A genetic risk score that includes common type 2 diabetes risk variants is associated with gestational diabetes

Understanding the potential contribution of polygenic risk scores to the prediction of gestational and type 2 diabetes in women from British Pakistani and Bangladeshi groups: a cohort study in Genes and Health

Background

British Pakistani and Bangladeshi (BPB) women have disproportionately high rates of gestational diabetes mellitus (GDM), with prevalence estimates up to three times higher than in the general population. They are also at increased risk of progressing to type 2 diabetes, leading to significant health complications. Despite this, predictive models tailored to this high-risk, yet understudied group are lacking.

Objective

To investigate whether combining genetic and traditional clinical data improves risk prediction of GDM and progression to type 2 diabetes among BPB women. We hypothesized that incorporating polygenic risk scores (PRS) would enhance the predictive accuracy of existing models.

Study Design

An observational cohort study utilizing the Genes & Health dataset, which includes comprehensive electronic health records. Women who gave birth between 2000 and 2023, both with and without a history of GDM, were included. Controls were defined as women without a GDM diagnosis during this period but who had a birth record. A total of 117 type 2 diabetes or GDM PRS were tested to determine the optimal PRS based on predictive performance metrics. The best-performing PRS was integrated with clinical variables for statistical analyses, including descriptive statistics, chi-square tests, logistic regression, and receiver operating characteristic curve analysis.

Results

Of 13,489 women with birth records, 10,931 were included in the analysis, with 29.3% developing GDM. Women with GDM were older (mean age 31.7 years, P<.001) and had a higher BMI (mean 28.4 kg/m2, P<.001) compared to controls. The optimal PRS demonstrated a strong association with GDM risk; women in the highest PRS decile had significantly increased odds of developing GDM (OR 5.66, 95% CI [4.59, 7.01], P=3.62×10-58). Furthermore, the risk of converting from GDM to type 2 diabetes was 30% in the highest PRS decile, compared to 19% among all GDM cases and 11% in the lowest decile. Incorporating genetic risk factors with clinical data improved the C-statistic for predicting type 2 diabetes following GDM from 0.62 to 0.67 (P=4.58×10-6), indicating better model discrimination.

Conclusion

The integration of genetic assessment with traditional clinical factors significantly enhances risk prediction for BPB women at high risk of developing type 2 diabetes after GDM. These findings support the implementation of targeted interventions and personalized monitoring strategies in this high-risk population. Future research should focus on validating these predictive models in external cohorts and exploring their integration into clinical practice to improve health outcomes.

Genetics and epigenetics in gestational diabetes contributing to type 2 diabetes

Gestational diabetes mellitus (GDM) is a common pregnancy complication and a risk factor for the subsequent development of type 2 diabetes (T2D) in mothers and of several metabolic diseases in offspring. However, the molecular underpinnings of these risks are not well understood. Genome-wide association studies (GWAS) and epigenetic studies may provide complementary insights into the causal relationships between GDM exposure and maternal/offspring metabolic outcomes. In this review we discuss the potential pathophysiological roles of specific genetic variants and commonly reported differentially methylated loci in GDM development, and their link to the progression to T2D in both the mother and the offspring in later life, pointing to the potential for tailored interventional strategies based on these genetic and epigenetic mechanisms.

Unveiling Gestational Diabetes: An Overview of Pathophysiology and Management

Gestational diabetes mellitus (GDM) is characterized by an inadequate pancreatic β-cell response to pregnancy-induced insulin resistance, resulting in hyperglycemia. The pathophysiology involves reduced incretin hormone secretion and signaling, specifically decreased glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), impairing insulinotropic effects. Pro-inflammatory cytokines, including tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), impair insulin receptor substrate-1 (IRS-1) phosphorylation, disrupting insulin-mediated glucose uptake. β-cell dysfunction in GDM is associated with decreased pancreatic duodenal homeobox 1 (PDX1) expression, increased endoplasmic reticulum stress markers (CHOP, GRP78), and mitochondrial dysfunction leading to impaired ATP production and reduced glucose-stimulated insulin secretion. Excessive gestational weight gain exacerbates insulin resistance through hyperleptinemia, which downregulates insulin receptor expression via JAK/STAT signaling. Additionally, hypoadiponectinemia decreases AMP-activated protein kinase (AMPK) activation in skeletal muscle, impairing GLUT4 translocation. Placental hormones such as human placental lactogen (hPL) induce lipolysis, increasing circulating free fatty acids which activate protein kinase C, inhibiting insulin signaling. Placental 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) overactivity elevates cortisol levels, which activate glucocorticoid receptors to further reduce insulin sensitivity. GDM diagnostic thresholds (≥92 mg/dL fasting, ≥153 mg/dL post-load) are lower than type 2 diabetes to prevent fetal hyperinsulinemia and macrosomia. Management strategies focus on lifestyle modifications, including dietary carbohydrate restriction and exercise. Pharmacological interventions, such as insulin or metformin, aim to restore AMPK signaling and reduce hepatic glucose output. Emerging therapies, such as glucagon-like peptide-1 receptor (GLP-1R) agonists, show potential in improving glycemic control and reducing inflammation. A mechanistic understanding of GDM pathophysiology is essential for developing targeted therapeutic strategies to prevent both adverse pregnancy outcomes and the progression to overt diabetes in affected women.

Association of gestational and childhood circulating C-peptide concentrations in the hyperglycemia and adverse pregnancy outcomes follow-up study

AIMS

This study examined the association of gravida C-peptide with progeny islet function and insulin sensitivity in the Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (HAPO FUS).

METHODS

Pregnancy 3rd trimester oral glucose tolerance test (OGTT), cord blood, and offspring OGTT glucose, C-peptide and insulin at age 10-14 years were analyzed for 4,121 mother-child dyads. Gravida fasting and 1-hour C-peptide concentration correlations with cord blood and childhood C-peptide, insulin, insulinogenic index and insulin sensitivity, and insulin resistance [HOMA-IR]), were assessed by multiple linear regression. Maternal covariates included age, gestational age, BMI and glucose at OGTT; child covariates included age, sex, pubertal stage, BMI z score and glucose.

RESULTS

Gravida fasting and 1-hour OGTT C-peptide was positively correlated with cord blood C-peptide, offspring OGTT C-peptide and insulin concentrations at fasting, 30 min, 1-hour and 2-hour at 10-14 years of age. Maternal fasting and 1-hour C-peptide concentrations were positively correlated with the insulinogenic index and HOMA-IR but inversely correlated with insulin sensitivity. Maternal C-peptide explained more variance than maternal glucose concentrations (3.0-17.9 % vs 0.2-3.5 %).

CONCLUSIONS/INTERPRETATION

The correlation between gravida and offspring C-peptide suggests that without crossing the placenta, insulin may influence the offspring pancreatic beta-cell development and insulin sensitivity.

Reduced circulating sphingolipids and CERS2 activity are linked to T2D risk and impaired insulin secretion

Gestational diabetes mellitus (GDM), a transient form of diabetes that resolves postpartum, is a major risk factor for type 2 diabetes (T2D) in women. While the progression from GDM to T2D is not fully understood, it involves both genetic and environmental components. By integrating clinical, metabolomic, and genome-wide association study (GWAS) data, we identified associations between decreased sphingolipid biosynthesis and future T2D, in part through the rs267738 allele of the CERS2 gene in Hispanic women shortly after a GDM pregnancy. To understand the impact of the CERS2 gene and risk allele on glucose regulation, we examined whole-body Cers2 knockout and rs267738 knock-in mice. Both models exhibited glucose intolerance and impaired insulin secretion in vivo. Islets isolated from these models also demonstrated reduced β cell function, as shown by decreased insulin secretion ex vivo. Overall, reduced circulating sphingolipids may indicate a high risk of GDM-to-T2D progression and reflect deficits in CERS2 activity that negatively affect glucose homeostasis and β cell function.

Identification and Potential Clinical Utility of Common Genetic Variants in Gestational Diabetes among Chinese Pregnant Women

BACKGRUOUND

The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications.

METHODS

We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants.

RESULTS

Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI], 1.38 to 1.96), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals.

CONCLUSION

Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

A Simplified Screening Model to Predict the Risk of Gestational Diabetes Mellitus in Caucasian and Latin American Pregnant Women

The pathophysiology of gestational diabetes mellitus (GDM) comprises clinical and genetic factors. In fact, GDM is associated with several single nucleotide polymorphisms (SNPs). This study aimed to build a prediction model of GDM combining clinical and genetic risk factors. A total of 1588 pregnant women from the San Carlos Cohort participated in the present study, including 1069 (67.3%) Caucasian (CAU) and 519 (32.7%) Latin American (LAT) individuals, and 255 (16.1%) had GDM. The incidence of GDM was similar in both groups (16.1% CAU and 16.0% LAT). Genotyping was performed via IPLEX Mass ARRAY PCR, selecting 110 SNPs based on literature references. SNPs showing the strongest likelihood of developing GDM were rs10830963, rs7651090, and rs1371614 in CAU and rs1387153 and rs9368222 in LAT. Clinical variables, including age, pre-pregnancy body mass index, and fasting plasma glucose (FPG) at 12 gestational weeks, predicted the risk of GDM (AUC 0.648, 95% CI 0.601-0.695 in CAU; AUC 0.688, 95% CI 0.628-9.748 in LAT), and adding SNPs modestly improved prediction (AUC 0.722, 95%CI 0.680-0.764 in CAU; AUC 0.769, 95% CI 0.711-0.826 in LAT). In conclusion, adding genetic variants enhanced the prediction model of GDM risk in CAU and LAT pregnant women.

