Genetic risk score to improve prediction and treatment in gestational diabetes mellitus.
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ABSTRACT:
SUBMITTER: Tian Y
PROVIDER: S-EPMC9627198 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
Genetic risk score to improve prediction and treatment in gestational diabetes mellitus.
Frontiers in endocrinology 20221019
Diabetes mellitus is a chronic disease caused by the interaction of genetics and the environment that can lead to chronic damage to many organ systems. Genome-wide association studies have identified accumulating single-nucleotide polymorphisms related to type 2 diabetes mellitus and gestational diabetes mellitus. Genetic risk score (GRS) has been utilized to evaluate the incidence risk to improve prediction and optimize treatments. This article reviews the research progress in the use of the GR ...[more]
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Project description:BackgroundGestational diabetes mellitus (GDM) can lead to adverse maternal and fetal outcomes, and early prevention is particularly important for their health, but there is no widely accepted approach to predict it in the early pregnancy. The aim of the present study is to build and evaluate predictive models for GDM using routine indexes, including maternal clinical characteristics and laboratory biomarkers, before 16 gestational weeks.MethodsA total of 2895 pregnant women were recruited and maternal clinical characteristics and laboratory biomarkers before 16 weeks of gestation were collected from two hospitals. All participants were randomly stratified into the training cohort and the internal validation cohort by the ratio of 7:3. Using multivariable logistic regression analysis, two nomogram models, including a basic model and an extended model, were built. The discrimination, calibration, and clinical validity were used to evaluate the models in the internal validation cohort.ResultsThe area under the receiver operating characteristic curve of the basic and the extended model was 0.736 and 0.756 in the training cohort, and was 0.736 and 0.763 in the validation cohort, respectively. The calibration curve analysis showed that the predicted values of the two models were not significantly different from the actual observations (p = 0.289 and 0.636 in the training cohort, p = 0.684 and 0.635 in the internal validation cohort, respectively). The decision-curve analysis showed a good clinical application value of the models.ConclusionsThe present study built simple and effective models, indicating that routine clinical and laboratory parameters can be used to predict the risk of GDM in the early pregnancy, and providing a novel reference for studying the prediction of GDM.
| S-EPMC9714187 | biostudies-literature
Project description:The aetiological mechanism of gestational diabetes mellitus (GDM) has still not been fully understood. The aim of this study was to explore the associations between functional genetic variants screened from a genome-wide association study (GWAS) and GDM risk among 554 GDM patients and 641 healthy controls in China. Functional analysis of single nucleotide polymorphisms (SNPs) positively associated with GDM was further performed. Univariate regression and multivariate logistic regression analyses were used to screen clinical risk factors, and a predictive nomogram model was established. After adjusting for age and prepregnancy BMI, rs9283638 was significantly associated with GDM susceptibility (P < 0.05). Moreover, an obvious interaction between rs9283638 and clinical variables was detected (Pinteraction < 0.05). Functional analysis confirmed that rs9283638 can regulate not only target gene transcription factor binding, but it also regulates the mRNA levels of SAMD7 (P < 0.05). The nomogram model constructed with the factors of age, FPG, 1hPG, 2hPG, HbA1c, TG and rs9283638 revealed an area under the ROC curve of 0.920 (95% CI 0.902-0.939). Decision curve analysis (DCA) suggested that the model had greater net clinical benefit. Conclusively, genetic variants can alter women's susceptibility to GDM by affecting the transcription of target genes. The predictive nomogram model constructed based on genetic and clinical variables can effectively distinguish individuals with different GDM risk factors.
| S-EPMC11300845 | biostudies-literature
Project description:ObjectiveWe aimed to explore the associations between common genetic risk variants with gestational diabetes mellitus (GDM) risk in Russian women and to assess their utility in the identification of GDM cases.MethodsWe conducted a case-control study including 1,142 pregnant women (688 GDM cases and 454 controls) enrolled at Almazov National Medical Research Centre. The International Association of Diabetes and Pregnancy Study Groups criteria were used to diagnose GDM. A total of 11 single- nucleotide polymorphisms (SNPs), including those in HKDC1 (rs10762264), GCK (rs1799884), MTNR1B (rs10830963 and rs1387153), TCF7L2 (rs7903146 and rs12255372), KCNJ11 (rs5219), IGF2BP2 (rs4402960), IRS1 (rs1801278), FTO (rs9939609), and CDKAL1 (rs7754840) were genotyped using Taqman assays. A logistic regression model was used to calculate odds ratios (ORs) and their confidence intervals (CIs). A simple-count genetic risk score (GRS) was calculated using 6 SNPs. The area under the receiver operating characteristic curve (c-statistic) was calculated for the logistic regression model predicting the risk of GDM using clinical covariates, SNPs that had shown a significant association with GDM in our study, GRS, and their combinations.ResultsTwo variants in MTNR1B (rs1387153 and rs10830963) demonstrated a significant association with an increased risk of GDM. The association remained significant after adjustment for age, pre-gestational BMI, arterial hypertension, GDM in history, impaired glucose tolerance, polycystic ovary syndrome, family history of diabetes, and parity (P = 0.001 and P < 0.001, respectively). After being conditioned by each other, the effect of rs1387153 on GDM predisposition weakened while the effect of rs10830963 remained significant (P = 0.004). The risk of GDM was predicted by clinical variables (c-statistic 0.712, 95 % CI: 0.675 - 0.749), and the accuracy of prediction was modestly improved by adding GRS to the model (0.719, 95 % CI 0.682 - 0.755), and more by adding only rs10830963 (0.729, 95 % CI 0.693 - 0.764).ConclusionAmong 11 SNPs associated with T2D and/or GDM in other populations, we confirmed significant association with GDM for two variants in MTNR1B in Russian women. However, these variants showed limited value in the identification of GDM cases.
