of items related to muscle pain, muscle tension, flu-like symptoms, fatigue, headache, autonomic and gastrointestinal symptoms. To create an integrated clinical severity index, we computed a z unit-based composite score by summing the z-scores of the pure HAMD, pure HAMA, pure STAI, and pure FF (labeled as overall severity of depression or OSOD. Weight loss was assessed using the relevant items from the HAMD. Adverse childhood experiences (ACEs) were evaluated using the Childhood Trauma Questionnaire-Short Form (CTQ-SF) (Bernstein et al., 2003 ). The total ACEs score was computed by summing the five subscales: emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect (Vasupanrajit et al., 2024 ). Physical measurements, such as height and weight, were documented. The BMI was determined by dividing the weight in kilograms by the square height in meters. Waist circumference (WC) was assessed at the midpoint between the iliac crest and the lowest rib. A composite z-score representing body composition was derived by aggregating BMI and WC (z BMI + z WC). MetS was delineated according to the 2009 Joint Scientific Statement of the American Heart Association and the National Heart, Lung, and Blood Institute (Alberti et al., 2009 ), necessitating the fulfillment of a minimum of three of the subsequent criteria: (1) Triglycerides ≥ 150 mg/dL; (2) WC ≥ 90 cm (men) or ≥ 80 cm (women); (3) Elevated blood pressure (systolic > 130 mmHg, diastolic > 85 mmHg, or current antihypertensive medication usage); (4) High-density lipoprotein cholesterol (HDL-C) < 40 mg/dL (men) or < 50 mg/dL (women); and (5) Fasting glucose ≥ 100 mg/dL or a diabetes diagnosis. Participants were graded according to the number of MetS criteria they satisfied. Assays Venous blood (30 mL) was obtained from each participant after a 10-hour fast, between 6:30 and 8:00 a.m., utilizing disposable syringes and serum tubes. After centrifugation at 3,500 rpm, the serum was divided into Eppendorf tubes and preserved at − 80°C until analysis. Fasting blood glucose (FBG) was quantified utilizing the glucose assay kit (glucose oxidase method, Gcell, Beijing Strong Biotechnologies, Inc.) on a fully automated biochemical analyzer (ADVIA 2400, Siemens Healthcare Diagnostics Inc.), exhibiting intra-assay and inter-assay analytical coefficients of variation (CVs) of 3.2% and 6.8%, respectively. Serum insulin was measured utilizing the Atellica IM insulin assay kit (direct chemiluminescence method, Siemens Healthcare Diagnostics Inc.) on the Atellica IM fully automated chemiluminescence immunoassay analyzer (Siemens Healthcare Diagnostics Inc.). The intra-assay and inter-assay analytical CVs are 1.8% and 3.6%, respectively. Serum triglycerides (TyG) were quantified using TyG assay kits (GPO-PAP method, Gcell, Beijing Strong Biotechnologies, Inc.) on a fully automated biochemical analyzer (ADVIA 2400, Siemens Healthcare Diagnostics Inc.), exhibiting intra-assay and inter-assay analytical CVs of 3.0% and 7.0%, respectively. Serum HDL-C was assayed by the direct method-select inhibition method (Beijing Strong Biotechnologies, Inc.) with the intra-assay and inter-assay CVs of < 4% and < 10%, respectively. Basal insulin resistance was evaluated using Homeostatic Model Assessment version 2 for insulin resistance (HOMA2-IR), whereas beta-cell function was measured by HOMA2-β and insulin sensitivity by HOMA2-IS. Calculations were conducted using the HOMA2 calculator ( http://www.dtu.ox.ac.uk ). We calculated indices of insulin resistance using z unit-based composite scores as z FBG + z insulin and indices of insulin sensitivity as z insulin – z FPG (Maes, Jirakran, et al., 2025b ). The TyG index, a metric for evaluating insulin resistance, was calculated using the formula: ln{fasting triglyceride (mg/dL) × fasting plasma glucose (mg/dL)/2} (Abbasi & Reaven, 2011 ). The Quantitative Insulin Sensitivity Check Index (QUICKI) was utilized as a validated approach for assessing insulin sensitivity in both diabetic and non-diabetic populations, including persons with obesity (Muniyappa et al., 2008 ). The QUICKI score is determined by the formula: QUICKI = 1 / {log (Fasting Insulin) + log (Fasting Glucose)}. This indicator exhibits a robust connection with glucose clamp studies (r = 0.78) (Muniyappa et al., 2008 ). Serum transferrin was quantified via an immunoturbidimetric assay (DIAYS DIAGNOSTIC SYSTEM, SHANGHAI CO., LTD) on a fully automated biochemical analyzer (ADVIA 2400, Siemens Healthcare Diagnostics Inc.) with a sensitivity of 0.03 g/L. The intra-assay and inter-assay analytical CVs were 1.96% and 0.67%, respectively. Serum albumin was quantified with the Bromocresol Green Method kit (Beijing Strong Biotechnologies, Inc.), exhibiting intra-assay and inter-assay CVs of 1.20% and 2.10%, respectively. To construct an integrated index of the AP response, a composite z-score was derived by summing the z-scores for z albumin and z transferrin. Statistics Pearson’s correlation calculation was employed to evaluate the connections between continuous variables. Contingency tables were used to analyze the associations between categorical variables. Differences in continuous variables among groups were assessed using analysis of variance (ANOVA). False Discovery Rate (FDR) was applied to correct multiple comparisons. The study considered potential confounders such as age, sex, smoking status, and BMI. Binary logistic regression analysis was employed to predict MDD (controls as the reference group) using insulin biomarkers, albumin, transferrin, ACEs, and demographic data. Key outcomes were Nagelkerke pseudo-R² (used as effect size), Wald statistics with corresponding p-values, odds ratios (OR) with 95% confidence intervals (CI), and unstandardized regression coefficients (B) with their standard errors (SE). Multiple regression analysis was used to predict the insulin biomarkers with albumin, transferrin, weight loss, and metabolic data as explanatory variables. Another series of multiple regression analyses examined the effects of insulin biomarkers and other predictors on the clinical rating scale scores. Both manual and automatic stepwise methods were employed. The automated procedure involved linear modeling with measures to prevent overfitting, with entry and removal criteria set at p = 0.05 and p = 0.07, respectively. The model outputs included standardized beta coefficients, degrees of freedom (df), p-values, R², and F-statistics. Heteroskedasticity was evaluated using the White and modified Breusch-Pagan tests, while collinearity was assessed using the tolerance and variance inflation factor (VIF). All analyses were two-tailed with a significance level of 0.05 for all tests. IBM SPSS Statistics 30 (Windows) was used for all analyses, and data transformations, including log10, square root, rank-order, and Winsorization, were applied where needed. Results Table 1 Demographic and clinical profile of patients with major depressive disorder (MDD) and healthy controls (HC). Variables HC (n = 40) MDD (n = 125) F / χ² df p-value Age (years) 37.1 (13.7) 35.7 (12.1) 0.37 1/163 0.542 Gender (m/f) 13/27 38/87 0.06a 1 0.845 Metabolic syndrome (MetS) (No/Yes) 29/10 102/22 1.173a 1 0.355 Ranking MetS 1.59 (1.46) 1.42 (1.26) 0.50 1/161 0.479 Body Mass Index (BMI) (kg/m²) 23.52 (4.07) 22.299 (3.36) 3.58 1/163 0.060 Waist circumference (WC) (cm) 79.38 (11.73) 78.27 (11.32) 0.28 1/163 0.595 z BMI + z WC (z score) 0.332 (2.020) -0.106 (1.840) 1.64 1/163 0.202 Weight loss (z score) -0.605 (0.000) 0.194 (1.080) 21.80 1/163 < 0.001 History of smoking (No/Yes) 37/3 101/24 3.03a 1 0.091 Educational years 13.9 (4.3) 13.5 (3.3) 0.26 1/163 0.613 Albumin (g/L) 43.64 (1.99) 41.18 (3.05) 22.44 1/161 < 0.001 Transferrin (mg/dL) 258.76 (37.44) 228.25 (38.66) 18.79 1/162 < 0.001 z albumin + z transferrin (z score) 0.723 (0.805) -0.227 (0.948) 31.92 1/161 < 0.001 Triglycerides (mmol/L) 1.39 (0.10) 1.36 (0.06) 0.205 1/161 0.479 Total HAMD 0.9 (2.0) 28.5 (5.2) MWUT - < 0.001 Pure HAMD 0.0 (0.0) 8.26 (4.04) MWUT - < 0.001 Total HAMA 1.1 (3.0) 26.6 (8.6) MWUT - < 0.001 Pure HAMA 0.1 (0.5) 9.0 (3.2) MWUT - < 0.001 Total FF 1.4 (2.9) 29.9 (9.9) MWUT - < 0.001 Pure FF 0.5 (1.0) 11.0 (6.0) MWUT - < 0.001 STAI state 36.8 (8.3) 58.1 (10.4) 137.23 1/153 < 0.001 Total ACEs -0.746 (0.821) 0.239 (0.935) 35.55 1/163 < 0.001 OSOD (PC score) -1.518 (0.237) 0.528 (0.491) 640.21 1/153 < 0.001 Data are presented as mean (SD); F: F-statistic from ANOVA; χ²: Chi-square test statistic (marked with the “a” in the table); MWUT: Mann–Whitney U test statistic. ACE: Adverse childhood experiences, HAMD: Hamilton Depression Rating Scale, HAMA: Hamilton Anxiety Rating Scale, FF: Fibro-Fatigue, STAI: State-Trait Anxiety Inventory, OSOD (PC score): overall severity of depression (principal component score). Socio-demographic and Clinical Characteristics Table 1 shows the socio-demographic and clinical characteristics of the study participants. No significant differences were observed between MDD and HC in terms of age, gender distribution, the prevalence of MetS, MetS ranking, BMI, WC, the z BMI + z WC score, history of smoking, or years of education. However, the MDD group demonstrated a significantly higher weight loss score compared to the HC group. Clinical assessments revealed significantly higher total scores on HAMD, HAMA, FF, and STAI in MDD patients relative to controls. Furthermore, MDD patients reported significantly higher levels of ACEs and a greater OSOD score. Serum albumin and transferrin levels were markedly lower in the MDD group, accompanied by a significant reduction in the z albumin + z transferrin score. Table 2 Results of general linear models (GLM) showing unadjusted and adjusted associations between metabolic indicators and major depressive disorder (MDD) versus healthy controls (HC) Variables HC n = 40 MDD n = 125 Unadjusted Adjusted (MetS, BMI, age) Adjusted (MetS, BMI, Age, Tf, Alb) F (df = 1/161) p F (df = 1/157) p F (df = 1/155) p FPG (mmol/L) 5.45 (0.51) 5.16 (0.59) 8.16 0.005 6.29 0.013 0.60 0.441 Insulin (mU/L) 8.04 (4.89) 6.01 (3.23) 8.97 0.003 19.93 < 0.001 3.92 0.050 z FPG + z INS (z score) 0.460 (1.110) -0.146 (0.920) 11.73 < 0.001 19.26 < 0.001 3.15 0.078 z INS - z GLU (z score) 0.018 (1.104) -0.006 (0.988) 0.02 0.899 3.25 0.073 0.96 0.328 HOMA2-IR 1.067 (0.653) 0.790 (0.428) 9.43 0.003 20.29 < 0.001 4.19 0.042 HOMA2-IS 124.9 (65.3) 164.0 (84.9) 6.97 0.009 10.12 0.002 0.00 0.985 HOMA2-β 79.0 (24.8) 73.7 (22.9) 1.49 0.224 8.65 0.004 0.13 0.722 QUICKI 0.356 (0.032) 0.375 (0.035) 9.00 0.003 15.05 < 0.001 0.24 0.629 TyG 8.070 (4.950) 7.136 (4.000) 1.44 0.232 0.00 0.963 0.00 0.950 Data are presented as mean (SD). F and P values were derived from ANOVA (unadjusted) and two GLM analyses: one adjusted for metabolic syndrome (MetS), body mass index (BMI), and age, and the second adjusted for MetS, BMI, transferrin (Tf), and albumin (Alb). FPG: fasting plasma glucose, HOMA2-IR: Homeostasis Model Assessment Version 2 for Insulin Resistance, HOMA2-IS: Homeostasis Model Assessment Version 2 for Insulin Sensitivity, HOMA2-β: Homeostasis Model Assessment Version 2 for β-cell Function, QUICKI: quantitative insulin sensitivity check index, TyG: triglyceride-glucose index. Insulin biomarkers in MDD Table 2 presents the results of GLM analyses examining the associations between metabolic indicators and MDD, both before and after adjustment for MetS, BMI, age, transferrin, and albumin. Prior to adjustment, MDD patients showed significantly lower levels of FPG, insulin, and z FPG + z INS and HOMA2-IR scores, alongside significantly higher levels of insulin sensitivity as indicated by HOMA2-IS and QUICKI, compared to healthy controls. All these differences remained significant after FDR p-correction (all at p < 0.0135). After controlling for MetS, BMI, and age, all those differences remained significant, whilst also HOMA-2β became significant (decreased in MDD). After excluding subjects with MetS, the decreases in insulin resistance and increases in insulin sensitivity (p < 0.01) in MDD versus controls remained significant. The differences also remained significant after FDR p correction (all at p = 0.0167). After adding the negative acute phase proteins as covariates, only HOMA2-IR remained significant. Nevertheless, after FDR p correction, this variable was no longer