What is the impact of gestational diabetes mellitus on frequency of structural chromosome aberrations in pregnant women and their offspring?

ADIPOQ gene (T45G and G276T) single nucleotide polymorphisms and their association with gestational diabetes mellitus in a Filipino population

Several studies have associated the presence of ADIPOQ gene polymorphisms with insulin resistance, adiponectin levels, and metabolic diseases such as diabetes, although with varying degrees of correlation depending on ethnicity. Here we aim to identify individual's susceptibility to gestational diabetes mellitus (GDM) in the presence of T45G and G276T single nucleotide polymorphisms (SNPs) within the ADIPOQ gene among Filipino pregnant women. A total of 285 pregnant women (95 GDM cases and 190 controls) were included in this study. Two ADIPOQ gene polymorphisms were genotyped using TaqMan assay. Results of SNP genotyping showed no significant differences in the frequencies of TT, TG and GG genotypes of T45G SNP between the GDM and control group [p = 1.0000, 0.6179, 0.5797; OR (95%CI) = 1.030 (0.582-1.874), 1.135 (0.683-1.828), 0.833 (0.481-1.420)]. Similarly, the frequencies of GG, GT, and TT genotypes of G276T SNP were comparable in both groups [p = 0.8002, 1.0000, 0.3466; OR (95%CI) = 1.090 (0.654-1.785), 1.022 (0.616-1.665), 0.433 (0.092-1.698)]. Moreover, although adiponectin levels were significantly decreased in GDM group (p = 0.0196) and have shown substantial negative correlations with FBS, 1-hour OGTT, 2-hour OGTT, and HOMA-IR (p < 0.05), they were not significantly different according to genotypes of T45G and G276T polymorphisms both in GDM and control group. Our results suggest that neither of the two ADIPOQ gene polymorphisms influence adiponectin levels and development of GDM in a Filipino population.

Is Gestational Diabetes Mellitus a Risk Factor of Maternal Breast Cancer? A Systematic Review of the Literature

The association between gestational diabetes mellitus (GDM) and breast cancer (BC) risk is complex. We aimed to examine this association in a systematic review of the literature. This review was done using the PubMed/Medline and Web of Science databases, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Newcastle-Ottawa Scale was used for the assessment of bias and quality of studies. Only English-language articles published before 1 June 2021, were included. Fourteen studies were included in this systematic review. Among them, eight did not find statistically significant results. Three studies showed a statistically significant increased risk of BC after GDM, and they explained this potential increased risk by hyperinsulinemia, hyperglycemia, and low-grade inflammation. However, three studies showed a statistically significant decreased risk of BC after GDM, suggesting a possible protective effect of hormonal changes induced by GDM during pregnancy. These controversial results should be interpreted with caution due to both quantitative and qualitative methodological shortcomings. Further investigations are thus needed in order to gain a better understanding of the associations between GDM and BC, and their underlying mechanisms.

Algorithm for diabetes risk evaluation from past gestational diabetes data

Gestational diabetes mellitus (GDM) is defined as glucose intolerance that is diagnosed in pregnancy period, leading to possible complications for both mother and fetus during pregnancy. The aim of this study was to build an objective method to evaluate diabetes mellitus (DM) risk from past GDM data recorded 15 years ago and find a short list of most informative indicators. The dataset consists of demographic, lifestyle, clinical, genetic and pregnancy related information recorded 15 years ago. Due to the large time gap data are limited and have missing values (MVs). Follow-up tests were performed to see if DM or impaired metabolism has developed after pregnancy with previously diagnosed GDM. The research steps involve pre-processing data to evaluate MVs, finding most informative attributes and testing standard classification algorithms to combine in to most effective voting meta-algorithm. Initially the attributes and records with large number of MVs were rejected. A small percentage (2.04%) was imputed using regression based methods. The data set was prepared for two scenarios: classification in two classes (1-healthy; 2-impaired metabolism including DM) and three classes (1-healthy; 2-impaired metabolism; 3-DM). Voting meta-algorithm combining best algorithms of 21 from five different groups including Bayesian, regression, lazy, rule, and decision trees makes classification more objective and not depending on preferences. Relative frequency of occurrence (RFO) analysis of attributes combined with voting meta-algorithm helped finding optimal amount of attributes giving best possible classification result. The algorithm applied to two class data set with 12 selected attributes produced accuracy of 75.85 and AUC = 0.82 with standard error of 0.11. Similarly for three class dataset the 9 attributes were selected allowing to reach classification accuracy 63.77 and AUC = 0.76 with standard error of 0.1. Meta-algorithm based classification of limited anamnestic GDM related data for DM prediction is proving to be effective. Testing multiple algorithms and performing RFO analysis appears to be natural and objective way of selecting most informative attributes and evaluating their importance.