Associations Between Hematologic Profiles and Depressive Disorder in Adolescents

Associations Between Hematologic Profiles and Depressive Disorder in Adolescents | 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 Associations Between Hematologic Profiles and Depressive Disorder in Adolescents Kang Yoon Jung, Jong-Hyun Jeong, HyeJin Tae, Yoo Hyun Um, Jae Hyun Yoo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8434228/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Adolescent depressive disorders are common and often interfere with adolescents’ everyday functioning, school life, and social relationships. Although there is growing interest in finding objective ways to support these disorders’ clinical assessment, blood-based measures for young people have been relatively overlooked. As low-grade inflammation and changes in hematopoiesis are thought to play a role in depression, we explored whether routinely obtained hematological measures show meaningful patterns in relation to depressive symptom severity among South Korean adolescents. We retrospectively analyzed the electronic health record data of 1,074 adolescents with depressive disorders and 1,220 healthy controls aged 13–18 years. Depressive symptoms were assessed using the 17-item Hamilton Depression Rating Scale. Blood samples were obtained from routine laboratory tests, and associations between peripheral blood parameters and depressive symptoms were examined using correlation analyses and multivariable least absolute shrinkage and selection operator (LASSO) regression analysis. Compared with the control group, the depression group exhibited lower red blood cell count, hemoglobin, hematocrit, and mean corpuscular hemoglobin concentration, but higher platelet and white blood cell counts. Correlation analyses showed that greater depression severity was associated with reduced hemoglobin, hematocrit, red blood cell count, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, and eosinophil levels. LASSO regression revealed that hemoglobin, hematocrit, and eosinophil counts were negatively associated with scores on the Hamilton Depression Rating Scale. Although these markers are unlikely to serve as standalone diagnostic indicators, they may offer complementary information regarding inflammatory or hematopoietic alterations in youths with depression. Blood Cell Count Depressive Disorder Adolescent Inflammation Hemoglobin 1. Introduction Depressive disorders are common psychiatric conditions in adolescents and are strongly associated with impaired academic and social functioning, heightened risk of self-harm and suicide, and long-term psychosocial disability [ 1 , 2 ]. Despite this growing burden, diagnosis of adolescent depression continues to rely primarily on subjective clinical assessments. Approximately 60% of adolescents show poor response to initial treatment [ 3 ], emphasizing the need for objective and easily accessible biomarkers that can aid in diagnosis and treatment selection [ 2 ]. Currently, the diagnosis of major depressive disorder in youths relies on clinical assessments of symptomatology, which can be particularly challenging because manifestations of depressive symptoms are often less typical in adolescents than in adults. Therefore, the identification of biomarkers specific to child and adolescent depression could refine diagnostic accuracy and improve treatment outcomes. Furthermore, biomarkers that predict antidepressant response would enable more personalized and effective treatment strategies, representing a key step toward improving long-term prognosis across the lifespan [ 1 ]. Evidence increasingly suggests that low-grade systemic inflammation plays an important role in the pathophysiology of depressive disorders. In adults, elevated levels of pro-inflammatory cytokines such as interleukin (IL), tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP) have been consistently observed in major depressive disorder [ 4 – 7 ]. In parallel, hematological indices derived from routine complete blood count tests—such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and mean platelet volume—have emerged as reliable indicators of systemic inflammation and have been shown to correlate with depression severity in adult populations [ 8 , 9 ]. Similarly, in adolescents, elevated levels of pro-inflammatory cytokines such as IL-6, IL-8, TNF-α, and IL-1β have been reported, with higher cytokine concentrations associated with both greater depressive symptom severity and increased odds of meeting the criteria for clinical depression [ 10 ]. Moreover, iron deficiency has been associated with increased likelihood of depressive symptoms in female adolescents, with lower hemoglobin levels and higher body weight linked to greater depression risk [ 11 , 12 ]. Female adolescents with depressive disorders also demonstrated significantly higher high-sensitivity CRP levels than healthy controls [ 13 ], further supporting the potential role of peripheral inflammatory and hematological markers in youth depression. In addition, a recent case-control study found significantly higher PLR values in children and adolescents with depressive disorders than in healthy controls [ 14 ]. However, studies focusing on adolescents are still limited and often rely on small samples or insufficient control of confounding factors such as age or body-mass index (BMI). Previous studies revealed that the maturation of hematopoietic cells is associated with the developmental stage and health status [ 15 , 16 ]. Thus, important gaps remain in clarifying how depressive symptoms relate to altered hematologic profiles and determining whether these indices can serve as reliable, objective, and easily accessible candidate markers for depression. The present study aimed to explore the hematologic markers associated with depressive symptoms in adolescents and clarify how deviations in these indices relate to symptom severity. We examined a range of hematologic parameters in adolescents with depressive disorders and healthy controls and compared the two groups to explore evidence supporting the inflammatory hypothesis in depression. To identify the indices most strongly associated with depressive symptoms among this large set of interrelated variables, we applied the least absolute shrinkage and selection operator (LASSO) regression. 2. Methods 2.1. Participants This retrospective study utilized the Clinical Data Warehouse of the Seoul St. Mary’s Hospital and St. Vincent’s Hospital, an electronic health record system that serves as an integrated repository of de-identified clinical data, including the diagnosis, prescription, laboratory result, and health screening information. The cohort was identified for the following period: January 1, 2014, to July 1, 2024. As this study involved a retrospective review of existing medical records, the requirement for informed consent was waived by the Institutional Review Board (approval no.: KC24WIDI0718; date of approval: November 5, 2024). The depression group included 1,160 adolescents aged 13–18 years with depressive disorders who visited the Seoul St. Mary’s Hospital or St. Vincent’s Hospital as inpatients, outpatients, or emergency department patients. Board-certified psychiatrists evaluated all participants in the depression group. Patients with depressive disorders were included if they could be categorized under one of the following International Statistical Classification of Diseases and Related Health Problems 10th Revision codes related to depressive disorders: F32, F320, F321, F322, F323, F328, F329, F33, F330, F331, F332, F333, F334, F339, F34, F341, F349, F38, F380, F381, F388, or F39 (Table S1 ). The control group included 1,243 adolescents aged 13–18 years who visited the Health Promotion Center at The Catholic University of Korea, Seoul St. Mary’s Hospital. Exclusion criteria for the study were as follows: signs of acute infection (white blood cell [WBC] ≥ 13,000 or segmented neutrophils ≥ 75%), coexisting physical illnesses that could affect various blood test results (e.g., cancer, diabetes, dyslipidemia, pulmonary tuberculosis, and hepatitis B), absence of hemoglobin values in the blood test results, and a Hamilton Depression Rating Scale (HAMD) score of 7 (normal range) for only the depression group. Accordingly, the following participants were excluded: 35 adolescents in the depression group and 15 in the control group with signs of acute infection; 5 adolescents in the depression group and 6 in the control group with underlying conditions such as cancer, diabetes, dyslipidemia, pulmonary tuberculosis, or hepatitis B; 30 adolescents in the depression group and 2 in the control group without hemoglobin values; and 17 adolescents in the depression group with an HAMD score of 7. The final sample comprised 1,074 adolescents with depressive disorders and 1,220 adolescents in the control group. For each participant, the dates of initial clinical visits, HAMD assessment, and blood sampling were aligned to ensure consistency in the baseline measurements. 2.2. Depressive symptoms Depressive symptoms were assessed using the 17-item HAMD [ 17 ], a clinician-administered instrument in which items are rated on either a 3-point (0–2) or 5-point (0–4) scale. The total scores range from 0 to 54, with higher scores indicating greater symptom severity. The HAMD has demonstrated good reliability and validity in both clinical and research settings [ 18 ]. Consistent with established guidelines, scores ≥ 8 indicated the presence of depression, with 8–16 classified as mild, 17–23 as moderate, and ≥ 24 as severe depression [ 19 ]. Participants scoring ≥ 8 were categorized into the depressive disorder group. To examine the association between inflammatory markers and incidence of depression, depressive symptoms were treated as binary variables. To analyze the relationship between inflammatory changes and depression severity across waves, HAMD scores were analyzed as continuous variables. All HAMD assessments were conducted by psychiatrists through direct clinical interviews. 2.3. Blood cell counts and inflammatory markers We retrospectively collected peripheral blood test results from subjects enrolled between January 2014 and July 2024. To exclude potential changes in blood indicators attributable to depression treatment, laboratory data obtained on the date closest to each patient’s first hospital visit were selected for analysis. The fundamental indices included red blood cell (RBC) count, WBC count, absolute neutrophil count (ANC), segmented neutrophil count (NC), basophils, lymphocyte count (LC), eosinophils, monocytes, hemoglobin, hematocrit, mean corpuscular volume (MCV), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular hemoglobin (MCH), platelet count (PLT), NLR, and PLR. Additionally, PLR and NLR, which reflect systemic inflammatory states, were computed (Yanwei Cheng, 2022) as follows : NLR = NC/LC and PLR = PLT/LC. 2.4. Covariate assessment Potential covariates included age, sex, and BMI at the time of blood sampling. These variables were selected because hemoglobin levels have been shown to be positively correlated with age and BMI, and sex differences in hemoglobin levels have also been reported [ 15 , 16 ]. 2.5. Statistical analyses Continuous variables for the baseline characteristics are represented as the mean ± standard deviation, while the categorical variables are described as numbers and percentages. Correlation analyses were performed to examine the associations between hematological markers and severity of depressive symptoms. To analyze the associations between peripheral blood cell counts, inflammatory markers, and depressive symptoms, we performed multivariable regression analyses in both the whole sample and sex-stratified samples. Because correlation analyses indicated substantial multicollinearity among the demographic and hematological variables (Table S2), we applied LASSO linear regression with Gaussian error distribution to examine associations between hematological variables and severity of depressive symptoms (HAMD scores). Age, height, weight, and BMI were forced to remain in the model as covariates. LASSO regression was chosen to mitigate instability associated with correlated variables, reduce overfitting of a prediction model. LASSO performs coefficient shrinkage in the presence of correlated predictors and provides a more stable variable selection than traditional stepwise methods. The optimal penalty parameter (λ) was selected using 10-fold cross-validation, and the value minimizing the mean cross-validated error (lambda.min) was chosen for model estimation. To enhance robustness and reduce sampling variability, the LASSO model was further combined with 1,000 bootstrap resamples, and the variable selection frequencies were examined to evaluate stability of the selected predictors. All analyses were conducted using R version 4.5.0. Missing data were excluded from the analyses. Statistical significance was set at a two-tailed p-value < 0.05. Age, sex, and BMI were included as covariates in all adjusted analyses to reduce potential confounding factors. 