significant. Our observations indicate that 92 patients received treatment with antidepressants, 10 were treated with mood stabilizers, 68 were administered benzodiazepines, and 44 were prescribed antipsychotic medications. GLM analyses revealed that treatment with these 4 types of drugs did not yield any significant effects on the insulin biomarkers, even in the absence of FDR p-value correction. As such, use of these drugs did not affect the biomarker results of the current study. Table 3 Intercorrelation matrix (Pearson’s correlation coefficients) between insulin biomarkers, the acute phase response and metabolic indicators. Variables Transferrin Albumin BMI WC Age Weight loss FPG 0.192* 0.243** 0.458** 0.425** 0.366** -0.225** Insulin 0.295** 0.369** 0.480** 0.383** 0.044 -0.185* z FPG + z INS 0.282** 0.355** 0.544** 0.468** 0.238** -0.238** z INS -z GLU 0.102 0.125 0.021 -0.042 -0.317** 0.039 HOMA2-IR 0.298** 0.370** 0.489** 0.391** 0.058 -0.191* HOMA2-IS -0.268** -0.370** -0.407** -0.381** -0.088 0.131 HOMA2-β 0.223** 0.293** 0.240** 0.165* -0.177* -0.047 QUICKI -0.292** -0.385** -0.478** -0.042 -0.124 0.178* TyG 0.100 0.227** 0.360** 0.373** 0.298** -0.124 ** Correlation is significant at the 0.01 level (2-tailed); * Correlation is significant at the 0.05 level (2-tailed). FPG: fasting plasma glucose, INS: insulin, GLU: glucose, HOMA2-IR: Homeostasis Model Assessment Version 2 for Insulin Resistance, HOMA2-IS: Homeostasis Model Assessment Version 2 for Insulin Sensitivity, HOMA2-β: Homeostasis Model Assessment Version 2 for β-cell Function, QUICKI: quantitative insulin sensitivity check index, TyG: triglyceride-glucose index, MetS: Metabolic syndrome. Correlations between insulin, negative APPs, metabolic markers, and weight loss Table 3 shows the Pearson correlation coefficients among negative APPs, metabolic indicators, anthropometric measures, and weight loss. Plasma levels of transferrin and albumin showed positive correlations with FPG, insulin, z FPG + z insulin, HOMA2-IR, and HOMA2-β. In contrast, both biomarkers were negatively correlated with insulin sensitivity indices (HOMA2-IS and QUICKI). The TyG index was positively correlated with albumin but not with transferrin. The anthropometric measures BMI and WC demonstrated strong positive correlations with insulin resistance parameters and strong negative correlations with insulin sensitivity indices. Weight loss was negatively correlated with FPG, insulin, z FPG + z insulin score, and HOMA2-IR, and positively correlated with QUICKI. Table 4 Intercorrelation matrix (Pearson’s correlation coefficients) between biomarkers and severity scales. Variables Pure HAMD Pure HAMA Pure FF STAI state FPG -0.204** -0.161* -0.146 -0.205* Insulin -0.223** -0.175* -0.189* -0.190* z FPG + z INS -0.247** -0.187* -0.197* -0.267** z INS - z GLUC -0.018 0.000 -0.048 -0.049 HOMA2-IR -0.226** -0.166* -0.196* -0.259** HOMA2-IS 0.197* 0.145 0.214** 0.237** HOMA2-β -0.099 -0.058 -0.128 -0.138 QUICKI 0.224** 0.163* 0.219** 0.261** TyG -0.067 -0.043 -0.078 -0.109 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). HAMD: Hamilton Depression Rating Scale, HAMA: Hamilton Anxiety Rating Scale, FF: Fibro-Fatigue, STAI: State-Trait Anxiety Inventory, FPG: fasting plasma glucose, INS: insulin, GLU: glucose, HOMA2-IR: Homeostasis Model Assessment Version 2 for Insulin Resistance, HOMA2-IS: Homeostasis Model Assessment Version 2 for Insulin Sensitivity, HOMA2-β: Homeostasis Model Assessment Version 2 for β-cell Function, QUICKI: quantitative insulin sensitivity check index, TyG: triglyceride-glucose index. Correlations Between Metabolic Indicators and Severity Scales Table 4 shows the correlations between metabolic biomarkers and symptom severity scales. FPG and insulin were significantly and negatively correlated with the HAMD, HAMA, FF, and STAI state scales. The z FPG + z insulin and HOMA-2IR scores exhibited significant negative correlations across all these scales. HOMA2-IS was positively correlated with these scales, except HAMA. QUICKI showed significant positive correlations with the severity scales. Table 5 Multiple regression with insulin metabolism biomarkers or rating scale scores as dependent variables. Dependent Variables Explanatory Variables Coefficient statistics Model statistics β t p R² F df P #1. FPG Model Age Transferrin Weight loss WC 0.355 0.190 -0.165 0.338 5.21 2.76 -2.41 4.90 < 0.001 0.007 0.017 < 0.001 0.359 20.76 4/148 < 0.001 #2. Insulin Model BMI Albumin Gender 0.453 0.358 -0.182 6.68 5.17 -2.60 < 0.001 < 0.001 0.010 0.348 26.49 3/149 < 0.001 #3 HOMA-IR Model BMI Albumin Gender 0.482 0.346 -0.167 7.22 5.07 -2.43 < 0.001 < 0.001 0.017 0.368 28.91 3/149 < 0.001 #4 z FPG + z INS Model BMI Albumin Transferrin Age 0.442 0.232 0.173 0.165 6.68 3.42 2.52 2.52 < 0.001 < 0.001 0.013 0.013 0.414 26.19 4/148 < 0.001 #5 QUICKI Model BMI Albumin Transferrin -0.403 -0.266 -0.160 -5.96 -3.72 -2.24 < 0.001 < 0.001 0.027 0.340 25.59 3/149 < 0.001 #6 Pure HAMD Model ACEs HOMA2-IR Gender 0.435 -0.173 0.163 5.90 -2.45 2.24 < 0.001 0.015 0.027 0.226 15.44 3/159 < 0.001 #7 Pure HAMA Model ACEs z FPG + z INS 0.358 -0.177 4.92 -2.43 < 0.001 0.016 0.184 18.04 2/160 < 0.001 #8 Pure FF Model ACEs QUICKI 0.272 0.192 3.64 2.57 < 0.001 0.011 0.127 11.64 2/160 < 0.001 #9 STAI Model ACEs HOMA2-IR 0.510 -0.158 7.43 -2.30 < 0.001 0.023 0.305 32.91 2/150 < 0.001 #10 OSOD Model ACEs HOMA2-IR 0.471 -0.183 6.73 -2.62 < 0.001 0.010 0.277 28.74 2/150 < 0.001 ACE: adverse childhood experiences, HAMD: Hamilton Depression Rating Scale, HAMA: Hamilton Anxiety Rating Scale, FF: Fibro-Fatigue, STAI: State-Trait Anxiety Inventory, FPG: fasting plasma glucose, INS: insulin, GLU: glucose, HOMA2-IR: Homeostasis Model Assessment Version 2 for Insulin Resistance, HOMA2-IS: Homeostasis Model Assessment Version 2 for Insulin Sensitivity, HOMA2-β: Homeostasis Model Assessment Version 2 for β-cell Function, QUICKI: quantitative insulin sensitivity check index. WC: Waist circumference, BMI: Body Mass Index, OSOD: overall severity of depression. Predictors of Metabolic Dysregulation Table 5 summarizes the results of multiple regression analyses examining the predictors of metabolic measures. Regression model #1 shows that 35.9% of the variance in FPG was explained by age, transferrin, and WC (all positive associations), together with weight loss (negative association). Regression model #2 demonstrates that 34.8% of the variance in insulin was explained by BMI and albumin (positive associations), along with female gender. Regression model #3 indicates that 36.8% of the variance in HOMA-IR was explained by BMI and albumin (positive associations), together with female gender. Regression model #4 shows that 41.4% of the variance in the z FPG + z insulin score was explained by BMI, albumin, transferrin, and age (all positive associations). Regression model #5 demonstrates that 34.0% of the variance in the QUICKI index was explained by BMI, albumin, and transferrin (all negative associations). Predictors of the clinical rating scale scores Table 5 , regression model #6 indicates that 22.6% of the variance in the pure HAMD score was explained by ACEs and female gender (positive associations), together with HOMA-IR (negative association). Regression #7 shows that 18.4% of the variance in the pure HAMA score was explained by ACEs (positive association) and the z FPG + z insulin score (negative association). Model #8 demonstrates that 12.7% of the variance in the pure FF score was explained by ACEs and the QUICKI index (both positive associations). Model #9 indicates that 30.5% of the variance in the STAI score was explained by ACEs (positive association) and HOMA2-IR (negative association). Regression #10 shows that 27.7% of the variance in the OSOD score was explained by ACEs (positive association) and HOMA2-IR (negative association). Impact of metabolic syndrome on insulin biomarkers in MDD Electronic Supplementary File (ESF) , Table 1 shows the findings from general linear models assessing the impact of MetS on insulin biomarkers, after controlling for potential confounders, including MDD. Individuals with MetS showed significantly higher FPG, insulin, z FPG + z insulin, HOMA2-IR, and TyG than those without MetS, whilst HOMA2-IS and the QUICKI index were significantly lower in those with MetS. Discussion Insulin resistance and sensitivity in Chinese MDD patients The first major finding of this study is that Chinese MDD patients exhibit lower insulin resistance than controls, whilst their insulin sensitivity was higher. Whether unadjusted or adjusted for confounding factors such as age, gender, BMI, and MetS, the MDD group showed significantly lower FPG, insulin levels, and HOMA2-IR scores compared to the healthy controls. Meanwhile, insulin sensitivity indices, such as HOMA2-IS and QUICKI, were significantly higher. In contrast, a meta-analysis by Fernandes et al. (Fernandes et al., 2022 ) reported that insulin levels, HOMA-IR, and insulin variability were significantly elevated in the acute phase of depression. However, this meta-analysis study did not adjust for potential confounding factors such as MetS or increased BMI scores (Fernandes et al., 2022 ). It is obvious that the absence of correction for metabolic factors might result in significant heterogeneity and bias. It is well established that these factors primarily affect insulin resistance and sensitivity, and that after controlling for these variables, no significant differences between MDD (and BD) and controls may be established (de Melo et al., 2017 ; Landucci Bonifácio et al., 2017 ; Morelli et al., 2021 ). Nevertheless, after considering the effects of other NIMETOX pathways (e.g., immune and oxidative stress), it was established that insulin resistance is associated with the severity of illness (Maes, Jirakran, et al., 2025b ). Therefore, the results of most studies included in the meta-analysis by Fernandes et al. are difficult to interpret. After adjusting for BMI, MetS and age (sex was not significant), the current study found that the increase in insulin sensitivity and lower insulin resistance were still prevalent in our Chinese MDD patients. The above-mentioned discrepancies may be due to significant differences in obesity and MetS prevalence between our study sample and other samples recruited from Western or some South American countries. Epidemiological data show that the age-standardized prevalence of obesity in China (approximately 16.0% in men and 14.4% in women) (Mu et al., 2021 ) is significantly lower than in Western countries (e.g., approximately 43.0% in men and 42.1% in women in the United States) (Hales et al., 2020 ). Furthermore, in Chengdu, China, the prevalence of obesity is approximately 11.0% and 16.7% for MetS (Zhou et al., 2024 ), significantly lower than the 37.6% seen in the U.S (Liang et al., 2023 ). Our representative cohort from Chengdu exhibited a comparatively low prevalence of MetS, comprising 17.7% of MDD patients and 25.6% of the controls. The lower incidence of obesity and MetS might explain the observed differences in insulin sensitivity in Chinese MDD patients versus studies that did not adjust for MetS or obesity. Nonetheless, the exact explanation is more intricate, as detailed in the subsequent two sections. Insulin sensitivity and the APP response The second major finding of this study is the significant association between enhanced insulin resistance in MDD and the negative APP. Despite the lower FPG, insulin levels, and HOMA2-IR scores in MDD, as well as the significantly increased insulin sensitivity, the significance of these variables disappeared after adjusting for negative APPs. This indicates that the APP response impacts insulin metabolism and might explain the lower insulin resistance and higher insulin sensitivity in MDD. Several studies have reported a decrease in albumin and transferrin levels in depression (Al-Marwani et al., 2023 ; Ambrus & Westling, 2019 ; Maes, 1993 ; Maes et al., 1991 ; Van Hunsel et al., 1996 ; Yin et al., 2022 ). Maes et al. ( 1991 ) found that depression is associated with a negative APP response and protein deficiency or homeostasis dysregulation, which was additionally associated with anorexia and weight loss, core symptoms of depression (Maes et al., 1991 ). Our study also supports this hypothesis, suggesting that