| S-EPMC8092356 | biostudies-literature
Project description:Genome-wide association studies (GWASs) showed that two single nucleotide polymorphisms (SNPs) (rs17584499 and rs649891) in the protein tyrosine phosphatase receptor type D (PTPRD) were associated with type 2 diabetes (T2D). We sought to determine the influence of the PTPRD variants on the gestational diabetes mellitus (GDM) risk. In this research, two SNPs in PTPRD reported in T2D GWASs and six PTPRD expression-related SNPs were genotyped in 964 GDM cases and 1,021 controls using the Sequenom platform. Logistic regression analyses in additive models showed consistently significant associations of PTPRD rs10511544 A>C, rs10756026 T>A and rs10809070 C>G with a decreased risk of GDM [adjusted OR (95% CI) = 0.83 (0.72-0.97) for rs10511544; adjusted OR (95% CI) = 0.81 (0.70-0.94) for rs10756026; adjusted OR (95% CI) = 0.78 (0.65-0.92) for rs10809070]. Furthermore, the risk of GDM was significantly decreased with an increasing number of variant alleles of the three SNPs in a dose-dependent manner (Ptrend = 0.008). Moreover, the haplotype containing variant alleles of the three SNPs were significantly associated with a decreased risk of GDM [adjusted OR (95% CI) = 0.77 (0.64-0.92), P = 0.005], when compared with the most frequent haplotype. However, there were no significant associations for the SNPs reported in the T2D GWASs. Altogether, these findings indicate that the variants of rs10511544, rs10756026 and rs10809070 in PTPRD may contribute to a decreased susceptibility to GDM. Further validation in different ethnic backgrounds and biological function analyses are needed.
| S-EPMC5340179 | biostudies-literature
Project description:ObjectiveTo build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors.MethodsTo develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands. The other was from an open-source database nuMoM2b including data of 10,038 nulliparous pregnant women, collected in the USA. Widely used maternal demographic and clinical risk factors were considered for modeling. A GDM prediction model based on elastic net logistic regression was trained from a subset of the MMC data. Internal validation was performed on the remaining MMC data to evaluate the model performance. For external validation, the prediction model was tested on an external test set from the nuMoM2b dataset.ResultsAn area under the receiver-operating-characteristic curve (AUC) of 0.81 was achieved for early prediction of GDM on the MMC test data, comparable to the performance reported in previous studies. While the performance markedly decreased to an AUC of 0.69 when testing the MMC-based model on the external nuMoM2b test data, close to the performance trained and tested on the nuMoM2b dataset only (AUC = 0.70).
| S-EPMC11021008 | biostudies-literature
Project description:AimsThe heterogeneity in Gestational Diabetes Mellitus (GDM) risk factors among different populations impose challenges in developing a generic prediction model. This study evaluates the predictive ability of existing UK NICE guidelines for assessing GDM risk in Singaporean women, and used machine learning to develop a non-invasive predictive model.MethodsData from 909 pregnancies in Singapore's most deeply phenotyped mother-offspring cohort study, Growing Up in Singapore Towards healthy Outcomes (GUSTO), was used for predictive modeling. We used a CatBoost gradient boosting algorithm, and the Shapley feature attribution framework for model building and interpretation of GDM risk attributes.ResultsUK NICE guidelines showed poor predictability in Singaporean women [AUC:0.60 (95% CI 0.51, 0.70)]. The non-invasive predictive model comprising of 4 non-invasive factors: mean arterial blood pressure in first trimester, age, ethnicity and previous history of GDM, greatly outperformed [AUC:0.82 (95% CI 0.71, 0.93)] the UK NICE guidelines.ConclusionsThe UK NICE guidelines may be insufficient to assess GDM risk in Asian women. Our non-invasive predictive model outperforms the current state-of-the-art machine learning models to predict GDM, is easily accessible and can be an effective approach to minimize the economic burden of universal testing & GDM associated healthcare in Asian populations.
