Metabolomic and genetic architecture of gestational diabetes subtypes.

AIMS/HYPOTHESIS: Physiological gestational diabetes mellitus (GDM) subtypes that may confer different risks for adverse pregnancy outcomes have been defined. The aim of this study was to characterise the metabolome and genetic architecture of GDM subtypes to address the hypothesis that they differ between GDM subtypes. METHODS: This was a cross-sectional study of participants in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study who underwent an OGTT at approximately 28 weeks' gestation. GDM was defined retrospectively using International Association of Diabetes and Pregnancy Study Groups/WHO criteria, and classified as insulin-deficient GDM (insulin secretion <25th percentile with preserved insulin sensitivity) or insulin-resistant GDM (insulin sensitivity <25th percentile with preserved insulin secretion). Metabolomic analyses were performed on fasting and 1 h serum samples in 3463 individuals (576 with GDM). Genome-wide genotype data were obtained for 8067 individuals (1323 with GDM). RESULTS: Regression analyses demonstrated striking differences between the metabolomes for insulin-deficient or insulin-resistant GDM compared to those with normal glucose tolerance. After adjustment for covariates, 33 fasting metabolites, including 22 medium- and long-chain acylcarnitines, were uniquely associated with insulin-deficient GDM; 23 metabolites, including the branched-chain amino acids and their metabolites, were uniquely associated with insulin-resistant GDM; two metabolites (glycerol and 2-hydroxybutyrate) were associated with the same direction of association with both subtypes. Subtype differences were also observed 1 h after a glucose load. In genome-wide association studies, variants within MTNR1B (rs10830963, p=3.43×10-18, OR 1.55) and GCKR (rs1260326, p=5.17×10-13, OR 1.43) were associated with GDM. Variants in GCKR (rs1260326, p=1.36×10-13, OR 1.60) and MTNR1B (rs10830963, p=1.22×10-9, OR 1.49) demonstrated genome-wide significant association with insulin-resistant GDM; there were no significant associations with insulin-deficient GDM. The lead SNP in GCKR, rs1260326, was associated with the levels of eight of the 25 fasting metabolites that were associated with insulin-resistant GDM and ten of 41 1 h metabolites that were associated with insulin-resistant GDM. CONCLUSIONS/INTERPRETATION: This study demonstrates that physiological GDM subtypes differ in their metabolome and genetic architecture. These findings require replication in additional cohorts, but suggest that these differences may contribute to subtype-related adverse pregnancy outcomes.

Fetal alleles predisposing to metabolically favorable adiposity are associated with higher birth weight.

BACKGROUND: Higher birthweight is associated with higher adult body mass index (BMI). Alleles that predispose to greater adult adiposity might act in fetal life to increase fetal growth and birthweight. Whether there are fetal effects of recently identified adult metabolically favorable adiposity alleles on birthweight is unknown. AIM: We aimed to test the effect on birthweight of fetal genetic predisposition to higher metabolically favorable adult adiposity and compare that with the effect of fetal genetic predisposition to higher adult BMI. METHODS: We used published genome wide association study data (n = upto 406 063) to estimate fetal effects on birthweight (adjusting for maternal genotype) of alleles known to raise metabolically favorable adult adiposity or BMI. We combined summary data across single nucleotide polymorphisms (SNPs) with random effects meta-analyses. We performed weighted linear regression of SNP-birthweight effects against SNP-adult adiposity effects to test for a dose-dependent association. RESULTS: Fetal genetic predisposition to higher metabolically favorable adult adiposity and higher adult BMI were both associated with higher birthweight (3 g per effect allele (95% CI: 1-5) averaged over 14 SNPs; P = 0.002; 0.5 g per effect allele (95% CI: 0-1) averaged over 76 SNPs; P = 0.042, respectively). SNPs with greater effects on metabolically favorable adiposity tended to have greater effects on birthweight (R2 = 0.2912, P = 0.027). There was no dose-dependent association for BMI (R2 = -0.0019, P = 0.602). CONCLUSIONS: Fetal genetic predisposition to both higher adult metabolically favorable adiposity and BMI is associated with birthweight. Fetal effects of metabolically favorable adiposity-raising alleles on birthweight are modestly proportional to their effects on future adiposity, but those of BMI-raising alleles are not.