Exploring the immunological landscape of osteomyelitis through mendelian randomization analysis

Background

Osteomyelitis is a severe bone marrow infection, whose pathogenesis is not yet fully understood. This study aims to explore the causal relationship between immune cell characteristics and osteomyelitis, hoping to provide new insights for the prevention and treatment of osteomyelitis.

Methods

Based on two independent samples, this study employed a two-sample Mendelian randomization (MR) analysis to assess the causal relationship between 731 immune cell characteristics (divided into seven groups) and osteomyelitis. Genetic variants were used as proxies for risk factors to ensure that the selected instrumental variables meet the three key assumptions of MR analysis. Genome-Wide Association Studies (GWAS) data for immune characteristics were obtained from the public GWAS catalog, while data for osteomyelitis was sourced from the FinnGen.

Results

At a significance level of 0.05, 21 immune phenotypes were identified as having a causal relationship with osteomyelitis development. In the B cell group, phenotypes such as Memory B cell % B cell (percentage of memory B cells within the total B cell population, % finger cell ratio), CD20- %B cell (percentage of B cells that do not express the CD20 marker on their surface), and Memory B cell % lymphocyte showed a positive causal relationship with osteomyelitis, while Naive-mature B cell %B cell and IgD-CD38-absolute cell counts (AC) phenotypes showed a negative causal relationship. In addition, specific immune phenotypes in the conventional dendritic cells (cDCs) group, Myeloid cell group, TBNK (T cells, B cells, natural killer cells) cell group, T cell maturation stage, and Treg cell group also showed significant associations with osteomyelitis. Through reverse MR analysis, it was found that osteomyelitis had no significant causal impact on these immune phenotypes, suggesting that the occurrence of osteomyelitis may not affect these immune cell phenotypes.

Conclusion

To our knowledge, this is the first study to shed light on the causal relationship between specific immune cell characteristics and the development of osteomyelitis, thereby providing a new perspective to understand the immune mechanism of osteomyelitis. These findings are significant for formulating targeted prevention and treatment strategies, and hold promise to improve the treatment outcomes for patients with osteomyelitis.

Distinct and shared genetic architectures of gestational diabetes mellitus and type 2 diabetes

Gestational diabetes mellitus (GDM) is a common metabolic disorder affecting more than 16 million pregnancies annually worldwide1,2. GDM is related to an increased lifetime risk of type 2 diabetes (T2D)1-3, with over a third of women developing T2D within 15 years of their GDM diagnosis. The diseases are hypothesized to share a genetic predisposition1-7, but few studies have sought to uncover the genetic underpinnings of GDM. Most studies have evaluated the impact of T2D loci only8-10, and the three prior genome-wide association studies of GDM11-13 have identified only five loci, limiting the power to assess to what extent variants or biological pathways are specific to GDM. We conducted the largest genome-wide association study of GDM to date in 12,332 cases and 131,109 parous female controls in the FinnGen study and identified 13 GDM-associated loci, including nine new loci. Genetic features distinct from T2D were identified both at the locus and genomic scale. Our results suggest that the genetics of GDM risk falls into the following two distinct categories: one part conventional T2D polygenic risk and one part predominantly influencing mechanisms disrupted in pregnancy. Loci with GDM-predominant effects map to genes related to islet cells, central glucose homeostasis, steroidogenesis and placental expression.

Associations between a polygenic risk score and the risk of gestational diabetes mellitus in a Chinese population: a case-control study

Our objective was to construct a polygenic risk score (PRS) and assess its utility and effectiveness in predicting the risk of gestational diabetes mellitus (GDM) in a Chinese population. We performed a case-control study involving 638 patients with GDM and 1,062 healthy controls. Genotyping was conducted utilizing a genome-wide association study (GWAS), and a PRS was constructed. We identified 12 susceptibility loci that exhibited significant associations with the risk of GDM at a p-value threshold of ≤5.0 × 10-8, of which four loci were newly discovered. A higher PRS was associated with an increased risk of GDM (OR: 1.44; 95% CI: 1.03, 2.01 for the highest quartile compared to the lowest quartile). The PRS demonstrated a clear linear relationship with the fasting plasma glucose (FPG), 1-hour postprandial glucose (1hPG), and 2-hour postprandial glucose (2hPG) levels. The maximally adjusted β coefficients and their corresponding 95% CIs were 0.181 (0.041, 0.320) for FPG, 0.225 (0.103, 0.346) for 1hPG, and 0.172 (0.036, 0.307) for 2hPG. Among the genetic variants examined, TCF7L2 rs7903146 displayed the strongest association with GDM risk (logOR = 0.18, p = 2.37 × 10-19), followed by ADAMTSL1 rs10963767 (logOR = 0.14, p = 3.58 × 10-15). The areas under the curve (AUCs) was significantly increased from 0.703 (0.678, 0.728) in the traditional risk factor model to 0.765 (0.741, 0.788) by including PRS. These findings indicate that pregnant women with a higher PRS could potentially derive considerable advantages from the implementation of a feasible PRS-based GDM screening program aimed at delivering precision prevention strategies within Chinese populations.

Genetics of diabetes

Diabetes mellitus is a complicated disease characterized by a complex interplay of genetic, epigenetic, and environmental variables. It is one of the world's fastest-growing diseases, with 783 million adults expected to be affected by 2045. Devastating macrovascular consequences (cerebrovascular disease, cardiovascular disease, and peripheral vascular disease) and microvascular complications (like retinopathy, nephropathy, and neuropathy) increase mortality, blindness, kidney failure, and overall quality of life in individuals with diabetes. Clinical risk factors and glycemic management alone cannot predict the development of vascular problems; multiple genetic investigations have revealed a clear hereditary component to both diabetes and its related complications. In the twenty-first century, technological advancements (genome-wide association studies, next-generation sequencing, and exome-sequencing) have led to the identification of genetic variants associated with diabetes, however, these variants can only explain a small proportion of the total heritability of the condition. In this review, we address some of the likely explanations for this "missing heritability", for diabetes such as the significance of uncommon variants, gene-environment interactions, and epigenetics. Current discoveries clinical value, management of diabetes, and future research directions are also discussed.

Genetics and Epigenetics: Implications for the Life Course of Gestational Diabetes

Gestational diabetes (GDM) is one of the most common complications of pregnancy, affecting as many as one in six pregnancies. It is associated with both short- and long-term adverse outcomes for the mother and fetus and has important implications for the life course of affected women. Advances in genetics and epigenetics have not only provided new insight into the pathophysiology of GDM but have also provided new approaches to identify women at high risk for progression to postpartum cardiometabolic disease. GDM and type 2 diabetes share similarities in their pathophysiology, suggesting that they also share similarities in their genetic architecture. Candidate gene and genome-wide association studies have identified susceptibility genes that are shared between GDM and type 2 diabetes. Despite these similarities, a much greater effect size for MTNR1B in GDM compared to type 2 diabetes and association of HKDC1, which encodes a hexokinase, with GDM but not type 2 diabetes suggest some differences in the genetic architecture of GDM. Genetic risk scores have shown some efficacy in identifying women with a history of GDM who will progress to type 2 diabetes. The association of epigenetic changes, including DNA methylation and circulating microRNAs, with GDM has also been examined. Targeted and epigenome-wide approaches have been used to identify DNA methylation in circulating blood cells collected during early, mid-, and late pregnancy that is associated with GDM. DNA methylation in early pregnancy had some ability to identify women who progressed to GDM, while DNA methylation in blood collected at 26-30 weeks gestation improved upon the ability of clinical factors alone to identify women at risk for progression to abnormal glucose tolerance post-partum. Finally, circulating microRNAs and long non-coding RNAs that are present in early or mid-pregnancy and associated with GDM have been identified. MicroRNAs have also proven efficacious in predicting both the development of GDM as well as its long-term cardiometabolic complications. Studies performed to date have demonstrated the potential for genetic and epigenetic technologies to impact clinical care, although much remains to be done.

