Gestational diabetes mellitus (GDM) among pregnant women increases the risk of both short-term and long-term complications, such as birth complications, babies large for gestational age (LGA), and type 2 diabetes in both mother and offspring. Lifestyle changes are essential in the management of GDM. In this review, we seek to provide an overview of the lifestyle changes which can be recommended in the management of GDM. The diet recommended for women with GDM should contain sufficient macronutrients and micronutrients to support the growth of the foetus and, at the same time, limit postprandial glucose excursions and encourage appropriate maternal gestational weight gain. Blood glucose excursions and hyperglycaemic episodes depend on carbohydrate-intake. Therefore, nutritional counselling should focus on the type, amount, and distribution of carbohydrates in the diet. Further, physical activity has beneficial effects on glucose and insulin levels and it can contribute to a better glycaemic control.
Carbohydrate is the macronutrient that has the greatest impact on blood glucose response. Limited data are available on how carbohydrate distribution throughout the day affects blood glucose in women with gestational diabetes mellitus (GDM). We aimed to assess how a high-carbohydrate morning-intake (HCM) versus a low-carbohydrate-morning-intake (LCM), affect glycemic variability and glucose control. In this randomized crossover study continuous glucose monitoring (CGM) was performed in 12 women with diet treated GDM (75 g, 2-h OGTT ≥ 8.5 mmol/L), who went through 2 × 3 days of HCM and LCM. A within-subject-analysis showed a significantly higher mean amplitude of glucose excursions (MAGE) (0.7 mmol/L, p = 0.004) and coefficient of variation (CV) (5.1%, p = 0.01) when comparing HCM with LCM, whereas a significantly lower mean glucose (MG) (−0.3 mmol/L, p = 0.002) and fasting blood glucose (FBG) were found (−0.4 mmol/L, p = 0.01) on the HCM diet compared to the LCM diet. In addition, insulin resistance, expressed as Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), decreased significantly during HCM. Results indicate that a carbohydrate distribution of 50% in the morning favors lower blood glucose and improvement in insulin sensitivity in women with GDM, but in contrary gives a higher glycemic variability.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes including large for gestational age infants. Individualizing the management of women with GDM based on the likelihood of needing insulin may improve pregnancy outcomes. The aim of this study is to identify characteristics associated with a need for insulin in women with GDM, and to develop a predictive model for insulin requirement. A historical cohort study was conducted among all women with GDM in a singleton pregnancy at Aarhus University Hospital from 2012 to 2017. Variables associated with insulin treatment were identified through multivariable logistic regression. The variables were dichotomized and included in a point scoring system aiming to predict the likelihood of needing insulin. Seven variables were associated with needing insulin: family history of diabetes, current smoker, multiparity, prepregnancy body mass index, gestational age at the oral glucose tolerance test (OGTT), 2-h glucose value at the OGTT and hemoglobin A1c at diagnosis. A risk score was calculated assigning one point to each variable. On ROC analysis, a cut-off value of ≥3 points optimally predicted a requirement for insulin. This prediction model may be clinically useful to predict requirement for insulin treatment after further validation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.