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Objective <p>This study aimed to identify objective biomarkers for unipolar and bipolar depressive episodes by analyzing the gut microbiome and serum metabolome of patients with major depressive disorder (MDD) and bipolar disorder (BD).</p> Methods <p>The study included 82 outpatients and inpatients (MDD, n = 38; BD, n = 44) from the First Hospital of Hebei Medical University, and 42 healthy controls. Demographic data were collected, and various psychological tests were administered. Stool and blood samples were collected for gut microbiome and non-targeted serum metabolomics analysis using 16S rRNA and ultra-high-performance liquid chromatography coupled with mass spectrometry. Additionally, multi-omics integration was performed using Joint Non-negative Matrix Factorization (JNMF) to construct a fused diagnostic model.</p> Results <p>Significant differences were observed in the α-diversity and β-diversity indices among the three groups. Seventeen significantly different bacterial populations were identified between BD and MDD groups. The BD group showed higher abundance of <i>g__Bifidobacterium</i>, <i>g__Prevotella_7</i>, and <i>g__un_f_Muribaculaceae</i>, and lower abundance of <i>g__Parasutterella.</i> Fifty differential metabolites were identified, with eight metabolites showing an area under the curve (AUC) greater than 0.7. <i>g__Bifidobacterium</i> was positively correlated with metabolites such as l-lactate and taurine. Notably, the diagnostic model based on integrated features achieved an AUC of 0.934, demonstrating superior discriminative ability compared to single-omics models.</p> Conclusions <p>Gut microbiota and serum metabolites effectively distinguished between unipolar and combination of four bacterial genera and eight serum metabolites showed potential value in differential diagnosis. Some differential gut microbiota and metabolites were related to clinical symptoms of the disease, suggesting a mutual influence between gut microbiota and serum metabolites.</p>

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Integrated multi-omics analysis of gut microbiome and serum metabolome in unipolar and bipolar depression

  • 期刊论文

  • 发表日期：

    2026年3月26日

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  • Yujing Wang,
  • Lan Wang,
  • Yaxin Zheng,
  • Ran Wang,
  • Fengya Zhen,
  • Zhangkai J. Cheng,
  • Baoqing Sun,
  • Stephen Kwok-Wing Tsui,
  • Cuixia An

摘要

Objective

This study aimed to identify objective biomarkers for unipolar and bipolar depressive episodes by analyzing the gut microbiome and serum metabolome of patients with major depressive disorder (MDD) and bipolar disorder (BD).

Methods

The study included 82 outpatients and inpatients (MDD, n = 38; BD, n = 44) from the First Hospital of Hebei Medical University, and 42 healthy controls. Demographic data were collected, and various psychological tests were administered. Stool and blood samples were collected for gut microbiome and non-targeted serum metabolomics analysis using 16S rRNA and ultra-high-performance liquid chromatography coupled with mass spectrometry. Additionally, multi-omics integration was performed using Joint Non-negative Matrix Factorization (JNMF) to construct a fused diagnostic model.

Results

Significant differences were observed in the α-diversity and β-diversity indices among the three groups. Seventeen significantly different bacterial populations were identified between BD and MDD groups. The BD group showed higher abundance of g__Bifidobacterium, g__Prevotella_7, and g__un_f_Muribaculaceae, and lower abundance of g__Parasutterella. Fifty differential metabolites were identified, with eight metabolites showing an area under the curve (AUC) greater than 0.7. g__Bifidobacterium was positively correlated with metabolites such as l-lactate and taurine. Notably, the diagnostic model based on integrated features achieved an AUC of 0.934, demonstrating superior discriminative ability compared to single-omics models.

Conclusions

Gut microbiota and serum metabolites effectively distinguished between unipolar and combination of four bacterial genera and eight serum metabolites showed potential value in differential diagnosis. Some differential gut microbiota and metabolites were related to clinical symptoms of the disease, suggesting a mutual influence between gut microbiota and serum metabolites.