METAGENOMIC PROFILING OF THE GUT MICROBIOME FOR EARLY PREDICTION OF METABOLIC DISORDERS: A STRUCTURED REVIEW AND INTEGRATIVE MACHINE LEARNING FRAMEWORK

Abstract

Obesity, type 2 diabetes mellitus (T2D), metabolic syndrome (MetS), and non-alcoholic fatty liver disease (NAFLD) are collectively the most rapidly growing group of non-communicable diseases, including global diabetes, which is expected to increase from 589 million adult cases in 2024 to 853 million in 2050. Current risk assessment strategies rely on anthropometric and biochemical variables that often become abnormal only after severe metabolic damage has occurred; a diagnostic window exists in which earlier identification of minimally invasive biomarkers could significantly shape clinical pathways. We now know of a new and modifiable organ like ecosystem, the gut microbiome, that is metabolically active and is a potentially useful substrate for such early warning biomarkers. This paper synthesises evidence on metagenomic profiling technologies and machine learning (ML) approaches to gut microbiome data used to identify metabolic disorders early, and outlines an integrative, clinically translatable approach. We searched for human cohort studies involving 16S rRNA or shotgun metagenomic sequencing and supervised ML to predict obesity, T2D, MetS, or NAFLD from 2006 to 2026. Results: Both longitudinal exploration and literature synthesis showed consistent directionality in dysbiotic, at-risk dynamics, including depletion of Akkermansia muciniphila, Faecalibacterium prausnitzii, and other short chain fatty acid producers, and a higher Firmicutes-to-Bacteroidetes ratio. Several classifiers, such as random forest and gradient-boosting classifiers trained on gut metagenomic features, achieved AUCs ranging from 0.76 to 0.87 for T2D discrimination, which improved when gut metagenomic features were combined with simple clinical covariates and slightly worsened in independent ethnic cohorts. In conclusion, non-invasive metabolic risk stratification via metagenomic profiling and interpretable machine learning is a biologically plausible and increasingly supported approach, with the caveat that routine implementation still requires evidence of biological causality, standardisation across populations, and longitudinal integration of multiple omics.