Vitamin D Status and Gestational Diabetes in Russian Pregnant Women in the Period between 2012 and 2021: A Nested Case-Control Study.

Several meta-analyses found an association between low maternal serum 25-hydroxyvitamin D (25(OH)D) level and gestational diabetes mellitus (GDM). However, some of them reported significant heterogeneity. We examined the association of serum 25(OH)D concentration measured in the first and in the second halves of pregnancy with the development of GDM in Russian women surveyed in the periods of 2012−2014 and 2018−2021. We conducted a case−control study (including 318 pregnant women) nested on two previous studies. In 2012−2014, a total of 214 women (83 GDM and 131 controls) were enrolled before 15 weeks of gestation and maternal serum 25(OH)D concentrations were measured twice: at 8th−14th week of gestation and simultaneously with two-hour 75 g oral glucose tolerance test (OGTT) at 24th−32nd week of gestation. In the period of 2018−2021, 104 women (56 GDM and 48 controls) were included after OGTT and 25(OH)D concentrations were measured at 24th−32nd week of gestation. Median 25(OH)D levels were 20.0 [15.1−25.7] vs. 20.5 [14.5−27.5] ng/mL (p = 0.565) in GDM and control group in the first half of pregnancy and 25.3 [19.8−33.0] vs. 26.7 [20.8−36.8] ng/mL (p = 0.471) in the second half of pregnancy, respectively. The prevalence rates for vitamin D deficiency (25(OH)D levels < 20 ng/mL) were 49.4% and 45.8% (p = 0.608) in the first half of pregnancy and 26.2% vs. 22.1% (p = 0.516) in the second half of pregnancy in women who developed GDM and in women without GDM, respectively. The frequency of vitamin D supplements intake during pregnancy increased in 2018−2021 compared to 2012−2014 (p = 0.001). However, the third trimester 25(OH)D levels and prevalence of vitamin D deficiency (25.5 vs. 23.1, p = 0.744) did not differ in women examined in the periods of 2012−2014 and 2018−2021. To conclude, there was no association between gestational diabetes risk and maternal 25(OH)D measured both in the first and in the second halves of pregnancy. The increased prevalence of vitamin D supplements intake during pregnancy by 2018−2021 did not lead to higher levels of 25(OH)D.

Improving nutrition for the prevention of gestational diabetes: Current status and perspectives.

Gestational diabetes mellitus (GDM) is a common complication of pregnancy and a serious public health problem. It carries significant risks of short-term and long-term adverse health effects for both mothers and their children. Risk factors, especially modifiable risk factors, must be considered to prevent GDM and its consequences. Observational studies have identified several nutritional and lifestyle factors associated with the risk of GDM. The results of intervention studies examining the effects of diet and lifestyle on the prevention of GDM are contradictory. Differences in the study populations, types and intensity of intervention, time frame of the intervention, and diagnostic criteria for GDM may explain the heterogeneity in the results of intervention studies. This review provides an overview of new diets and other factors that may help prevent GDM. The main results of epidemiological studies assessing the risk factors for GDM, as well as the results and methodological problems of intervention studies on the prevention of GDM and their meta-analyses, are discussed. In addition, the evidence that gene and lifestyle interactions influence the development of GDM, as well as prospects for increasing the effectiveness of interventions designed to prevent GDM, including new data on the possible uses of personalized diet therapy, are highlighted.

The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients With Gestational Diabetes.

The incorporation of glycemic index (GI) and glycemic load (GL) is a promising way to improve the accuracy of postprandial glycemic response (PPGR) prediction for personalized treatment of gestational diabetes (GDM). Our aim was to assess the prediction accuracy for PPGR prediction models with and without GI data in women with GDM and healthy pregnant women. The GI values were sourced from University of Sydney's database and assigned to a food database used in the mobile app DiaCompanion. Weekly continuous glucose monitoring (CGM) data for 124 pregnant women (90 GDM and 34 control) were analyzed together with records of 1489 food intakes. Pearson correlation (R) was used to quantify the accuracy of predicted PPGRs from the model relative to those obtained from CGM. The final model for incremental area under glucose curve (iAUC120) prediction chosen by stepwise multiple linear regression had an R of 0.705 when GI/GL was included among input variables and an R of 0.700 when GI/GL was not included. In linear regression with coefficients acquired using regularization methods, which was tested on the data of new patients, R was 0.584 for both models (with and without inclusion of GI/GL). In conclusion, the incorporation of GI and GL only slightly improved the accuracy of PPGR prediction models when used in remote monitoring.

Development and Evaluation of a Mobile Personalized Blood Glucose Prediction System for Patients With Gestational Diabetes Mellitus.

BACKGROUND: Personalized blood glucose (BG) prediction for diabetes patients is an important goal that is pursued by many researchers worldwide. Despite many proposals, only a few projects are dedicated to the development of complete recommender system infrastructures that incorporate BG prediction algorithms for diabetes patients. The development and implementation of such a system aided by mobile technology is of particular interest to patients with gestational diabetes mellitus (GDM), especially considering the significant importance of quickly achieving adequate BG control for these patients in a short period (ie, during pregnancy) and a typically higher acceptance rate for mobile health (mHealth) solutions for short- to midterm usage. OBJECTIVE: This study was conducted with the objective of developing infrastructure comprising data processing algorithms, BG prediction models, and an appropriate mobile app for patients' electronic record management to guide BG prediction-based personalized recommendations for patients with GDM. METHODS: A mobile app for electronic diary management was developed along with data exchange and continuous BG signal processing software. Both components were coupled to obtain the necessary data for use in the personalized BG prediction system. Necessary data on meals, BG measurements, and other events were collected via the implemented mobile app and continuous glucose monitoring (CGM) system processing software. These data were used to tune and evaluate the BG prediction model, which included an algorithm for dynamic coefficients tuning. In the clinical study, 62 participants (GDM: n=49; control: n=13) took part in a 1-week monitoring trial during which they used the mobile app to track their meals and self-measurements of BG and CGM system for continuous BG monitoring. The data on 909 food intakes and corresponding postprandial BG curves as well as the set of patients' characteristics (eg, glycated hemoglobin, body mass index [BMI], age, and lifestyle parameters) were selected as inputs for the BG prediction models. RESULTS: The prediction results by the models for BG levels 1 hour after food intake were root mean square error=0.87 mmol/L, mean absolute error=0.69 mmol/L, and mean absolute percentage error=12.8%, which correspond to an adequate prediction accuracy for BG control decisions. CONCLUSIONS: The mobile app for the collection and processing of relevant data, appropriate software for CGM system signals processing, and BG prediction models were developed for a recommender system. The developed system may help improve BG control in patients with GDM; this will be the subject of evaluation in a subsequent study.