3. Results 3.1. Descriptive statistics Table 1 describes the demographic and clinical characteristics. The depression group comprised 60.06% female participants, and the control group comprised 56.64% female participants. There was no significant difference in sex distribution between the control and depression groups (χ²=2.60, p = 0.107). The depression group had a significantly lower age (p < 0.001) and height (p < 0.001) than the control group. There were no significant differences in sex, weight, and BMI. In the depression group, the average HAMD score was 19.80, indicating that most adolescents with depression had moderate depressive symptoms at the time of evaluation. Table 1 Group comparison of demographic variables in the total sample Control Group (n = 1,220) Depression Group (n = 1,074) p value Age, yr 17.19 ± 1.69 16.05 ± 1.59 < 0.001*** Female (%) 691(56.64) 645(60.06) 0.107 Height 165.60 ± 10.71 163.65 ± 10.06 < 0.001*** Weight 59.41 ± 13.61 60.80 ± 16.70 0.087 BMI 22.40 ± 29.85 22.57 ± 6.36 0.880 HAMD n.a. 19.83 ± 5.44 n.a. Asterisk indicates * p < 0.05, ** p < 0.01, *** p < 0.001 BMI = Body Mass Index; HAMD = Hamilton Depression Rating Scale Regarding hematological parameters, the control group had a higher RBC count (p < 0.001), hemoglobin levels (p < 0.001), hematocrit (p < 0.001), and MCHC (p = 0.001, Table 2 ). MCV and MCH was higher in the depression group, but showed no significant group differences after controlling covariates. Adolescents with depressive disorders had higher WBC counts (p = 0.001), whereas the basophil proportion was higher in the control group (p = 0.027). Comparison of other indices, including LC, NC, ANC, and monocyte and eosinophil proportions, did not reach statistical significance after adjustment. PLTs were higher in the depression group and remained significant after adjustment (p = 0.019). Inflammatory markers, including NLR and PLR, were elevated in the depression group, but became non-significant after adjustment. Table 2 Group comparison of hematologic variables in the total sample Hematologic variables Control Group (n = 1,220) Depression Group (n = 1,074) t value p value Adjusted p value RBC count (10 12 /L) 4.88 ± 0.44 4.70 ± 0.50 6.62 < 0.001*** < 0.001*** Hemoglobin (g/dl) 14.30 ± 1.43 13.74 ± 1.73 8.42 < 0.001*** < 0.001*** Hematocrit (%) 42.89 ± 3.84 41.42 ± 4.46 8.38 < 0.001*** < 0.001*** MCV (fl) 87.33 ± 4.97 88.20 ± 4.86 -3.04 0.003** 0.458 MCHC (%) 33.43 ± 1.03 33.12 ± 1.26 4.75 < 0.001*** 0.001** MCH (pg) 29.20 ± 1.90 29.23 ± 2.09 -0.29 0.772 0.117 WBC count (10 9 /L) 6.40 ± 1.56 6.73 ± 1.69 -3.51 0.001** 0.001** LC (%) 39.14 ± 8.65 38.04 ± 8.50 2.08 0.038* 0.398 NC (%) 50.06 ± 9.32 51.25 ± 8.95 -2.11 0.035* 0.294 ANC (10 9 /L) 3.20 ± 1.11 3.51 ± 1.26 -4.43 < 0.001*** 0.052 Basophils (%) 0.50 ± 0.30 0.46 ± 0.26 2.16 0.031* 0.027* Monocytes (%) 7.33 ± 2.12 7.44 ± 1.75 -0.89 0.375 0.103 Eosinophils (%) 2.98 ± 2.03 2.80 ± 2.07 1.43 0.153 0.787 PLT (10 9 /L) 265.45 ± 52.3 275.79 ± 57.40 -3.32 0.001** 0.019* NLR 1.40 ± 0.56 1.47 ± 0.59 -2.16 0.031* 0.561 PLR 7.18 ± 2.38 7.68 ± 2.51 -3.40 0.001** 0.217 Asterisk indicates * p < 0.05, ** p < 0.01, *** p < 0.001 RBC count = Red Blood Cell count; MCV = Mean Corpuscular Volume; MCHC = Mean Corpuscular Hemoglobin Concentration; MCH = Mean Corpuscular Hemoglobin; WBC count = White Blood Cell count; LC = Lymphocytes count; NC = Segmented Neutrophils count; ANC = Absolute Neutrophil Count; PLT = Platelet count; NLR = Neutrophil-to-Lymphocyte Ratio; PLR = Platelet-to-Lymphocyte Ratio 3.2. Correlation analyses Correlation analyses demonstrated significant negative associations between HAMD scores and several hematological indices, including hemoglobin (r=–0.195, p < 0.001), hematocrit (r=–0.184, p < 0.001), RBC count (r=–0.160, p < 0.001), MCH (r=–0.107, p = 0.017), MCHC (r=–0.146, p = 0.001), and eosinophils (r=–0.115, p = 0.011). Although these correlations were modest, they suggested a trend wherein greater depression severity was associated with lower RBC parameters and eosinophil counts in adolescents (Table 3 ). Table 3 Correlation analyses between depressive symptom severity (HAMD scores) and hematological indices Hematologic variables Correlation coefficient (r) P value RBC count (10 12 /L) -0.160 < 0.001*** Hemoglobin (g/dl) -0.195 < 0.001*** Hematocrit (%) -0.184 < 0.001*** MCV (fl) -0.058 0.195 MCHC (%) -0.146 0.001** MCH (pg) -0.107 0.017* WBC count (10 9 /L) 0.006 0.892 LC (%) 0.031 0.492 NC (%) -0.004 0.923 ANC (10 9 /L) 0.013 0.775 Basophils (%) -0.038 0.403 Monocytes (%) 0.027 0.550 Eosinophils (%) -0.115 0.011* PLT (10 9 /L) 0.084 0.062 NLR -0.014 0.754 PLR 0.039 0.382 Asterisk indicates * p < 0.05, ** p < 0.01, *** p < 0.001 HAMD = Hamilton Depression Rating Scale; RBC count = Red Blood Cell count; MCV = Mean Corpuscular Volume; MCHC = Mean Corpuscular Hemoglobin Concentration; MCH = Mean Corpuscular Hemoglobin; WBC count = White Blood Cell count; LC = Lymphocytes count; NC = Segmented Neutrophils count; ANC = Absolute Neutrophil Count; PLT = Platelet count; NLR = Neutrophil-to-Lymphocyte Ratio; PLR = Platelet-to-Lymphocyte Ratio 3.3. Linear regressions In the depression group, LASSO regression with bootstrapping identified distinct patterns among the hematologic predictors of depressive symptom severity (Table 4 ). Several erythrocyte-related markers—including RBC count, hemoglobin, hematocrit, MCV, MCHC, and MCH—showed consistently negative coefficients, indicating that lower RBC parameters tended to be associated with greater severity of depressive symptoms. In addition, WBC count, eosinophil count, basophil count, and NLR were negative coefficients. In contrast, platelet-related indices—including PLT and PLR—demonstrated positive coefficients, suggesting that higher platelet activation levels may be associated with more severe depressive symptoms. Further, the LC and monocyte counts showed positive coefficients. Table 4 Lasso regression model for demographic and hematological predictors of depression severity (HAMD score) Variables Coefficient 95% CI Selection Frequency P value (Sign Test) Age -0.032 [-0.378, 0.307] 1.000 < 0.001 Height 0.022 [-0.220, 0.274] 1.000 < 0.001 Weight -0.128 [-0.555, 0.258] 1.000 < 0.001 BMI 0.451 [-0.613, 1.571] 1.000 < 0.001 RBC count (10 12 /L) -0.104 [-0.998, 0.000] 0.191 < 0.001 Hemoglobin (g/dl) -0.126 [-0.574, 0.000] 0.462 < 0.001 Hematocrit (%) -0.069 [-0.230, 0.000] 0.592 < 0.001 MCV (fl) -0.004 [-0.057, 0.000] 0.121 < 0.001 MCHC (%) -0.031 [-0.286, 0.000] 0.188 < 0.001 MCH (pg) -0.013 [-0.142, 0.000] 0.161 < 0.001 WBC count (10 9 /L) -0.007 [-0.127, 0.000] 0.087 < 0.001 LC (%) 0.003 [0.000, 0.035] 0.159 < 0.001 NC (%) 0 [0.000, 0.000] 0.024 1 ANC (10 9 /L) 0 [0.000, 0.000] 0.027 0.701 Basophils (%) -0.071 [-1.022, 0.058] 0.172 < 0.001 Monocytes (%) 0.026 [0.000, 0.225] 0.277 < 0.001 Eosinophils (%) -0.106 [-0.310, 0.000] 0.766 < 0.001 PLT (10 9 /L) 0.001 [0.000, 0.009] 0.324 < 0.001 NLR -0.008 [-0.132, 0.000] 0.041 < 0.001 PLR 0.004 [0.000, 0.056] 0.061 < 0.001 HAMD = Hamilton Depression Rating Scale; BMI = Body Mass Index; RBC count = Red Blood Cell count; MCV = Mean Corpuscular Volume; MCHC = Mean Corpuscular Hemoglobin Concentration; MCH = Mean Corpuscular Hemoglobin; WBC count = White Blood Cell count; LC = Lymphocytes count; NC = Segmented Neutrophils count; ANC = Absolute Neutrophil Count; PLT = Platelet count; NLR = Neutrophil-to-Lymphocyte Ratio; PLR = Platelet-to-Lymphocyte Based on the magnitude of the LASSO coefficients, predictors with the greatest relative contribution were hemoglobin (–0.126), followed by eosinophil proportion (–0.106) and RBC count (–0.104). Consistent with this pattern, bootstrap resampling demonstrated that eosinophil proportion (76.6%), hematocrit (59.2%), and hemoglobin (46.2%) showed the highest selection stability. Other parameters were selected less frequently but displayed directionally consistent coefficients across bootstrap iterations (p < 0.001). 3.4 Sex-specific differences in hematological indices In female adolescents, the depression group showed lower age (p < 0.001) and height (p = 0.021) but higher weight (p < 0.001) and BMI (p < 0.001) compared with the control group (Table S3). Among the hematologic markers, the depression group showed significantly lower levels of hemoglobin (p < 0.001), hematocrit (p < 0.001), MCHC (p < 0.001), RBC count (p < 0.001), and MCH (p = 0.031) compared to healthy controls after adjusting for age and BMI. Additionally, the monocyte percentage (p = 0.002) and NC (p = 0.026) were significantly higher in female participants. However, no significant group differences were observed in platelet indices (PLT and PLR) or NLR among female participants (Table S4). Among male adolescents, only age was significantly lower in the depression group (p < 0.001, Table S5). The depression group demonstrated a significantly higher WBC count (p = 0.019) and MCV (p = 0.012) than the control group. In contrast, hemoglobin (p = 0.019), hematocrit (p = 0.010), and RBC count (p = 0.030) were significantly lower in male patients, although the degree of reduction was less pronounced. Platelet indices (PLT and PLR) were numerically higher in the depression group but did not remain significant after adjustment (Table S6). 4. Discussion The present study investigated the associations between depressive symptom severity and hematological biomarkers in a large sample of South Korean adolescents. Compared to the control group, adolescents with depressive disorder showed lower RBC parameters, including RBC count, hemoglobin, hematocrit, and MCHC, even after adjusting for demographic and BMI covariates. The WBC and platelet counts were elevated in the depression group, while the basophil proportion was higher in the depression group. Regression analysis also demonstrated a significant association between lower RBC parameters and eosinophil proportion and greater depression severity in the depression group. The current findings do not imply causality; however, they indicate that changes in hematological indices may accompany depressive states in adolescents. These patterns aligned with the frameworks suggesting potential links between low-grade inflammation and hematopoiesis. In addition to these primary findings, several other features of this study strengthen the value of our observations. Although the present study employed a retrospective design, it analyzed a relatively large clinical sample of adolescents, allowing for a more stable estimation of hematological patterns than many earlier studies with smaller cohorts. To the best of our knowledge, this is the first investigation to examine specific markers such as hemoglobin may relate to depressive symptom severity. These analyses provided preliminary evidence that routinely collected blood parameters may capture clinically meaningful dimensions of adolescent depression. Nevertheless, important limitations of this study should be acknowledged. Because structural clinical interviews were not conducted, we could not systematically exclude certain comorbid psychiatric conditions. In addition, key biological regulators of hematopoiesis and inflammation—such as iron status; ferritin; and cytokines including IL-1, IL-6, and TNF-α—were not directly measured, restricting our ability to clarify the underlying mechanisms. These strengths and limitations should be considered when interpreting the implications of our findings. A growing body of literature suggests that inflammatory processes are involved in the development of depression. Inflammatory processes can alter monoamine neurotransmission by reducing the availability of serotonin, dopamine, and norepinephrine [ 20 ]. These changes may contribute to the development of core depressive symptoms such as anhedonia, fatigue, and psychomotor slowing [ 21 ]. Under inflammatory conditions, hematopoiesis undergoes a shift toward emergency hematopoiesis, characterized by enhanced myelopoiesis, to meet the increased immune demands [ 22 ]. This adaptive process promotes the rapid production of myeloid cells, such as neutrophils and monocytes, but concurrently suppresses erythropoiesis [ 23 ]. During inflammation, pro-inflammatory cytokines including IL-6 and TNF-α further inhibit erythropoietin synthesis and disturb iron homeostasis through hepcidin induction and ferroportin downregulation, leading to iron sequestration in macrophages and reduced availability for RBC formation [ 24 , 25 ]. Consequently, chronic inflammation may cause anemia by suppressing erythroid differentiation and limiting systemic iron supply. Such evidence is consistent with the current findings that adolescents with depressive disorders have lower RBC parameters. Further, we found an association between lower hemoglobin levels and depressive symptom severity. Given that anemia can lead to fatigue, low energy, and cognitive slowing, core features commonly reported in adolescent depression, peripheral anemia-related indices may serve as accessible indicators of the inflammatory processes underlying depressive