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Background <p>This study aims to compare the differences in clinical features and inflammatory factors between unipolar depression and bipolar depression, and to investigate potential clinical characteristics and peripheral blood biomarkers that could be used to differentiate between these two conditions. Furthermore, the study seeks to establish a predictive model.</p> Methods <p>Inpatients from the Shanghai Mental Health Center, admitted between June 2022 and June 2024, were selected as study participants. Based on diagnosis records, 274 patients were assigned to the unipolar depression group, and 128 patients to the bipolar depression group. A total of 128 patients were matched between the two groups using the propensity score matching method. Demographic data, clinical characteristics, and biological indicators were compared between the two groups. The biological markers assessed included neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), platelet/lymphocyte ratio (PLR), C-reactive protein (CRP), serum triiodothyronine (T3), thyroxine (T4), free thyroid hormones (fT3, fT4), thyroid-stimulating hormone (TSH), complement 3 (C3), complement 4 (C4), immunoglobulin A (IgA), immunoglobulin G (IgG), and immunoglobulin M (IgM). Binomial logistic regression analysis was employed to control for confounding factors and to explore the predictors of bipolar depression. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive value of clinical features and biological markers for bipolar depression.</p> Results <p>Statistically significant differences were observed between the unipolar depression and bipolar depression groups with respect to life events (χ² = 15.397, <i>P</i> = 0.000), CRP (Z = 6.717, <i>P</i> = 0.000), TSH (Z = 1.988, <i>P</i> = 0.047), C3 (Z = 5.682, <i>P</i> = 0.000), C4 (Z = 4.216, <i>P</i> = 0.000), and IgM (Z = 2.304, <i>P</i> = 0.021). Logistic regression analysis indicated that life events (OR = 4.552, 95% CI = 2.238–9.257), CRP (OR = 13.886, 95% CI = 5.290-36.452), and IgM (OR = 0.561, 95% CI = 0.325–0.970) were associated with bipolar depression. ROC curve analysis revealed that the area under the curve (AUC) for the logistic regression model predicting bipolar depression was 0.806, with a sensitivity of 61.7% and a specificity of 85.9%.</p> Conclusions <p>Compared to unipolar depression, bipolar depression was associated with the absence of life events, elevated CRP levels, and reduced IgM levels. The combined diagnostic model proved more effective in distinguishing bipolar depression from unipolar depression.</p>

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Comparison of clinical features and inflammatory factors between patients with bipolar depression and unipolar depression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月10日

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  • Tianwei Zhang,
  • Changjun Ji,
  • Jiayu Zhu,
  • Xiaoxiao Wang,
  • Chengjia Shen,
  • Fei Liang,
  • Yajun Hou,
  • Yan Sun,
  • Chongze Wang,
  • Peijuan Wang,
  • Guoqiang Lu,
  • Xiaohui Wang,
  • Qinyu Lv,
  • Zhenghui Yi

摘要

Background

This study aims to compare the differences in clinical features and inflammatory factors between unipolar depression and bipolar depression, and to investigate potential clinical characteristics and peripheral blood biomarkers that could be used to differentiate between these two conditions. Furthermore, the study seeks to establish a predictive model.

Methods

Inpatients from the Shanghai Mental Health Center, admitted between June 2022 and June 2024, were selected as study participants. Based on diagnosis records, 274 patients were assigned to the unipolar depression group, and 128 patients to the bipolar depression group. A total of 128 patients were matched between the two groups using the propensity score matching method. Demographic data, clinical characteristics, and biological indicators were compared between the two groups. The biological markers assessed included neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), platelet/lymphocyte ratio (PLR), C-reactive protein (CRP), serum triiodothyronine (T3), thyroxine (T4), free thyroid hormones (fT3, fT4), thyroid-stimulating hormone (TSH), complement 3 (C3), complement 4 (C4), immunoglobulin A (IgA), immunoglobulin G (IgG), and immunoglobulin M (IgM). Binomial logistic regression analysis was employed to control for confounding factors and to explore the predictors of bipolar depression. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive value of clinical features and biological markers for bipolar depression.

Results

Statistically significant differences were observed between the unipolar depression and bipolar depression groups with respect to life events (χ² = 15.397, P = 0.000), CRP (Z = 6.717, P = 0.000), TSH (Z = 1.988, P = 0.047), C3 (Z = 5.682, P = 0.000), C4 (Z = 4.216, P = 0.000), and IgM (Z = 2.304, P = 0.021). Logistic regression analysis indicated that life events (OR = 4.552, 95% CI = 2.238–9.257), CRP (OR = 13.886, 95% CI = 5.290-36.452), and IgM (OR = 0.561, 95% CI = 0.325–0.970) were associated with bipolar depression. ROC curve analysis revealed that the area under the curve (AUC) for the logistic regression model predicting bipolar depression was 0.806, with a sensitivity of 61.7% and a specificity of 85.9%.

Conclusions

Compared to unipolar depression, bipolar depression was associated with the absence of life events, elevated CRP levels, and reduced IgM levels. The combined diagnostic model proved more effective in distinguishing bipolar depression from unipolar depression.