Integrating multiple lines of evidence to assess the effects of maternal BMI on pregnancy and perinatal outcomes.

BACKGROUND: Higher maternal pre-pregnancy body mass index (BMI) is associated with adverse pregnancy and perinatal outcomes. However, whether these associations are causal remains unclear. METHODS: We explored the relation of maternal pre-/early-pregnancy BMI with 20 pregnancy and perinatal outcomes by integrating evidence from three different approaches (i.e. multivariable regression, Mendelian randomisation, and paternal negative control analyses), including data from over 400,000 women. RESULTS: All three analytical approaches supported associations of higher maternal BMI with lower odds of maternal anaemia, delivering a small-for-gestational-age baby and initiating breastfeeding, but higher odds of hypertensive disorders of pregnancy, gestational hypertension, preeclampsia, gestational diabetes, pre-labour membrane rupture, induction of labour, caesarean section, large-for-gestational age, high birthweight, low Apgar score at 1 min, and neonatal intensive care unit admission. For example, higher maternal BMI was associated with higher risk of gestational hypertension in multivariable regression (OR = 1.67; 95% CI = 1.63, 1.70 per standard unit in BMI) and Mendelian randomisation (OR = 1.59; 95% CI = 1.38, 1.83), which was not seen for paternal BMI (OR = 1.01; 95% CI = 0.98, 1.04). Findings did not support a relation between maternal BMI and perinatal depression. For other outcomes, evidence was inconclusive due to inconsistencies across the applied approaches or substantial imprecision in effect estimates from Mendelian randomisation. CONCLUSIONS: Our findings support a causal role for maternal pre-/early-pregnancy BMI on 14 out of 20 adverse pregnancy and perinatal outcomes. Pre-conception interventions to support women maintaining a healthy BMI may reduce the burden of obstetric and neonatal complications. FUNDING: Medical Research Council, British Heart Foundation, European Research Council, National Institutes of Health, National Institute for Health Research, Research Council of Norway, Wellcome Trust.

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.

Path-level interpretation of Gaussian graphical models using the pair-path subscore.

BACKGROUND : Construction of networks from cross-sectional biological data is increasingly common. Many recent methods have been based on Gaussian graphical modeling, and prioritize estimation of conditional pairwise dependencies among nodes in the network. However, challenges remain on how specific paths through the resultant network contribute to overall 'network-level' correlations. For biological applications, understanding these relationships is particularly relevant for parsing structural information contained in complex subnetworks. RESULTS: We propose the pair-path subscore (PPS), a method for interpreting Gaussian graphical models at the level of individual network paths. The scoring is based on the relative importance of such paths in determining the Pearson correlation between their terminal nodes. PPS is validated using human metabolomics data from the Hyperglycemia and adverse pregnancy outcome (HAPO) study, with observations confirming well-documented biological relationships among the metabolites. We also highlight how the PPS can be used in an exploratory fashion to generate new biological hypotheses. Our method is implemented in the R package pps, available at https://github.com/nathan-gill/pps . CONCLUSIONS: The PPS can be used to probe network structure on a finer scale by investigating which paths in a potentially intricate topology contribute most substantially to marginal behavior. Adding PPS to the network analysis toolkit may enable researchers to ask new questions about the relationships among nodes in network data.

Early emergence of sexual dimorphism in offspring leukocyte telomere length was associated with maternal and children's glucose metabolism-a longitudinal study.

