Evidence map›Paper›PMID 38599848›Full record

ArticleJournal of diabetes2024

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

Ziyu Liu, Tong Yue, Xueying Zheng, Sihui Luo, Wen Xu, Jinhua Yan, Jianping Weng, Daizhi Yang, Chaofan Wang

Abstract read
In one paragraph

Article in Journal of diabetes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ziyu LiuDepartment 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.
Tong YueDepartment 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.
Xueying ZhengDepartment 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.
Sihui LuoDepartment 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.ORCID https://orcid.org/0000-0001-8503-0310
Wen XuDepartment 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.ORCID https://orcid.org/0000-0002-8258-2707
Jinhua YanDepartment 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.
Jianping WengDepartment 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.
Daizhi YangDepartment 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.
Chaofan WangDepartment 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.ORCID https://orcid.org/0000-0001-5292-5564

Funding

Anhui Provincial Natural science foundation 2008085MH248Anhui Provincial Natural science foundation 2008085MH278Guangdong Basic and Applied Basic Research Foundation 2019A1515010979National Key R&D Program of China 2017YFC1309600National Natural Science Foundation of China 82100822Science and Technology Projects in Guangzhou 2023A04J1087
6 · The paper itself

Abstract

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.

Indexed as

Diabetes Mellitus, Type 1Gastrointestinal MicrobiomeCase-Control StudiesDepressionHumansRNA, Ribosomal, 16SRNA, Ribosomal, 16Sdepressionmetabolomicsmicrobiomicstype 1 diabetes

Identifiers

PMID38599848
PMCPMC11006619

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.