protein malnutrition associated with weight loss is associated with the negative APP response in MDD. Additionally, recent research indicates that the reduction in negative APPs in MDD patients, along with changes in positive APPs (such as high-sensitivity monomeric C-reactive protein), reflects a mild smoldering inflammatory state (Maes, Niu, et al., 2025c ). This state may be associated with chronic malnutrition, the activation of chronic inflammatory factors (such as TNF-α and IL-6), and impaired liver synthesis function of positive APPs (Cao et al., 2022 ; Maes, Almulla, et al., 2025 a; Maes et al., 1991 ). The liver is the primary organ responsible for synthesizing negative APPs (Ceciliani et al., 2002 ). Under normal physiological conditions, the liver efficiently synthesizes and secretes these proteins to maintain metabolic balance and immune function. However, in MDD, chronic inflammatory factors may inhibit liver function, resulting in a reduction in the synthesis of negative APPs (Maes, 1993 ; Maes et al., 1991 ; Maes, Niu, et al., 2025c ; Shao et al., 2021 ). For example, TNF-α can suppress the synthesis of albumin and transferrin by activating the Nuclear Factor-κB signaling pathway, resulting in significantly lower plasma concentrations of these proteins (Chojkier, 2005 ). This inflammation-mediated inhibition of liver synthesis may lead to a “malnutrition-inflammation” vicious cycle. Albumin and transferrin are not only important nutritional markers but also play a key function in sustaining homeostasis and various physiological functions (Levitt & Levitt, 2016 ; Talukder, 2021 ). Their sustained low levels may exacerbate the pathogenesis of MDD. On one hand, hypoalbuminemia may lead to an imbalance in colloid osmotic pressure, reduced oxidative stress buffering capacity, and impaired toxin clearance (Dubois et al., 2006 ; Levitt & Levitt, 2016 ). On the other hand, reduced transferrin may impair iron transport and metabolism, increasing the risk of the anemia of inflammation (Maes et al., 1996 ) and affecting mitochondrial energy production, further exacerbating fatigue in MDD patients (Rybka et al., 2013 ; Talukder, 2021 ). Metabolic flexibility and enhanced insulin sensitivity Metabolic flexibility refers to the ability of the organism to maintain normal physiological function by adjusting energy metabolism in the context of chronic stress or chronic disease (Smith et al., 2018 ). This adaptive mechanism involves the reallocation of energy resources to prioritize the needs of critical organs (Smith et al., 2018 ). Particularly under the stress of chronic inflammation and protein malnutrition, the body may optimize the use of limited energy resources by altering metabolic pathways, especially to ensure the energy supply of vital organs such as the brain (Olson et al., 2020 ). In this study, we observed abnormally elevated insulin sensitivity in MDD in the context of a negative APP response, suggesting that this change in metabolic status may be a metabolic adaptive response of the body. Long-term activation of chronic inflammatory factors may have metabolic consequences, leading to adaptive adjustments in energy allocation and use by the organism (Straub, 2011 ; Wang & Ye, 2015 ). In response to a chronic inflammatory state, the organism may optimize glucose utilization by increasing insulin sensitivity and reducing the waste of energy resources (Berbudi et al., 2025 ). In people without MetS, mild, smoldering inflammation can transiently lower insulin resistance. For example, small elevations in IL-6 may activate AMPK and increase insulin-stimulated glucose disposal in humans, improving glucose uptake (Carey et al., 2006 ). This hormetic window closes as inflammation strengthens or when MetS is present (Koenen et al., 2021 ). In MetS, the same stimulus typically worsens insulin resistance because inflammatory pathways are pre-primed, including JNK/IKKβ signaling, macrophage infiltration, and lipotoxic mediators blunting PI3K-Akt thereby promoting hepatic and muscle insulin resistance (Kelly et al., 2009 ; Pedersen & Febbraio, 2008 ; Shoelson et al., 2006 ). In adipose tissue, proinflammatory remodeling with macrophage infiltration elevates serum cytokines and free fatty acids, reinforcing systemic insulin resistance dysfunction (Apovian et al., 2008 ). Furthermore, reciprocal interactions between neurotoxicity-associated cytokine networks and MetS are associated with increasing clinical severity of MDD (Maes, Jirakran, et al., 2025b ). Overall, mild smoldering inflammation may be hormetic with respect to insulin metabolism in subjects without MetS, but in those with MetS it might, via interactions among activated cytokine networks and MetS, sustain or aggravate insulin resistance and severity of MDD (Maes, Jirakran, et al., 2025b ). Limitations This cross-sectional study has several limitations. First, this is a case-control study, which limits the ability to draw definitive conclusions about causality. Moreover, this study was conducted at a single site in southwestern China, and lifestyle factors (such as dietary intake, dietary habits, and physical activity) in this region differ from those in the West. Still, they may also differ from those in other Chinese areas. Conclusions This study shows that Chinese MDD patients exhibit enhanced insulin sensitivity and lower insulin resistance. Furthermore, these changes are mediated by a mild smoldering inflammatory response with protein malnutrition. A mild inflammatory response in study samples without a high prevalence of subjects with MetS or obesity (as in our Chengdu study sample) may be accompanied by a hormetic response briefly enhancing insulin sensitivity. On the other hand, stronger long-standing inflammatory responses, especially in populations with increased prevalence of MetS and obesity (such as Western populations), might cause and maintain impaired insulin resistance and cause increased severity of depression. Therefore, under the dual stress of chronic inflammation and protein malnutrition, MDD is characterized by increased insulin efficiency by prioritizing energy supply to critical organs, thereby optimizing glucose resource utilization to maintain normal function of vital organs, especially when energy supply is limited. However, the presence of MetS or obesity obscures these primary biomarkers of MDD, since interactions between immunological and metabolic factors become more pronounced. Further research should examine these immune and MetS interactions in MDD. Consequently, it is likely erroneous to assert that MDD is inherently linked to heightened insulin resistance; rather, the accurate interpretation is that MDD in the absence of MetS is accompanied by increased insulin sensitivity, and in the presence of immune activation and MetS might elicit elevated insulin resistance, which affects the severity of the condition. Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Institutional Review Board of Sichuan Provincial People’s Hospital, Chengdu, China [Ethics (Research) 2024 − 203] and all participants provided written informed consent per the Declaration of Helsinki. Consent for publication : Not applicable Funding This research was funded by the Sichuan Science and Technology Program “PIANJI” Project (Grant No.: 2025HJPJ0004). Author Contribution M.M. and Y.Z. Conception and design of the study. M.N. and J.L. Data collection. Y.L., T.C., and A.A. Performed the experiments. Y.L. and M.M. Data analysis. Y.L. Writing of the original draft. M.M. and Y.Z. Critical revision of the article. All authors read and approved the final manuscript. Acknowledgements: Not Applicable Data Availability The dataset supporting this study is available from the corresponding author (MM) upon reasonable request and after a thorough data review. References Abbasi F, Reaven GM. Comparison of two methods using plasma triglyceride concentration as a surrogate estimate of insulin action in nondiabetic subjects: Triglycerides × glucose versus triglyceride/high-density lipoprotein cholesterol. Metab Clin Exp. 2011;60(12):1673–6. https://doi.org/10.1016/j.metabol.2011.04.006 . 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8943225","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610187663,"identity":"c047a148-b03f-479f-83a4-f3b3ee392fb9","order_by":0,"name":"Yueyang Luo","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Yueyang","middleName":"","lastName":"Luo","suffix":""},{"id":610187666,"identity":"b1f91a3f-41f2-46ee-8c86-5dca8fc63114","order_by":1,"name":"Mengqi Niu","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Mengqi","middleName":"","lastName":"Niu","suffix":""},{"id":610187668,"identity":"eef7e598-2e7d-4ce0-8a3e-a09661e80a1a","order_by":2,"name":"Tangcong Chen","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Tangcong","middleName":"","lastName":"Chen","suffix":""},{"id":610187669,"identity":"dfa52451-5432-4c50-b5aa-7bf547ff3fc2","order_by":3,"name":"Jing Li","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Li","suffix":""},{"id":610187670,"identity":"b1757dae-6b81-466b-8f1f-36afb7c73942","order_by":4,"name":"Abbas F. Almulla","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Abbas","middleName":"F.","lastName":"Almulla","suffix":""},{"id":610187673,"identity":"e5ddf961-9a6d-4cf3-aa0a-d03d0e3f0899","order_by":5,"name":"Yingqian Zhang","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Yingqian","middleName":"","lastName":"Zhang","suffix":""},{"id":610187675,"identity":"db8e1f63-1560-4648-b513-2be58c8f89e7","order_by":6,"name":"Michael Maes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACZgY2IGkBYjI+gAlKEKEFrIbZAMbCr4UBoYVNgigtuu3Mzx78qJCI5m9vf1bNU1FXx9/AfPA2D4NdHi4tZofZzA17zkjkzjhzxuw2z5nDEhIH2JKteRiSi3Fr4WGT4G2TyG24kcN2m7ftgIQBA4+ZNA/DgcQGPFok//6TyJ1///mzYt5/dUAt/N8IapHmbZDI3XCDwYyZt4EZZAsbAS1sZtIyxyRyN57JMZacc+yw5IzDbMaWcwyScWs5f/iZ5Jsam9x5x48//PCmpo6fv7354Y03FXY4tWABzCDCgHj1o2AUjIJRMAowAQAjOk6OPsxhZQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":true,"prefix":"","firstName":"Michael","middleName":"","lastName":"Maes","suffix":""}],"badges":[],"createdAt":"2026-02-23 05:39:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8943225/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8943225/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105564874,"identity":"5d4ef70e-ac90-48f9-8fd8-e2080b0890b5","added_by":"auto","created_at":"2026-03-27 12:51:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1307828,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8943225/v1/eb627f24-1f33-4ee1-9bb9-88b7e0adbc43.pdf"},{"id":105308746,"identity":"f236651c-0a9b-4c80-a444-f9c4f469221a","added_by":"auto","created_at":"2026-03-24 14:58:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":22543,"visible":true,"origin":"","legend":"","description":"","filename":"ESF.docx","url":"https://assets-eu.researchsquare.com/files/rs-8943225/v1/17195a6c0d04c15fc1e31b3f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lower insulin resistance in Chinese patients with severe major depressive disorder: associations with the inflammatory response","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) is increasingly recognized as a result of dysregulation of neuroimmune, metabolic, and oxidative stress (NIMETOX) pathways (Maes, Almulla, et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea; Maes, Jirakran, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). Recent studies indicate that MDD is significantly linked to persistent low-grade inflammation, insulin resistance (IR), and cardiometabolic disorders, such as metabolic syndrome (MetS), type 2 diabetes mellitus (T2DM), and cardiovascular disease (Ma et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Maes, Almulla, et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea; Maes, Niu, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025c\u003c/span\u003e). Within this immunometabolic framework, activation of M1 macrophages and elevated levels of proinflammatory cytokines, such as interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF)-α, might impair insulin signaling, triggering peripheral and central insulin resistance that leads to hyperglycemia and compensatory hyperinsulinemia (Maes, Almulla, et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea; Maes, Jirakran, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). This