BACKGROUND: Leukocyte telomere length (LTL) is suggested to be a biomarker of biological age and reported to be associated with metabolic diseases such as type 2 diabetes. Glucose metabolic traits including glucose and insulin levels have been reported to be associated with LTL in adulthood. However, there is relatively little research focusing on children's LTL and the association with prenatal exposures. This study investigates the relationship between maternal and offspring glucose metabolism with offspring LTL in early life. METHODS: This study included 882 mother-child pairs from the HAPO Hong Kong Field Centre, with children evaluated at age 7.0 ± 0.4 (mean ± SD) years. Glucose metabolic traits including maternal post-load glucose during pregnancy, children's glucose and insulin levels, and their derived indices at follow-up were measured or calculated. Offspring LTL was assessed using real-time polymerase chain reaction. RESULTS: Sex- and age-adjusted children's LTL was found to be associated with children's HOMA-IR (ß=-0.046 ± 0.016, p=0.005). Interestingly, both children's and maternal post-load glucose levels were positively associated with children's LTL. However, negative associations were observed between children's LTL and children's OGTT insulin levels. In addition, the LTL in females was more strongly associated with pancreatic beta-cell function whilst LTL in males was more strongly associated with OGTT glucose levels. CONCLUSIONS: Our findings suggest a close association between maternal and offspring glucose metabolic traits with early life LTL, with the offspring sex as an important modifier of the disparate relationships in insulin production and response.

Hyperglycemia and Adverse Pregnancy Outcome Follow-Up Study: newborn anthropometrics and childhood glucose metabolism.

AIMS/HYPOTHESIS: We aimed to examine associations of newborn anthropometric measures with childhood glucose metabolism with the hypothesis that greater newborn birthweight, adiposity and cord C-peptide are associated with higher childhood glucose levels and lower insulin sensitivity. METHODS: Data from the international, multi-ethnic, population-based Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study and the HAPO Follow-Up Study were used. The analytic cohort included 4155 children (mean age [SD], 11.4 [1.2] years; 51.0% male). Multiple linear regression was used to examine associations of primary predictors, birthweight, newborn sum of skinfolds (SSF) and cord C-peptide, from HAPO with continuous child glucose outcomes from the HAPO Follow-Up Study. RESULTS: In an initial model that included family history of diabetes and maternal BMI during pregnancy, birthweight and SSF demonstrated a significant, inverse association with 30 min and 1 h plasma glucose levels. In the primary model, which included further adjustment for maternal sum of glucose z scores from an oral glucose tolerance test during pregnancy, the associations were strengthened, and birthweight and SSF were inversely associated with fasting, 30 min, 1 h and 2 h plasma glucose levels. Birthweight and SSF were also associated with higher insulin sensitivity (Matsuda index) (ß = 1.388; 95% CI 0.870, 1.906; p < 0.001; ß = 0.792; 95% CI 0.340, 1.244; p < 0.001, for birthweight and SSF higher by 1 SD, respectively) in the primary model, while SSF, but not birthweight, was positively associated with the disposition index, a measure of beta cell compensation for insulin resistance (ß = 0.034; 95% CI 0.012, 0.056; p = 0.002). Cord C-peptide levels were inversely associated with Matsuda index (ß = -0.746; 95% CI -1.188, -0.304; p < 0.001 for cord C-peptide higher by 1 SD) in the primary model. CONCLUSIONS/INTERPRETATION: This study demonstrates that higher birthweight and SSF are associated with greater childhood insulin sensitivity and lower glucose levels following a glucose load, associations that were further strengthened after adjustment for maternal glucose levels during pregnancy. Graphical abstract.

Higher maternal adiposity reduces offspring birthweight if associated with a metabolically favourable profile.

