Volume 16, Issue 4 e13542
ORIGINAL ARTICLE
Open Access

Microbial and metabolomic profiles of type 1 diabetes with depression: A case–control study

Ziyu Liu

Ziyu Liu

Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, Guangdong Diabetes Prevention and Control Research Center, Guangdong Provincial Key Laboratory of Diabetology, Guangzhou, China

Department of Endocrinology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

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Tong Yue

Tong Yue

Department of Endocrinology, Institute of Endocrine and Metabolic Diseases, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Clinical Research Hospital of the Chinese Academy of Sciences (Hefei), University of Science and Technology of China, Hefei, China

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Xueying Zheng

Xueying Zheng

Department of Endocrinology, Institute of Endocrine and Metabolic Diseases, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Clinical Research Hospital of the Chinese Academy of Sciences (Hefei), University of Science and Technology of China, Hefei, China

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Sihui Luo

Sihui Luo

Department of Endocrinology, Institute of Endocrine and Metabolic Diseases, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Clinical Research Hospital of the Chinese Academy of Sciences (Hefei), University of Science and Technology of China, Hefei, China

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Wen Xu

Wen Xu

Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, Guangdong Diabetes Prevention and Control Research Center, Guangdong Provincial Key Laboratory of Diabetology, Guangzhou, China

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Jinhua Yan

Jinhua Yan

Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, Guangdong Diabetes Prevention and Control Research Center, Guangdong Provincial Key Laboratory of Diabetology, Guangzhou, China

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Jianping Weng

Jianping Weng

Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, Guangdong Diabetes Prevention and Control Research Center, Guangdong Provincial Key Laboratory of Diabetology, Guangzhou, China

Department of Endocrinology, Institute of Endocrine and Metabolic Diseases, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Clinical Research Hospital of the Chinese Academy of Sciences (Hefei), University of Science and Technology of China, Hefei, China

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Daizhi Yang

Corresponding Author

Daizhi Yang

Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, Guangdong Diabetes Prevention and Control Research Center, Guangdong Provincial Key Laboratory of Diabetology, Guangzhou, China

Correspondence

Chaofan Wang and Daizhi Yang, Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou 510630, China.

Email: [email protected] and [email protected]

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Chaofan Wang

Corresponding Author

Chaofan Wang

Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, Guangdong Diabetes Prevention and Control Research Center, Guangdong Provincial Key Laboratory of Diabetology, Guangzhou, China

Correspondence

Chaofan Wang and Daizhi Yang, Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou 510630, China.

Email: [email protected] and [email protected]

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First published: 10 April 2024

Abstract

Background

Depression is the most common psychological disorder in patients with type 1 diabetes (T1D). However, the characteristics of microbiota and metabolites in these patients remain unclear. This study aimed to investigate microbial and metabolomic profiles and identify novel biomarkers for T1D with depression.

Methods

A case–control study was conducted in a total of 37 T1D patients with depression (TD+), 35 T1D patients without depression (TD−), and 29 healthy controls (HCs). 16S rRNA gene sequencing and liquid chromatography–mass spectrometry (LC–MS) metabolomics analysis were conducted to investigate the characteristics of microbiota and metabolites. The association between altered microbiota and metabolites was explored by Spearman's rank correlation and visualized by a heatmap. The microbial signatures to discriminate TD+ from TD− were identified by a random forest (RF) classifying model.

Results

In microbiota, 15 genera enriched in TD− and 2 genera enriched in TD+, and in metabolites, 14 differential metabolites (11 upregulated and 3 downregulated) in TD+ versus TD− were identified. Additionally, 5 genera (including Phascolarctobacterium, Butyricimonas, and Alistipes from altered microbiota) demonstrated good diagnostic power (area under the curve [AUC] = 0.73; 95% CI, 0.58–0.87). In the correlation analysis, Butyricimonas was negatively correlated with glutaric acid (r = −0.28, p = 0.015) and malondialdehyde (r = −0.30, p = 0.012). Both Phascolarctobacterium (r = 0.27, p = 0.022) and Alistipes (r = 0.31, p = 0.009) were positively correlated with allopregnanolone.

Conclusions

T1D patients with depression were characterized by unique profiles of gut microbiota and serum metabolites. Phascolarctobacterium, Butyricimonas, and Alistipes could predict the risk of T1D with depression. These findings provide further evidence that the microbiota–gut–brain axis is involved in T1D with depression.

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