inflammatory response triggers an acute-phase reaction in the liver, reducing negative acute-phase proteins (APPs) levels, particularly albumin and transferrin (Maes, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough scientific research often suggests a bidirectional association between insulin resistance and MDD, the findings vary across studies (Fanelli et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Some reviews indicate significantly elevated fasting insulin levels and HOMA-IR indices in MDD patients (Fernandes et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), whereas other studies did not find increased insulin resistance indices in MDD compared to controls (de Melo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Maes, Jirakran, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; Morelli et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A meta-analysis showed that during the acute phase of depression, patients had significantly elevated insulin levels, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) indices, and insulin variability (Fernandes et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, these markers showed no significant changes during remission or following antidepressant treatment (Fernandes et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the latter analysis has a key methodological limitation: the strategy for controlling confounding factors relied solely on matching sex, age, and BMI during the study design phase, without further adjusting for MetS, overweight or obesity, in the statistical models. The inclusion of people with MetS or high BMI values in the analyses may have caused bias in metabolic data, resulting in biased findings (Maes et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Maes, Jirakran, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, the associations between insulin resistance and MDD go far beyond the possible differences between MDD and healthy controls. Thus, increased insulin resistance in MDD contributes to increased oxidative and nitrosative stress, and together with enhanced activation of cytokine networks, leads to increased severity of the affective and physio-somatic symptoms in MDD (Maes, Jirakran, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; Morelli et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such changes may be explained by intertwined causal relationships between insulin resistance, oxidative stress and activated cytokine networks (Maes, Jirakran, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe inconsistent association between insulin resistance and MDD limits its reliability as a robust biomarker of MDD. In contrast, the negative APP response with low albumin and transferrin levels demonstrates great consistency in their association with MDD (Maes et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Wainberg et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These two negative APPs are closely linked to immune activation, adverse childhood experiences (ACEs), and the severity of somatic and affective symptoms (Almulla et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Maes, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). While alterations in negative APPs (albumin and transferrin) have consistently been linked to MDD and immune activation, their interactions with insulin resistance in MDD patients remain unclear. In addition, research specifically examining the relationship between insulin resistance and inflammation in Chinese MDD patients remains scarce.\u003c/p\u003e \u003cp\u003eThus, this study aimed to investigate the insulin resistance status in Chinese MDD patients and further elucidate the association between insulin resistance and the negative APP response, as reflected by the negative APPs albumin and transferrin. We hypothesize that changes in insulin resistance are associated with the severity of MDD and that these changes are associated with the negative APP response.\u003c/p\u003e"},{"header":"Methods","content":"\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThis research utilized a cross-sectional case-control approach and included 165 participants, comprising 125 people diagnosed with MDD and 40 healthy controls. Recruitment occurred at the Psychiatric Center of Sichuan Provincial People\u0026rsquo;s Hospital in Chengdu, China. The eligibility criteria for participants with MDD were: (1) a validated diagnosis of MDD as per the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5); (2) a score exceeding 18 on the 21-item Hamilton Depression Rating Scale (HAMD-21) (Hamilton, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1960\u003c/span\u003e); (3) an age range of 18 to 65 years, irrespective of gender; and (4) the capacity to furnish written informed consent, with guardian consent when necessary. Healthy controls were matched to MDD patients according to sex, age, body mass index (BMI), and educational attainment, and were recruited among hospital workers and their connections.\u003c/p\u003e \u003cp\u003eThe exclusion criteria for both MDD patients and healthy controls included: (1) pregnancy or lactation; (2) a history of acute infections or recent surgeries within the last three months; (3) severe allergic reactions in the preceding month; (4) treatment with immunosuppressive or immunomodulatory agents, including glucocorticoids; (5) use of therapeutic-dose antioxidants or omega-3 supplements in the last three months; (6) frequent or chronic analgesic use; (7) serious medical illnesses such as autoimmune disorders, systemic lupus erythematosus, rheumatoid arthritis, type 1 diabetes, inflammatory bowel disease, psoriasis, chronic obstructive pulmonary disease, or malignancy; (8) neurological disorders including epilepsy, stroke, brain tumors, Parkinson\u0026rsquo;s disease, Alzheimer\u0026rsquo;s disease, or multiple sclerosis; (9) personality or developmental disorders (e.g., borderline, antisocial, or severe intellectual disability); (10) other major psychiatric diagnoses, such as bipolar disorder, schizophrenia, schizoaffective disorder, psycho-organic conditions, or substance-use disorders (except nicotine dependence). Furthermore, healthy controls were excluded if they had a history of MDD or dysthymia, any DSM-IV anxiety disorder, or a family history of mood disorders, suicide, or substance use disorders other than nicotine dependence.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClinical Assessments\u003c/h2\u003e \u003cp\u003eAll participants underwent a semi-structured interview conducted by a trained physician to collect demographic and clinical information, including age, sex, education, medical and psychiatric history, and family history. Psychiatric diagnoses were assessed using the Mini International Neuropsychiatric Interview (MINI) (Sheehan et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), which screens for both current and lifetime psychiatric disorders according to the DSM-IV and ICD-10 criteria.\u003c/p\u003e \u003cp\u003eOn the same day, a single evaluator assessed depression, anxiety, and somatic symptoms. Depression severity was measured using the total score on the 21-item Hamilton Depression Rating Scale (HAMD) (Hamilton, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1960\u003c/span\u003e), and anxiety severity was assessed using the Hamilton Anxiety Rating Scale (HAMA) (Hamilton, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1959\u003c/span\u003e). Self-reported anxiety was evaluated using the state version of the State-Trait Anxiety Inventory (STAI) (Spielberger et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). Fibromyalgia and Chronic Fatigue Syndrome (CFS) symptoms were quantified using the 12-item Fibro Fatigue Scale (FF) (Zachrisson et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo minimize the influence of overlapping somatic items across scales, \u003cem\u003epure scores\u003c/em\u003e were derived for each affective domain. Pure HAMD and pure HAMA were computed by selecting items specific to depression, anxiety, and subjective anxiety, while excluding confounding somatic items. Pure HAMD was calculated as the sum of the individual items related to depression, guilt, suicidal ideation, and loss of interest, excluding any somatic items. The pure HAMA score is the sum of items that assess anxiety, tension, fear, and anxious behavior. Pure FF score is calculated by summing the z-scores of items related to muscle pain, muscle tension, flu-like symptoms, fatigue, headache, autonomic and gastrointestinal symptoms. To create an integrated clinical severity index, we computed a z unit-based composite score by summing the z-scores of the pure HAMD, pure HAMA, pure STAI, and pure FF (labeled as overall severity of depression or OSOD. Weight loss was assessed using the relevant items from the HAMD. Adverse childhood experiences (ACEs) were evaluated using the Childhood Trauma Questionnaire-Short Form (CTQ-SF) (Bernstein et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The total ACEs score was computed by summing the five subscales: emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect (Vasupanrajit et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePhysical measurements, such as height and weight, were documented. The BMI was determined by dividing the weight in kilograms by the square height in meters. Waist circumference (WC) was assessed at the midpoint between the iliac crest and the lowest rib. A composite z-score representing body composition was derived by aggregating BMI and WC (z BMI\u0026thinsp;+\u0026thinsp;z WC). MetS was delineated according to the 2009 Joint Scientific Statement of the American Heart Association and the National Heart, Lung, and Blood Institute (Alberti et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), necessitating the fulfillment of a minimum of three of the subsequent criteria: (1) Triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL; (2) WC\u0026thinsp;\u0026ge;\u0026thinsp;90 cm (men) or \u0026ge;\u0026thinsp;80 cm (women); (3) Elevated blood pressure (systolic\u0026thinsp;\u0026gt;\u0026thinsp;130 mmHg, diastolic\u0026thinsp;\u0026gt;\u0026thinsp;85 mmHg, or current antihypertensive medication usage); (4) High-density lipoprotein cholesterol (HDL-C)\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL (men) or \u0026lt;\u0026thinsp;50 mg/dL (women); and (5) Fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/dL or a diabetes diagnosis. Participants were graded according to the number of MetS criteria they satisfied.\u003c/p\u003e \u003cp\u003eAssays\u003c/p\u003e \u003cp\u003eVenous blood (30 mL) was obtained from each participant after a 10-hour fast, between 6:30 and 8:00 a.m., utilizing disposable syringes and serum tubes. After centrifugation at 3,500 rpm, the serum was divided into Eppendorf tubes and preserved at \u0026minus;\u0026thinsp;80\u0026deg;C until analysis. Fasting blood glucose (FBG) was quantified utilizing the glucose assay kit (glucose oxidase method, Gcell, Beijing Strong Biotechnologies, Inc.) on a fully automated biochemical analyzer (ADVIA 2400, Siemens Healthcare Diagnostics Inc.), exhibiting intra-assay and inter-assay analytical coefficients of variation (CVs) of 3.2% and 6.8%, respectively. Serum insulin was measured utilizing the Atellica IM insulin assay kit (direct chemiluminescence method, Siemens Healthcare Diagnostics Inc.) on the Atellica IM fully automated chemiluminescence immunoassay analyzer (Siemens Healthcare Diagnostics Inc.). The intra-assay and inter-assay analytical CVs are 1.8% and 3.6%, respectively. Serum triglycerides (TyG) were quantified using TyG assay kits (GPO-PAP method, Gcell, Beijing Strong Biotechnologies, Inc.) on a fully automated biochemical analyzer (ADVIA 2400, Siemens Healthcare Diagnostics Inc.), exhibiting intra-assay and inter-assay analytical CVs of 3.0% and 7.0%, respectively. Serum HDL-C was assayed by the direct method-select inhibition method (Beijing Strong Biotechnologies, Inc.) with the intra-assay and inter-assay CVs of \u0026lt;\u0026thinsp;4% and \u0026lt;\u0026thinsp;10%, respectively.