A preliminary report on the association between maternal serum organophosphate ester concentrations and gestational diabetes mellitus

Organophosphate esters (OPEs) are extensively manufactured and used in China. Whether exposure to OPEs during pregnancy increases the risk of gestational diabetes mellitus (GDM) is unknown. Between 2011 and 2012, a case-control study including 130 and 67 women with and without GDM, respectively, was conducted in Hangzhou, China. The levels of 10 OPEs in maternal serum samples at delivery were quantified, and the relationships between the OPE concentrations and GDM risk were investigated. The results show that in all participants, tri-n-butyl phosphate (TNBP, median: 2.02 ng/mL) was the most common OPE present in the serum, followed by tri-phenyl phosphate (TPHP, median: 1.74 ng/mL) and tri-iso-butyl phosphate (median: 1.68 ng/mL). With one-unit elevation in the tris (2-chloroethyl) phosphate, TNBP, TPHP, and tris (2-butoxy ethyl) phosphate (TBOEP) concentrations in maternal serum, 1-h glucose levels increased by 0.19 (95% confidence interval (CI): -0.01, 0.29), 0.11 (95% CI: -0.18, 0.62), 0.29 (95% CI: 0.12, 0.58), and 0.20 units (95% CI: 0.01, 0.44), respectively. In addition, a unit increase in TBOEP levels in maternal serum was associated with an increase of 0.26 units (95% CI: 0.09, 0.61) in 2-h glucose levels. After adjusting for covariate factors, serum TNBP (odds ratio (OR) = 2.07; 95% CI: 1.27, 3.41), TBOEP (OR = 2.63; 95% CI: 1.68, 4.11), and TPHP (OR = 1.03; 95% CI: 1.05, 1.51) concentrations were associated with GDM incidence in pregnant women. Overall, TNBP, TBOEP, and TPHP exposure during pregnancy is associated with GDM risk and increased glucose levels.

Distinct and shared genetic architectures of Gestational diabetes mellitus and Type 2 Diabetes Mellitus

Gestational diabetes mellitus (GDM) affects more than 16 million pregnancies annually worldwide and is related to an increased lifetime risk of Type 2 diabetes (T2D). The diseases are hypothesized to share a genetic predisposition, but there are few GWAS studies of GDM and none of them is sufficiently powered to assess whether any variants or biological pathways are specific to GDM. We conducted the largest genome-wide association study of GDM to date in 12,332 cases and 131,109 parous female controls in the FinnGen Study and identified 13 GDM-associated loci including 8 novel loci. Genetic features distinct from T2D were identified both at the locus and genomic scale. Our results suggest that the genetics of GDM risk falls into two distinct categories - one part conventional T2D polygenic risk and one part predominantly influencing mechanisms disrupted in pregnancy. Loci with GDM-predominant effects map to genes related to islet cells, central glucose homeostasis, steroidogenesis, and placental expression. These results pave the way for an improved biological understanding of GDM pathophysiology and its role in the development and course of T2D.

THADA, SDHAF4, and MACF1 Gene Polymorphisms and Placental Expression in Women with Gestational Diabetes

Gestational diabetes mellitus (GDM) is a metabolic disorder in pregnant women leading to various complications. Consequently, factors predisposing its development are being sought. Previous studies have shown that the pathogenesis of GDM is similar to that of type 2 diabetes, and it is therefore thought that the two diseases may have a common genetic basis. The aim of this study was to examine the associations between thyroid adenoma-associated (THADA) rs7578597 T>C, succinate dehydrogenase complex assembly factor 4 (SDHAF4) rs1048886 A>G, and microtubule-actin crosslinking factor 1 (MACF1) rs2296172 A>G gene polymorphisms and the risk of GDM development as well as selected clinical parameters in women with GDM. We also examined the expression of these genes in the placenta of women with and without GDM in association with clinical parameters. This case-control study included 272 pregnant women with GDM and 348 pregnant women with normal glucose tolerance. There were no statistically significant differences in the distribution of the THADA rs7578597 T>C, SDHAF4 rs1048886 A>G, and MACF1 rs2296172 A>G gene polymorphisms between pregnant control women and women with GDM. The associations between clinical parameters such as body mass before pregnancy, body mass at birth, body mass increase during pregnancy, BMI before pregnancy, BMI at birth, BMI increase during pregnancy, glycated hemoglobin (HbA1c), daily insulin requirement, childbirth time, and newborn body mass and APGAR score, and the THADA rs7578597 T>C, SDHAF4 rs1048886 A>G, and MACF1 rs2296172 A>G genotypes were statistically non-significant. We only observed lower values of body mass before pregnancy and body mass at birth in women with the SDHAF4 rs1048886 AG genotype in comparison with AA genotype carriers. There was no statistically significant difference in the expression of THADA, SDHAF4, and MACF1 genes in the placenta between women with GDM and healthy women. There were also no statistically significant correlations between THADA, SDHAF4, and MACF1 gene expression in the placenta and clinical parameters. The results of our study suggest that THADA rs7578597 T>C, SDHAF4 rs1048886 A>G, and MACF1 rs2296172 A>G gene polymorphisms are not significant factors associated with GDM onset. In addition, SDHAF4 rs1048886 A>G may be associated with body mass before pregnancy and body mass at birth in pregnant women.

Genetic Risk Factors and Gene-Lifestyle Interactions in Gestational Diabetes

Paralleling the increasing trends of maternal obesity, gestational diabetes (GDM) has become a global health challenge with significant public health repercussions. In addition to short-term adverse outcomes, such as hypertensive pregnancy disorders and fetal macrosomia, in the long term, GDM results in excess cardiometabolic morbidity in both the mother and child. Recent data suggest that women with GDM are characterized by notable phenotypic and genotypic heterogeneity and that frequencies of adverse obstetric and perinatal outcomes are different between physiologic GDM subtypes. However, as of yet, GDM treatment protocols do not differentiate between these subtypes. Mapping the genetic architecture of GDM, as well as accurate phenotypic and genotypic definitions of GDM, could potentially help in the individualization of GDM treatment and assessment of long-term prognoses. In this narrative review, we outline recent studies exploring genetic risk factors of GDM and later type 2 diabetes (T2D) in women with prior GDM. Further, we discuss the current evidence on gene-lifestyle interactions in the development of these diseases. In addition, we point out specific research gaps that still need to be addressed to better understand the complex genetic and metabolic crosstalk within the mother-placenta-fetus triad that contributes to hyperglycemia in pregnancy.

Genetic variants for prediction of gestational diabetes mellitus and modulation of susceptibility by a nutritional intervention based on a Mediterranean diet

Hypothesis

Gestational 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.

Methods

2418 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.

Results

Quality 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.

Conclusions

We 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.

Multi-ancestry genome-wide association study of gestational diabetes mellitus highlights genetic links with type 2 diabetes

Gestational diabetes mellitus (GDM) is associated with increased risk of pregnancy complications and adverse perinatal outcomes. GDM often reoccurs and is associated with increased risk of subsequent diagnosis of type 2 diabetes (T2D). To improve our understanding of the aetiological factors and molecular processes driving the occurrence of GDM, including the extent to which these overlap with T2D pathophysiology, the GENetics of Diabetes In Pregnancy Consortium assembled genome-wide association studies of diverse ancestry in a total of 5485 women with GDM and 347 856 without GDM. Through multi-ancestry meta-analysis, we identified five loci with genome-wide significant association (P < 5 × 10-8) with GDM, mapping to/near MTNR1B (P = 4.3 × 10-54), TCF7L2 (P = 4.0 × 10-16), CDKAL1 (P = 1.6 × 10-14), CDKN2A-CDKN2B (P = 4.1 × 10-9) and HKDC1 (P = 2.9 × 10-8). Multiple lines of evidence pointed to the shared pathophysiology of GDM and T2D: (i) four of the five GDM loci (not HKDC1) have been previously reported at genome-wide significance for T2D; (ii) significant enrichment for associations with GDM at previously reported T2D loci; (iii) strong genetic correlation between GDM and T2D and (iv) enrichment of GDM associations mapping to genomic annotations in diabetes-relevant tissues and transcription factor binding sites. Mendelian randomization analyses demonstrated significant causal association (5% false discovery rate) of higher body mass index on increased GDM risk. Our results provide support for the hypothesis that GDM and T2D are part of the same underlying pathology but that, as exemplified by the HKDC1 locus, there are genetic determinants of GDM that are specific to glucose regulation in pregnancy.

Polygenic Risk Score and Risk Factors for Gestational Diabetes

Gestational diabetes mellitus (GDM) is a common complication of pregnancy that adversely affects maternal and offspring health. A variety of risk factors, such as BMI and age, have been associated with increased risks of gestational diabetes. However, in many cases, gestational diabetes occurs in healthy nulliparous women with no obvious risk factors. Emerging data suggest that the tendency to develop gestational diabetes has genetic and environmental components. Here we develop a polygenic risk score for GDM and investigate relationships between its genetic architecture and genetically constructed risk factors and biomarkers. Our results demonstrate that the polygenic risk score can be used as an early screening tool that identifies women at higher risk of GDM before its onset allowing comprehensive monitoring and preventative programs to mitigate the risks.

