Obesity, parental history (PH) of type 2 diabetes (T2D), and genes play an important role in T2D development. However, the influence of each factor on T2D variability is unclear. This study aimed to investigate the influence of obesity (body mass index [BMI], waist/hip ratio), PH, and 16 single-nucleotide polymorphisms (SNPs) associated with T2D on T2D variability in Mexico, comparing 1234 non-diabetic controls and 1219 diabetic patients. To replicate the data, a case-control (n = 2904) and a cross-sectional (n = 1901) study were also included. In a multivariate logistic regression model, all factors accounted for only 27.3% of T2D variability: SNPs (8.4%); PH (11.8%) and obesity (7.1%). These factors contributed more in men (33.2%) than in women (25%), specifically when the disease was diagnosed before the age of 46 (46.7% vs. 30%). Genes played a substantially more important role in men than in women (14.9% vs. 5.5%), while obesity and PH played a similar role in both genders. Genes and PH appeared to play a greater role than obesity in T2D. However, obesity contribution was calculated at the time of recruitment and may be underestimated in patients because the BMI decreased linearly with the number of years with the disease. The data suggest that sexual hormones may play important roles in genes that are associated with T2D.

HypothesisGestational diabetes mellitus (GDM) entails a complex underlying pathogenesis, with a specific genetic background and the effect of environmental factors. This study examines the link between a set of single nucleotide polymorphisms (SNPs) associated with diabetes and the development of GDM in pregnant women with different ethnicities, and evaluates its potential modulation with a clinical intervention based on a Mediterranean diet.Methods2418 women from our hospital-based cohort of pregnant women screened for GDM from January 2015 to November 2017 (the San Carlos Cohort, randomized controlled trial for the prevention of GDM ISRCTN84389045 and real-world study ISRCTN13389832) were assessed for evaluation. Diagnosis of GDM was made according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. Genotyping was performed by IPLEX MassARRAY PCR using the Agena platform (Agena Bioscience, SanDiego, CA). 110 SNPs were selected for analysis based on selected literature references. Statistical analyses regarding patients’ characteristics were performed in SPSS (Chicago, IL, USA) version 24.0. Genetic association tests were performed using PLINK v.1.9 and 2.0 software. Bioinformatics analysis, with mapping of SNPs was performed using STRING, version 11.5.ResultsQuality controls retrieved a total 98 SNPs and 1573 samples, 272 (17.3%) with GDM and 1301 (82.7%) without GDM. 1104 (70.2%) were Caucasian (CAU) and 469 (29.8%) Hispanic (HIS). 415 (26.4%) were from the control group (CG), 418 (26.6%) from the nutritional intervention group (IG) and 740 (47.0%) from the real-world group (RW). 40 SNPs (40.8%) presented some kind of significant association with GDM in at least one of the genetic tests considered. The nutritional intervention presented a significant association with GDM, regardless of the variant considered. In CAU, variants rs4402960, rs7651090, IGF2BP2; rs1387153, rs10830963, MTNR1B; rs17676067, GLP2R; rs1371614, DPYSL5; rs5215, KCNJ1; and rs2293941, PDX1 were significantly associated with an increased risk of GDM, whilst rs780094, GCKR; rs7607980, COBLL1; rs3746750, SLC17A9; rs6048205, FOXA2; rs7041847, rs7034200, rs10814916, GLIS3; rs3783347, WARS; and rs1805087, MTR, were significantly associated with a decreased risk of GDM, In HIS, variants significantly associated with increased risk of GDM were rs9368222, CDKAL1; rs2302593, GIPR; rs10885122, ADRA2A; rs1387153, MTNR1B; rs737288, BACE2; rs1371614, DPYSL5; and rs2293941, PDX1, whilst rs340874, PROX1; rs2943634, IRS1; rs7041847, GLIS3; rs780094, GCKR; rs563694, G6PC2; and rs11605924, CRY2 were significantly associated with decreased risk for GDM.ConclusionsWe identify a core set of SNPs in their association with diabetes and GDM in a large cohort of patients from two main ethnicities from a single center. Identification of these genetic variants, even in the setting of a nutritional intervention, deems useful to design preventive and therapeutic strategies.