\u003c/p\u003e \u003cp\u003eBasal insulin resistance was evaluated using Homeostatic Model Assessment version 2 for insulin resistance (HOMA2-IR), whereas beta-cell function was measured by HOMA2-β and insulin sensitivity by HOMA2-IS. Calculations were conducted using the HOMA2 calculator (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.dtu.ox.ac.uk\u003c/span\u003e\u003cspan address=\"http://www.dtu.ox.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We calculated indices of insulin resistance using z unit-based composite scores as z FBG\u0026thinsp;+\u0026thinsp;z insulin and indices of insulin sensitivity as z insulin \u0026ndash; z FPG (Maes, Jirakran, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). The TyG index, a metric for evaluating insulin resistance, was calculated using the formula: ln{fasting triglyceride (mg/dL) \u0026times; fasting plasma glucose (mg/dL)/2} (Abbasi \u0026amp; Reaven, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The Quantitative Insulin Sensitivity Check Index (QUICKI) was utilized as a validated approach for assessing insulin sensitivity in both diabetic and non-diabetic populations, including persons with obesity (Muniyappa et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The QUICKI score is determined by the formula: QUICKI\u0026thinsp;=\u0026thinsp;1 / {log (Fasting Insulin)\u0026thinsp;+\u0026thinsp;log (Fasting Glucose)}. This indicator exhibits a robust connection with glucose clamp studies (r\u0026thinsp;=\u0026thinsp;0.78) (Muniyappa et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSerum transferrin was quantified via an immunoturbidimetric assay (DIAYS DIAGNOSTIC SYSTEM, SHANGHAI CO., LTD) on a fully automated biochemical analyzer (ADVIA 2400, Siemens Healthcare Diagnostics Inc.) with a sensitivity of 0.03 g/L. The intra-assay and inter-assay analytical CVs were 1.96% and 0.67%, respectively. Serum albumin was quantified with the Bromocresol Green Method kit (Beijing Strong Biotechnologies, Inc.), exhibiting intra-assay and inter-assay CVs of 1.20% and 2.10%, respectively. To construct an integrated index of the AP response, a composite z-score was derived by summing the z-scores for z albumin and z transferrin.\u003c/p\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003cp\u003ePearson\u0026rsquo;s correlation calculation was employed to evaluate the connections between continuous variables. Contingency tables were used to analyze the associations between categorical variables. Differences in continuous variables among groups were assessed using analysis of variance (ANOVA). False Discovery Rate (FDR) was applied to correct multiple comparisons. The study considered potential confounders such as age, sex, smoking status, and BMI. Binary logistic regression analysis was employed to predict MDD (controls as the reference group) using insulin biomarkers, albumin, transferrin, ACEs, and demographic data. Key outcomes were Nagelkerke pseudo-R\u0026sup2; (used as effect size), Wald statistics with corresponding p-values, odds ratios (OR) with 95% confidence intervals (CI), and unstandardized regression coefficients (B) with their standard errors (SE). Multiple regression analysis was used to predict the insulin biomarkers with albumin, transferrin, weight loss, and metabolic data as explanatory variables. Another series of multiple regression analyses examined the effects of insulin biomarkers and other predictors on the clinical rating scale scores. Both manual and automatic stepwise methods were employed. The automated procedure involved linear modeling with measures to prevent overfitting, with entry and removal criteria set at p\u0026thinsp;=\u0026thinsp;0.05 and p\u0026thinsp;=\u0026thinsp;0.07, respectively. The model outputs included standardized beta coefficients, degrees of freedom (df), p-values, R\u0026sup2;, and F-statistics. Heteroskedasticity was evaluated using the White and modified Breusch-Pagan tests, while collinearity was assessed using the tolerance and variance inflation factor (VIF). All analyses were two-tailed with a significance level of 0.05 for all tests. IBM SPSS Statistics 30 (Windows) was used for all analyses, and data transformations, including log10, square root, rank-order, and Winsorization, were applied where needed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic and clinical profile of patients with major depressive disorder (MDD) and healthy controls (HC).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMDD (n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF / \u0026chi;\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.1 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.7 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (m/f)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13/27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38/87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolic syndrome (MetS) (No/Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102/22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.173a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRanking MetS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59 (1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42 (1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody Mass Index (BMI) (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.52 (4.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.299 (3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaist circumference (WC) (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.38 (11.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.27 (11.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ez BMI\u0026thinsp;+\u0026thinsp;z WC (z score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.332 (2.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.106 (1.840)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeight loss (z score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.605 (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.194 (1.080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistory of smoking (No/Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101/24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.9 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.5 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.64 (1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.18 (3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransferrin (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258.76 (37.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228.25 (38.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ez albumin\u0026thinsp;+\u0026thinsp;z transferrin (z score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.723 (0.805)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.227 (0.948)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39 (0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal HAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.5 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMWUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePure HAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.26 (4.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMWUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal HAMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.6 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMWUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePure HAMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMWUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal FF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.9 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMWUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePure FF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMWUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTAI state\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.8 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.1 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal ACEs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.746 (0.821)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.239 (0.935)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOSOD (PC score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.518 (0.237)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.528 (0.491)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e640.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData are presented as mean (SD); F: F-statistic from ANOVA; \u0026chi;\u0026sup2;: Chi-square test statistic (marked with the \u0026ldquo;a\u0026rdquo; in the table); MWUT: Mann\u0026ndash;Whitney U test statistic.\u003c/p\u003e\n\u003cp\u003eACE: Adverse childhood experiences, HAMD: Hamilton Depression Rating Scale, HAMA: Hamilton Anxiety Rating Scale, FF: Fibro-Fatigue, STAI: State-Trait Anxiety Inventory, OSOD (PC score): overall severity of depression (principal component score).\u003c/p\u003e\n\u003ch3\u003eSocio-demographic and Clinical Characteristics\u003c/h3\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the socio-demographic and clinical characteristics of the study participants. No significant differences were observed between MDD and HC in terms of age, gender distribution, the prevalence of MetS, MetS ranking, BMI, WC, the z BMI\u0026thinsp;+\u0026thinsp;z WC score, history of smoking, or years of education. However, the MDD group demonstrated a significantly higher weight loss score compared to the HC group. Clinical assessments revealed significantly higher total scores on HAMD, HAMA, FF, and STAI in MDD patients relative to controls. Furthermore, MDD patients reported significantly higher levels of ACEs and a greater OSOD score. Serum albumin and transferrin levels were markedly lower in the MDD group, accompanied by a significant reduction in the z albumin\u0026thinsp;+\u0026thinsp;z transferrin score.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of general linear models (GLM) showing unadjusted and adjusted associations between metabolic indicators and major depressive disorder (MDD) versus healthy controls (HC)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eHC\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;40\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;125\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eUnadjusted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAdjusted (MetS, BMI, age)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAdjusted (MetS, BMI, Age, Tf, Alb)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF (df\u0026thinsp;=\u0026thinsp;1/161)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF (df\u0026thinsp;=\u0026thinsp;1/157)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF (df\u0026thinsp;=\u0026thinsp;1/155)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.45 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.16 (0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e6.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsulin (mU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8.04 (4.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e6.01 (3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e19.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ez FPG\u0026thinsp;+\u0026thinsp;z INS (z score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.460 (1.110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.146 (0.920)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e11.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e19.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ez INS - z GLU (z score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.018 (1.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.006 (0.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA2-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.067 (0.653)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.790 (0.428)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e9.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e20.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA2-IS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e124.9 (65.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e164.0 (84.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e6.