COBLL1 and IRS1 Gene Polymorphisms and Placental Expression in Women with Gestational Diabetes

Gestational diabetes mellitus (GDM) is carbohydrate intolerance in pregnant women leading to various complications. Currently, there is a search for factors predisposing to GDM. Among them are genetic polymorphisms of genes involved in insulin secretion as well as carbohydrate metabolism. Due to the similar pathogenesis of GDM to type 2 diabetes (T2DM), genetic polymorphisms associated with T2DM are considered. The aim of this study was to examine the associations between the COBLL1 rs7607980 T > C and IRS1 rs2943641 T > C gene polymorphisms and the risk of GDM as well as selected clinical parameters in women with GDM. Additionally, we examined the expression of these genes in the placenta of women with and without GDM in correlation with selected clinical parameters. This study included 328 pregnant women with normal glucose tolerance (NGT) and 251 pregnant women with GDM diagnosed on the basis of a 75 g oral glucose tolerance test (OGTT) at 24−28 weeks gestation. There were no statistically significant differences in the distribution of IRS1 rs2943641 gene polymorphisms between women with GDM and pregnant women with NGT. In the GDM group, we observed a decreased frequency of COBLL1 rs7607980 CC homozygous women (CC vs. TC+TT, p = 0.048); however, there was no statistically significant difference in the frequency of alleles between women with GDM and the control group. There were no statistically significant associations between COBLL1 rs7607980 gene polymorphism and clinical parameters in women with GDM. In GDM women with the IRS1 rs2943641 TT genotype, fasting glucose levels were significantly higher than in women with CC and TC genotypes. There was no statistically significant difference in the expression of COBLL1 and IRS1 genes in the placenta between women with GDM and healthy women. There were no statistically significant correlations between COBLL1 gene expression in the placenta and clinical parameters. The expression of IRS1 correlated significantly with an increase in BMI during pregnancy. The results of this study suggest that COBLL1 rs7607980 and IRS1 rs2943641 gene polymorphisms are not significant risk factors for GDM in our population. The IRS1 TT genotype may be associated with higher fasting glucose levels in women with GDM. Expression of the IRS1 gene in the placenta positively correlates with an increase in BMI during pregnancy in women with GDM.

Common single-nucleotide polymorphisms combined with a genetic risk score provide new insights regarding the etiology of gestational diabetes mellitus

AIMS

Few studies have constructed a genetic risk score (GRS) to predict the risk of gestaional diabetes mellitus (GDM). We tested the hypothesis that single-nucleotide polymorphisms (SNPs) confirmed for diabetes and obesity and the GRS are associated with GDM.

METHODS

We conducted a case-control study comprising 971 GDM cases and 1682 controls from the University of Hong Kong Shenzhen Hospital. A total of 1448 SNPs reported with type 2 diabetes (T2D), type 1 diabetes (T1D), and obesity were selected and the GRS based on SNPs associated with GDM was created.

RESULTS

We confirmed that rs10830963 (OR = 1.41,95% CI = 1.25, 1.59) in MTNR1B and rs2206734 (OR = 1.38, 95% CI = 1.22, 1.55) in CDKAL1 were strongly associated with the risk of GDM. Compared with participants with GRS based on T2D SNPs in the low tertile, the ORs of GDM across increasing GRS tertiles were 1.63 (95% CI 1.29, 2.06) and 2.72 (95% CI 2.18, 3.38) in the middle and high tertile, respectively. The positive associations between the GRS and the risk of GDM were also observed in GRS based on obesity/waist-to-hip ratio (WHR)/body mass index (BMI) SNPs. The resulting GRS for each allele increase was significantly associated with higher glycemic indices and lower HOMA-B values for GRS based on T2D SNPs, but not for GRS based on T1D SNPs and GRS based on obesity/WHR/BMI SNPs.

CONCLUSION

These findings indicate that GDM may share a common genetic background with T2D and obesity and that SNPs associated with insulin secretion defects have a vital role in the development of GDM.

Genetic risk of type 2 diabetes modifies the effects of a lifestyle intervention aimed at the prevention of gestational and postpartum diabetes

AIMS/HYPOTHESIS

The aim of this study was to assess the interaction between genetic risk and lifestyle intervention on the occurrence of gestational diabetes mellitus (GDM) and postpartum diabetes.

METHODS

The RADIEL study is an RCT aimed at prevention of GDM and postpartum diabetes through lifestyle intervention. Participants with a BMI ≥30 kg/m2 and/or prior GDM were allocated to intervention and control groups before pregnancy or in early pregnancy. The study visits took place every 3 months before pregnancy, once in each trimester, and at 6 weeks and 6 and 12 months postpartum. We calculated a polygenic risk score (PRS) based on 50 risk variants for type 2 diabetes.

RESULTS

Altogether, 516 participants provided genetic and GDM data. The PRS was associated with higher glycaemic levels (fasting glucose and/or HbA1c) and a lower insulin secretion index in the second and third trimesters and at 12 months postpartum, as well as with a higher occurrence of GDM and glycaemic abnormalities at 12 months postpartum (n = 356). There was an interaction between the PRS and lifestyle intervention (p=0.016 during pregnancy and p=0.024 postpartum) when analysing participants who did not have GDM at the first study visit during pregnancy (n = 386). When analysing women in tertiles according to the PRS, the intervention was effective in reducing the age-adjusted occurrence of GDM only among those with the highest genetic risk (OR 0.37; 95% CI 0.17, 0.82). The risk of glycaemic abnormalities at 12 months postpartum was reduced in the same group after adjusting additionally for BMI, parity, smoking and education (OR 0.35; 95% CI 0.13, 0.97).

CONCLUSIONS/INTERPRETATION

Genetic predisposition to diabetes modifies the response to a lifestyle intervention aimed at prevention of GDM and postpartum diabetes. This suggests that lifestyle intervention may benefit from being tailored according to genetic risk.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT01698385.

PPARG, TMEM163, UBE2E2 and WFS1 Gene Polymorphisms Are Not Significant Risk Factors for Gestational Diabetes in the Polish Population

Gestational diabetes mellitus (GDM) is a common disorder that occurs in pregnant women, leading to many maternal and neonatal complications. The pathogenesis of GDM is complex and includes risk factors, such as: age, obesity, and family history of diabetes. Studies have shown that genetic factors also play a role in the pathogenesis of GDM. The present study investigated whether polymorphisms in the PPARG (rs1801282), TMEM163 (rs6723108 and rs998451), UBE2E2 (rs6780569), and WFS1 (rs4689388) genes are risk factors for the development of GDM and whether they affect selected clinical parameters in women with GDM. This study included 204 pregnant women with GDM and 207 pregnant women with normal glucose tolerance (NGT). The diagnosis of GDM was based on a 75 g oral glucose tolerance test (OGTT) at 24–28 weeks gestation, according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. There were no statistically significant differences in the distribution of polymorphisms studied between women with GDM and pregnant women with normal carbohydrate tolerance, which suggests that these polymorphisms are not risk factors for GDM. We also examined the associations between studied gene polymorphisms and clinical parameters: fasting glucose, daily insulin requirement, body mass before pregnancy, body mass at birth, body mass increase during pregnancy, BMI before pregnancy, BMI at birth, BMI increase during pregnancy, new-born body mass, and APGAR score in women with GDM. We observed lower BMI values before pregnancy and at birth in women with PPARG rs17036160 TT genotype. The results of this study suggest that the PPARG (rs1801282), TMEM163 (rs6723108 and rs998451), UBE2E2 (rs6780569), and WFS1 (rs4689388) gene polymorphisms are not significant risk factors for GDM development in the Polish population and do not affect the clinical parameters in women with GDM; only rs1801282 of the PPARG gene may influence BMI values in women with GDM.

An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China

OBJECTIVES

To evaluate the influence of genetic variants and clinical characteristics on the risk of gestational diabetes mellitus (GDM) and to construct and verify a prediction model of GDM in early pregnancy.

METHODS

Four hundred seventy five women with GDM and 487 women without, as a control, were included to construct the prediction model of GDM in early pregnancy. Both groups had complete genotyping results and clinical data. They were randomly divided into a trial cohort (70%) and a test cohort (30%). Then, the model validation cohort, including 985 pregnant women, was used for the external validation of the GDM early pregnancy prediction model.

RESULTS

We found maternal age, gravidity, parity, BMI and family history of diabetes were significantly associated with GDM (OR > 1; P < 0.001), and assisted reproduction was a critical risk factor for GDM (OR = 1.553, P = 0.055). MTNR1B rs10830963, C2CD4A/B rs1436953 and rs7172432, CMIP rs16955379 were significantly correlated with the incidence of GDM (AOR > 1, P < 0.05). Therefore, these four genetic susceptible single nucleotide polymorphisms (SNPs) and six clinical characteristics were included in the construction of the GDM early pregnancy prediction model. In the trial cohort, a predictive model of GDM in early pregnancy was constructed, in which genetic risk score was independently associated with GDM (AOR = 2.061, P < 0.001) and was the most effective predictor with the exception of family history of diabetes. The ROC-AUC of the prediction model was 0.727 (95% CI 0.690-0.765), and the sensitivity and specificity were 69.9% and 64.0%, respectively. The predictive power was also verified in the test cohort and the validation cohort.

CONCLUSIONS

Based on the genetic variants and clinical characteristics, this study developed and verified the early pregnancy prediction model of GDM. This model can help screen out the population at high-risk for GDM in early pregnancy, and lifestyle interventions can be performed for them in a timely manner in early pregnancy.

