Health
Gestational diabetes may be detected early and prevented by microbiome analysis
moreover”BMJ Gut microbiotaThe gut microbiota appears to play a role in inflammation-induced GDM pathogenesis, and interleukin-6 is thought to be a potential contributor to pathogenesis.
“Several studies have found alterations in the gut microbiome composition of women with GDM, most of which were based on samples collected after diagnosis.” the report said.
“Our findings suggest that microbial differences between GDM and control groups exist in T1 when controlling for confounding variables. [the first trimester] Subtle differences in composition are driven by specific taxa rather than community-wide changes. “na
Providing evidence for their claims, the team was able to accurately predict future GDM onset in T1 with a machine learning approach for predicting GDM based on patient characteristics.
“Our joint model predicts GDM with very high accuracy, and even microbiota-centric models are able to predict disease onset in two geographically distinct cohorts. Accurate early prediction, care planning and potential prevention of this disease will be possible, improving both maternal and fetal outcomes.”na
they add: “…overall, local microbiota characteristics can (and probably should) be used to improve predictions, but in the absence of local data, microbiome genus Differences in levels can be used as a general predictor.”na
problem
Gestational diabetes (GDM) is the development of glucose intolerance during pregnancy in women without diabetes, affecting approximately 10% of pregnant women. Additionally, the incidence of GDM is increasing worldwide. This is largely due to the prevalence of overweight and obesity, aging maternal aging, and an increasing population at risk.
Outcomes of GDM include a wide range of obstetric and metabolic complications in both the mother (preeclampsia, type 2 diabetes, cardiovascular disease, etc.) and the neonate (mainly macrosomia and hypoglycemia). Detecting and properly managing GDM can prevent many complications, but early detection may completely ameliorate the short- and long-term risks associated with GDM.
the study
The team prospectively recruited 394 women in the first trimester of pregnancy, of whom 44 (11%) developed GDM, diagnosed by the second trimester glucose tolerance test (GTT). Another her 350 women were used as a control group (“healthy pregnant women”).
The team identified biomarkers for GDM in the first trimester of pregnancy by profiling the gut microbiome, metabolome, and inflammatory cytokine profiles of women who were later diagnosed with GDM and those who were not. The team then used germ-free (GF) mice to investigate whether the early pregnancy microbiome drives her development of GDM.
Using a combination of ‘omics’ tools, they identified biomarkers for GDM onset as early as the first trimester of pregnancy. Her T1 female, who later developed her GDM, showed abnormal gut microbiota, as well as increased inflammatory serum cytokines and decreased levels of SCFAs in her faeces. Moreover, specific microbial alterations in their microbiota were directly associated with GDM phenotypic features (insulin resistance and mild inflammation).
Finally, they used a machine learning approach to predict GDM based on patient characteristics, T1 microbiome, and clinical information to identify early timeframes for therapeutic intervention.
They demonstrated that microbiota samples from T1 alone could be used to predict the onset of GDM, and that parameters from patient medical records could be used to improve these predictions, suggesting that there is a need for early prediction of GDM. provided a robust tool.
The authors point out that their study has several limitations. That is, bacterial enterobacteriosis may be the first reaction to the development of the disease, rather than the cause. Furthermore, the phenotypic transmission they observed could be caused by other fecal agents such as metabolites, eukaryotic microbes, human viruses and bacteriophages, but again, the identified bacterial biomarkers It may be related to diagnosis.
Finally, throughout this study, researchers processed the major risk factors of GDM, BMI, and age using matching or related statistical methods. They could not exclude the influence of other clinical or demographic characteristics on outcome, highlighting the potentially important contribution of these two ‘confounding’ risk factors.
Although limited, the addition of microbiome data to our machine learning model has improved our ability to predict GDM, and now also serves as a standalone snapshot predictor. These results may be useful when considering preventive measures for GDM in the future.
The report concludes: “In summary, in a large prospective cohort of pregnant women, we found broad and consistent evidence that the pathology of GDM begins as early as T1. Furthermore, the precursors of GDM are derived from the gut microbiota. , early-onset GDM has a bacterial signature that is at least partially responsible for the GDM phenotype, as evidenced by phenotypic transfer after FMT. Future studies based on our findings may help elucidate the underlying mechanisms.na
sauce: BMJ Gut microbiotana
doi:10.1136/gutjnl-2022-328406
“Gestational diabetes is caused by microbiota-induced inflammation months before diagnosis.”na
Author:: Pinto Y, Frishman S, Turjeman S, et al.
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