symptoms. In the depression group, we found a significantly higher WBC count and lower basophil proportion than in the control group. However, eosinophil proportion was the only hematologic variable that showed a significant association with HAMD scores. Eosinophil proportion was consistently selected as a relevant predictor in the LASSO model with bootstrapping. In the Avon Longitudinal Study of Parents and Children birth cohort, adolescents with persistent depressive symptoms showed higher WBC counts, whereas no clear association was observed with eosinophil levels [ 26 ]. Another recent cohort study reported that depression severity is more strongly associated with higher cell counts of monocytes, basophils, and eosinophils [ 27 ]. Findings remain inconsistent on eosinophils as an important predictor of depression, and further studies are needed to clarify these relationships. Meta-analytic evidence supported the association between inflammation-based hematologic ratios and depression. Cheng et al. [ 8 ] reported that individuals with depression exhibit significantly higher NLR and PLR than controls. In addition, a case-control study with adolescent data found an increased PLR in a depression group. However, NLR and PLR did not remain significant after adjustment in the current analysis, whereas hemoglobin- and eosinophil-related indices showed stronger associations with depression severity. RBC-related indices (e.g., hemoglobin, hematocrit, RBC count, and MCV) showed minimal biological variation in healthy individuals, with a within-subject coefficient of variation of approximately 2–3% in the European Federation of Clinical Chemistry and Laboratory Medicine study, indicating high stability. In contrast, leukocyte and platelet measures display much greater variability (> 7–15%) and are more sensitive to short-term physiological factors [ 28 ]. Given their lower biological variability, RBC indices may offer more stable and potentially more informative markers of depressive states than NLR or PLR. The sex-specific patterns observed in our study further suggest that depressive disorders in adolescents may involve distinct biological pathways for male and female patients. Female patients showed more pronounced reductions in erythrocyte-related indices, whereas male patients demonstrated elevated WBC counts and higher MCV, along with milder decreases in hemoglobin and hematocrit. These differences may reflect the well-established sex-dependent modulation of immune responses and hematopoiesis. Previous studies have demonstrated that sex hormones such as estrogen and testosterone exert divergent effects on both innate and adaptive immune activities, contributing to differential inflammatory profiles across sexes [ 29 , 30 ]. Similarly, transcriptional studies have shown that depression itself is associated with sex-specific molecular signatures, indicating that the biological underpinnings of depressive disorders differ between male and female patients at the genomic and immune regulatory levels [ 31 ]. In addition, hematological regulation—including erythropoiesis and baseline hemoglobin levels—is strongly influenced by sex-linked hormonal pathways, which may partially explain the more marked erythrocyte-related alterations in female adolescents with depression [ 32 ]. However, it is important to note that pubertal stage and age-related changes in hematological indices can significantly affect sex-based comparisons. Although we adjusted for age in our primary analyses, we could not fully account for pubertal status or age-by-sex interaction effects, which may have contributed to the observed differences. Moreover, the menstrual status and phase—which are known to influence hemoglobin and other red blood cell indices—were not systematically assessed in female participants. The lack of control for these factors limits the interpretation of sex-specific hematological changes. Future studies should incorporate detailed assessments of pubertal development, hormonal status, and menstrual cycle phases to better delineate sex-specific biological mechanisms in adolescent depression. Together, these findings support the possibility that distinct immuno-hematological mechanisms contribute to depressive symptomatology in male and female patients, underscoring the need for sex-informed approaches in future biomarker research. Taken together, the present findings indicate that the most prominent hematological feature in the depression group was a reduction in RBC-related indices—particularly hemoglobin—which was also associated with greater severity of depressive symptoms. However, erythropoiesis in physiologically healthy individuals is primarily regulated by oxygen availability and renal erythropoietin signaling [ 33 ] and depends on sufficient iron, vitamin B12, and folate status [ 34 ]. Moreover, low-grade inflammatory activity can modestly suppress erythropoiesis through hepcidin-mediated iron restriction [ 35 ]. Given these multiple determinants, our cross-sectional data cannot establish whether reduced hemoglobin functions as a specific biomarker of depression. Nonetheless, previous studies have repeatedly reported alterations in hemoglobin and other RBC indices among individuals with depressive symptoms [ 11 , 12 ], suggesting that erythrocyte-related measures may represent a potential ancillary dimension in future assessments of depressive states. Our findings should be interpreted in light of several limitations. First, its cross-sectional design limits causal inferences. Longitudinal studies are warranted to determine whether inflammatory and hematological changes precede the onset of depressive symptoms in a prospective cohort. Second, several unmeasured factors that could influence inflammation and hematological profiles were not captured in this study, limiting the interpretation of our results. Future studies should exclude factors that directly influence erythropoiesis and incorporate direct measures of stress, stress hormones, and inflammatory cytokines (e.g., IL-1, IL-6, and TNF-α) to more clearly delineate the biological pathways leading to depression. Third, several biologically relevant confounders that may influence the inflammatory and hematological profiles—such as iron status (including serum ferritin), vitamin B12 and folate levels, and dietary or nutritional patterns—were not assessed. Given their essential roles in erythropoiesis and immune function, the absence of such measures limits the biological interpretation of our findings. Fourth, information on psychiatric comorbidities (e.g., anxiety disorders, neurodevelopmental disorders, stress-related disorders, and eating disorders) and psychotropic or anti-inflammatory medication use was not available, and these factors may independently affect the hematological and inflammatory parameters. Future studies should consider these variables more comprehensively to clarify the observed associations. Finally, our sample consisted of adolescents who visited a hospital, which introduced the risk of sampling bias. Validation of community-based cohorts is thus needed to determine the generalizability of these findings. In summary, adolescents with depression showed lower RBC-related indices and modest elevations in WBC and platelet counts, which are patterns broadly consistent with models linking low-grade inflammation to altered hematopoiesis. However, the cross-sectional, retrospective design and the many physiological factors influencing RBC production are important limitations. Evidence regarding eosinophils and other leukocyte markers remains inconsistent, further emphasizing the need for caution. Longitudinal, community-based studies incorporating repeated hematologic measures, iron-related biomarkers, stress indicators, and inflammatory cytokines are needed to clarify the temporal relationships and better define the biological pathways underlying adolescent depression. Declarations Acknowledgments: Author contributions: All authors contributed to the conception and design of the study. Material preparation, data collection, and analysis were performed by Kang Yoon Jung and Jae Hyun Yoo. The first draft of the manuscript was written by Kang Yoon Jung, and all the authors have revised the previous versions of the manuscript. All the authors have read and approved the final manuscript. Funding: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea. (grant number RS2025-02293110) Ethical approval: This study involved a retrospective review of existing medical records, and the requirement for informed consent was waived by the Institutional Review Board (Approval no.: KC24WIDI0718; date of approval: November 5, 2024). Consent for publication: The manuscript and publication have been approved by all authors. Competing interests: All authors certify that they have no affiliations to or involvement with in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this manuscript. References Zwolińska W, Dmitrzak-Weglarz M, Słopień A (2023) Biomarkers in child and adolescent depression. Child Adolesc Ment Health 28(2):141–150. https://doi.org/10.1111/camh.12616 Trebatická J, Vatrál M, Katrenčíková B, Muchová J, Ďuračková Z (2025) Current insight into biological markers of depressive disorder in children and adolescents: A narrative review. Antioxid (Basel) 14(6):699. https://doi.org/10.3390/antiox14060699 Fava M (2003) Diagnosis and definition of treatment-resistant depression. Biol Psychiatry 53(8):649–659. https://doi.org/10.1016/S0006-3223(03)00231-2 Zonca V, Marizzoni M, Saleri S, Zajkowska Z, Manfro PH, Souza L, Viduani A, Sforzini L, Swartz JR, Fisher HL, Kohrt BA, Kieling C, Riva MA, Cattaneo A, Mondelli V (2024) Inflammation and immune system pathways as biological signatures of adolescent depression—the IDEA-Risco study. Transl Psychiatry 14(1):230. https://doi.org/10.1038/s41398-024-02959-z Osimo EF, Baxter LJ, Lewis G, Jones PB, Khandaker GM (2019) Prevalence of low-grade inflammation in depression: a systematic review and meta-analysis of CRP levels. Psychol Med 49(12):1958–1970. https://doi.org/10.1017/S0033291719001454 Osimo EF, Pillinger T, Rodriguez IM, Khandaker GM, Pariante CM, Howes OD (2020) Inflammatory markers in depression: a meta-analysis of mean differences and variability in 5,166 patients and 5,083 controls. Brain Behav Immun 87:901–909. https://doi.org/10.1016/j.bbi.2020.02.010 Köhler CA, Freitas TH, Maes M, de Andrade NQ, Liu CS, Fernandes BS, Stubbs B, Solmi M, Veronese N, Herrmann N, Raison CL, Miller BJ, Lanctôt KL, Carvalho AF (2017) Peripheral cytokine and chemokine alterations in depression: a meta-analysis of 82 studies. Acta Psychiatr Scand 135(5):373–387. https://doi.org/10.1111/acps.12698 Cheng Y, Wang Y, Wang X, Jiang Z, Zhu L, Fang S (2022) Neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio in depression: an updated systematic review and meta-analysis. Front Psychiatry 13:893097. https://doi.org/10.3389/fpsyt.2022.893097 Canan F, Dikici S, Kutlucan A, Celbek G, Coskun H, Gungor A, Aydin Y, Kocaman G (2012) Association of mean platelet volume with DSM-IV major depression in a large community-based population: the MELEN study. J Psychiatr Res 46(3):298–302. https://doi.org/10.1016/j.jpsychires.2011.11.016 Leung CY, Weiss SJ (2025) Cytokines and depressive symptoms among adolescents. Biol Res Nurs 27(3):400–410. https://doi.org/10.1177/10998004251318385 Zarate-Ortiz AG, Verhoef H, Melse-Boonstra A, Woods BJ, Lee-Bazaldúa EE, Feskens EJM, Quiroga-Garza A, Cepeda-Lopez AC (2022) Depressive symptoms among Mexican adolescent girls in relation to iron status, anaemia, body weight and pubertal status: results from a latent class analysis. Public Health Nutr 26(2):408–415. https://doi.org/10.1017/S1368980022001203 Chen M-H, Su T-P, Chen Y-S, Hsu J-W, Huang K-L, Chang W-H, Chen T-J, Bai Y-M (2013a) Association between psychiatric disorders and iron deficiency anemia among children and adolescents: a nationwide population-based study. BMC Psychiatry 13(1):161. https://doi.org/10.1186/1471-244X-13-161 Tabatabaeizadeh SA, Abdizadeh MF, Meshkat Z, Khodashenas E, Darroudi S, Fazeli M, Ferns GA, Avan A, Ghayour-Mobarhan M (2018) There is an association between serum high-sensitivity C-reactive protein (hs-CRP) concentrations and depression score in adolescent girls. Psychoneuroendocrinology 88:102–104. https://doi.org/10.1016/j.psyneuen.2017.11.014 Önen Ö, Özek Erkuran H, Bağ Ö, Abacıgil F (2021) Blood count parameters as inflammation indicators in children and adolescents diagnosed with depressive disorder. Psychiatry Clin Psychopharmacol 31(4):425–433. https://doi.org/10.5152/pcp.2021.21137 Su F, Cao L, Ren X, Hu J, Tavengana G, Wu H, Zhou Y, Fu Y, Jiang M, Wen Y (2023) Age and sex trend differences in hemoglobin levels in China: a cross-sectional study. BMC Endocr Disord 23(1):8. https://doi.org/10.1186/s12902-022-01218-w PMID: 36624464, PMCID: PMC9827637 Jeong HR, Lee HS, Shim YS, Hwang JS (2022) Positive associations between body mass index and hematological parameters, including RBCs, WBCs, and platelet counts, in Korean children and adolescents. Child (Basel) 9(1):109. https://doi.org/10.3390/children9010109 PMID: 35053734, PMCID: PMC8774222 Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23(1):56–62. https://doi.org/10.1136/jnnp.23.1.56 Bagby RM, Ryder AG, Schuller DR, Marshall MB (2004) The Hamilton Depression Rating Scale: has the gold standard become a lead weight? Am J Psychiatry 161(12):2163–2177. https://doi.org/10.1176/appi.ajp.161.12.2163 Zimmerman MA (2013) Resiliency theory: a strengths-based approach to research and practice for adolescent health. Health Educ Behav 40(4):381–383. https://doi.org/10.1177/1090198113493782 Felger JC, Lotrich FE (2013) Inflammatory cytokines in depression: neurobiological mechanisms and therapeutic implications. Neuroscience 246:199–229. https://doi.org/10.1016/j.neuroscience.2013.04.060 Capuron L, Miller AH (2011) Immune system to brain signaling: neuropsychopharmacological implications. Pharmacol Ther 130(2):226–238. https://doi.org/10.1016/j.pharmthera.2011.01.014 EPub. PMID: 21334376, PMCID: PMC3072299 Wang J, Erlacher M, Fernandez-Orth J (2022) The role of inflammation in hematopoiesis and bone marrow failure: what can we learn from mouse models? Front Immunol 13:951937. https://doi.org/10.3389/fimmu.2022.951937 Loftus TJ, Mohr AM, Moldawer LL (2018) Dysregulated myelopoiesis and hematopoietic function following acute physiologic insult. Curr Opin Hematol 25(1):37–43. https://doi.org/10.1097/MOH.0000000000000395 Weiss G, Goodnough LT (2005) Anemia of chronic disease. N Engl J Med 352(10):1011–1023. https://doi.org/10.1056/NEJMra041809 Boettcher S, Manz MG (2017) Regulation of inflammation- and infection-driven hematopoiesis. Trends Immunol 38(5):345–357. https://doi.org/10.1016/j.it.2017.01.004 Tsang RSM, Stow D, Kwong ASF, Donnelly NA, Fraser H, Barroso I, Holmans PA, Owen MJ, Wood ML, LINC Consortium, van den Bree MBM, Timpson NJ, Khandaker GM (2025) Immunometabolic blood biomarkers of developmental trajectories of depressive symptoms: findings from the ALSPAC birth cohort. Mol Psychiatry. https://doi.org/10.1038/s41380-025-03311-7 Seizer L, Renner TJ, Löchner J (2026) Associations between depression severity and immune activity in children and adolescents. J Affect Disord 393(A):120190. https://doi.org/10.1016/j.jad.2025.120190 García-Tardón N, Sikkink LJ, Kranenborg J, den Besten G (2024) Hematological monitoring of oncology patients using the HemoScreen point of care analyser. J Lab Precis Med 9:21. https://doi.org/10.21037/jlpm-23-95 Klein SL, Flanagan KL (2016) Sex differences in immune responses. Nat Rev Immunol 16(10):626–638. https://doi.org/10.1038/nri.2016.90 Khan D, Ansar Ahmed S (2015) The immune system is a natural target for estrogen action: opposing effects of estrogen in two prototypical autoimmune diseases. Front Immunol 6:635. https://doi.org/10.3389/fimmu.2015.00635 Labonté B, Engmann O, Purushothaman I et al (2017) Sex-specific transcriptional signatures in human depression. Nat Med 23(9):1102–1111. https://doi.org/10.1038/nm.4386 Murphy WG (2014) The sex difference in haemoglobin levels in adults—mechanisms, causes, and consequences. Blood Rev 28(2):41–47. https://doi.org/10.1016/j.blre.2013.12.003 Jelkmann W (2011) Regulation of erythropoietin production. J Physiol 589(6):1251–1258. https://doi.org/10.1113/jphysiol.2010.195057 O’Leary F, Samman S (2010) Vitamin B12 in health and disease. Nutrients 2(3):299–316. https://doi.org/10.3390/nu2030299 Ganz T, Nemeth E (2012) Hepcidin and iron homeostasis. Biochim Biophys Acta 1823(9):1434–1443. https://doi.org/10.1016/j.bbamcr.2012.01.014 Additional Declarations No competing interests reported. Supplementary Files Supplementtable1222.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8434228","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":566828165,"identity":"7db8316e-d11c-4beb-bd2e-187ce439eee1","order_by":0,"name":"Kang Yoon Jung","email":"","orcid":"","institution":"The Catholic University of Korea","correspondingAuthor":false,"prefix":"","firstName":"Kang","middleName":"Yoon","lastName":"Jung","suffix":""},{"id":566828166,"identity":"49d1f66f-dc57-441a-980b-500e69bbcc48","order_by":1,"name":"Jong-Hyun Jeong","email":"","orcid":"","institution":"St. Vincent’s Hospital, The Catholic University of Korea","correspondingAuthor":false,"prefix":"","firstName":"Jong-Hyun","middleName":"","lastName":"Jeong","suffix":""},{"id":566828168,"identity":"382a86b4-c60c-4d2e-b019-a7e38307f860","order_by":2,"name":"HyeJin Tae","email":"","orcid":"","institution":"The Catholic University of Korea","correspondingAuthor":false,"prefix":"","firstName":"HyeJin","middleName":"","lastName":"Tae","suffix":""},{"id":566828169,"identity":"5f69a24f-4d61-4604-ad31-2beb32ba09e0","order_by":3,"name":"Yoo Hyun Um","email":"","orcid":"","institution":"St. Vincent’s Hospital, The Catholic University of Korea","correspondingAuthor":false,"prefix":"","firstName":"Yoo","middleName":"Hyun","lastName":"Um","suffix":""},{"id":566828171,"identity":"d28ce4ab-c3d2-4e31-829d-ade311ab2c45","order_by":4,"name":"Jae Hyun Yoo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACxgYILcfYDmIZWBCvxZi55wBIiwTxtiW2z0gA0URoYW4/Y8DMU2FjzDvz+dUNPwokGPjbuxPwO6wnB6jlTJqc5Oycsps9QIdJnDm7Ab+WBqAW3rbDxoazc9Ju8AC1GEjkEtDS/wasJXH/zTNpN/8QpWUGxJbExhnsx24TZ8uMZwUH55xJMwZ6iu22jIEED0G/GPYnb3zwpsIGGJXHn91888dGjr+9l4CWBg6DQzxgJo8BmMSrHATkGdgfMP4AM9kfEFQ9CkbBKBgFIxMAAOncSNfupKpNAAAAAElFTkSuQmCC","orcid":"","institution":"The Catholic University of Korea","correspondingAuthor":true,"prefix":"","firstName":"Jae","middleName":"Hyun","lastName":"Yoo","suffix":""}],"badges":[],"createdAt":"2025-12-23 13:24:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8434228/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8434228/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99283925,"identity":"cb815fd1-eb29-458a-9586-e2cadac6828f","added_by":"auto","created_at":"2025-12-31 09:05:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80187,"visible":true,"origin":"","legend":"","description":"","filename":"ECAPAnnotatedManuscript1222.docx","url":"https://assets-eu.researchsquare.com/files/rs-8434228/v1/db671b4c0bbe4dbd672771c7.docx"},{"id":99321180,"identity":"af243f4e-48d0-4f79-ba10-3bba2f3f6333","added_by":"auto","created_at":"2025-12-31 16:39:15","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7255,"visible":true,"origin":"","legend":"","description":"","filename":"58d0001e5ab040d891a41a88982ce88b.json","url":"https://assets-eu.researchsquare.com/files/rs-8434228/v1/6246d78d669b8b65fab6ec8d.json"},{"id":99283927,"identity":"183d2491-d57d-4f1c-86ab-47449e3d8cf1","added_by":"auto","created_at":"2025-12-31 09:05:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27222,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementtable1222.docx","url":"https://assets-eu.researchsquare.com/files/rs-8434228/v1/235444a5e21a84d1bbfc6147.docx"},{"id":99319467,"identity":"23e9caa0-bd28-4c0f-9f20-105534b6de18","added_by":"auto","created_at":"2025-12-31 16:37:19","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129624,"visible":true,"origin":"","legend":"","description":"","filename":"58d0001e5ab040d891a41a88982ce88b1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8434228/v1/e1221301fb0efc21cc3a8430.xml"},{"id":99320524,"identity":"a1856f6f-edce-4d60-8a13-a2401482e59c","added_by":"auto","created_at":"2025-12-31 16:38:43","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126694,"visible":true,"origin":"","legend":"","description":"","filename":"58d0001e5ab040d891a41a88982ce88b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8434228/v1/7514d0e311b66bd4ea69c77b.xml"},{"id":99283930,"identity":"7af35630-17cb-49a4-98d6-427aa7a0db7d","added_by":"auto","created_at":"2025-12-31 09:05:00","extension":"html","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137569,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8434228/v1/e94af15cec650062527a4f6e.html"},{"id":102055407,"identity":"4cf4ebaf-7607-40e1-ac76-a2650ada2e23","added_by":"auto","created_at":"2026-02-06 15:41:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":826243,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8434228/v1/2936c0ed-d19a-48bc-8b56-b35fc54c732e.pdf"},{"id":99283924,"identity":"80074eae-6668-462f-b836-052fb180e891","added_by":"auto","created_at":"2025-12-31 09:05:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27222,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementtable1222.docx","url":"https://assets-eu.researchsquare.com/files/rs-8434228/v1/2ec61f874c62a1096fc7a988.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations Between Hematologic Profiles and Depressive Disorder in Adolescents","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDepressive disorders are common psychiatric conditions in adolescents and are strongly associated with impaired academic and social functioning, heightened risk of self-harm and suicide, and long-term psychosocial disability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite this growing burden, diagnosis of adolescent depression continues to rely primarily on subjective clinical assessments. Approximately 60% of adolescents show poor response to initial treatment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], emphasizing the need for objective and easily accessible biomarkers that can aid in diagnosis and treatment selection [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, the diagnosis of major depressive disorder in youths relies on clinical assessments of symptomatology, which can be particularly challenging because manifestations of depressive symptoms are often less typical in adolescents than in adults. Therefore, the identification of biomarkers specific to child and adolescent depression could refine diagnostic accuracy and improve treatment outcomes. Furthermore, biomarkers that predict antidepressant response would enable more personalized and effective treatment strategies, representing a key step toward improving long-term prognosis across the lifespan [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvidence increasingly suggests that low-grade systemic inflammation plays an important role in the pathophysiology of depressive disorders. In adults, elevated levels of pro-inflammatory cytokines such as interleukin (IL), tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP) have been consistently observed in major depressive disorder [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In parallel, hematological indices derived from routine complete blood count tests\u0026mdash;such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and mean platelet volume\u0026mdash;have emerged as reliable indicators of systemic inflammation and have been shown to correlate with depression severity in adult populations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, in adolescents, elevated levels of pro-inflammatory cytokines such as IL-6, IL-8, TNF-α, and IL-1β have been reported, with higher cytokine concentrations associated with both greater depressive symptom severity and increased odds of meeting the criteria for clinical depression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, iron deficiency has been associated with increased likelihood of depressive symptoms in female adolescents, with lower hemoglobin levels and higher body weight linked to greater depression risk [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Female adolescents with depressive disorders also demonstrated significantly higher high-sensitivity CRP levels than healthy controls [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], further supporting the potential role of peripheral inflammatory and hematological markers in youth depression. In addition, a recent case-control study found significantly higher PLR values in children and adolescents with depressive disorders than in healthy controls [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, studies focusing on adolescents are still limited and often rely on small samples or insufficient control of confounding factors such as age or body-mass index (BMI). Previous studies revealed that the maturation of hematopoietic cells is associated with the developmental stage and health status [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Thus, important gaps remain in clarifying how depressive symptoms relate to altered hematologic profiles and determining whether these indices can serve as reliable, objective, and easily accessible candidate markers for depression.\u003c/p\u003e \u003cp\u003eThe present study aimed to explore the hematologic markers associated with depressive symptoms in adolescents and clarify how deviations in these indices relate to symptom severity. We examined a range of hematologic parameters in adolescents with depressive disorders and healthy controls and compared the two groups to explore evidence supporting the inflammatory hypothesis in depression. To identify the indices most strongly associated with depressive symptoms among this large set of interrelated variables, we applied the least absolute shrinkage and selection operator (LASSO) regression.