Association of tribbles homologue 1 gene expression in human umbilical vein endothelial cells with duration of intrauterine exposure to hyperglycaemia.

Maternal gestational diabetes mellitus (GDM) is considered to be an important factor that epigenetically predisposes offspring to metabolic and cardiovascular diseases. However, the mechanisms of how intrauterine hyperglycaemia affects offspring have not been thoroughly studied. The mammalian tribbles homologue 1 (TRIB1) gene is associated with plasma lipid concentrations and coronary artery disease (CAD). Our aim was to study the effect of GDM and its treatment terms on the level of TRIB1 gene expression in human umbilical vein endothelial cells (HUVECs) of newborns from women with and without GDM. The study included 50 women with GDM and 25 women without GDM (control group). Women with GDM were divided into three groups according to their gestational age when the treatment of GDM started: 24-28 weeks (GDM1, N = 16), 29-32 weeks (GDM2, N = 25) and >34 weeks (GDM3, N = 9). The levels of TRIB1 gene expression in GDM3, GDM2, GDM1 and control groups were 2.8 ± 1.1, 4.2 ± 2.4, 6.0 ± 3.4 and 8.1 ± 6.1, respectively (p = 0.001). After comparison in pairs the difference was significant for the following pairs: GDM2-control (p = 0.004), GDM3-control (p = 0.002), GDM1-GDM3 (p = 0.012). Notably, if treatment had been started before the 28th week of gestation, the difference in TRIB1 gene expression in HUVECs was not significant (p = 0.320 for comparison between GDM1 and control groups). Our findings support the hypothesis that TRIB1 gene expression in HUVECs depends on the duration of intrauterine exposure to hyperglycaemia.

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.

Effect of gene-lifestyle interaction on gestational diabetes risk.

We hypothesized that the association of certain lifestyle parameters with gestational diabetes mellitus (GDM) risk would depend on susceptibility loci. In total, 278 Russian women with GDM and 179 controls completed questionnaires about lifestyle habits (food consumption, physical activity and smoking). GDM was diagnosed according to the criteria of the International Association of Diabetes and Pregnancy Study Groups. Maternal blood was sampled for genotyping single-nucleotide polymorphisms (SNPs) in MTNR1B (rs10830963 and rs1387153), GCK (rs1799884), KCNJ11 (rs5219), IGF2BP2 (rs4402960), TCF7L2 (rs7903146 and rs12255372), CDKAL1 (rs7754840), IRS1 (rs1801278) and FTO (rs9939609). Binary logistic regression revealed an interaction effect of sausage intake and the number of risk alleles of two SNPs (rs10830963 in MTNR1B and rs1799884 in GCK) on GDM risk (P < 0.001). Among women without risk alleles of these two SNPs, sausage consumption was positively associated with GDM risk (P trend = 0.045). This difference was not revealed in women carrying 1 or more risk alleles. The risk of GDM increased as the number of risk alles increased in participants with low and moderate sausage consumption (P trend <0.001 and 0.006, respectively), while the risk of GDM in women with high sausage consumption remained relatively high, independent of the number of risk alleles. These findings indicate that the association of sausage consumption with GDM risk can be determined based on the number of risk alleles of rs10830963 in MTNR1B and rs1799884 in GCK.