ArticleScientific reports2025
Predicting diabetic kidney disease with serum metabolomics and gut microbiota.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Metabolomics profile in diabetes and blood glucose regulation: a systematic review.Frontiers in nutrition · 2026Pooled it
- Metabolomics approaches for the early detection and therapeutics: type 2 diabetes-induced diabetic kidney disease-a systematic review and meta-analysis.Metabolomics : Official journal of the Metabolomic Society · 2025Pooled it
- A novel mouse model for cardiovascular-kidney-metabolic syndrome: Bridging metabolic, renal and cardiac dysfunction.Molecular metabolism · 2026Article
- Carnitine dysregulation in diabetic kidney disease: from pathogenic mechanism to precision biomarker.Journal of translational medicine · 2026Review
- Integrated metabolomics and 16S rRNA sequencing reveal the mechanism of total flavones of Abelmoschus manihot (L.) Medic against liver fibrosis.Scientific reports · 2026Article
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Effects of Balanced Dietary Patterns and/or Integrated Exercise on Serum 1,5-Anhydroglucitol and CVD Risk Factors in Individuals with Prediabetes.Life (Basel, Switzerland) · 2026Article
- Renal tissue microbiota and metabolite profiling reveal dysregulated signatures in diabetic kidney disease.Frontiers in microbiology · 2026Article
- New Insights into the Role of Mitochondrial Dysfunction in Diabetic Kidney Disease in the Omics Era.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Review
- Sex-specific modulation of gut microbiota by empagliflozin contributes to renoprotection in diabetic kidney disease mice.Frontiers in microbiology · 2026Article
- Integrated multi-omics analysis unveils microbiota-metabolite-host interactions and novel biomarkers for early diabetic kidney disease diagnosis.Frontiers in immunology · 2026Article
- Diabetic kidney disease: integrating multi-omics insights, artificial intelligence, and novel therapeutics for precision medicine.Frontiers in genetics · 2026Review
- Pathological Alterations in Human Blood Microbiome-An Updated Review.International journal of molecular sciences · 2025Review
- Tirzepatide modulates gut microbiota homeostasis to protect against diabetic kidney disease.Frontiers in molecular biosciences · 2025Article
- Gut microbiota dysbiosis in diabetic nephropathy: mechanisms and therapeutic targeting via the gut-kidney axis.Frontiers in endocrinology · 2025Review
- Gut microbiota-derived short-chain fatty acids mediate the antifibrotic effects of traditional Chinese medicine in diabetic nephropathy.Frontiers in endocrinology · 2025Review
- Targeting ion channel networks in diabetic kidney disease: from molecular crosstalk to precision therapeutics and clinical innovation.Frontiers in medicine · 2025Review
- The effect of Zuogui-Jiangtang-Yishen decoction on the intestinal flora's response to L-α-phosphatidylcholine and L-tyrosine in patients with diabetic kidney disease: anFrontiers in pharmacology · 2025Article
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13 authors.
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Abstract
This study aims to identify biomarkers for reliably predicting diabetic kidney disease (DKD), systematically characterize serum metabolites and gut microbiota in DKD patients, and investigate the correlation between differential serum metabolites and gut microbiota. From September 2021 to January 2023, 90 subjects were recruited: 30 with DKD, 30 with type 2 diabetes mellitus (T2DM), and 30 normal controls (NCs). Serum metabolites, including 180 different metabolites, were analyzed using untargeted metabolomics UPLC-MS/MS, and gut microbiota were assessed via 16S rRNA sequencing. Differential metabolites were identified through univariate (t-test or Mann-Whitney U-test, P < 0.05) and multivariate analyses (OPLS-DA model, VIP > 1, P < 0.05), followed by selection using the least absolute shrinkage and selection operator (LASSO). The selected overlapping serum metabolites, along with DKD-associated differential gut microbiota, were used to develop a logistic regression prediction model for DKD based on six markers. In the DKD group compared to the DM and NC groups, 39 and 60 differential serum metabolites were identified, respectively (VIP > 1, P < 0.01). Among these, 36 serum metabolites, including alpha-Hydroxyisobutyric acid, were significantly elevated in DKD patients compared to those with DM. Of these, 28 metabolites showed a negative correlation with estimated glomerular filtration rate (eGFR), while 29 showed a positive correlation with urine albumin creatinine ratio (UACR). Patients with DKD were further categorized into subgroups (DKD middle and DKD early) based on eGFR (eGFR < 90 ml/min/1.73m
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