Understanding Patterns of the Gut Microbiome May Contribute to the Early Detection and Prevention of Type 2 Diabetes Mellitus: A Systematic Review.

Abstract

The rising burden of type 2 diabetes mellitus (T2DM) is a growing global public health problem, particularly prominent in developing countries. The early detection of T2DM and prediabetes is vital for reversing the outcome of disease, allowing early intervention. In the past decade, various microbiome-metabolome studies have attempted to address the question of whether there are any common microbial patterns that indicate either prediabetic or diabetic gut microbial signatures. Because current studies have a high methodological heterogeneity and risk of bias, we have selected studies that adhered to similar design and methodology. We performed a systematic review to assess if there were any common changes in microbiome belonging to diabetic, prediabetic and healthy individuals. The cross-sectional studies presented here collectively covered a population of 65,754 people, with 1800 in the 2TD group, 2770 in the prediabetic group and 61,184 in the control group. The overall microbial diversity scores were lower in the T2D and prediabetes cohorts in 86% of the analyzed studies. Re-programming of the microbiome is potentially one of the safest and long-lasting ways to eliminate diabetes in its early stages. The differences in the abundance of certain microbial species could serve as an early warning for a dysbiotic gut environment and could be easily modified before the onset of disease by changes in lifestyle, taking probiotics, introducing diet modifications or stimulating the vagal nerve. This review shows how metagenomic studies have and will continue to identify novel therapeutic targets (probiotics, prebiotics or targets for elimination from flora). This work clearly shows that gut microbiome intervention studies, if performed according to standard operating protocols using a predefined analytic framework (e.g., STORMS), could be combined with other similar studies, allowing broader conclusions from collating all global cohort studies efforts and eliminating the effect-size statistical insufficiency of a single study.