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA2-\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e79.0 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e73.7 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQUICKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.356 (0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.375 (0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e15.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8.070 (4.950)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e7.136 (4.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData are presented as mean (SD). F and P values were derived from ANOVA (unadjusted) and two GLM analyses: one adjusted for metabolic syndrome (MetS), body mass index (BMI), and age, and the second adjusted for MetS, BMI, transferrin (Tf), and albumin (Alb).\u003c/p\u003e\n\u003cp\u003eFPG: fasting plasma glucose, HOMA2-IR: Homeostasis Model Assessment Version 2 for Insulin Resistance, HOMA2-IS: Homeostasis Model Assessment Version 2 for Insulin Sensitivity, HOMA2-\u0026beta;: Homeostasis Model Assessment Version 2 for \u0026beta;-cell Function, QUICKI: quantitative insulin sensitivity check index, TyG: triglyceride-glucose index.\u003c/p\u003e\n\u003ch3\u003eInsulin biomarkers in MDD\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of GLM analyses examining the associations between metabolic indicators and MDD, both before and after adjustment for MetS, BMI, age, transferrin, and albumin. Prior to adjustment, MDD patients showed significantly lower levels of FPG, insulin, and z FPG\u0026thinsp;+\u0026thinsp;z INS and HOMA2-IR scores, alongside significantly higher levels of insulin sensitivity as indicated by HOMA2-IS and QUICKI, compared to healthy controls. All these differences remained significant after FDR p-correction (all at p\u0026thinsp;\u0026lt;\u0026thinsp;0.0135). After controlling for MetS, BMI, and age, all those differences remained significant, whilst also HOMA-2\u0026beta; became significant (decreased in MDD). After excluding subjects with MetS, the decreases in insulin resistance and increases in insulin sensitivity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in MDD versus controls remained significant. The differences also remained significant after FDR p correction (all at p\u0026thinsp;=\u0026thinsp;0.0167). After adding the negative acute phase proteins as covariates, only HOMA2-IR remained significant. Nevertheless, after FDR p correction, this variable was no longer significant.\u003c/p\u003e\n\u003cp\u003eOur observations indicate that 92 patients received treatment with antidepressants, 10 were treated with mood stabilizers, 68 were administered benzodiazepines, and 44 were prescribed antipsychotic medications. GLM analyses revealed that treatment with these 4 types of drugs did not yield any significant effects on the insulin biomarkers, even in the absence of FDR p-value correction. As such, use of these drugs did not affect the biomarker results of the current study.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIntercorrelation matrix (Pearson\u0026rsquo;s correlation coefficients) between insulin biomarkers, the acute phase response and metabolic indicators.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTransferrin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeight loss\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.192*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.243**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.458**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.425**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.366**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.225**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.295**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.369**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.480**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.383**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.185*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ez FPG\u0026thinsp;+\u0026thinsp;z INS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.282**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.355**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.544**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.468**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.238**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.238**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ez INS -z GLU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.317**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA2-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.298**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.370**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.489**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.391**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.191*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA2-IS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.268**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.370**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.407**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.381**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA2-\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.223**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.293**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.240**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.165*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.177*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQUICKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.292**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.385**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.478**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.178*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.227**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.360**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.373**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.298**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e** Correlation is significant at the 0.01 level (2-tailed); * Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e\n\u003cp\u003eFPG: fasting plasma glucose, INS: insulin, GLU: glucose, HOMA2-IR: Homeostasis Model Assessment Version 2 for Insulin Resistance, HOMA2-IS: Homeostasis Model Assessment Version 2 for Insulin Sensitivity, HOMA2-\u0026beta;: Homeostasis Model Assessment Version 2 for \u0026beta;-cell Function, QUICKI: quantitative insulin sensitivity check index, TyG: triglyceride-glucose index, MetS: Metabolic syndrome.\u003c/p\u003e\n\u003ch3\u003eCorrelations between insulin, negative APPs, metabolic markers, and weight loss\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the Pearson correlation coefficients among negative APPs, metabolic indicators, anthropometric measures, and weight loss. Plasma levels of transferrin and albumin showed positive correlations with FPG, insulin, z FPG\u0026thinsp;+\u0026thinsp;z insulin, HOMA2-IR, and HOMA2-\u0026beta;. In contrast, both biomarkers were negatively correlated with insulin sensitivity indices (HOMA2-IS and QUICKI). The TyG index was positively correlated with albumin but not with transferrin. The anthropometric measures BMI and WC demonstrated strong positive correlations with insulin resistance parameters and strong negative correlations with insulin sensitivity indices. Weight loss was negatively correlated with FPG, insulin, z FPG\u0026thinsp;+\u0026thinsp;z insulin score, and HOMA2-IR, and positively correlated with QUICKI.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIntercorrelation matrix (Pearson\u0026rsquo;s correlation coefficients) between biomarkers and severity scales.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePure HAMD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePure HAMA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePure FF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSTAI state\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.204**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.161*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.205*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.223**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.175*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.189*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.190*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ez FPG\u0026thinsp;+\u0026thinsp;z INS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.247**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.187*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.197*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.267**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ez INS - z GLUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA2-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.226**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.166*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.196*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.259**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA2-IS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.197*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.214**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.237**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA2-\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQUICKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.224**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.163*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.219**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.261**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e** Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e\n\u003cp\u003e* Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e\n\u003cp\u003eHAMD: Hamilton Depression Rating Scale, HAMA: Hamilton Anxiety Rating Scale, FF: Fibro-Fatigue, STAI: State-Trait Anxiety Inventory, FPG: fasting plasma glucose, INS: insulin, GLU: glucose, HOMA2-IR: Homeostasis Model Assessment Version 2 for Insulin Resistance, HOMA2-IS: Homeostasis Model Assessment Version 2 for Insulin Sensitivity, HOMA2-\u0026beta;: Homeostasis Model Assessment Version 2 for \u0026beta;-cell Function, QUICKI: quantitative insulin sensitivity check index, TyG: triglyceride-glucose index.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelations Between Metabolic Indicators and Severity Scales\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the correlations between metabolic biomarkers and symptom severity scales. FPG and insulin were significantly and negatively correlated with the HAMD, HAMA, FF, and STAI state scales. The z FPG\u0026thinsp;+\u0026thinsp;z insulin and HOMA-2IR scores exhibited significant negative correlations across all these scales. HOMA2-IS was positively correlated with these scales, except HAMA. QUICKI showed significant positive correlations with the severity scales.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultiple regression with insulin metabolism biomarkers or rating scale scores as dependent variables.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eDependent Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eExplanatory Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eCoefficient statistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eModel statistics\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e#1. FPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003eTransferrin\u003c/p\u003e\n \u003cp\u003eWeight loss\u003c/p\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003cp\u003e-0.165\u003c/p\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.21\u003c/p\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003cp\u003e-2.41\u003c/p\u003e\n \u003cp\u003e4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e20.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4/148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e#2. Insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003cp\u003e-0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.68\u003c/p\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003cp\u003e-2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e26.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e#3 HOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003cp\u003e-0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.22\u003c/p\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003cp\u003e-2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e28.