Genetic risk score to improve prediction and treatment in gestational diabetes mellitus

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 GRS in diabetes mellitus in recent years and discusses future prospects.

Interaction of Polygenetic Variants for Gestational Diabetes Mellitus Risk with Breastfeeding and Korean Balanced Diet to Influence Type 2 Diabetes Risk in Later Life in a Large Hospital-Based Cohort

The etiologies of gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM) are similar. Genetic and environmental factors interact to influence the risk of both types of diabetes. We aimed to determine if the polygenetic risk scores (PRS) for GDM risk interacted with lifestyles to influence type 2 diabetes risk in women aged >40 years in a large hospital-based city cohort. The participants with GDM diagnosis without T2DM before pregnancy were considered the case group (n = 384) and those without GDM and T2DM as the control (n = 33,956) to explore GDM-related genetic variants. The participants with T2DM were the case (n = 2550), and the control (n = 33,956) was the same as GDM genetic analysis for the interaction analysis of GDM genetic risk with lifestyles to influence T2DM risk. The genetic variants for the GDM risk were selected from a genome-wide association study (GWAS), and their PRS from the best model with gene-gene interactions were generated. GDM was positively associated with age at first pregnancy, body mass index (BMI) at age 20, and education level. A previous GDM diagnosis increased the likelihood of elevated fasting serum glucose concentrations and HbA1c contents by 8.42 and 9.23 times in middle-aged and older women. However, it was not associated with the risk of any other metabolic syndrome components. Breast-feeding (≥1 year) was inversely associated with the T2DM risk in later life. In the genetic variant-genetic variant interaction, the best model with 5-SNPs included PTPRD_rs916855529, GPC6_rs9589710, CDKAL1_rs7754840, PRKAG2_rs11975504, and PTPRM_rs80164908. The PRS calculated from the 5-SNP model was positively associated with the GDM risk by 3.259 (2.17-4.89) times after adjusting GDM-related covariates. The GDM experience interacted with PRS for the T2DM risk. Only in non-GDM women PRS was positively associated with T2DM risk by 1.36-times. However, long breastfeeding did not interact with the PRS for T2DM risk. Among dietary patterns, only a Korean-style balanced diet (KBD) showed an interaction with PRS for the T2DM risk. Participants with a low-PRS had the lowest serum glucose concentrations in the high KBD intake but not low KBD intake. In conclusion, participants with a high PRS for GDM risk are positively associated with T2DM risk, and breastfeeding for ≥1 year and consuming KBD offset the PRS for GDM risk to influence T2DM risk in middle-aged and older.

Serum concentrations of SFAs and CDKAL1 single-nucleotide polymorphism rs7747752 are related to an increased risk of gestational diabetes mellitus

BACKGROUND

Interactions between genetic and nutritional factors can contribute to the risk of gestational diabetes mellitus (GDM).

OBJECTIVES

We aimed to explore the associations of cyclin-dependent kinase 5 regulatory subunit associated protein 1-like 1 (CDKAL1) single-nucleotide polymorphism (SNP) rs7747752 and serum concentrations of SFAs with the risk of GDM in Chinese women.

METHODS

We conducted a 1:1 case-control study in a prospective cohort of pregnant women in Tianjin, China. Serum SFA data were collected from a total of 243 women with GDM and their controls matched by maternal age (±1 y). Among them, 207 case-control pairs had high-quality sequencing data. P/L and S/P ratios were defined as palmitic acid (16:0)/lauric acid (12:0) and stearic acid (18:0)/palmitic acid, respectively. Conditional logistic regression analysis was performed to estimate associations of CDKAL1 SNP rs7747752 and serum concentrations of SFAs with the risk of GDM. An additive interaction between rs7747752 and palmitic acid was analyzed to test the contribution of their interaction to the risk of GDM.

RESULTS

Among the 5 tested SFAs, palmitic acid was positively whereas lauric acid was negatively associated with the risk of GDM. A P/L ratio ≥12.2 and an S/P ratio ≤0.71 were independently and synergistically associated with an increased risk of GDM. The CDKAL1 rs7747752 G > C variant was significantly associated with an increased risk of GDM (P < 0.05). Furthermore, the presence of the rs7747752 G > C variant increased the OR (95% CI) of high palmitic acid concentration from 1.55 (0.61, 3.97) to 4.34 (2.04, 9.23), with a significant additive interaction.

CONCLUSIONS

The interaction between high serum palmitic acid concentration and the CDKAL1 rs7747752 G > C variant played a critical role in GDM. Given that a hypocaloric low-carbohydrate diet can lower palmitic acid concentrations, it is worthwhile to test whether such a diet is effective in reducing the risk of GDM, especially among women who have both risk factors.

Genetic variants associated with beta-cell function and insulin sensitivity potentially influence bile acid metabolites and gestational diabetes mellitus in a Chinese population

INTRODUCTION

To investigate associations between genetic variants related to beta-cell (BC) dysfunction or insulin resistance (IR) in type 2 diabetes (T2D) and bile acids (BAs), as well as the risk of gestational diabetes mellitus (GDM).

RESEARCH DESIGN AND METHODS

We organized a case-control study of 230 women with GDM and 217 without GDM nested in a large prospective cohort of 22 302 Chinese women in Tianjin, China. Two weighted genetic risk scores (GRSs), namely BC-GRS and IR-GRS, were established by combining 39 and 23 single nucleotide polymorphisms known to be associated with BC dysfunction and IR, respectively. Regression and mediation analyses were performed to evaluate the relationship of GRSs with BAs and GDM.

RESULTS

We found that the BC-GRS was inversely associated with taurodeoxycholic acid (TDCA) after adjustment for confounders (Beta (SE)=-0.177 (0.048); p=2.66×10^-4). The BC-GRS was also associated with the risk of GDM (OR (95% CI): 1.40 (1.10 to 1.77); p=0.005), but not mediated by TDCA. Compared with individuals in the low tertile of BC-GRS, the OR for GDM was 2.25 (95% CI 1.26 to 4.01) in the high tertile. An interaction effect of IR-GRS with taurochenodeoxycholic acid (TCDCA) on the risk of GDM was evidenced (p=0.005). Women with high IR-GRS and low concentration of TCDCA had a markedly higher OR of 14.39 (95% CI 1.59 to 130.16; p=0.018), compared with those with low IR-GRS and high TCDCA.

CONCLUSIONS

Genetic variants related to BC dysfunction and IR in T2D potentially influence BAs at early pregnancy and the development of GDM. The identification of both modifiable and non-modifiable risk factors may facilitate the identification of high-risk individuals to prevent GDM.

All thresholds of maternal hyperglycaemia from the WHO 2013 criteria for gestational diabetes identify women with a higher genetic risk for type 2 diabetes

Background: Using genetic scores for fasting plasma glucose (FPG GS) and type 2 diabetes (T2D GS), we investigated whether the fasting, 1-hour and 2-hour glucose thresholds from the WHO 2013 criteria for gestational diabetes (GDM) have different implications for genetic susceptibility to raised fasting glucose and type 2 diabetes in women from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) and Atlantic Diabetes in Pregnancy (DIP) studies. Methods: Cases were divided into three subgroups: (i) FPG ≥5.1 mmol/L only, n=222; (ii) 1-hour glucose post 75 g oral glucose load ≥10 mmol/L only, n=154 (iii) 2-hour glucose ≥8.5 mmol/L only, n=73; and (iv) both FPG ≥5.1 mmol/L and either of a 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, n=172. We compared the FPG and T2D GS of these groups with controls (n=3,091) in HAPO and DIP separately. Results: In HAPO and DIP, the mean FPG GS in women with a FPG ≥5.1 mmol/L, either on its own or with 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, was higher than controls (all P <0.01). Mean T2D GS in women with a raised FPG alone or with either a raised 1-hour or 2-hour glucose was higher than controls (all P <0.05). GDM defined by 1-hour or 2-hour hyperglycaemia only was also associated with a higher T2D GS than controls (all P <0.05). Conclusions: The different diagnostic categories that are part of the WHO 2013 criteria for GDM identify women with a genetic predisposition to type 2 diabetes as well as a risk for adverse pregnancy outcomes.

A Genetic Risk Score Improves the Prediction of Type 2 Diabetes Mellitus in Mexican Youths but Has Lower Predictive Utility Compared With Non-Genetic Factors

Background

Type 2 diabetes (T2D) is a multifactorial disease caused by a complex interplay between environmental risk factors and genetic predisposition. To date, a total of 10 single nucleotide polymorphism (SNPs) have been associated with pediatric-onset T2D in Mexicans, with a small individual effect size. A genetic risk score (GRS) that combines these SNPs could serve as a predictor of the risk for pediatric-onset T2D.

Objective

To assess the clinical utility of a GRS that combines 10 SNPs to improve risk prediction of pediatric-onset T2D in Mexicans.