AIMS/HYPOTHESIS: Higher maternal BMI during pregnancy is associated with higher offspring birthweight, but it is not known whether this is solely the result of adverse metabolic consequences of higher maternal adiposity, such as maternal insulin resistance and fetal exposure to higher glucose levels, or whether there is any effect of raised adiposity through non-metabolic (e.g. mechanical) factors. We aimed to use genetic variants known to predispose to higher adiposity, coupled with a favourable metabolic profile, in a Mendelian randomisation (MR) study comparing the effect of maternal 'metabolically favourable adiposity' on offspring birthweight with the effect of maternal general adiposity (as indexed by BMI). METHODS: To test the causal effects of maternal metabolically favourable adiposity or general adiposity on offspring birthweight, we performed two-sample MR. We used variants identified in large, published genetic-association studies as being associated with either higher adiposity and a favourable metabolic profile, or higher BMI (n = 442,278 and n = 322,154 for metabolically favourable adiposity and BMI, respectively). We then extracted data on the metabolically favourable adiposity and BMI variants from a large, published genetic-association study of maternal genotype and offspring birthweight controlling for fetal genetic effects (n = 406,063 with maternal and/or fetal genotype effect estimates). We used several sensitivity analyses to test the reliability of the results. As secondary analyses, we used data from four cohorts (total n = 9323 mother-child pairs) to test the effects of maternal metabolically favourable adiposity or BMI on maternal gestational glucose, anthropometric components of birthweight and cord-blood biomarkers. RESULTS: Higher maternal adiposity with a favourable metabolic profile was associated with lower offspring birthweight (-94 [95% CI -150, -38] g per 1 SD [6.5%] higher maternal metabolically favourable adiposity, p = 0.001). By contrast, higher maternal BMI was associated with higher offspring birthweight (35 [95% CI 16, 53] g per 1 SD [4 kg/m2] higher maternal BMI, p = 0.0002). Sensitivity analyses were broadly consistent with the main results. There was evidence of outlier SNPs for both exposures; their removal slightly strengthened the metabolically favourable adiposity estimate and made no difference to the BMI estimate. Our secondary analyses found evidence to suggest that a higher maternal metabolically favourable adiposity decreases pregnancy fasting glucose levels while a higher maternal BMI increases them. The effects on neonatal anthropometric traits were consistent with the overall effect on birthweight but the smaller sample sizes for these analyses meant that the effects were imprecisely estimated. We also found evidence to suggest that higher maternal metabolically favourable adiposity decreases cord-blood leptin while higher maternal BMI increases it. CONCLUSIONS/INTERPRETATION: Our results show that higher adiposity in mothers does not necessarily lead to higher offspring birthweight. Higher maternal adiposity can lead to lower offspring birthweight if accompanied by a favourable metabolic profile. DATA AVAILABILITY: The data for the genome-wide association studies (GWAS) of BMI are available at https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files . The data for the GWAS of body fat percentage are available at https://walker05.u.hpc.mssm.edu .

Bayesian estimation of genetic regulatory effects in high-throughput reporter assays.

MOTIVATION: High-throughput reporter assays dramatically improve our ability to assign function to noncoding genetic variants, by measuring allelic effects on gene expression in the controlled setting of a reporter gene. Unlike genetic association tests, such assays are not confounded by linkage disequilibrium when loci are independently assayed. These methods can thus improve the identification of causal disease mutations. While work continues on improving experimental aspects of these assays, less effort has gone into developing methods for assessing the statistical significance of assay results, particularly in the case of rare variants captured from patient DNA. RESULTS: We describe a Bayesian hierarchical model, called Bayesian Inference of Regulatory Differences, which integrates prior information and explicitly accounts for variability between experimental replicates. The model produces substantially more accurate predictions than existing methods when allele frequencies are low, which is of clear advantage in the search for disease-causing variants in DNA captured from patient cohorts. Using the model, we demonstrate a clear tradeoff between variant sequencing coverage and numbers of biological replicates, and we show that the use of additional biological replicates decreases variance in estimates of effect size, due to the properties of the Poisson-binomial distribution. We also provide a power and sample size calculator, which facilitates decision making in experimental design parameters. AVAILABILITY AND IMPLEMENTATION: The software is freely available from www.geneprediction.org/bird. The experimental design web tool can be accessed at http://67.159.92.22:8080. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Associations of gestational cardiovascular health with pregnancy outcomes: the Hyperglycemia and Adverse Pregnancy Outcome study.