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e#4 z FPG\u0026thinsp;+\u0026thinsp;z INS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003cp\u003eTransferrin\u003c/p\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.68\u003c/p\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e26.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4/148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e#5 QUICKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003cp\u003eTransferrin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.403\u003c/p\u003e\n \u003cp\u003e-0.266\u003c/p\u003e\n \u003cp\u003e-0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-5.96\u003c/p\u003e\n \u003cp\u003e-3.72\u003c/p\u003e\n \u003cp\u003e-2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e25.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e#6 Pure HAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003cp\u003eACEs\u003c/p\u003e\n \u003cp\u003eHOMA2-IR\u003c/p\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.90\u003c/p\u003e\n \u003cp\u003e-2.45\u003c/p\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e15.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e#7 Pure HAMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003cp\u003eACEs\u003c/p\u003e\n \u003cp\u003ez FPG\u0026thinsp;+\u0026thinsp;z INS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003cp\u003e-0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.92\u003c/p\u003e\n \u003cp\u003e-2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e18.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2/160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e#8 Pure FF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003cp\u003eACEs\u003c/p\u003e\n \u003cp\u003eQUICKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e11.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2/160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e#9 STAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003cp\u003eACEs\u003c/p\u003e\n \u003cp\u003eHOMA2-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003cp\u003e-0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003cp\u003e-2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e32.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2/150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e#10 OSOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003cp\u003eACEs\u003c/p\u003e\n \u003cp\u003eHOMA2-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003cp\u003e-0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.73\u003c/p\u003e\n \u003cp\u003e-2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e28.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2/150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eACE: adverse childhood experiences, HAMD: Hamilton Depression Rating Scale, HAMA: Hamilton Anxiety Rating Scale, FF: Fibro-Fatigue, STAI: State-Trait Anxiety Inventory, FPG: fasting plasma glucose, INS: insulin, GLU: glucose, HOMA2-IR: Homeostasis Model Assessment Version 2 for Insulin Resistance, HOMA2-IS: Homeostasis Model Assessment Version 2 for Insulin Sensitivity, HOMA2-\u0026beta;: Homeostasis Model Assessment Version 2 for \u0026beta;-cell Function, QUICKI: quantitative insulin sensitivity check index. WC: Waist circumference, BMI: Body Mass Index, OSOD: overall severity of depression.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePredictors of Metabolic Dysregulation\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes the results of multiple regression analyses examining the predictors of metabolic measures. Regression model #1 shows that 35.9% of the variance in FPG was explained by age, transferrin, and WC (all positive associations), together with weight loss (negative association). Regression model #2 demonstrates that 34.8% of the variance in insulin was explained by BMI and albumin (positive associations), along with female gender. Regression model #3 indicates that 36.8% of the variance in HOMA-IR was explained by BMI and albumin (positive associations), together with female gender. Regression model #4 shows that 41.4% of the variance in the z FPG\u0026thinsp;+\u0026thinsp;z insulin score was explained by BMI, albumin, transferrin, and age (all positive associations). Regression model #5 demonstrates that 34.0% of the variance in the QUICKI index was explained by BMI, albumin, and transferrin (all negative associations).\u003c/p\u003e\n\u003ch3\u003ePredictors of the clinical rating scale scores\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, regression model #6 indicates that 22.6% of the variance in the pure HAMD score was explained by ACEs and female gender (positive associations), together with HOMA-IR (negative association). Regression #7 shows that 18.4% of the variance in the pure HAMA score was explained by ACEs (positive association) and the z FPG\u0026thinsp;+\u0026thinsp;z insulin score (negative association). Model #8 demonstrates that 12.7% of the variance in the pure FF score was explained by ACEs and the QUICKI index (both positive associations). Model #9 indicates that 30.5% of the variance in the STAI score was explained by ACEs (positive association) and HOMA2-IR (negative association). Regression #10 shows that 27.7% of the variance in the OSOD score was explained by ACEs (positive association) and HOMA2-IR (negative association).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eImpact of metabolic syndrome on insulin biomarkers in MDD\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eElectronic Supplementary File (ESF)\u003c/strong\u003e, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the findings from general linear models assessing the impact of MetS on insulin biomarkers, after controlling for potential confounders, including MDD. Individuals with MetS showed significantly higher FPG, insulin, z FPG\u0026thinsp;+\u0026thinsp;z insulin, HOMA2-IR, and TyG than those without MetS, whilst HOMA2-IS and the QUICKI index were significantly lower in those with MetS.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInsulin resistance and sensitivity in Chinese MDD patients\u003c/h2\u003e \u003cp\u003eThe first major finding of this study is that Chinese MDD patients exhibit lower insulin resistance than controls, whilst their insulin sensitivity was higher. Whether unadjusted or adjusted for confounding factors such as age, gender, BMI, and MetS, the MDD group showed significantly lower FPG, insulin levels, and HOMA2-IR scores compared to the healthy controls. Meanwhile, insulin sensitivity indices, such as HOMA2-IS and QUICKI, were significantly higher.\u003c/p\u003e \u003cp\u003eIn contrast, a meta-analysis by Fernandes et al. (Fernandes et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported that insulin levels, HOMA-IR, and insulin variability were significantly elevated in the acute phase of depression. However, this meta-analysis study did not adjust for potential confounding factors such as MetS or increased BMI scores (Fernandes et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is obvious that the absence of correction for metabolic factors might result in significant heterogeneity and bias. It is well established that these factors primarily affect insulin resistance and sensitivity, and that after controlling for these variables, no significant differences between MDD (and BD) and controls may be established (de Melo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Landucci Bonif\u0026aacute;cio et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Morelli et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, after considering the effects of other NIMETOX pathways (e.g., immune and oxidative stress), it was established that insulin resistance is associated with the severity of illness (Maes, Jirakran, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). Therefore, the results of most studies included in the meta-analysis by Fernandes et al. are difficult to interpret. After adjusting for BMI, MetS and age (sex was not significant), the current study found that the increase in insulin sensitivity and lower insulin resistance were still prevalent in our Chinese MDD patients.\u003c/p\u003e \u003cp\u003eThe above-mentioned discrepancies may be due to significant differences in obesity and MetS prevalence between our study sample and other samples recruited from Western or some South American countries. Epidemiological data show that the age-standardized prevalence of obesity in China (approximately 16.0% in men and 14.4% in women) (Mu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) is significantly lower than in Western countries (e.g., approximately 43.0% in men and 42.1% in women in the United States) (Hales et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, in Chengdu, China, the prevalence of obesity is approximately 11.0% and 16.7% for MetS (Zhou et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), significantly lower than the 37.6% seen in the U.S (Liang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our representative cohort from Chengdu exhibited a comparatively low prevalence of MetS, comprising 17.7% of MDD patients and 25.6% of the controls. The lower incidence of obesity and MetS might explain the observed differences in insulin sensitivity in Chinese MDD patients versus studies that did not adjust for MetS or obesity. Nonetheless, the exact explanation is more intricate, as detailed in the subsequent two sections.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eInsulin sensitivity and the APP response\u003c/h2\u003e \u003cp\u003eThe second major finding of this study is the significant association between enhanced insulin resistance in MDD and the negative APP. Despite the lower FPG, insulin levels, and HOMA2-IR scores in MDD, as well as the significantly increased insulin sensitivity, the significance of these variables disappeared after adjusting for negative APPs. This indicates that the APP response impacts insulin metabolism and might explain the lower insulin resistance and higher insulin sensitivity in MDD.\u003c/p\u003e \u003cp\u003eSeveral studies have reported a decrease in albumin and transferrin levels in depression (Al-Marwani et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ambrus \u0026amp; Westling, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Maes, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Maes et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Van Hunsel et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Maes et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) found that depression is associated with a negative APP response and protein deficiency or homeostasis dysregulation, which was additionally associated with anorexia and weight loss, core symptoms of depression (Maes et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Our study also supports this hypothesis, suggesting that protein malnutrition associated with weight loss is associated with the negative APP response in MDD.