Methods

This case-control study included 97 individuals with pediatric-onset T2D and 84 controls below 18 years old without T2D. Information regarding family history of T2D, demographics, perinatal risk factors, anthropometric measurements, biochemical variables, lifestyle, and fitness scores were then obtained. Moreover, 10 single nucleotide polymorphisms (SNPs) previously associated with pediatric-onset T2D in Mexicans were genotyped. The GRS was calculated by summing the 10 risk alleles. Pediatric-onset T2D risk variance was assessed using multivariable logistic regression models and the area under the receiver operating characteristic curve (AUC).

Results

The body mass index Z-score (Z-BMI) [odds ratio (OR) = 1.7; p = 0.009] and maternal history of T2D (OR = 7.1; p < 0.001) were found to be independently associated with pediatric-onset T2D. No association with other clinical risk factors was observed. The GRS also showed a significant association with pediatric-onset T2D (OR = 1.3 per risk allele; p = 0.006). The GRS, clinical risk factors, and GRS plus clinical risk factors had an AUC of 0.66 (95% CI 0.56-0.75), 0.72 (95% CI 0.62-0.81), and 0.78 (95% CI 0.70-0.87), respectively (p < 0.01).

Conclusion

The GRS based on 10 SNPs was associated with pediatric-onset T2D in Mexicans and improved its prediction with modest significance. However, clinical factors, such the Z-BMI and family history of T2D, continue to have the highest predictive utility in this population.

CDK5 Regulatory Subunit-Associated Protein 1-Like 1 Gene Polymorphisms and Gestational Diabetes Mellitus Risk: A Trial Sequential Meta-Analysis of 13,306 Subjects

OBJECTIVES

The CDK5 regulatory subunit-associated protein 1-like 1 (CDKAL1) contributes to islet β-cell function and insulin secretion by inhibiting the activation of CDK5. The current studies on the relationship between CDKAL1 polymorphisms rs7756992 A>G and rs7754840 C>G and the risk of gestational diabetes mellitus (GDM) have drawn contradictory conclusions.

MATERIALS AND METHODS

A meta-analysis with a fixed- or random-effects model was conducted to estimate the correlation between studied CDKAL1 polymorphisms and GDM risk with the summary odds ratio (OR) and 95% confidence interval (CI). In addition, trial sequential analysis (TSA) and false-positive report probability (FPRP) analysis were performed to confirm the study findings.

RESULTS

A total of 13,306 subjects were included in the present study. Meta-analysis results showed that the variant heterozygous and homozygous genotypes of the two polymorphisms were associated with increased GDM risk in comparison with the wild-type AA genotype (AG vs. AA: OR = 1.23, 95% CI = 1.08, 1.41, p = 0.002; GG vs. AA: OR = 1.47, 95% CI = 1.05, 2.05, p = 0.024 for rs7756992; and CG vs. GG: OR = 1.36, 95% CI = 1.13, 1.65, p = 0.002; CC vs. GG: OR = 1.76, 95% CI = 1.37, 2.26, p < 0.001 for rs7754840). The TSA confirmed a significant association between rs7754840 and the susceptibility to GDM because the cumulative Z-curve crossed both the conventional cutoff value and the TSA boundaries under the heterozygote and homozygote models.

CONCLUSIONS

This study supported the finding that rs7756992 and rs7754840 are associated with susceptibility to GDM. However, further functional studies are warranted to clarify the mechanism.

Association of Common Genetic Risk Variants With Gestational Diabetes Mellitus and Their Role in GDM Prediction

OBJECTIVE

We 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.

METHODS

We 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.

RESULTS

Two 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).

CONCLUSION

Among 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.

Common Variants in NUS1 and GP2 Genes Contributed to the Risk of Gestational Diabetes Mellitus

BACKGROUND

Recently, NUS1 and GP2 genes were reported to be associated with the risk of type 2 diabetes (T2D) in a Japanese population. Given the sharing of pathogenic contribution from genetic factors between T2D and gestational diabetes mellitus (GDM), we conducted the study to systematically examine the relationship of NUS1 and GP2 genes with the susceptibility to GDM in Chinese Han population.

METHODS

A total of 4,250 subjects comprised of 1,282 patients with GDM and 2,968 controls were recruited, and 20 tag single nucleotide polymorphisms (SNPs) (10 from NUS1 and 10 from GP2) were selected for genotyping. Association analyses were conducted for GDM and its related biomedical indexes including fasting glucose and HbA1c levels.

RESULTS

Two SNPs, rs80196932 from NUS1 (P=2.93×10^-5) and rs117267808 from GP2 (P=5.68×10^-5), were identified to be significantly associated with the risk of GDM. Additionally, SNP rs80196932 was significantly associated with HbA1c level in both patients with GDM (P=0.0009) and controls (P=0.0003), while SNP rs117267808 was significantly associated with fasting glucose level in both patients with GDM (P=0.0008) and controls (P=0.0007). Serum levels of protein NUS1 and GP2 were measured for the study subjects, and significant differences were identified among groups with different genotypes of SNP rs80196932 and rs117267808, respectively.

CONCLUSIONS

Our findings indicate that NUS1 and GP2 genes contribute to the risk of GDM, which would help to offer the potential to improve our understanding of the etiology of GDM and, in turn, could facilitate the development of novel medicines and treatments for GDM.

No deleterious effect of an additional pregnancy on glucose metabolism in women with previous gestational diabetes mellitus

OBJECTIVE

Women with gestational diabetes mellitus (GDM) often develop type 2 diabetes later in life. It remains unclear whether this results solely from a common underlying predisposition or, whether a pregnancy itself persistently impairs glucose metabolism in predisposed women. We therefore examined how an additional pregnancy affected different aspects of glucose metabolism in women with previous GDM.

RESEARCH DESIGN AND METHODS

Nested case-control study within the prospective cohort study PPSDiab, recruited in Munich, Germany from 2011-16. Cases (n = 41): women with previous GDM who completed an additional pregnancy; controls: no additional pregnancy, pairwise matching.

ENDPOINTS

change of the area under the glucose curve (AUGC) of an oral glucose tolerance, of plasma glucose at 60' of the test (PG 60'), of the insulin sensitivity index (ISI) and of the disposition index (DI), all between before and after the additional pregnancy in cases/the corresponding observation period in controls.

RESULTS

We observed no significant difference between cases and controls in the primary [ratio AUGC 1.05(0.92-1.15) vs. 0.97(0.85-1.14); p = 0.21] and in the secondary endpoints [difference PG 60', ratio ISI and ratio DI.

CONCLUSION

We did not find a deleterious effect of an additional pregnancy on glucose metabolism in women with previous GDM.

Association of a four-gene model with allergic diseases: Two-year follow-up of a birth cohort study

Background

Our previous study has developed a four‐gene model involving IL13 rs20541, IL4 rs2243250, ADRB2 rs1042713, and FCER1B rs569108 associated with asthma and atopy in Chinese Han children. However, whether the gene model is associated with allergies in early life has yet to be determined. This study aimed to apply the gene model in a birth cohort to investigate its associations with the development of allergic diseases in Chinese Han toddlers.

Methods

Five hundred and ninety‐seven children from a birth cohort completing 2‐year follow‐up were included. Epidemiologic information and cord blood were collected. Children were genotyped for the above polymorphisms and divided into high or low genetic risk groups based on the genotypes. Subjects were followed at 6, 12, and 24 months, with information on allergic diseases collected via standard questionnaires and assessed by specialists.

Results

Two hundred and eighty‐four children were divided into a high‐risk group and 313 into a low‐risk group. Between the two groups, a significant difference was only found in delivery mode among all the subject characteristics (p = .025). After stratification for delivery mode, children at high risk were more likely to develop eczema (relative risk [RR] = 1.46, p = .040) over 2 years of follow‐up compared with those at low risk. No significant associations were found between genetic risk and food allergy, wheezing and allergic rhinitis (p > .05).

Conclusion

The gene model was significantly associated with the development of eczema in Chinese Han toddlers. Long‐term follow‐up along with functional and replication studies on the gene model are still needed in future.

Update on the genetic and epigenetic etiology of gestational diabetes mellitus: a review

Background

Many studies have been conducted on the genetic and epigenetic etiology of gestational diabetes mellitus (GDM) in the last two decades because of the disease’s increasing prevalence and role in global diabetes mellitus (DM) explosion. An update on the genetic and epigenetic etiology of GDM then becomes imperative to better understand and stem the rising incidence of the disease. This review, therefore, articulated GDM candidate genes and their pathophysiology for the awareness of stakeholders.

Main body (genetic and epigenetic etiology, GDM)

The search discovered 83 GDM candidate genes, of which TCF7L2, MTNR1B, CDKAL1, IRS1, and KCNQ1 are the most prevalent. Certain polymorphisms of these genes can modulate beta-cell dysfunction, adiposity, obesity, and insulin resistance through several mechanisms. Environmental triggers such as diets, pollutants, and microbes may also cause epigenetic changes in these genes, resulting in a loss of insulin-boosting and glucose metabolism functions. Early detection and adequate management may resolve the condition after delivery; otherwise, it will progress to maternal type 2 diabetes mellitus (T2DM) and fetal configuration to future obesity and DM. This shows that GDM is a strong risk factor for T2DM and, in rare cases, type 1 diabetes mellitus (T1DM) and maturity-onset diabetes of the young (MODY). This further shows that GDM significantly contributes to the rising incidence and burden of DM worldwide and its prevention may reverse the trend.