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants\u003c/h2\u003e \u003cp\u003eThis retrospective study utilized the Clinical Data Warehouse of the Seoul St. Mary\u0026rsquo;s Hospital and St. Vincent\u0026rsquo;s Hospital, an electronic health record system that serves as an integrated repository of de-identified clinical data, including the diagnosis, prescription, laboratory result, and health screening information. The cohort was identified for the following period: January 1, 2014, to July 1, 2024. As this study involved a retrospective review of existing medical records, the requirement for informed consent was waived by the Institutional Review Board (approval no.: KC24WIDI0718; date of approval: November 5, 2024).\u003c/p\u003e \u003cp\u003eThe depression group included 1,160 adolescents aged 13\u0026ndash;18 years with depressive disorders who visited the Seoul St. Mary\u0026rsquo;s Hospital or St. Vincent\u0026rsquo;s Hospital as inpatients, outpatients, or emergency department patients. Board-certified psychiatrists evaluated all participants in the depression group. Patients with depressive disorders were included if they could be categorized under one of the following International Statistical Classification of Diseases and Related Health Problems 10th Revision codes related to depressive disorders: F32, F320, F321, F322, F323, F328, F329, F33, F330, F331, F332, F333, F334, F339, F34, F341, F349, F38, F380, F381, F388, or F39 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The control group included 1,243 adolescents aged 13\u0026ndash;18 years who visited the Health Promotion Center at The Catholic University of Korea, Seoul St. Mary\u0026rsquo;s Hospital.\u003c/p\u003e \u003cp\u003eExclusion criteria for the study were as follows: signs of acute infection (white blood cell [WBC]\u0026thinsp;\u0026ge;\u0026thinsp;13,000 or segmented neutrophils\u0026thinsp;\u0026ge;\u0026thinsp;75%), coexisting physical illnesses that could affect various blood test results (e.g., cancer, diabetes, dyslipidemia, pulmonary tuberculosis, and hepatitis B), absence of hemoglobin values in the blood test results, and a Hamilton Depression Rating Scale (HAMD) score of 7 (normal range) for only the depression group. Accordingly, the following participants were excluded: 35 adolescents in the depression group and 15 in the control group with signs of acute infection; 5 adolescents in the depression group and 6 in the control group with underlying conditions such as cancer, diabetes, dyslipidemia, pulmonary tuberculosis, or hepatitis B; 30 adolescents in the depression group and 2 in the control group without hemoglobin values; and 17 adolescents in the depression group with an HAMD score of 7. The final sample comprised 1,074 adolescents with depressive disorders and 1,220 adolescents in the control group. For each participant, the dates of initial clinical visits, HAMD assessment, and blood sampling were aligned to ensure consistency in the baseline measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Depressive symptoms\u003c/h2\u003e \u003cp\u003eDepressive symptoms were assessed using the 17-item HAMD [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], a clinician-administered instrument in which items are rated on either a 3-point (0\u0026ndash;2) or 5-point (0\u0026ndash;4) scale. The total scores range from 0 to 54, with higher scores indicating greater symptom severity. The HAMD has demonstrated good reliability and validity in both clinical and research settings [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Consistent with established guidelines, scores\u0026thinsp;\u0026ge;\u0026thinsp;8 indicated the presence of depression, with 8\u0026ndash;16 classified as mild, 17\u0026ndash;23 as moderate, and \u0026ge;\u0026thinsp;24 as severe depression [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Participants scoring\u0026thinsp;\u0026ge;\u0026thinsp;8 were categorized into the depressive disorder group. To examine the association between inflammatory markers and incidence of depression, depressive symptoms were treated as binary variables. To analyze the relationship between inflammatory changes and depression severity across waves, HAMD scores were analyzed as continuous variables. All HAMD assessments were conducted by psychiatrists through direct clinical interviews.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Blood cell counts and inflammatory markers\u003c/h2\u003e \u003cp\u003eWe retrospectively collected peripheral blood test results from subjects enrolled between January 2014 and July 2024. To exclude potential changes in blood indicators attributable to depression treatment, laboratory data obtained on the date closest to each patient\u0026rsquo;s first hospital visit were selected for analysis. The fundamental indices included red blood cell (RBC) count, WBC count, absolute neutrophil count (ANC), segmented neutrophil count (NC), basophils, lymphocyte count (LC), eosinophils, monocytes, hemoglobin, hematocrit, mean corpuscular volume (MCV), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular hemoglobin (MCH), platelet count (PLT), NLR, and PLR. Additionally, PLR and NLR, which reflect systemic inflammatory states, were computed (Yanwei Cheng, 2022) as follows : \u003cem\u003eNLR\u0026thinsp;=\u0026thinsp;NC/LC\u003c/em\u003e and \u003cem\u003ePLR\u0026thinsp;=\u0026thinsp;PLT/LC.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Covariate assessment\u003c/h2\u003e \u003cp\u003ePotential covariates included age, sex, and BMI at the time of blood sampling. These variables were selected because hemoglobin levels have been shown to be positively correlated with age and BMI, and sex differences in hemoglobin levels have also been reported [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analyses\u003c/h2\u003e \u003cp\u003eContinuous variables for the baseline characteristics are represented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while the categorical variables are described as numbers and percentages. Correlation analyses were performed to examine the associations between hematological markers and severity of depressive symptoms. To analyze the associations between peripheral blood cell counts, inflammatory markers, and depressive symptoms, we performed multivariable regression analyses in both the whole sample and sex-stratified samples.\u003c/p\u003e \u003cp\u003eBecause correlation analyses indicated substantial multicollinearity among the demographic and hematological variables (Table S2), we applied LASSO linear regression with Gaussian error distribution to examine associations between hematological variables and severity of depressive symptoms (HAMD scores). Age, height, weight, and BMI were forced to remain in the model as covariates.\u003c/p\u003e \u003cp\u003eLASSO regression was chosen to mitigate instability associated with correlated variables, reduce overfitting of a prediction model. LASSO performs coefficient shrinkage in the presence of correlated predictors and provides a more stable variable selection than traditional stepwise methods. The optimal penalty parameter (λ) was selected using 10-fold cross-validation, and the value minimizing the mean cross-validated error (lambda.min) was chosen for model estimation. To enhance robustness and reduce sampling variability, the LASSO model was further combined with 1,000 bootstrap resamples, and the variable selection frequencies were examined to evaluate stability of the selected predictors.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using R version 4.5.0. Missing data were excluded from the analyses. Statistical significance was set at a two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Age, sex, and BMI were included as covariates in all adjusted analyses to reduce potential confounding factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Descriptive statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes the demographic and clinical characteristics. The depression group comprised 60.06% female participants, and the control group comprised 56.64% female participants. There was no significant difference in sex distribution between the control and depression groups (χ\u0026sup2;=2.60, p\u0026thinsp;=\u0026thinsp;0.107). The depression group had a significantly lower age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and height (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than the control group. There were no significant differences in sex, weight, and BMI. In the depression group, the average HAMD score was 19.80, indicating that most adolescents with depression had moderate depressive symptoms at the time of evaluation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGroup comparison of demographic variables in the total sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl Group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,220)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepression Group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,074)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e691(56.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e645(60.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165.60\u0026thinsp;\u0026plusmn;\u0026thinsp;10.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163.65\u0026thinsp;\u0026plusmn;\u0026thinsp;10.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.41\u0026thinsp;\u0026plusmn;\u0026thinsp;13.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.80\u0026thinsp;\u0026plusmn;\u0026thinsp;16.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.40\u0026thinsp;\u0026plusmn;\u0026thinsp;29.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.57\u0026thinsp;\u0026plusmn;\u0026thinsp;6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAsterisk indicates * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI\u0026thinsp;=\u0026thinsp;Body Mass Index; HAMD\u0026thinsp;=\u0026thinsp;Hamilton Depression Rating Scale\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding hematological parameters, the control group had a higher RBC count (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hemoglobin levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hematocrit (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and MCHC (p\u0026thinsp;=\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). MCV and MCH was higher in the depression group, but showed no significant group differences after controlling covariates. Adolescents with depressive disorders had higher WBC counts (p\u0026thinsp;=\u0026thinsp;0.001), whereas the basophil proportion was higher in the control group (p\u0026thinsp;=\u0026thinsp;0.027). Comparison of other indices, including LC, NC, ANC, and monocyte and eosinophil proportions, did not reach statistical significance after adjustment. PLTs were higher in the depression group and remained significant after adjustment (p\u0026thinsp;=\u0026thinsp;0.019). Inflammatory markers, including NLR and PLR, were elevated in the depression group, but became non-significant after adjustment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGroup comparison of hematologic variables in the total sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematologic variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl Group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,220)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepression Group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,074)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted p value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC count (10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e14.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e13.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e42.89\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e41.42\u0026thinsp;\u0026plusmn;\u0026thinsp;4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV (fl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e87.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e88.20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCHC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e33.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e33.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCH (pg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e29.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e29.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e39.14\u0026thinsp;\u0026plusmn;\u0026thinsp;8.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e38.04\u0026thinsp;\u0026plusmn;\u0026thinsp;8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e50.06\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e51.25\u0026thinsp;\u0026plusmn;\u0026thinsp;8.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophils (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.027*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophils (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e265.45\u0026thinsp;\u0026plusmn;\u0026thinsp;52.