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J Endocrinol Invest ; 44(6): 1139-1149, 2021 Jun.
Article in English | MEDLINE | ID: mdl-33226626

ABSTRACT

PURPOSE: A systematic review and meta-analysis were conducted to quantitatively synthesize the current evidence regarding the risk of developing metabolic syndrome (MetS) in women with a personal history of gestational diabetes mellitus (GDM), without pre-existing diabetes, compared with those without a history of GDM. METHODS: Four electronic databases [MEDLINE (via PubMed), Scopus, Cochrane Library, PROSPERO] were searched for relevant literature until July 29th 2020. Cochran's Q test was applied for the assessment of heterogeneity. The random-effects model was applied by calculating the odds ratio (OR) and 95% confidence interval (CI) for each study. Publication bias was estimated with Egger's linear regression test. RESULTS: The results from 23 studies (10,230 pregnant women; 5169 cases, 5061 controls), indicated that women with a history of GDM had a higher risk of developing MetS compared with those without such a history (OR 3.45; 95% CI 2.80-4.25, p < 0.0001). This risk remained higher, independently of maternal age and ethnicity (although the risk was not as high in Asians; OR 2.11; 95% CI 1.27-3.52). The risk of developing MetS was even higher in studies where women with GDM had increased body mass index (BMI) compared with the controls (OR 4.14; 95% CI 3.18-5.38). CONCLUSIONS: The risk for developing MetS following delivery is higher in women with a history of GDM compared with women without such a history. Timely recognition and appropriate intervention are critical to halt progression to MetS and its associated morbidity.


Subject(s)
Diabetes, Gestational , Metabolic Syndrome , Risk Assessment , Cardiometabolic Risk Factors , Diabetes, Gestational/diagnosis , Diabetes, Gestational/epidemiology , Female , Humans , Metabolic Syndrome/epidemiology , Metabolic Syndrome/prevention & control , Pregnancy , Preventive Health Services , Reproductive History , Risk Assessment/methods , Risk Assessment/statistics & numerical data
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