Research Article: Risk factors for gestational diabetes: An umbrella review of meta-analyses of observational studies

Date Published: April 19, 2019

Publisher: Public Library of Science

Author(s): Konstantinos Giannakou, Evangelos Evangelou, Panayiotis Yiallouros, Costas A. Christophi, Nicos Middleton, Evgenia Papatheodorou, Stefania I. Papatheodorou, Liwei Chen.

http://doi.org/10.1371/journal.pone.0215372

Abstract

Gestational diabetes mellitus (GDM) is a common pregnancy complication, with complex disease mechanisms, and several risk factors may contribute to its onset. We performed an umbrella review to summarize the evidence from meta-analyses of observational studies on risk factors associated with GDM, evaluate whether there are indications of biases in this literature and identify which of the previously reported associations are supported by convincing evidence.

We searched PubMed and ISI Web of Science from inception to December 2018 to identify meta-analyses examining associations between putative risk factors for GDM. For each meta-analysis we estimated the summary effect size, the 95% confidence interval, the 95% prediction interval, the between-study heterogeneity, evidence of small-study effects, and evidence of excess-significance bias.

Thirty eligible meta-analyses were identified, providing data on 61 associations. Fifty (82%) associations had nominally statistically significant findings (P<0.05), while only 15 (25%) were significant at P<10−6 under the random-effects model. Only four risk factors presented convincing evidence:, low vs. normal BMI (cohort studies), BMI ~30–35 kg/m2 vs. normal BMI, BMI >35 kg/m2 vs. normal BMI, and hypothyroidism.

The compilation of results from synthesis of observational studies suggests that increased BMI and hypothyroidism show the strongest consistent evidence for an association with GDM. Diet and lifestyle modifications in pregnancy should be tested in large randomized trials. Our findings suggest that women with known thyroid disease may be offered screening for GDM earlier in pregnancy.

Partial Text

Gestational diabetes mellitus (GDM) is a common pregnancy complication, defined as glucose intolerance with onset or first recognition during pregnancy, in women without prior diabetes history prior to pregnancy.[1, 2] During the last 20 years the prevalence of GDM has increased worldwide and it is expected to continue to rise along with the increase in pre-conception obesity and pregnant women affected by obesity.[3] GDM affects approximately 15% of all pregnancies, depending on population characteristics, and this prevalence may in fact be higher under the new diagnostic criteria.[4, 5] GDM is associated with an increased risk of maternal and infant morbidity, including macrosomia, large for gestational age (LGA), cesarean section delivery and preterm birth, but it is also considered to be a risk factor for long-term complications, such as type 2 diabetes mellitus and cardiovascular disease in the mother and the offspring.[6–9] The etiology of GDM is multifactorial and has not completely been established yet, while several risk factors may contribute to its onset. Age, overweight or obesity, ethnicity, family history of diabetes, and history of GDM are some of the proposed risk factors for GDM.[10–13]

The present umbrella review of meta-analyses identified 61 unique risk factors for GDM. Our analysis identified four risk factors with convincing evidence and strong epidemiological credibility pertaining to hypothyroidism and BMI (specifically, low vs. normal BMI (cohort studies), BMI ~30–35 vs. normal weight, BMI >35 vs. normal weight). Diet and lifestyle modifications in pregnancy should be tested in large randomized trials. Our findings suggest that women with known thyroid disease could be offered screening for GDM earlier in pregnancy. As previously suggested, the use of standardized definitions and protocols for exposures, outcomes, and statistical analyses may diminish the threat of biases, allow for the computation of more precise estimates and will promote the development and training of prediction models that could promote public health.

 

Source:

http://doi.org/10.1371/journal.pone.0215372

 

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