| S-EPMC7612635 | biostudies-literature
Project description:BackgroundSeveral studies have examined associations between genetic variants and the risk of gestational diabetes mellitus (GDM). However, inferences from these studies were often hindered by limited statistical power and conflicting results. We aimed to systematically review and quantitatively summarize the association of commonly studied single nucleotide polymorphisms (SNPs) with GDM risk and to identify important gaps that remain for consideration in future studies.MethodsGenetic association studies of GDM published through 1 October 2012 were searched using the HuGE Navigator and PubMed databases. A SNP was included if the SNP-GDM associations were assessed in three or more independent studies. Two reviewers independently evaluated the eligibility for inclusion and extracted the data. The allele-specific odds ratios (ORs) and 95% confidence intervals (CIs) were pooled using random effects models accounting for heterogeneity.ResultsOverall, 29 eligible articles capturing associations of 12 SNPs from 10 genes were included for the systematic review. The minor alleles of rs7903146 (TCF7L2), rs12255372 (TCF7L2), rs1799884 (-30G/A, GCK), rs5219 (E23K, KCNJ11), rs7754840 (CDKAL1), rs4402960 (IGF2BP2), rs10830963 (MTNR1B), rs1387153 (MTNR1B) and rs1801278 (Gly972Arg, IRS1) were significantly associated with a higher risk of GDM. Among them, genetic variants in TCF7L2 showed the strongest association with GDM risk, with ORs (95% CIs) of 1.44 (1.29-1.60, P < 0.001) per T allele of rs7903146 and 1.46 (1.15-1.84, P = 0.002) per T allele of rs12255372.ConclusionsIn this systematic review, we found significant associations of GDM risk with nine SNPs in seven genes, most of which have been related to the regulation of insulin secretion.
| S-EPMC3682671 | biostudies-literature
Project description:Many common genetic polymorphisms are associated with glycemic traits and type 2 diabetes (T2D), but knowledge about genetic determinants of glycemic traits in pregnancy is limited. We tested genetic variants known to be associated with glycemic traits and T2D in the general population for associations with glycemic traits in pregnancy and gestational diabetes mellitus (GDM). Participants in two cohorts (Genetics of Glucose regulation in Gestation and Growth [Gen3G] and Hyperglycemia and Adverse Pregnancy Outcome [HAPO]) underwent oral glucose tolerance testing at 24-32 weeks' gestation. We built genetic risk scores (GRSs) for elevated fasting glucose and insulin, reduced insulin secretion and sensitivity, and T2D, using variants discovered in studies of nonpregnant individuals. We tested for associations between these GRSs, glycemic traits in pregnancy, and GDM. In both cohorts, the fasting glucose GRS was strongly associated with fasting glucose. The insulin secretion and sensitivity GRSs were also significantly associated with these traits in Gen3G, where insulin measurements were available. The fasting insulin GRS was weakly associated with fasting insulin (Gen3G) or C-peptide (HAPO). In HAPO (207 GDM case subjects), all five GRSs (T2D, fasting glucose, fasting insulin, insulin secretion, and insulin sensitivity) were significantly associated with GDM. In Gen3G (43 GDM case subjects), both the T2D and insulin secretion GRSs were associated with GDM; effect sizes for the other GRSs were similar to those in HAPO. Thus, despite the profound changes in glycemic physiology during pregnancy, genetic determinants of fasting glucose, fasting insulin, insulin secretion, and insulin sensitivity discovered outside of pregnancy influence GDM risk.
| S-EPMC6245229 | biostudies-literature
Project description:OBJECTIVES/SPECIFIC AIMS: This study aims to identify genetic biomarkers of GDM and facilitate the understanding of its molecular underpinnings. METHODS/STUDY POPULATION: We identified a cohort of mothers diagnosed with GDM in our longitudinal birth study by mining Electronic Health Records of participants utilizing PheCode map with ICD-9 and ICD-10 codes. We verified each case using ACOG’s GDM diagnosis criteria. RESULTS/ANTICIPATED RESULTS: Whole genome sequencing (WGS) data were available for 111 confirmed cases (out of 205) and 706 controls (out of 1,429) from different ancestries (412 EUR, 256 AMR, 56 EAS, 26 SAS and 18 AFR; 49 OTHER). SAS had the highest incidence of GDM at 38.46% and EUR had the lowest at 6.55%. We performed logistic regression using computed ancestry, age and BMI as covariates to determine if any variants are associated with GDM. The top variant (rs139014401) was found in an intron of DFFB gene, which is p53-bound and regulates DNA fragmentation during apoptosis. We will investigate the robustness of 49 identified variants and will separate the cohort by ancestry to detect population-specific differences in the top loci. DISCUSSION/SIGNIFICANCE OF IMPACT: Identification of molecular biomarkers in GDM across different ancestral backgrounds will address a gap in current GDM research. Findings may enhance screening and enable clinicians to identify those at risk for developing GDM earlier in the pregnancy. Early management of mothers at risk may lead to better health outcomes for mother and baby.
| S-EPMC6808337 | biostudies-literature
Project description:We compared the plasma miRNA expression profiles between healthy and GDM women by microarray analysis.Our study offers new insights into circulating biomarkers of GDM and thus provides a valuable resource for future investigations.
2017-04-22 | GSE98043 | GEO