Background Interactions between polymorphisms of the melatonin receptor 1B gene (MTNR1B) and lifestyle intervention for gestational diabetes have been described. Whether these are specific for physical activity or healthy eating intervention is unknown. Objectives The aim was to assess the interaction between MTNR1B rs10830962 and rs10830963 polymorphisms and lifestyle interventions during pregnancy. Methods Women with a BMI of ≥29 kg/m2 (n = 436) received counseling on healthy eating (HE), physical activity (PA) or both. The control group received usual care. This secondary analysis had a factorial design with comparison of HE versus no HE and PA versus no PA. Maternal outcomes at 24–28 weeks were gestational weight gain (GWG), maternal fasting glucose, insulin, insulin resistance (HOMA-IR), disposition index, and development of GDM. Neonatal outcomes were cord blood leptin and C-peptide and estimated neonatal fat percentage. Interaction between receiving either HE or PA intervention and genotypes of both rs10830962 and rs10830963 was assessed using multilevel regression analysis. Results GDM risk was increased in women homozygous for the G allele of rs10830962 or rs10830963 (OR 2.60 [95% CI 1.34, 5.06] and 2.83 [1.24, 6.47], respectively). Significant interactions between rs10830962 and interventions were found: In women homozygous for the G allele, but not in the other genotypes, the PA intervention reduced maternal fasting insulin (beta –0.16 [95%CI –0.33, 0.02], P = 0.08) and HOMA-IR (–0.17 [–0.35, 0.01], P = 0.06), and reduced cord blood leptin (–0.84 [–1.42, –0.25], P = 0.01) and C-peptide (–0.62 [–1.07, –0.17], P = 0.01) In heterozygous women, HE intervention had no effect, whereas in women homozygous for the C allele, HE intervention reduced GWG (−1.6 kg [−2.4, −0.8]). No interactions were found. Discussion In women homozygous for the risk allele of MTNR1B rs10830962, GDM risk was increased and PA intervention might be more beneficial than HE intervention for reducing maternal insulin resistance, cord blood C-peptide and cord blood leptin.

IntroductionGestational diabetes mellitus (GDM) is underdiagnosed in Mexico. Early GDM risk stratification through prediction modeling is expected to improve preventative care. We developed a GDM risk assessment model that integrates both genetic and clinical variables.Research design and methodsData from pregnant Mexican women enrolled in the ‘Cuido mi Embarazo’ (CME) cohort were used for development (107 cases, 469 controls) and data from the ‘Mónica Pretelini Sáenz’ Maternal Perinatal Hospital (HMPMPS) cohort were used for external validation (32 cases, 199 controls). A 2-hour oral glucose tolerance test (OGTT) with 75 g glucose performed at 24–28 gestational weeks was used to diagnose GDM. A total of 114 single-nucleotide polymorphisms (SNPs) with reported predictive power were selected for evaluation. Blood samples collected during the OGTT were used for SNP analysis. The CME cohort was randomly divided into training (70% of the cohort) and testing datasets (30% of the cohort). The training dataset was divided into 10 groups, 9 to build the predictive model and 1 for validation. The model was further validated using the testing dataset and the HMPMPS cohort.ResultsNineteen attributes (14 SNPs and 5 clinical variables) were significantly associated with the outcome; 11 SNPs and 4 clinical variables were included in the GDM prediction regression model and applied to the training dataset. The algorithm was highly predictive, with an area under the curve (AUC) of 0.7507, 79% sensitivity, and 71% specificity and adequately powered to discriminate between cases and controls. On further validation, the training dataset and HMPMPS cohort had AUCs of 0.8256 and 0.8001, respectively.ConclusionsWe developed a predictive model using both genetic and clinical factors to identify Mexican women at risk of developing GDM. These findings may contribute to a greater understanding of metabolic functions that underlie elevated GDM risk and support personalized patient recommendations.

Polymorphisms of genes involved in the metabolism and transport of folate and cobalamin could play relevant roles in pregnancy outcomes. This study assessed the prevalence of genetic polymorphisms of folate and cobalamin metabolism-related genes such as MTHFR, MTR, CUBN, and SLC19A1 in pregnant women of a homogeneous Spanish population according to conception, pregnancy, delivery, and newborns complications. This study was conducted on 149 nulliparous women with singleton pregnancies. Sociodemographic and obstetrics variables were recorded, and all patients were genotyped in the MTHFR, MTR, CUBN, and SLC10A1 polymorphisms. The distribution of genotypes detected in this cohort was similar to the population distribution reported in Europe, highlighting that more than 50% of women were carriers of risk alleles of the studied genes. In women with the MTHFR risk allele, there was a statistically significant higher frequency of assisted fertilisation and a higher frequency of preeclampsia and preterm birth. Moreover, CUBN (rs1801222) polymorphism carriers showed a statistically significantly lower frequency of complications during delivery. In conclusion, the prevalence of genetic variants related to folic acid and vitamin B12 metabolic genes in pregnant women is related to mother and neonatal outcomes. Knowing the prevalence of these polymorphisms may lead to a personalised prescription of vitamin intake.