BACKGROUND: The American Heart Association's formal characterization of cardiovascular health combines several metrics in a health-oriented, rather than disease-oriented, framework. Although cardiovascular health assessment during pregnancy has been recommended, its significance for pregnancy outcomes is unknown. OBJECTIVE: The purpose of this study was to examine the association of gestational cardiovascular health-formally characterized by a combination of 5 metrics-with adverse maternal and newborn outcomes. STUDY DESIGN: We analyzed data from the Hyperglycemia and Adverse Pregnancy Outcome study, including 2304 mother-newborn dyads from 6 countries. Maternal cardiovascular health was defined by the combination of the following 5 metrics measured at a mean of 28 (24-32) weeks' gestation: body mass index, blood pressure, lipids, glucose, and smoking. Levels of each metric were categorized using pregnancy guidelines, and the total cardiovascular health was scored (0-10 points, where 10 was the most favorable). Cord blood was collected at delivery, newborn anthropometrics were measured within 72 hours, and medical records were abstracted for obstetrical outcomes. Modified Poisson and multinomial logistic regression were used to test the associations of gestational cardiovascular health with pregnancy outcomes, adjusted for center and maternal and newborn characteristics. RESULTS: The average age of women at study exam was 29.6 years old, and they delivered at a mean gestational age of 39.8 weeks. The mean total gestational cardiovascular health score was 8.6 (of 10); 36.3% had all ideal metrics and 7.5% had 2+ poor metrics. In fully adjusted models, each 1 point higher (more favorable) cardiovascular health score was associated with lower risks for preeclampsia (relative risk, 0.67 [95% confidence interval, 0.61-0.73]), unplanned primary cesarean delivery (0.88 [0.82-0.95]), newborn birthweight >90th percentile (0.81 [0.75-0.87]), sum of skinfolds >90th percentile (0.84 [0.77-0.92]), and insulin sensitivity <10th percentile (0.83 [0.77-0.90]). Cardiovascular health categories demonstrated graded associations with outcomes; for example, relative risks (95% confidence intervals) for preeclampsia were 3.13 (1.39-7.06), 5.34 (2.44-11.70), and 9.30 (3.95-21.86) for women with ≥1 intermediate, 1 poor, or ≥2 poor (vs all ideal) metrics, respectively. CONCLUSION: More favorable cardiovascular health at 24 to 32 weeks' gestation was associated with lower risks for several adverse pregnancy outcomes in a multinational cohort.

Associations of Maternal Cardiovascular Health in Pregnancy With Offspring Cardiovascular Health in Early Adolescence.

Importance: Pregnancy may be a key window to optimize cardiovascular health (CVH) for the mother and influence lifelong CVH for her child. Objective: To examine associations between maternal gestational CVH and offspring CVH. Design, Setting, and Participants: This cohort study used data from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study (examinations: July 2000-April 2006) and HAPO Follow-Up Study (examinations: February 2013-December 2016). The analyses included 2302 mother-child dyads, comprising 48% of HAPO Follow-Up Study participants, in an ancillary CVH study. Participants were from 9 field centers across the United States, Barbados, United Kingdom, China, Thailand, and Canada. Exposures: Maternal gestational CVH at a target of 28 weeks' gestation, based on 5 metrics: body mass index, blood pressure, total cholesterol level, glucose level, and smoking. Each metric was categorized as ideal, intermediate, or poor using pregnancy guidelines. Total CVH was categorized as follows: all ideal metrics, 1 or more intermediate (but 0 poor) metrics, 1 poor metric, or 2 or more poor metrics. Main Outcomes and Measures: Offspring CVH at ages 10 to 14 years, based on 4 metrics: body mass index, blood pressure, total cholesterol level, and glucose level. Total CVH was categorized as for mothers. Results: Among 2302 dyads, the mean (SD) ages were 29.6 (2.7) years for pregnant mothers and 11.3 (1.1) years for children. During pregnancy, the mean (SD) maternal CVH score was 8.6 (1.4) out of 10. Among pregnant mothers, the prevalence of all ideal metrics was 32.8% (95% CI, 30.6%-35.1%), 31.7% (95% CI, 29.4%-34.0%) for 1 or more intermediate metrics, 29.5% (95% CI, 27.2%-31.7%) for 1 poor metric, and 6.0% (95% CI, 3.8%-8.3%) for 2 or more poor metrics. Among children of mothers with all ideal metrics, the prevalence of all ideal metrics was 42.2% (95% CI, 38.4%-46.2%), 36.7% (95% CI, 32.9%-40.7%) for 1 or more intermediate metrics, 18.4% (95% CI, 14.6%-22.4%) for 1 poor metric, and 2.6% (95% CI, 0%-6.6%) for 2 or more poor metrics. Among children of mothers with 2 or more poor metrics, the prevalence of all ideal metrics was 30.7% (95% CI, 22.0%-40.4%), 28.3% (95% CI, 19.7%-38.1%) for 1 or more intermediate metrics, 30.7% (95% CI, 22.0%-40.4%) for 1 poor metric, and 10.2% (95% CI, 1.6%-20.0%) for 2 or more poor metrics. The adjusted relative risks associated with 1 or more intermediate, 1 poor, and 2 or more poor (vs all ideal) metrics, respectively, in mothers during pregnancy were 1.17 (95% CI, 0.96-1.42), 1.66 (95% CI, 1.39-1.99), and 2.02 (95% CI, 1.55-2.64) for offspring to have 1 poor (vs all ideal) metrics, and the relative risks were 2.15 (95% CI, 1.23-3.75), 3.32 (95% CI,1.96-5.62), and 7.82 (95% CI, 4.12-14.85) for offspring to have 2 or more poor (vs all ideal) metrics. Additional adjustment for categorical birth factors (eg, preeclampsia) did not fully explain these significant associations (eg, relative risk for association between 2 or more poor metrics among mothers during pregnancy and 2 or more poor metrics among offspring after adjustment for an extended set of birth factors, 6.23 [95% CI, 3.03-12.82]). Conclusions and Relevance: In this multinational cohort, better maternal CVH at 28 weeks' gestation was significantly associated with better offspring CVH at ages 10 to 14 years.

