How Can Maternal Lifestyle Interventions Modify the Effects of Gestational Diabetes in the Neonate and the Offspring? A Systematic Review of Meta-Analyses

Mitanchez, Delphine; Ciangura, C.; Jacqueminet, S.

doi:10.3390/nu12020353

Cited by 21 publications

(17 citation statements)

References 49 publications

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“…In predicting GDM, at or above the cutoff point of 0.7, the observed positive predictive value was 93.2%, and the corresponding sensitivity was 99%, representing values in predicting the presence of GDM and identifying high-risk GDM women. Following the identification of highrisk women, proper management, including early diagnosis by OGTT, lifestyle changes, and exercise, may be beneficial in controlling maternal and neonatal complications [31], although additional data are needed. A predictive value of 0.7 or higher also had value in predicting the presence of fetal adverse outcomes, including macrosomia, preterm delivery, and low Apgar scores, as well as cesarean delivery and preeclampsia, with a positive predictive value of 79.2% and a specificity of 99%.…”

Section: Comparisons Andmentioning

confidence: 99%

Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study

Xiong

Zhou

et al. 2020

Journal of Diabetes Research

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Background. Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression. Objective. The purpose of this study was to use machine learning methods to predict GDM and compare their performance with that of logistic regressions. Methods. We performed a retrospective, observational study including women who attended their routine first hospital visits during early pregnancy and had Down’s syndrome screening at 16-20 gestational weeks in a tertiary maternity hospital in China from 2013.1.1 to 2017.12.31. A total of 22,242 singleton pregnancies were included, and 3182 (14.31%) women developed GDM. Candidate predictors included maternal demographic characteristics and medical history (maternal factors) and laboratory values at early pregnancy. The models were derived from the first 70% of the data and then validated with the next 30%. Variables were trained in different machine learning models and traditional logistic regression models. Eight common machine learning methods (GDBT, AdaBoost, LGB, Logistic, Vote, XGB, Decision Tree, and Random Forest) and two common regressions (stepwise logistic regression and logistic regression with RCS) were implemented to predict the occurrence of GDM. Models were compared on discrimination and calibration metrics. Results. In the validation dataset, the machine learning and logistic regression models performed moderately (AUC 0.59-0.74). Overall, the GBDT model performed best (AUC 0.74, 95% CI 0.71-0.76) among the machine learning methods, with negligible differences between them. Fasting blood glucose, HbA1c, triglycerides, and BMI strongly contributed to GDM. A cutoff point for the predictive value at 0.3 in the GBDT model had a negative predictive value of 74.1% (95% CI 69.5%-78.2%) and a sensitivity of 90% (95% CI 88.0%-91.7%), and the cutoff point at 0.7 had a positive predictive value of 93.2% (95% CI 88.2%-96.1%) and a specificity of 99% (95% CI 98.2%-99.4%). Conclusion. In this study, we found that several machine learning methods did not outperform logistic regression in predicting GDM. We developed a model with cutoff points for risk stratification of GDM.

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Section: Comparisons Andmentioning

confidence: 99%

Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study

Xiong

Zhou

et al. 2020

Journal of Diabetes Research

View full text Add to dashboard Cite

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“…Maternal lifestyle interventions did not significantly affect birth weight, and had limited impact on neonatal outcomes [22,[25][26][27]. This could be attributable to the specific characteristics of the LI we implemented, which included caloric restriction and several strict follow-ups during pregnancy to monitor adherence to the program.…”

Section: Discussionmentioning

confidence: 95%

The Impact of an Early Lifestyle Intervention on Pregnancy Outcomes in a Cohort of Insulin-Resistant Overweight and Obese Women

et al. 2020

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Obese women are more likely to have decreased insulin sensitivity and are at increased risk for many adverse pregnancy outcomes. An early lifestyle intervention (LI) may have the potential to reduce the impact of insulin resistance (IR) on perinatal outcomes. We report post hoc analysis of an open-label randomized control trial that includes IR women with body-mass index ≥25 randomly assigned to a LI with a customized low glycemic index diet or to standard care (SC) involving generic counseling about healthy diet and physical activity. Women were evaluated at 16, 20, 28, and 36 weeks of gestation, at which times perinatal outcomes were collected and analyzed. An oral-glucose-tolerance test (OGTT) showed that women in the LI group had lower plasma glucose levels at 120 min at 16–18 weeks of gestation, and at 60 and 120 min at 24–28 weeks. More importantly, these women had a lower rate of large-for-gestational-age (LGA) infants (p = 0.04). Interestingly, the caloric restriction and low-glycemic index diet did not increase the rate of small-for-gestational-age (SGA) babies in the LI group. A lifestyle intervention started early in pregnancy on overweight and obese women had the potential to restore adequate glucose tolerance and mitigate the detrimental role of IR on neonatal outcomes, especially on fetal growth.

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“…These findings are similar to those from other recent reviews, including a review conducted for the U.S. PA guidelines, which cited "strong" evidence of an inverse relationship between PA and risk of GDM. 67,68 Overall, current evidence suggests that being physically active before, and in the early stages of pregnancy, is important for the prevention of GDM. 53,59,64,65 In intervention studies, those that commenced early in pregnancy (prior to 20 weeks of gestation) and those that were structured (usually in a group, with an exercise leader, e.g., in a health care setting) were most effective, especially if adherence and compliance were high.…”

Section: Physical Activity/exercise Interventions For the Prevention mentioning

confidence: 99%

The Role of Lifestyle Intervention in the Prevention and Treatment of Gestational Diabetes

et al. 2020

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Obesity during pregnancy is associated with the development of adverse outcomes, including gestational diabetes mellitus (GDM). GDM is highly associated with obesity and independently increases the risk of both complications during pregnancy and future impaired glycemic control and risk factors for cardiovascular disease for both the mother and child. Despite extensive research evaluating the effectiveness of lifestyle interventions incorporating diet and/or exercise, there remains a lack of definitive consensus on their overall efficacy alone or in combination for both the prevention and treatment of GDM. Combination of diet and physical activity/exercise interventions for GDM prevention demonstrates limited success, whereas exercise-only interventions report of risk reductions ranging from 3 to 49%. Similarly, combination therapy of diet and exercise is the first-line treatment of GDM, with positive effects on maternal weight gain and the prevalence of infants born large-for-gestational age. Yet, there is inconclusive evidence on the effects of diet or exercise as standalone therapies for GDM treatment. In clinical care, women with GDM should be treated with a multidisciplinary approach, starting with lifestyle modification and escalating to pharmacotherapy if needed. Several key knowledge gaps remain, including how lifestyle interventions can be optimized during pregnancy, and whether intervention during preconception is effective for preventing the rising prevalence of GDM.

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How Can Maternal Lifestyle Interventions Modify the Effects of Gestational Diabetes in the Neonate and the Offspring? A Systematic Review of Meta-Analyses

Cited by 21 publications

References 49 publications

Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study

Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study

The Impact of an Early Lifestyle Intervention on Pregnancy Outcomes in a Cohort of Insulin-Resistant Overweight and Obese Women

The Role of Lifestyle Intervention in the Prevention and Treatment of Gestational Diabetes

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