Furthermore the report published in 'BMJ Gut Microbiota' poses that the gut microbiome appears to play a role in inflammation-induced GDM pathogenesis, with interleukin-6 as a potential contributor to pathogenesis.
"Several studies have found altered gut microbiome composition in women with GDM; most were based on samples collected post-diagnosis," the report states.
"Our findings suggest that microbial differences between GDM and control groups, when controlling for confounding variables, exist in T1 [the first trimester] and are driven by specific taxa rather than community-wide shifts, leading to subtle differences in composition."
Providing evidence for their assertion, the team were able to accurately predict future GDM onset in T1 in a machine learning approach to predict GDM based on patient characteristics.
"Our combined model predicts GDM with very high accuracy, and even a microbiota-centric model could predict disease onset in two geographically diverse cohorts. This tool allows for accurate early prediction, care plans and potential prevention of this disease, improving both maternal and fetal outcomes."
They add: "...On the whole, prediction could (and likely should) be improved using local microbiota characteristics, but genus-level differences in the microbiome can be used as general predictors in the absence of local data."
Gestational diabetes mellitus (GDM) is the development of glucose intolerance during pregnancy in women without diabetes and it effects approximately 10% of pregnant women. What's more, the incidence of GDM is increasing worldwide, due primarily to the increase in prevalence of overweight and obesity, advanced maternal age and growth of at-risk populations.
Consequences of GDM include a wide range of obstetrical and metabolic complications for both the mother (eg, pre-eclampsia, type 2 diabetes and cardiovascular diseases) and the neonate (mainly macrosomia and hypoglycaemia). Many complications are preventable if GDM is detected and appropriately managed but earlier detection might allow for complete amelioration of GDM-associated short-term and long-term risks.
The team prospectively recruited 394 women during T1, 44 (11%) of which went on to develop GDM, as diagnosed by glucose tolerance test (GTT) during the second trimester of pregnancy. Another 350 women served as the control group (‘healthy pregnant women’).
The team identified biomarkers of GDM in the first trimester of pregnancy by profiling the gut microbiome, metabolome and inflammatory cytokine profiles of women who would and would not later be diagnosed with GDM. The team then investigated whether the early pregnancy microbiome drove GDM development using germ-free (GF) mice.
Using a combination of ‘omics’ tools, they identified biomarkers of GDM onset as early as the first trimester of pregnancy. Women in T1, who later developed GDM, exhibited gut microbiota dysbiosis as well as increased proinflammatory serum cytokines and lower levels of faecal SCFAs. Further, the specific microbial changes in their microbiota were directly associated with GDM phenotype features (insulin resistance and low-grade inflammation).
Finally, they used a machine learning approach to predict GDM based on patient characteristics, T1 microbiome and clinical information, to identify earlier time frames for therapeutic intervention.
They demonstrated that microbiota samples from T1 alone can be used to predict GDM onset and that parameters from patient medical records can improve these predictions, providing a robust tool for early prediction of GDM.
The authors note several limitations to their study. Namely, bacterial dysbiosis could be a first response to disease onset rather than a cause. Additionally, the phenotype transfer they observed may be caused by other faecal material including metabolites, eukaryotic microorganisms, human viruses and bacteriophages, though in this case as well, the bacterial biomarkers identified can be relevant for diagnostics.
Lastly, throughout this study, the researchers treated the major risk factors of GDM, BMI and age, using either matching or relevant statistical methods. They can't exclude the effect of other clinical or demographic features on our results and they highlight the potentially important contribution of these two ‘confounding’ risk factors.
Despite limitations, addition of microbiome data to a machine learning model improved our ability to predict GDM and can even serve as a standalone snapshot predictor. These results may be of use in the future when exploring preventive measures for GDM.
The report concludes: "In summary, we found broad and consistent evidence that GDM pathology begins as early as T1 in a large prospective cohort of pregnant women. Additionally, we successfully demonstrated that the precursors of GDM originate in the gut microbiota and that early-onset GDM has a bacterial signature at least partially responsible for the GDM phenotype, evident from phenotype transfer following FMT. Our findings suggest that GDM is induced through heightened inflammation, initiated by microbial dysbiosis. Future research based on our findings can help unravel the underlying mechanisms."
Source: BMJ Gut Microbiota
"Gestational diabetes is driven by microbiota-induced inflammation months before diagnosis"
Authors:: Pinto Y, Frishman S, Turjeman S, et al