Metabolomic and genetic associations with insulin resistance in pregnancy.

AIMS/HYPOTHESIS: Our study aimed to integrate maternal metabolic and genetic data related to insulin sensitivity during pregnancy to provide novel insights into mechanisms underlying pregnancy-induced insulin resistance. METHODS: Fasting and 1 h serum samples were collected from women in the Hyperglycemia and Adverse Pregnancy Outcome study who underwent an OGTT at ∼28 weeks' gestation. We obtained targeted and non-targeted metabolomics and genome-wide association data from 1600 and 4528 mothers, respectively, in four ancestry groups (Northern European, Afro-Caribbean, Mexican American and Thai); 1412 of the women had both metabolomics and genome-wide association data. Insulin sensitivity was calculated using a modified insulin sensitivity index that included fasting and 1 h glucose and C-peptide levels after a 75 g glucose load. RESULTS: Per-metabolite and network analyses across the four ancestries identified numerous metabolites associated with maternal insulin sensitivity before and 1 h after a glucose load, ranging from amino acids and carbohydrates to fatty acids and lipids. Genome-wide association analyses identified 12 genetic variants in the glucokinase regulatory protein gene locus that were significantly associated with maternal insulin sensitivity, including a common functional missense mutation, rs1260326 (ß = -0.2004, p = 4.67 × 10-12 in a meta-analysis across the four ancestries). This SNP was also significantly associated with multiple fasting and 1 h metabolites during pregnancy, including fasting and 1 h triacylglycerols and 2-hydroxybutyrate and 1 h lactate, 2-ketoleucine/ketoisoleucine and palmitoleic acid. Mediation analysis suggested that 1 h palmitoleic acid contributes, in part, to the association of rs1260326 with maternal insulin sensitivity, explaining 13.7% (95% CI 4.0%, 23.3%) of the total effect. CONCLUSIONS/INTERPRETATION: The present study demonstrates commonalities between metabolites and genetic variants associated with insulin sensitivity in the gravid and non-gravid states and provides insights into mechanisms underlying pregnancy-induced insulin resistance. Graphical abstract.

Maternal metabolites during pregnancy are associated with newborn outcomes and hyperinsulinaemia across ancestries.

