# Integrated multi-omics analysis unveils microbiota-metabolite-host interactions and novel biomarkers for early diabetic kidney disease diagnosis

## TL;DR

This study uses multi-omics and machine learning to identify early biomarkers for diabetic kidney disease in East Asian populations.

## Contribution

The study introduces a novel multi-omics and ML approach to identify population-specific biomarkers for early DKD diagnosis in East Asians.

## Key findings

- MR analysis found significant associations between specific microbiota taxa and metabolites linked to DKD in East Asians.
- ML models achieved over 90% accuracy in distinguishing T2DM from DKD in the East Asian cohort.
- Microbial dysbiosis and altered metabolites were observed in DKD patients, including increased Klebsiella and reduced Faecalibaculum.

## Abstract

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease (ESRD), and its early diagnosis remains a major global challenge because conventional biomarkers lack sensitivity. The East Asian population is characterized by distinct genetic, environmental, and lifestyle factors that may influence the development and progression of DKD, highlighting the importance of population-specific research. The primary objective of this study was to apply a multi-omics strategy, including Mendelian randomization (MR) analysis, within an East Asian cohort to investigate potential causal relationships among microbiota, metabolites, and DKD, with the aim of identifying candidate biomarkers relevant to this population. Secondary objectives included the analysis of clinical samples from East Asian participants to characterize microbiota composition, metabolomic profiles, and tongue image features (TIFs), as well as the development of machine learning (ML) models to distinguish patients with type 2 diabetes mellitus (T2DM) from those with DKD.

MR analysis was performed to investigate potential causal associations between more than 190 microbiota taxa and 404 differential metabolites in relation to DKD within the East Asian cohort. Clinical samples (n = 535) were collected from East Asian individuals and analyzed for microbiota composition, metabolomic profiling, and TIFs. Subsequently, ML models were constructed to differentiate patients with T2DM from those with DKD in this cohort.

MR analysis identified significant associations between specific microbiota taxa (e.g., Haemophilus-A, TM7x, Lachnoanaerobaculum, and Bacteroides) and metabolites (e.g., tyrosine and glutamine) in relation to DKD within the East Asian cohort. However, the causal nature of these associations requires further experimental or longitudinal validation. Clinical analyses revealed microbial dysbiosis in patients with DKD, including a 2.5-fold increase in Klebsiella and a 60% reduction in Faecalibaculum and Dubosiella. Metabolomic profiling demonstrated alterations in branched-chain amino acids (BCAAs) and fatty acids. Integrated multi-omics analysis suggested complex interactions among microbiota and metabolites that may contribute to DKD progression. The ML models achieved an accuracy exceeding 90% in distinguishing T2DM from DKD in the East Asian cohort.

Multi-omics integration combined with ML may provide candidate biomarkers for the early detection of DKD in the East Asian population. These approaches could improve the accuracy of non-invasive diagnosis and support the development of personalized management strategies. Nevertheless, further studies are required to validate the identified associations and confirm their clinical applicability in real-world East Asian settings.