Conclusion

Mutations and epigenetic changes in certain genes are strong risk factors for GDM. For affected individuals with such etiologies, medical practitioners should formulate drugs and treatment procedures that target these genes and their pathophysiology.

Genetic Studies of Gestational Diabetes and Glucose Metabolism in Pregnancy

PURPOSE OF REVIEW

In this review, we summarize studies investigating genetics of gestational diabetes mellitus (GDM) and glucose metabolism in pregnancy. We describe these studies in the context of the larger body of literature on type 2 diabetes (T2D) and glycemic trait genomics.

RECENT FINDINGS

We reviewed 23 genetic association studies for GDM and performed a meta-analysis, which revealed variants at eight T2D loci significantly associated with GDM after the Bonferroni correction. These studies suggest that GDM and T2D share a number of genetic risk loci. Only two unbiased genome-wide association studies (GWASs) have successfully revealed genetic associations for GDM and related glycemic traits in pregnancy. A GWAS for GDM in Korean women identified two loci (near CDKAL1 and MTNR1B) known to be associated with T2D, though the association of the MTNR1B locus with GDM appears to be stronger than that for T2D. A multi-ethnic GWAS for glycemic traits in pregnancy identified two novel loci (near HKDC1 and BACE2) which appear to be associated with post-load glucose and fasting c-peptide specifically in pregnant women. There are ongoing efforts to use this genetic information, in the form of polygenic scores, to predict risk of GDM and postpartum T2D. The body of literature examining genetic associations with GDM is limited, especially when compared to the available literature on T2D and glycemic trait genomics. Additional genetic discovery for glucose metabolism in pregnant women will require larger pregnancy cohorts and international collaborative efforts. Studies on the clinical implications of these findings are also warranted.

All thresholds of maternal hyperglycaemia from the WHO 2013 criteria for gestational diabetes identify women with a higher genetic risk for type 2 diabetes

Background: Using genetic scores for fasting plasma glucose (FPG GS) and type 2 diabetes (T2D GS), we investigated whether the fasting, 1-hour and 2-hour glucose thresholds from the WHO 2013 criteria for gestational diabetes (GDM) have different implications for genetic susceptibility to raised fasting glucose and type 2 diabetes in women from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) and Atlantic Diabetes in Pregnancy (DIP) studies. Methods: Cases were divided into three subgroups: (i) FPG ≥5.1 mmol/L only, n=222; (ii) 1-hour glucose post 75 g oral glucose load ≥10 mmol/L only, n=154 (iii) 2-hour glucose ≥8.5 mmol/L only, n=73; and (iv) both FPG ≥5.1 mmol/L and either of a 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, n=172. We compared the FPG and T2D GS of these groups with controls (n=3,091) in HAPO and DIP separately. Results: In HAPO and DIP, the mean FPG GS in women with a FPG ≥5.1 mmol/L, either on its own or with 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, was higher than controls (all P <0.01). Mean T2D GS in women with a raised FPG alone or with either a raised 1-hour or 2-hour glucose was higher than controls (all P <0.05). GDM defined by 1-hour or 2-hour hyperglycaemia only was also associated with a higher T2D GS than controls (all P <0.05). Conclusions: The different diagnostic categories that are part of the WHO 2013 criteria for GDM identify women with a genetic predisposition to type 2 diabetes as well as a risk for adverse pregnancy outcomes.

Defining Heterogeneity Among Women With Gestational Diabetes Mellitus

Attention to precision medicine in type 2 diabetes (T2D) has provided two favored approaches to subclassifying affected individuals and parsing heterogeneity apparent in this condition: phenotype-based and genotype-based. Gestational diabetes mellitus (GDM) shares phenotypic characteristics with T2D. However, unlike T2D, GDM emerges in the setting of profound pregnancy-related physiologic changes in glucose metabolism. T2D and GDM also share common genetic architecture, but there are likely to be unique genetic influences on pregnancy glycemic regulation that contribute to GDM. In this Perspective, we describe efforts to decipher heterogeneity in T2D and detail how we and others are applying approaches developed for T2D to the study of heterogeneity in GDM. Emerging results reveal the potential of phenotype- and genotype-based subclassification of GDM to deliver the promise of precision medicine to the obstetric population.

PCA-based GRS analysis enhances the effectiveness for genetic correlation detection

Genetic risk score (GRS, also known as polygenic risk score) analysis is an increasingly popular method for exploring genetic architectures and relationships of complex diseases. However, complex diseases are usually measured by multiple correlated phenotypes. Analyzing each disease phenotype individually is likely to reduce statistical power due to multiple testing correction. In order to conquer the disadvantage, we proposed a principal component analysis (PCA)-based GRS analysis approach. Extensive simulation studies were conducted to compare the performance of PCA-based GRS analysis and traditional GRS analysis approach. Simulation results observed significantly improved performance of PCA-based GRS analysis compared to traditional GRS analysis under various scenarios. For the sake of verification, we also applied both PCA-based GRS analysis and traditional GRS analysis to a real Caucasian genome-wide association study (GWAS) data of bone geometry. Real data analysis results further confirmed the improved performance of PCA-based GRS analysis. Given that GWAS have flourished in the past decades, our approach may help researchers to explore the genetic architectures and relationships of complex diseases or traits.

All thresholds of maternal hyperglycaemia from the WHO 2013 criteria for gestational diabetes identify women with a higher genetic risk for type 2 diabetes

Background: Using genetic scores for fasting plasma glucose (FPG GS) and type 2 diabetes (T2D GS), we investigated whether the fasting, 1-hour and 2-hour glucose thresholds from the WHO 2013 criteria for gestational diabetes (GDM) have different implications for genetic susceptibility to raised fasting glucose and type 2 diabetes in women from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) and Atlantic Diabetes in Pregnancy (DIP) studies. Methods: Cases were divided into three subgroups: (i) FPG ≥5.1 mmol/L only, n=222; (ii) 1-hour glucose post 75 g oral glucose load ≥10 mmol/L only, n=154 (iii) 2-hour glucose ≥8.5 mmol/L only, n=73; and (iv) both FPG ≥5.1 mmol/L and either of a 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, n=172. We compared the FPG and T2D GS of these groups with controls (n=3,091) in HAPO and DIP separately. Results: In HAPO and DIP, the mean FPG GS in women with a FPG ≥5.1 mmol/L, either on its own or with 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, was higher than controls (all P <0.01). Mean T2D GS in women with a raised FPG alone or with either a raised 1-hour or 2-hour glucose was higher than controls (all P <0.05). GDM defined by 1-hour or 2-hour hyperglycaemia only was also associated with a higher T2D GS than controls (all P <0.05). Conclusions: The different diagnostic categories that are part of the WHO 2013 criteria for GDM identify women with a genetic predisposition to type 2 diabetes as well as a risk for adverse pregnancy outcomes.

Genetic determinants of gestational diabetes mellitus: a case-control study in two independent populations

BACKGROUND

Genetic risk score (GRS) is more informative to identify the complicated associations between variants of genes and disease. Considering similar pathogenesis and shared genetic predispositions between gestational diabetes mellitus (GDM) and type 2 diabetes/obesity, we conducted this study to explore whether the GRS model integrating variants related to type 2 diabetes/obesity is also associated with GDM risk.

METHODS

A population-based case-control study that included 1429 subjects was conducted to investigate the association between the GRS model and GDM risk, which were analyzed employing stratified logistic regression analysis with the adjustment for age, BMI, parity and family history of diabetes.

RESULTS

We have screened 23 SNPs and further filtered six SNPs that were significantly associated with the risk of GDM: four risk SNPs (MTNR1B: rs10830963, rs1387153, rs2166706; MC4R: rs2229616) and two protective SNPs (MTNR1B: rs1447352 and rs4753426). The GRS model with a higher score indicated a higher genetic predisposition to develop GDM, especially in the highest quartile of GRS (all P < 0.001) and the strata of advanced maternal age (all P < 0.001) and obesity (all P = 0.005).

CONCLUSION

In this study, six SNPs were explored and further identified to be associated with GDM risk, which suggested GRSs including these polymorphisms might participate in facilitating GDM risk. These findings offer the potential to improve our understanding of the etiology of GDM.

