Gut microbiome encoded purine and amino acid pathways present prospective biomarkers for predicting metformin therapy efficacy in newly diagnosed T2D patients.
Ilze ElbereZigmunds OrlovskisLaura AnsoneIvars SilamikelisLauma JagareLiga BirznieceKaspars MegnisKristaps LeskovskisAnnija VaskaMaris TurksKristaps KlavinsValdis PiragsMonta BrivibaJanis KlovinsPublished in: Gut microbes (2024)
Metformin is widely used for treating type 2 diabetes mellitus (T2D). However, the efficacy of metformin monotherapy is highly variable within the human population. Understanding the potential indirect or synergistic effects of metformin on gut microbiota composition and encoded functions could potentially offer new insights into predicting treatment efficacy and designing more personalized treatments in the future. We combined targeted metabolomics and metagenomic profiling of gut microbiomes in newly diagnosed T2D patients before and after metformin therapy to identify potential pre-treatment biomarkers and functional signatures for metformin efficacy and induced changes in metformin therapy responders. Our sequencing data were largely corroborated by our metabolic profiling and identified that pre-treatment enrichment of gut microbial functions encoding purine degradation and glutamate biosynthesis was associated with good therapy response. Furthermore, we identified changes in glutamine-associated amino acid (arginine, ornithine, putrescine) metabolism that characterize differences in metformin efficacy before and after the therapy. Moreover, metformin Responders' microbiota displayed a shifted balance between bacterial lipidA synthesis and degradation as well as alterations in glutamate-dependent metabolism of N-acetyl-galactosamine and its derivatives (e.g. CMP-pseudaminate) which suggest potential modulation of bacterial cell walls and human gut barrier, thus mediating changes in microbiome composition. Together, our data suggest that glutamine and associated amino acid metabolism as well as purine degradation products may potentially condition metformin activity via its multiple effects on microbiome functional composition and therefore serve as important biomarkers for predicting metformin efficacy.
Keyphrases
- newly diagnosed
- amino acid
- end stage renal disease
- endothelial cells
- single cell
- chronic kidney disease
- stem cells
- ejection fraction
- type diabetes
- gene expression
- nitric oxide
- clinical trial
- prognostic factors
- electronic health record
- dna methylation
- cardiovascular disease
- microbial community
- genome wide
- cancer therapy
- climate change
- risk assessment
- machine learning
- weight loss
- artificial intelligence
- liver injury
- wastewater treatment
- smoking cessation
- deep learning
- data analysis
- drug induced
- cell therapy
- antibiotic resistance genes