\u003c/p\u003e \u003cp\u003eAdditionally, recent research indicates that the reduction in negative APPs in MDD patients, along with changes in positive APPs (such as high-sensitivity monomeric C-reactive protein), reflects a mild smoldering inflammatory state (Maes, Niu, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025c\u003c/span\u003e). This state may be associated with chronic malnutrition, the activation of chronic inflammatory factors (such as TNF-α and IL-6), and impaired liver synthesis function of positive APPs (Cao et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Maes, Almulla, et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea; Maes et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). The liver is the primary organ responsible for synthesizing negative APPs (Ceciliani et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Under normal physiological conditions, the liver efficiently synthesizes and secretes these proteins to maintain metabolic balance and immune function. However, in MDD, chronic inflammatory factors may inhibit liver function, resulting in a reduction in the synthesis of negative APPs (Maes, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Maes et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Maes, Niu, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025c\u003c/span\u003e; Shao et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For example, TNF-α can suppress the synthesis of albumin and transferrin by activating the Nuclear Factor-κB signaling pathway, resulting in significantly lower plasma concentrations of these proteins (Chojkier, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis inflammation-mediated inhibition of liver synthesis may lead to a \u0026ldquo;malnutrition-inflammation\u0026rdquo; vicious cycle. Albumin and transferrin are not only important nutritional markers but also play a key function in sustaining homeostasis and various physiological functions (Levitt \u0026amp; Levitt, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Talukder, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Their sustained low levels may exacerbate the pathogenesis of MDD. On one hand, hypoalbuminemia may lead to an imbalance in colloid osmotic pressure, reduced oxidative stress buffering capacity, and impaired toxin clearance (Dubois et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Levitt \u0026amp; Levitt, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). On the other hand, reduced transferrin may impair iron transport and metabolism, increasing the risk of the anemia of inflammation (Maes et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and affecting mitochondrial energy production, further exacerbating fatigue in MDD patients (Rybka et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Talukder, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic flexibility and enhanced insulin sensitivity\u003c/h2\u003e \u003cp\u003eMetabolic flexibility refers to the ability of the organism to maintain normal physiological function by adjusting energy metabolism in the context of chronic stress or chronic disease (Smith et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This adaptive mechanism involves the reallocation of energy resources to prioritize the needs of critical organs (Smith et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Particularly under the stress of chronic inflammation and protein malnutrition, the body may optimize the use of limited energy resources by altering metabolic pathways, especially to ensure the energy supply of vital organs such as the brain (Olson et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we observed abnormally elevated insulin sensitivity in MDD in the context of a negative APP response, suggesting that this change in metabolic status may be a metabolic adaptive response of the body. Long-term activation of chronic inflammatory factors may have metabolic consequences, leading to adaptive adjustments in energy allocation and use by the organism (Straub, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wang \u0026amp; Ye, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In response to a chronic inflammatory state, the organism may optimize glucose utilization by increasing insulin sensitivity and reducing the waste of energy resources (Berbudi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn people without MetS, mild, smoldering inflammation can transiently lower insulin resistance. For example, small elevations in IL-6 may activate AMPK and increase insulin-stimulated glucose disposal in humans, improving glucose uptake (Carey et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This hormetic window closes as inflammation strengthens or when MetS is present (Koenen et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In MetS, the same stimulus typically worsens insulin resistance because inflammatory pathways are pre-primed, including JNK/IKKβ signaling, macrophage infiltration, and lipotoxic mediators blunting PI3K-Akt thereby promoting hepatic and muscle insulin resistance (Kelly et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pedersen \u0026amp; Febbraio, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Shoelson et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In adipose tissue, proinflammatory remodeling with macrophage infiltration elevates serum cytokines and free fatty acids, reinforcing systemic insulin resistance dysfunction (Apovian et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Furthermore, reciprocal interactions between neurotoxicity-associated cytokine networks and MetS are associated with increasing clinical severity of MDD (Maes, Jirakran, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). Overall, mild smoldering inflammation may be hormetic with respect to insulin metabolism in subjects without MetS, but in those with MetS it might, via interactions among activated cytokine networks and MetS, sustain or aggravate insulin resistance and severity of MDD (Maes, Jirakran, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eThis cross-sectional study has several limitations. First, this is a case-control study, which limits the ability to draw definitive conclusions about causality. Moreover, this study was conducted at a single site in southwestern China, and lifestyle factors (such as dietary intake, dietary habits, and physical activity) in this region differ from those in the West. Still, they may also differ from those in other Chinese areas.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study shows that Chinese MDD patients exhibit enhanced insulin sensitivity and lower insulin resistance. Furthermore, these changes are mediated by a mild smoldering inflammatory response with protein malnutrition. A mild inflammatory response in study samples without a high prevalence of subjects with MetS or obesity (as in our Chengdu study sample) may be accompanied by a hormetic response briefly enhancing insulin sensitivity. On the other hand, stronger long-standing inflammatory responses, especially in populations with increased prevalence of MetS and obesity (such as Western populations), might cause and maintain impaired insulin resistance and cause increased severity of depression.\u003c/p\u003e \u003cp\u003eTherefore, under the dual stress of chronic inflammation and protein malnutrition, MDD is characterized by increased insulin efficiency by prioritizing energy supply to critical organs, thereby optimizing glucose resource utilization to maintain normal function of vital organs, especially when energy supply is limited. However, the presence of MetS or obesity obscures these primary biomarkers of MDD, since interactions between immunological and metabolic factors become more pronounced. Further research should examine these immune and MetS interactions in MDD.\u003c/p\u003e \u003cp\u003eConsequently, it is likely erroneous to assert that MDD is inherently linked to heightened insulin resistance; rather, the accurate interpretation is that MDD in the absence of MetS is accompanied by increased insulin sensitivity, and in the presence of immune activation and MetS might elicit elevated insulin resistance, which affects the severity of the condition.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e Ethical approval for this study was obtained from the Institutional Review Board of Sichuan Provincial People\u0026rsquo;s Hospital, Chengdu, China [Ethics (Research) 2024\u0026thinsp;\u0026minus;\u0026thinsp;203] and all participants provided written informed consent per the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003e \u003cem\u003eConsent for publication\u003c/em\u003e:\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by the Sichuan Science and Technology Program \u0026ldquo;PIANJI\u0026rdquo; Project (Grant No.: 2025HJPJ0004).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.M. and Y.Z. Conception and design of the study. M.N. and J.L. Data collection. Y.L., T.C., and A.A. Performed the experiments. Y.L. and M.M. Data analysis. Y.L. Writing of the original draft. M.M. and Y.Z. Critical revision of the article. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eNot Applicable\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset supporting this study is available from the corresponding author (MM) upon reasonable request and after a thorough data review.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbasi F, Reaven GM. Comparison of two methods using plasma triglyceride concentration as a surrogate estimate of insulin action in nondiabetic subjects: Triglycerides \u0026times; glucose versus triglyceride/high-density lipoprotein cholesterol. 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Front Endocrinol. 2024;15:1365658. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fendo.2024.1365658\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2024.1365658\" 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":"inflammation, major depressive disorder, neuroimmune, biomarkers, metabolic syndrome, insulin resistance","lastPublishedDoi":"10.21203/rs.3.rs-8943225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8943225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Major depressive disorder (MDD) is widely acknowledged as stemming from the dysregulation of neuroimmune, metabolic, and oxidative stress (NIMETOX) pathways. \u003cbr\u003e\nThe objective of this study was to examine insulin metabolism in Chinese patients with MDD and to examine the relationship between insulin resistance and the acute phase protein (APP) response, as shown by lower albumin and transferrin. \u003cbr\u003e\nMethods: This investigation utilized a cross-sectional case-control approach, enrolling 125 inpatients with MDD and 40 healthy controls. \u003cbr\u003e\nResults: Patients with MDD exhibited markedly reduced levels of fasting plasma glucose, insulin, and insulin resistance, and heightened insulin sensitivity, in comparison to healthy controls. The significance of these alterations persisted after controlling for metabolic syndrome, body mass index (BMI), and age, but was nullified with adjustment for both negative APPs. We determined that elevated BMI, albumin, transferrin, and age levels accounted for 41.4% of the variance in insulin resistance. Insulin resistance was significantly and inversely associated with weight loss. We found that 27.7% of the variance in overall depression severity was accounted for by adverse childhood experiences (positive correlation) and insulin resistance (negative correlation). \u003cbr\u003e\nConclusions: This work demonstrates that Chinese MDD patients display increased insulin sensitivity and reduced insulin resistance, with these alterations being associated with a modest smoldering inflammatory response. MDD is characterized by a hormetic response that enhances insulin efficacy, hence optimizing glucose consumption to sustain normal organ function. It is incorrect to claim that MDD is intrinsically associated with increased insulin resistance.\u003c/p\u003e","manuscriptTitle":"Lower insulin resistance in Chinese patients with severe major depressive disorder: associations with the inflammatory response","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 14:57:55","doi":"10.21203/rs.3.rs-8943225/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-08T12:55:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T02:15:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T15:48:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62283656150970674397942782153596191361","date":"2026-03-22T16:26:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281822864439637081164290847862340964922","date":"2026-03-20T01:33:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-20T01:04:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-20T00:52:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-02T18:16:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-02T01:48:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2026-03-02T01:43:55+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":"1a41a3a2-1397-4bbf-96ac-477ca63c833c","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:54:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 14:57:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8943225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8943225","identity":"rs-8943225","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}