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e275.79\u0026thinsp;\u0026plusmn;\u0026thinsp;57.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAsterisk indicates * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eRBC count\u0026thinsp;=\u0026thinsp;Red Blood Cell count; MCV\u0026thinsp;=\u0026thinsp;Mean Corpuscular Volume; MCHC\u0026thinsp;=\u0026thinsp;Mean Corpuscular Hemoglobin Concentration; MCH\u0026thinsp;=\u0026thinsp;Mean Corpuscular Hemoglobin; WBC count\u0026thinsp;=\u0026thinsp;White Blood Cell count; LC\u0026thinsp;=\u0026thinsp;Lymphocytes count; NC\u0026thinsp;=\u0026thinsp;Segmented Neutrophils count; ANC\u0026thinsp;=\u0026thinsp;Absolute Neutrophil Count; PLT\u0026thinsp;=\u0026thinsp;Platelet count; NLR\u0026thinsp;=\u0026thinsp;Neutrophil-to-Lymphocyte Ratio; PLR\u0026thinsp;=\u0026thinsp;Platelet-to-Lymphocyte Ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Correlation analyses\u003c/h2\u003e \u003cp\u003eCorrelation analyses demonstrated significant negative associations between HAMD scores and several hematological indices, including hemoglobin (r=\u0026ndash;0.195, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hematocrit (r=\u0026ndash;0.184, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RBC count (r=\u0026ndash;0.160, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MCH (r=\u0026ndash;0.107, p\u0026thinsp;=\u0026thinsp;0.017), MCHC (r=\u0026ndash;0.146, p\u0026thinsp;=\u0026thinsp;0.001), and eosinophils (r=\u0026ndash;0.115, p\u0026thinsp;=\u0026thinsp;0.011). Although these correlations were modest, they suggested a trend wherein greater depression severity was associated with lower RBC parameters and eosinophil counts in adolescents (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analyses between depressive symptom severity (HAMD scores) and hematological indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematologic variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003cp\u003ecoefficient (r)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC count (10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV (fl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCHC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCH (pg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophils (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophils (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAsterisk indicates * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eHAMD\u0026thinsp;=\u0026thinsp;Hamilton Depression Rating Scale; RBC count\u0026thinsp;=\u0026thinsp;Red Blood Cell count; MCV\u0026thinsp;=\u0026thinsp;Mean Corpuscular Volume; MCHC\u0026thinsp;=\u0026thinsp;Mean Corpuscular Hemoglobin Concentration; MCH\u0026thinsp;=\u0026thinsp;Mean Corpuscular Hemoglobin; WBC count\u0026thinsp;=\u0026thinsp;White Blood Cell count; LC\u0026thinsp;=\u0026thinsp;Lymphocytes count; NC\u0026thinsp;=\u0026thinsp;Segmented Neutrophils count; ANC\u0026thinsp;=\u0026thinsp;Absolute Neutrophil Count; PLT\u0026thinsp;=\u0026thinsp;Platelet count; NLR\u0026thinsp;=\u0026thinsp;Neutrophil-to-Lymphocyte Ratio; PLR\u0026thinsp;=\u0026thinsp;Platelet-to-Lymphocyte Ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Linear regressions\u003c/h2\u003e \u003cp\u003eIn the depression group, LASSO regression with bootstrapping identified distinct patterns among the hematologic predictors of depressive symptom severity (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Several erythrocyte-related markers\u0026mdash;including RBC count, hemoglobin, hematocrit, MCV, MCHC, and MCH\u0026mdash;showed consistently negative coefficients, indicating that lower RBC parameters tended to be associated with greater severity of depressive symptoms. In addition, WBC count, eosinophil count, basophil count, and NLR were negative coefficients. In contrast, platelet-related indices\u0026mdash;including PLT and PLR\u0026mdash;demonstrated positive coefficients, suggesting that higher platelet activation levels may be associated with more severe depressive symptoms. Further, the LC and monocyte counts showed positive coefficients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLasso regression model for demographic and hematological predictors of depression severity (HAMD score)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSelection\u003c/p\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003cp\u003e(Sign Test)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.378, 0.307]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.220, 0.274]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.555, 0.258]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.613, 1.571]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC count (10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.998, 0.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.574, 0.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.230, 0.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV (fl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.057, 0.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCHC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.286, 0.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCH (pg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.142, 0.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.127, 0.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.000, 0.035]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.000, 0.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.000, 0.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophils (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-1.022, 0.058]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.000, 0.225]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophils (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.310, 0.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.000, 0.009]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.132, 0.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.000, 0.056]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eHAMD\u0026thinsp;=\u0026thinsp;Hamilton Depression Rating Scale; BMI\u0026thinsp;=\u0026thinsp;Body Mass Index; RBC count\u0026thinsp;=\u0026thinsp;Red Blood Cell count; MCV\u0026thinsp;=\u0026thinsp;Mean Corpuscular Volume; MCHC\u0026thinsp;=\u0026thinsp;Mean Corpuscular Hemoglobin Concentration; MCH\u0026thinsp;=\u0026thinsp;Mean Corpuscular Hemoglobin; WBC count\u0026thinsp;=\u0026thinsp;White Blood Cell count; LC\u0026thinsp;=\u0026thinsp;Lymphocytes count; NC\u0026thinsp;=\u0026thinsp;Segmented Neutrophils count; ANC\u0026thinsp;=\u0026thinsp;Absolute Neutrophil Count; PLT\u0026thinsp;=\u0026thinsp;Platelet count; NLR\u0026thinsp;=\u0026thinsp;Neutrophil-to-Lymphocyte Ratio; PLR\u0026thinsp;=\u0026thinsp;Platelet-to-Lymphocyte\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the magnitude of the LASSO coefficients, predictors with the greatest relative contribution were hemoglobin (\u0026ndash;0.126), followed by eosinophil proportion (\u0026ndash;0.106) and RBC count (\u0026ndash;0.104). Consistent with this pattern, bootstrap resampling demonstrated that eosinophil proportion (76.6%), hematocrit (59.2%), and hemoglobin (46.2%) showed the highest selection stability. Other parameters were selected less frequently but displayed directionally consistent coefficients across bootstrap iterations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Sex-specific differences in hematological indices\u003c/h2\u003e \u003cp\u003eIn female adolescents, the depression group showed lower age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and height (p\u0026thinsp;=\u0026thinsp;0.021) but higher weight (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared with the control group (Table S3). Among the hematologic markers, the depression group showed significantly lower levels of hemoglobin (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hematocrit (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MCHC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RBC count (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and MCH (p\u0026thinsp;=\u0026thinsp;0.031) compared to healthy controls after adjusting for age and BMI. Additionally, the monocyte percentage (p\u0026thinsp;=\u0026thinsp;0.002) and NC (p\u0026thinsp;=\u0026thinsp;0.026) were significantly higher in female participants. However, no significant group differences were observed in platelet indices (PLT and PLR) or NLR among female participants (Table S4).\u003c/p\u003e \u003cp\u003eAmong male adolescents, only age was significantly lower in the depression group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table S5). The depression group demonstrated a significantly higher WBC count (p\u0026thinsp;=\u0026thinsp;0.019) and MCV (p\u0026thinsp;=\u0026thinsp;0.012) than the control group. In contrast, hemoglobin (p\u0026thinsp;=\u0026thinsp;0.019), hematocrit (p\u0026thinsp;=\u0026thinsp;0.010), and RBC count (p\u0026thinsp;=\u0026thinsp;0.030) were significantly lower in male patients, although the degree of reduction was less pronounced. Platelet indices (PLT and PLR) were numerically higher in the depression group but did not remain significant after adjustment (Table S6).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study investigated the associations between depressive symptom severity and hematological biomarkers in a large sample of South Korean adolescents. Compared to the control group, adolescents with depressive disorder showed lower RBC parameters, including RBC count, hemoglobin, hematocrit, and MCHC, even after adjusting for demographic and BMI covariates. The WBC and platelet counts were elevated in the depression group, while the basophil proportion was higher in the depression group. Regression analysis also demonstrated a significant association between lower RBC parameters and eosinophil proportion and greater depression severity in the depression group. The current findings do not imply causality; however, they indicate that changes in hematological indices may accompany depressive states in adolescents. These patterns aligned with the frameworks suggesting potential links between low-grade inflammation and hematopoiesis.\u003c/p\u003e \u003cp\u003eIn addition to these primary findings, several other features of this study strengthen the value of our observations. Although the present study employed a retrospective design, it analyzed a relatively large clinical sample of adolescents, allowing for a more stable estimation of hematological patterns than many earlier studies with smaller cohorts. To the best of our knowledge, this is the first investigation to examine specific markers such as hemoglobin may relate to depressive symptom severity. These analyses provided preliminary evidence that routinely collected blood parameters may capture clinically meaningful dimensions of adolescent depression. Nevertheless, important limitations of this study should be acknowledged. Because structural clinical interviews were not conducted, we could not systematically exclude certain comorbid psychiatric conditions. In addition, key biological regulators of hematopoiesis and inflammation\u0026mdash;such as iron status; ferritin; and cytokines including IL-1, IL-6, and TNF-α\u0026mdash;were not directly measured, restricting our ability to clarify the underlying mechanisms. These strengths and limitations should be considered when interpreting the implications of our findings.\u003c/p\u003e \u003cp\u003eA growing body of literature suggests that inflammatory processes are involved in the development of depression. Inflammatory processes can alter monoamine neurotransmission by reducing the availability of serotonin, dopamine, and norepinephrine [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These changes may contribute to the development of core depressive symptoms such as anhedonia, fatigue, and psychomotor slowing [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnder inflammatory conditions, hematopoiesis undergoes a shift toward emergency hematopoiesis, characterized by enhanced myelopoiesis, to meet the increased immune demands [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This adaptive process promotes the rapid production of myeloid cells, such as neutrophils and monocytes, but concurrently suppresses erythropoiesis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. During inflammation, pro-inflammatory cytokines including IL-6 and TNF-α further inhibit erythropoietin synthesis and disturb iron homeostasis through hepcidin induction and ferroportin downregulation, leading to iron sequestration in macrophages and reduced availability for RBC formation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Consequently, chronic inflammation may cause anemia by suppressing erythroid differentiation and limiting systemic iron supply. Such evidence is consistent with the current findings that adolescents with depressive disorders have lower RBC parameters. Further, we found an association between lower hemoglobin levels and depressive symptom severity. Given that anemia can lead to fatigue, low energy, and cognitive slowing, core features commonly reported in adolescent depression, peripheral anemia-related indices may serve as accessible indicators of the inflammatory processes underlying depressive symptoms.