AIMS/HYPOTHESIS: We aimed to determine the association of maternal metabolites with newborn adiposity and hyperinsulinaemia in a multi-ethnic cohort of mother-newborn dyads. METHODS: Targeted and non-targeted metabolomics assays were performed on fasting and 1 h serum samples from a total of 1600 mothers in four ancestry groups (Northern European, Afro-Caribbean, Mexican American and Thai) who participated in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, underwent an OGTT at ~28 weeks gestation and whose newborns had anthropometric measurements at birth. RESULTS: In this observational study, meta-analyses demonstrated significant associations of maternal fasting and 1 h metabolites with birthweight, cord C-peptide and/or sum of skinfolds across ancestry groups. In particular, maternal fasting triacylglycerols were associated with newborn sum of skinfolds. At 1 h, several amino acids, fatty acids and lipid metabolites were associated with one or more newborn outcomes. Network analyses revealed clusters of fasting acylcarnitines, amino acids, lipids and fatty acid metabolites associated with cord C-peptide and sum of skinfolds, with the addition of branched-chain and aromatic amino acids at 1 h. CONCLUSIONS/INTERPRETATION: The maternal metabolome during pregnancy is associated with newborn outcomes. Maternal levels of amino acids, acylcarnitines, lipids and fatty acids and their metabolites during pregnancy relate to fetal growth, adiposity and cord C-peptide, independent of maternal BMI and blood glucose levels.

Maternal glucose levels during pregnancy and childhood adiposity in the Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study.

AIMS/HYPOTHESIS: Maternal type 2 diabetes during pregnancy and gestational diabetes are associated with childhood adiposity; however, associations of lower maternal glucose levels during pregnancy with childhood adiposity, independent of maternal BMI, remain less clear. The objective was to examine associations of maternal glucose levels during pregnancy with childhood adiposity in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) cohort. METHODS: The HAPO Study was an observational epidemiological international multi-ethnic investigation that established strong associations of glucose levels during pregnancy with multiple adverse perinatal outcomes. The HAPO Follow-up Study (HAPO FUS) included 4832 children from ten HAPO centres whose mothers had a 75 g OGTT at ~28 weeks gestation 10-14 years earlier, with glucose values blinded to participants and clinical caregivers. The primary outcome was child adiposity, including: (1) being overweight/obese according to sex- and age-specific cut-offs based on the International Obesity Task Force (IOTF) criteria; (2) IOTF-defined obesity only; and (3) measurements >85th percentile for sum of skinfolds, waist circumference and per cent body fat. Primary predictors were maternal OGTT and HbA1c values during pregnancy. RESULTS: Fully adjusted models that included maternal BMI at pregnancy OGTT indicated positive associations between maternal glucose predictors and child adiposity outcomes. For one SD difference in pregnancy glucose and HbA1c measures, ORs for each child adiposity outcome were in the range of 1.05-1.16 for maternal fasting glucose, 1.11-1.19 for 1 h glucose, 1.09-1.21 for 2 h glucose and 1.12-1.21 for HbA1c. Associations were significant, except for associations of maternal fasting glucose with offspring being overweight/obese or having waist circumference >85th percentile. Linearity was confirmed in all adjusted models. Exploratory sex-specific analyses indicated generally consistent associations for boys and girls. CONCLUSIONS/INTERPRETATION: Exposure to higher levels of glucose in utero is independently associated with childhood adiposity, including being overweight/obese, obesity, skinfold thickness, per cent body fat and waist circumference. Glucose levels less than those diagnostic of diabetes are associated with greater childhood adiposity; this may have implications for long-term metabolic health.

Genomic approaches for understanding the genetics of complex disease.

There are thousands of known associations between genetic variants and complex human phenotypes, and the rate of novel discoveries is rapidly increasing. Translating those associations into knowledge of disease mechanisms remains a fundamental challenge because the associated variants are overwhelmingly in noncoding regions of the genome where we have few guiding principles to predict their function. Intersecting the compendium of identified genetic associations with maps of regulatory activity across the human genome has revealed that phenotype-associated variants are highly enriched in candidate regulatory elements. Allele-specific analyses of gene regulation can further prioritize variants that likely have a functional effect on disease mechanisms; and emerging high-throughput assays to quantify the activity of candidate regulatory elements are a promising next step in that direction. Together, these technologies have created the ability to systematically and empirically test hypotheses about the function of noncoding variants and haplotypes at the scale needed for comprehensive and systematic follow-up of genetic association studies. Major coordinated efforts to quantify regulatory mechanisms across genetically diverse populations in increasingly realistic cell models would be highly beneficial to realize that potential.

Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort.