Fine-tuning of Genome-Wide Polygenic Risk Scores and Prediction of Gestational Diabetes in South Asian Women

Gestational diabetes Mellitus (GDM) affects 1 in 7 births and is associated with numerous adverse health outcomes for both mother and child. GDM is suspected to share a large common genetic background with type 2 diabetes (T2D). The aim of our study was to characterize different GDM polygenic risk scores (PRSs) and test their association with GDM using data from the South Asian Birth Cohort (START). PRSs were derived for 832 South Asian women from START using the pruning and thresholding (P + T), LDpred, and GraBLD methods. Weights were derived from a multi-ethnic and a white Caucasian study of the DIAGRAM consortium. GDM status was defined using South Asian-specific glucose values in response to an oral glucose tolerance test. Association with GDM was tested using logistic regression. Results were replicated in South Asian women from the UK Biobank (UKB) study. The top ranking P + T, LDpred and GraBLD PRSs were all based on DIAGRAM's multi-ethnic study. The best PRS was highly associated with GDM in START (AUC = 0.62, OR = 1.60 [95% CI = 1.44-1.69]), and in South Asian women from UKB (AUC = 0.65, OR = 1.69 [95% CI = 1.28-2.24]). Our results highlight the importance of combining genome-wide genotypes and summary statistics from large multi-ethnic studies to optimize PRSs in South Asians.

Assessing the Relationship Between Gut Microbiota and Bone Mineral Density

Background

Recent study demonstrates the comprehensive effects of gut microbiota on complex diseases or traits. However, limited effort has been conducted to explore the potential relationships between gut microbiota and BMD.

Methods

We performed a polygenetic risk scoring (PRS) analysis to systematically explore the relationships between gut microbiota and body BMD. Significant SNP sets associated with gut microbiota were derived from previous genome-wide association study (GWAS). In total, 2,294 to 5,065 individuals with BMD values of different sites and their genotype data were obtained from UK Biobank cohort. The gut microbiota PRS of each individual was computed from the SNP genotype data for each study subject of UK Biobank by PLINK software. Using computed PRS as the instrumental variables of gut microbiota, Pearson correlation analysis of individual PRS values and BMD values was finally conducted to test the potential association between gut microbiota and target trait.

Results

In total, 31 BMD traits were selected as outcome to assess their relationships with gut microbiota. After adjusted for age, sex, body mass index, and the first 5 principal components (PCs) as the covariates using linear regression model, pelvis BMD (P = 0.0437) showed suggestive association signal with gut microbiota after multiple testing correction.

Conclusion

Our study findings support the weak relevance of gut microbiota with the development of BMD.

Variants in BMI-Associated Genes and Adrenergic Genes are not Associated with Gestational Weight Trajectory

OBJECTIVE

The aim of this study is to define the association between a genetic risk score (GRS) that combined the effect of multiple BMI-associated variants and gestational weight trajectory. Because pregnancy is a state of sympathetic activation, the association between gestational weight trajectory and variants in adrenergic pathways previously associated with weight was examined.

METHODS

In a previously defined cohort of pregnant women with (n = 1,504) and without gestational diabetes (GDM) (n = 435), weight trajectory was calculated using all weights during pregnancy. A GRS for BMI (GRS_BMI ) was calculated using 31 common variants associated with BMI, and 10 variants in the adrenergic pathways were genotyped. Clinical and genetic factors were studied using generalized linear models.

RESULTS

Prepregnancy BMI was associated with the GRS_BMI (P = 9.3 × 10^-11 ) and parity (P = 4.54 × 10^-17 ). The GRS_BMI was associated with gestational weight trajectory in women with and without GDM (P = 0.041 and P < 0.0001, respectively); however, when prepregnancy BMI was included in the models, the associations disappeared (P > 0.05). Variants in adrenergic genes were not associated with gestational weight trajectory.

CONCLUSIONS

A GRS for BMI was associated with prepregnancy BMI but was not independently associated with gestational weight trajectory in women with and without GDM. Selected variants in adrenergic genes were not associated with gestational weight trajectory.

Assessing the Genetic Correlations Between Blood Plasma Proteins and Osteoporosis: A Polygenic Risk Score Analysis

Osteoporosis is a common metabolic bone disease. The impact of global blood plasma proteins on the risk of osteoporosis remains elusive now. We performed a large-scale polygenic risk score (PRS) analysis to evaluate the potential effects of blood plasma proteins on the development of osteoporosis in 2286 Caucasians, including 558 males and 1728 females. Bone mineral density (BMD) and bone areas at ulna & radius, hip, and spine were measured using Hologic 4500W DXA. BMD/bone areas values were adjusted for age, sex, height, and weight as covariates. Genome-wide SNP genotyping of 2286 Caucasian subjects was performed using Affymetrix Human SNP Array 6.0. The 267 blood plasma proteins-associated SNP loci and their genetic effects were obtained from recently published genome-wide association study (GWAS) using a highly multiplexed aptamer-based affinity proteomics platform. The polygenetic risk score (PRS) of study subjects for each blood plasma protein was calculated from the genotypes data of the 2286 Caucasian subjects by PLINK software. Pearson correlation analysis of individual PRS values and BMD/bone area value was performed using R. Additionally, gender-specific analysis also was performed by Pearson correlation analysis. 267 blood plasma proteins were analyzed in this study. For BMD, we observed association signals between 41 proteins and BMD, mainly including whole body total BMD versus Factor H (p value = 9.00 × 10-3), whole body total BMD versus BGH3 (p value = 1.40 × 10-2), spine total BMD versus IGF-I (p value = 2.15 × 10-2), and spine total BMD versus SAP (p value = 3.90 × 10-2). As for bone areas, association evidence was observed between 45 blood plasma proteins and bone areas, such as ferritin versus spine area (p value = 1.90 × 10-2), C4 versus hip area (p value = 1.25 × 10-2), and hemoglobin versus right ulna and radius area (p value = 2.70 × 10-2). Our study results suggest the modest impact of blood plasma proteins on the variations of BMD/bone areas, and identify several candidate blood plasma proteins for osteoporosis.

Maternal genetic contribution to pre-pregnancy obesity, gestational weight gain, and gestational diabetes mellitus

INTRODUCTION

Pre-pregnancy obesity, gestational diabetes mellitus (GDM), and gestational weight gain (GWG) are associated with each other. This is the first study to investigate whether genetic variants were associated with having GDM, and whether genetic variants-related GDM were associated with adiposity including pre-pregnancy obesity and excessive GWG in Turkish women.

PATIENTS AND METHODS

Women with GDM (n = 160) and without GDM (n = 145) were included in case-controlled study. Genotyping of the HNF1A gene (p.I27L rs1169288, p.98V rs1800574, p.S487N rs2464196), the VDR gene (p.BsmI rs1544410, p.ApaI rs7975232, p.TaqI rs731236, p.FokI rs2228570), and FTO gene (rs9939609) SNPs were performed by using RT-PCR.

RESULTS

The FTO AA genotype was associated with an increased risk of having GDM (AA vs. AT + TT, 24.4% vs. 12.4%, OR = 2.27, 95% CI [1.23-4.19], p = 0.007). The HNF1A p.I27L GT/TT genotype was associated with increased GDM risk (GT + TT vs. GG-wild, 79.4% vs. 65.5%, OR = 2.02, 95% CI 1.21-3.38], p = 0.007). However, all VDR gene SNPs and the HNF1A p.A98V, p.S487N were not associated with having GDM (p > 0.05). The FTO AA genotype was associated with an increased risk for pre-pregnancy overweight/obesity (OR = 1.43, 95% CI [1.25-3.4], p = 0.035), but not associated with excessive GWG after adjusting for pre-pregnancy weight (p > 0.05). Pre-pregnancy weight, weight at delivery, and GWG did not differ in both VDR and HNF1A gene carriers (p > 0.05). HOMA-IR and HbA1c were increased in both p.I27L TT and FTO AA genotype carriers (p < 0.05).

CONCLUSION

The adiposity-related gene FTO is associated with GDM by the effect of FTO on pre-pregnancy obesity. The diabetes-related p.I27L gene is associated with GDM by increasing insulin resistance.

Genetic Determinants of Glycemic Traits and the Risk of Gestational Diabetes Mellitus

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.

Gene coexpression network analysis identified potential biomarkers in gestational diabetes mellitus progression

Background

Gestational diabetes mellitus (GDM) is one of the most common problems during pregnancy. Lack of international consistent diagnostic procedures has limit improvement of current therapeutic effectiveness. Here, we aimed to screen potential gene biomarkers that might play vital roles in GDM progression for assistance of its diagnostic and treatment.

Methods

Gene expression profiles in four GDM placentae at first trimester, four GDM placentae at second trimester, and four normal placentae were obtained from the publicly available Gene Expression Omnibus (GEO). Weighted gene coexpression network analysis (WGCNA) indicated two gene modules, that is, black and brown module, that was significantly positively and negatively correlated with GDM progression time points, respectively. Additionally, a significant positive correlation between module membership (MM) and degree in protein–protein interaction network of brown module genes was observed.

Results

KIF2C, CENPE, CCNA2, AURKB, MAD2L1, CCNB2, CDC20, PLK1, CCNB1, and CDK1 all have degree larger than 50 and MM larger than 0.9, so they might be valuable biomarkers in GDM. Gene set enrichment analysis inferred tight relations between carbohydrate metabolism or steroid biosynthesis‐related processes and GDM progression.

Conclusions

All in all, our study should provide several novel references for GDM diagnosis and therapeutic.