\u003c/p\u003e \u003cp\u003eIn the depression group, we found a significantly higher WBC count and lower basophil proportion than in the control group. However, eosinophil proportion was the only hematologic variable that showed a significant association with HAMD scores. Eosinophil proportion was consistently selected as a relevant predictor in the LASSO model with bootstrapping. In the Avon Longitudinal Study of Parents and Children birth cohort, adolescents with persistent depressive symptoms showed higher WBC counts, whereas no clear association was observed with eosinophil levels [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Another recent cohort study reported that depression severity is more strongly associated with higher cell counts of monocytes, basophils, and eosinophils [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Findings remain inconsistent on eosinophils as an important predictor of depression, and further studies are needed to clarify these relationships.\u003c/p\u003e \u003cp\u003eMeta-analytic evidence supported the association between inflammation-based hematologic ratios and depression. Cheng et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reported that individuals with depression exhibit significantly higher NLR and PLR than controls. In addition, a case-control study with adolescent data found an increased PLR in a depression group. However, NLR and PLR did not remain significant after adjustment in the current analysis, whereas hemoglobin- and eosinophil-related indices showed stronger associations with depression severity. RBC-related indices (e.g., hemoglobin, hematocrit, RBC count, and MCV) showed minimal biological variation in healthy individuals, with a within-subject coefficient of variation of approximately 2\u0026ndash;3% in the European Federation of Clinical Chemistry and Laboratory Medicine study, indicating high stability. In contrast, leukocyte and platelet measures display much greater variability (\u0026gt;\u0026thinsp;7\u0026ndash;15%) and are more sensitive to short-term physiological factors [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Given their lower biological variability, RBC indices may offer more stable and potentially more informative markers of depressive states than NLR or PLR.\u003c/p\u003e \u003cp\u003eThe sex-specific patterns observed in our study further suggest that depressive disorders in adolescents may involve distinct biological pathways for male and female patients. Female patients showed more pronounced reductions in erythrocyte-related indices, whereas male patients demonstrated elevated WBC counts and higher MCV, along with milder decreases in hemoglobin and hematocrit. These differences may reflect the well-established sex-dependent modulation of immune responses and hematopoiesis. Previous studies have demonstrated that sex hormones such as estrogen and testosterone exert divergent effects on both innate and adaptive immune activities, contributing to differential inflammatory profiles across sexes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Similarly, transcriptional studies have shown that depression itself is associated with sex-specific molecular signatures, indicating that the biological underpinnings of depressive disorders differ between male and female patients at the genomic and immune regulatory levels [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, hematological regulation\u0026mdash;including erythropoiesis and baseline hemoglobin levels\u0026mdash;is strongly influenced by sex-linked hormonal pathways, which may partially explain the more marked erythrocyte-related alterations in female adolescents with depression [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, it is important to note that pubertal stage and age-related changes in hematological indices can significantly affect sex-based comparisons. Although we adjusted for age in our primary analyses, we could not fully account for pubertal status or age-by-sex interaction effects, which may have contributed to the observed differences. Moreover, the menstrual status and phase\u0026mdash;which are known to influence hemoglobin and other red blood cell indices\u0026mdash;were not systematically assessed in female participants. The lack of control for these factors limits the interpretation of sex-specific hematological changes. Future studies should incorporate detailed assessments of pubertal development, hormonal status, and menstrual cycle phases to better delineate sex-specific biological mechanisms in adolescent depression. Together, these findings support the possibility that distinct immuno-hematological mechanisms contribute to depressive symptomatology in male and female patients, underscoring the need for sex-informed approaches in future biomarker research.\u003c/p\u003e \u003cp\u003eTaken together, the present findings indicate that the most prominent hematological feature in the depression group was a reduction in RBC-related indices\u0026mdash;particularly hemoglobin\u0026mdash;which was also associated with greater severity of depressive symptoms. However, erythropoiesis in physiologically healthy individuals is primarily regulated by oxygen availability and renal erythropoietin signaling [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and depends on sufficient iron, vitamin B12, and folate status [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, low-grade inflammatory activity can modestly suppress erythropoiesis through hepcidin-mediated iron restriction [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Given these multiple determinants, our cross-sectional data cannot establish whether reduced hemoglobin functions as a specific biomarker of depression. Nonetheless, previous studies have repeatedly reported alterations in hemoglobin and other RBC indices among individuals with depressive symptoms [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], suggesting that erythrocyte-related measures may represent a potential ancillary dimension in future assessments of depressive states.\u003c/p\u003e \u003cp\u003eOur findings should be interpreted in light of several limitations. First, its cross-sectional design limits causal inferences. Longitudinal studies are warranted to determine whether inflammatory and hematological changes precede the onset of depressive symptoms in a prospective cohort.\u003c/p\u003e \u003cp\u003eSecond, several unmeasured factors that could influence inflammation and hematological profiles were not captured in this study, limiting the interpretation of our results. Future studies should exclude factors that directly influence erythropoiesis and incorporate direct measures of stress, stress hormones, and inflammatory cytokines (e.g., IL-1, IL-6, and TNF-α) to more clearly delineate the biological pathways leading to depression.\u003c/p\u003e \u003cp\u003eThird, several biologically relevant confounders that may influence the inflammatory and hematological profiles\u0026mdash;such as iron status (including serum ferritin), vitamin B12 and folate levels, and dietary or nutritional patterns\u0026mdash;were not assessed. Given their essential roles in erythropoiesis and immune function, the absence of such measures limits the biological interpretation of our findings.\u003c/p\u003e \u003cp\u003eFourth, information on psychiatric comorbidities (e.g., anxiety disorders, neurodevelopmental disorders, stress-related disorders, and eating disorders) and psychotropic or anti-inflammatory medication use was not available, and these factors may independently affect the hematological and inflammatory parameters. Future studies should consider these variables more comprehensively to clarify the observed associations.\u003c/p\u003e \u003cp\u003eFinally, our sample consisted of adolescents who visited a hospital, which introduced the risk of sampling bias. Validation of community-based cohorts is thus needed to determine the generalizability of these findings.\u003c/p\u003e \u003cp\u003eIn summary, adolescents with depression showed lower RBC-related indices and modest elevations in WBC and platelet counts, which are patterns broadly consistent with models linking low-grade inflammation to altered hematopoiesis. However, the cross-sectional, retrospective design and the many physiological factors influencing RBC production are important limitations. Evidence regarding eosinophils and other leukocyte markers remains inconsistent, further emphasizing the need for caution. Longitudinal, community-based studies incorporating repeated hematologic measures, iron-related biomarkers, stress indicators, and inflammatory cytokines are needed to clarify the temporal relationships and better define the biological pathways underlying adolescent depression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e All authors contributed to the conception and design of the study. Material preparation, data collection, and analysis were performed by Kang Yoon Jung and Jae Hyun Yoo. The first draft of the manuscript was written by Kang Yoon Jung, and all the authors have revised the previous versions of the manuscript. All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was supported by a grant of the Korea Health Technology R\u0026amp;D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health \u0026amp; Welfare, Republic of Korea. (grant number RS2025-02293110)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e This study involved a retrospective review of existing medical records, and the requirement for informed consent was waived by the Institutional Review Board (Approval no.: KC24WIDI0718; date of approval: November 5, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e The manuscript and publication have been approved by all authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e All authors certify that they have no affiliations to or involvement with in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZwolińska W, Dmitrzak-Weglarz M, Słopień A (2023) Biomarkers in child and adolescent depression. Child Adolesc Ment Health 28(2):141\u0026ndash;150. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/camh.12616\u003c/span\u003e\u003cspan address=\"10.1111/camh.12616\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrebatick\u0026aacute; J, Vatr\u0026aacute;l M, Katrenč\u0026iacute;kov\u0026aacute; B, Muchov\u0026aacute; J, Ďuračkov\u0026aacute; Z (2025) Current insight into biological markers of depressive disorder in children and adolescents: A narrative review. Antioxid (Basel) 14(6):699. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/antiox14060699\u003c/span\u003e\u003cspan address=\"10.3390/antiox14060699\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFava M (2003) Diagnosis and definition of treatment-resistant depression. Biol Psychiatry 53(8):649\u0026ndash;659. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0006-3223(03)00231-2\u003c/span\u003e\u003cspan address=\"10.1016/S0006-3223(03)00231-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZonca V, Marizzoni M, Saleri S, Zajkowska Z, Manfro PH, Souza L, Viduani A, Sforzini L, Swartz JR, Fisher HL, Kohrt BA, Kieling C, Riva MA, Cattaneo A, Mondelli V (2024) Inflammation and immune system pathways as biological signatures of adolescent depression\u0026mdash;the IDEA-Risco study. Transl Psychiatry 14(1):230. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41398-024-02959-z\u003c/span\u003e\u003cspan address=\"10.1038/s41398-024-02959-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsimo EF, Baxter LJ, Lewis G, Jones PB, Khandaker GM (2019) Prevalence of low-grade inflammation in depression: a systematic review and meta-analysis of CRP levels. Psychol Med 49(12):1958\u0026ndash;1970. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0033291719001454\u003c/span\u003e\u003cspan address=\"10.1017/S0033291719001454\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsimo EF, Pillinger T, Rodriguez IM, Khandaker GM, Pariante CM, Howes OD (2020) Inflammatory markers in depression: a meta-analysis of mean differences and variability in 5,166 patients and 5,083 controls. Brain Behav Immun 87:901\u0026ndash;909. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbi.2020.02.010\u003c/span\u003e\u003cspan address=\"10.1016/j.bbi.2020.02.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK\u0026ouml;hler CA, Freitas TH, Maes M, de Andrade NQ, Liu CS, Fernandes BS, Stubbs B, Solmi M, Veronese N, Herrmann N, Raison CL, Miller BJ, Lanct\u0026ocirc;t KL, Carvalho AF (2017) Peripheral cytokine and chemokine alterations in depression: a meta-analysis of 82 studies. Acta Psychiatr Scand 135(5):373\u0026ndash;387. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/acps.12698\u003c/span\u003e\u003cspan address=\"10.1111/acps.12698\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng Y, Wang Y, Wang X, Jiang Z, Zhu L, Fang S (2022) Neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio in depression: an updated systematic review and meta-analysis. Front Psychiatry 13:893097. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyt.2022.893097\u003c/span\u003e\u003cspan address=\"10.3389/fpsyt.2022.893097\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanan F, Dikici S, Kutlucan A, Celbek G, Coskun H, Gungor A, Aydin Y, Kocaman G (2012) Association of mean platelet volume with DSM-IV major depression in a large community-based population: the MELEN study. J Psychiatr Res 46(3):298\u0026ndash;302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jpsychires.2011.11.016\u003c/span\u003e\u003cspan address=\"10.1016/j.jpsychires.2011.11.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeung CY, Weiss SJ (2025) Cytokines and depressive symptoms among adolescents. Biol Res Nurs 27(3):400\u0026ndash;410. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/10998004251318385\u003c/span\u003e\u003cspan address=\"10.1177/10998004251318385\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZarate-Ortiz AG, Verhoef H, Melse-Boonstra A, Woods BJ, Lee-Bazald\u0026uacute;a EE, Feskens EJM, Quiroga-Garza A, Cepeda-Lopez AC (2022) Depressive symptoms among Mexican adolescent girls in relation to iron status, anaemia, body weight and pubertal status: results from a latent class analysis. Public Health Nutr 26(2):408\u0026ndash;415. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1368980022001203\u003c/span\u003e\u003cspan address=\"10.1017/S1368980022001203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen M-H, Su T-P, Chen Y-S, Hsu J-W, Huang K-L, Chang W-H, Chen T-J, Bai Y-M (2013a) Association between psychiatric disorders and iron deficiency anemia among children and adolescents: a nationwide population-based study. BMC Psychiatry 13(1):161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-244X-13-161\u003c/span\u003e\u003cspan address=\"10.1186/1471-244X-13-161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabatabaeizadeh SA, Abdizadeh MF, Meshkat Z, Khodashenas E, Darroudi S, Fazeli M, Ferns GA, Avan A, Ghayour-Mobarhan M (2018) There is an association between serum high-sensitivity C-reactive protein (hs-CRP) concentrations and depression score in adolescent girls. Psychoneuroendocrinology 88:102\u0026ndash;104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.psyneuen.2017.11.014\u003c/span\u003e\u003cspan address=\"10.1016/j.psyneuen.2017.11.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ouml;nen \u0026Ouml;, \u0026Ouml;zek Erkuran H, Bağ \u0026Ouml;, Abacıgil F (2021) Blood count parameters as inflammation indicators in children and adolescents diagnosed with depressive disorder. Psychiatry Clin Psychopharmacol 31(4):425\u0026ndash;433. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5152/pcp.2021.21137\u003c/span\u003e\u003cspan address=\"10.5152/pcp.2021.21137\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu F, Cao L, Ren X, Hu J, Tavengana G, Wu H, Zhou Y, Fu Y, Jiang M, Wen Y (2023) Age and sex trend differences in hemoglobin levels in China: a cross-sectional study. BMC Endocr Disord 23(1):8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12902-022-01218-w\u003c/span\u003e\u003cspan address=\"10.1186/s12902-022-01218-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 36624464, PMCID: PMC9827637\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeong HR, Lee HS, Shim YS, Hwang JS (2022) Positive associations between body mass index and hematological parameters, including RBCs, WBCs, and platelet counts, in Korean children and adolescents. Child (Basel) 9(1):109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/children9010109\u003c/span\u003e\u003cspan address=\"10.3390/children9010109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 35053734, PMCID: PMC8774222\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23(1):56\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jnnp.23.1.56\u003c/span\u003e\u003cspan address=\"10.1136/jnnp.23.1.56\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBagby RM, Ryder AG, Schuller DR, Marshall MB (2004) The Hamilton Depression Rating Scale: has the gold standard become a lead weight? Am J Psychiatry 161(12):2163\u0026ndash;2177. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1176/appi.ajp.161.12.2163\u003c/span\u003e\u003cspan address=\"10.1176/appi.ajp.161.12.2163\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimmerman MA (2013) Resiliency theory: a strengths-based approach to research and practice for adolescent health. Health Educ Behav 40(4):381\u0026ndash;383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1090198113493782\u003c/span\u003e\u003cspan address=\"10.1177/1090198113493782\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFelger JC, Lotrich FE (2013) Inflammatory cytokines in depression: neurobiological mechanisms and therapeutic implications. Neuroscience 246:199\u0026ndash;229. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroscience.2013.04.060\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroscience.2013.04.060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCapuron L, Miller AH (2011) Immune system to brain signaling: neuropsychopharmacological implications. Pharmacol Ther 130(2):226\u0026ndash;238. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pharmthera.2011.01.014\u003c/span\u003e\u003cspan address=\"10.1016/j.pharmthera.2011.01.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEPub. PMID: 21334376, PMCID: PMC3072299\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Erlacher M, Fernandez-Orth J (2022) The role of inflammation in hematopoiesis and bone marrow failure: what can we learn from mouse models? Front Immunol 13:951937. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2022.951937\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2022.951937\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoftus TJ, Mohr AM, Moldawer LL (2018) Dysregulated myelopoiesis and hematopoietic function following acute physiologic insult. Curr Opin Hematol 25(1):37\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/MOH.0000000000000395\u003c/span\u003e\u003cspan address=\"10.1097/MOH.0000000000000395\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiss G, Goodnough LT (2005) Anemia of chronic disease. N Engl J Med 352(10):1011\u0026ndash;1023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMra041809\u003c/span\u003e\u003cspan address=\"10.1056/NEJMra041809\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoettcher S, Manz MG (2017) Regulation of inflammation- and infection-driven hematopoiesis. Trends Immunol 38(5):345\u0026ndash;357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.it.2017.01.004\u003c/span\u003e\u003cspan address=\"10.1016/j.it.2017.01.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsang RSM, Stow D, Kwong ASF, Donnelly NA, Fraser H, Barroso I, Holmans PA, Owen MJ, Wood ML, LINC Consortium, van den Bree MBM, Timpson NJ, Khandaker GM (2025) Immunometabolic blood biomarkers of developmental trajectories of depressive symptoms: findings from the ALSPAC birth cohort. Mol Psychiatry. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41380-025-03311-7\u003c/span\u003e\u003cspan address=\"10.1038/s41380-025-03311-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeizer L, Renner TJ, L\u0026ouml;chner J (2026) Associations between depression severity and immune activity in children and adolescents. J Affect Disord 393(A):120190. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2025.120190\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2025.120190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Tard\u0026oacute;n N, Sikkink LJ, Kranenborg J, den Besten G (2024) Hematological monitoring of oncology patients using the HemoScreen point of care analyser. J Lab Precis Med 9:21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21037/jlpm-23-95\u003c/span\u003e\u003cspan address=\"10.21037/jlpm-23-95\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlein SL, Flanagan KL (2016) Sex differences in immune responses. Nat Rev Immunol 16(10):626\u0026ndash;638. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nri.2016.90\u003c/span\u003e\u003cspan address=\"10.1038/nri.2016.90\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan D, Ansar Ahmed S (2015) The immune system is a natural target for estrogen action: opposing effects of estrogen in two prototypical autoimmune diseases. Front Immunol 6:635. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2015.00635\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2015.00635\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLabont\u0026eacute; B, Engmann O, Purushothaman I et al (2017) Sex-specific transcriptional signatures in human depression. Nat Med 23(9):1102\u0026ndash;1111. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nm.4386\u003c/span\u003e\u003cspan address=\"10.1038/nm.4386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy WG (2014) The sex difference in haemoglobin levels in adults\u0026mdash;mechanisms, causes, and consequences. Blood Rev 28(2):41\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.blre.2013.12.003\u003c/span\u003e\u003cspan address=\"10.1016/j.blre.2013.12.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJelkmann W (2011) Regulation of erythropoietin production. J Physiol 589(6):1251\u0026ndash;1258. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1113/jphysiol.2010.195057\u003c/span\u003e\u003cspan address=\"10.1113/jphysiol.2010.195057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Leary F, Samman S (2010) Vitamin B12 in health and disease. Nutrients 2(3):299\u0026ndash;316. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu2030299\u003c/span\u003e\u003cspan address=\"10.3390/nu2030299\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanz T, Nemeth E (2012) Hepcidin and iron homeostasis. Biochim Biophys Acta 1823(9):1434\u0026ndash;1443. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbamcr.2012.01.014\u003c/span\u003e\u003cspan address=\"10.1016/j.bbamcr.2012.01.014\" 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":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Blood Cell Count, Depressive Disorder, Adolescent, Inflammation, Hemoglobin","lastPublishedDoi":"10.21203/rs.3.rs-8434228/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8434228/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAdolescent depressive disorders are common and often interfere with adolescents\u0026rsquo; everyday functioning, school life, and social relationships. Although there is growing interest in finding objective ways to support these disorders\u0026rsquo; clinical assessment, blood-based measures for young people have been relatively overlooked. As low-grade inflammation and changes in hematopoiesis are thought to play a role in depression, we explored whether routinely obtained hematological measures show meaningful patterns in relation to depressive symptom severity among South Korean adolescents. We retrospectively analyzed the electronic health record data of 1,074 adolescents with depressive disorders and 1,220 healthy controls aged 13\u0026ndash;18 years. Depressive symptoms were assessed using the 17-item Hamilton Depression Rating Scale. Blood samples were obtained from routine laboratory tests, and associations between peripheral blood parameters and depressive symptoms were examined using correlation analyses and multivariable least absolute shrinkage and selection operator (LASSO) regression analysis. Compared with the control group, the depression group exhibited lower red blood cell count, hemoglobin, hematocrit, and mean corpuscular hemoglobin concentration, but higher platelet and white blood cell counts. Correlation analyses showed that greater depression severity was associated with reduced hemoglobin, hematocrit, red blood cell count, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, and eosinophil levels. LASSO regression revealed that hemoglobin, hematocrit, and eosinophil counts were negatively associated with scores on the Hamilton Depression Rating Scale. Although these markers are unlikely to serve as standalone diagnostic indicators, they may offer complementary information regarding inflammatory or hematopoietic alterations in youths with depression.\u003c/p\u003e","manuscriptTitle":"Associations Between Hematologic Profiles and Depressive Disorder in Adolescents","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 09:04:49","doi":"10.21203/rs.3.rs-8434228/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89f7556e-6106-4374-8b3a-112134e369fe","owner":[],"postedDate":"December 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-06T15:39:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-31 09:04:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8434228","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8434228","identity":"rs-8434228","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}