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

被引:2
作者
Liu, Ziyu [1 ,2 ]
Yue, Tong [3 ]
Zheng, Xueying [3 ]
Luo, Sihui [3 ]
Xu, Wen [1 ]
Yan, Jinhua [1 ]
Weng, Jianping [1 ,3 ]
Yang, Daizhi [1 ,4 ]
Wang, Chaofan [1 ,4 ]
机构
[1] Sun Yat Sen Univ, Affiliated Hosp 3, Guangdong Diabet Prevent & Control Res Ctr, Dept Endocrinol & Metab,Guangdong Prov Key Lab Dia, Guangzhou, Peoples R China
[2] Sun Yat Sen Univ, Affiliated Hosp 6, Dept Endocrinol, Guangzhou, Peoples R China
[3] Univ Sci & Technol China, Affiliated Hosp USTC 1, Inst Endocrine & Metab Dis, Clin Res Hosp,Dept Endocrinol,Div Life Sci & Med,C, Hefei, Peoples R China
[4] Sun Yat Sen Univ, Affiliated Hosp 3, Dept Endocrinol & Metab, 600 Tianhe Rd, Guangzhou 510630, Peoples R China
关键词
depression; metabolomics; microbiomics; type; 1; diabetes; OXIDATIVE STRESS; GUT MICROBIOTA; SERUM; ALLOPREGNANOLONE; CHROMATOGRAPHY; SIGNATURES; DISORDERS; SYMPTOMS; MELLITUS; ANXIETY;
D O I
10.1111/1753-0407.13542
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
BackgroundDepression 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.MethodsA 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.ResultsIn 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.ConclusionsT1D 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. imageConclusionsT1D 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. image HighlightsType 1 diabetes (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. image
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页数:16
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