We report a novel high-throughput method to empirically quantify individual-specific regulatory element activity at the population scale. The approach combines targeted DNA capture with a high-throughput reporter gene expression assay. As demonstration, we measured the activity of more than 100 putative regulatory elements from 95 individuals in a single experiment. In agreement with previous reports, we found that most genetic variants have weak effects on distal regulatory element activity. Because haplotypes are typically maintained within but not between assayed regulatory elements, the approach can be used to identify causal regulatory haplotypes that likely contribute to human phenotypes. Finally, we demonstrate the utility of the method to functionally fine map causal regulatory variants in regions of high linkage disequilibrium identified by expression quantitative trait loci (eQTL) analyses.

Association of Gestational Diabetes With Maternal Disorders of Glucose Metabolism and Childhood Adiposity.

Importance: The sequelae of gestational diabetes (GD) by contemporary criteria that diagnose approximately twice as many women as previously used criteria are unclear. Objective: To examine associations of GD with maternal glucose metabolism and childhood adiposity 10 to 14 years' postpartum. Design, Setting, and Participants: The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study established associations of glucose levels during pregnancy with perinatal outcomes and the follow-up study evaluated the long-term outcomes (4697 mothers and 4832 children; study visits occurred between February 13, 2013, and December 13, 2016). Exposures: Gestational diabetes was defined post hoc using criteria from the International Association of Diabetes and Pregnancy Study Groups consisting of 1 or more of the following 75-g oral glucose tolerance test results (fasting plasma glucose ≥92 mg/dL; 1-hour plasma glucose level ≥180 mg/dL; 2-hour plasma glucose level ≥153 mg/dL). Main Outcomes and Measures: Primary maternal outcome: a disorder of glucose metabolism (composite of type 2 diabetes or prediabetes). Primary outcome for children: being overweight or obese; secondary outcomes: obesity, body fat percentage, waist circumference, and sum of skinfolds (>85th percentile for latter 3 outcomes). Results: The analytic cohort included 4697 mothers (mean [SD] age, 41.7 [5.7] years) and 4832 children (mean [SD] age, 11.4 [1.2] years; 51.0% male). The median duration of follow-up was 11.4 years. The criteria for GD were met by 14.3% (672/4697) of mothers overall and by 14.1% (683/4832) of mothers of participating children. Among mothers with GD, 52.2% (346/663) developed a disorder of glucose metabolism vs 20.1% (791/3946) of mothers without GD (odds ratio [OR], 3.44 [95% CI, 2.85 to 4.14]; risk difference [RD], 25.7% [95% CI, 21.7% to 29.7%]). Among children of mothers with GD, 39.5% (269/681) were overweight or obese and 19.1% (130/681) were obese vs 28.6% (1172/4094) and 9.9% (405/4094), respectively, for children of mothers without GD. Adjusted for maternal body mass index during pregnancy, the OR was 1.21 (95% CI, 1.00 to 1.46) for children who were overweight or obese and the RD was 3.7% (95% CI, -0.16% to 7.5%); the OR was 1.58 (95% CI, 1.24 to 2.01) for children who were obese and the RD was 5.0% (95% CI, 2.0% to 8.0%); the OR was 1.35 (95% CI, 1.08 to 1.68) for body fat percentage and the RD was 4.2% (95% CI, 0.9% to 7.4%); the OR was 1.34 (95% CI, 1.08 to 1.67) for waist circumference and the RD was 4.1% (95% CI, 0.8% to 7.3%); and the OR was 1.57 (95% CI, 1.27 to 1.95) for sum of skinfolds and the RD was 6.5% (95% CI, 3.1% to 9.9%). Conclusions and Relevance: Among women with GD identified by contemporary criteria compared with those without it, GD was significantly associated with a higher maternal risk for a disorder of glucose metabolism during long-term follow-up after pregnancy. Among children of mothers with GD vs those without it, the difference in childhood overweight or obesity defined by body mass index cutoffs was not statistically significant; however, additional measures of childhood adiposity may be relevant in interpreting the study findings.

Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data.

BACKGROUND: Metabolomics offers a unique integrative perspective for health research, reflecting genetic and environmental contributions to disease-related phenotypes. Identifying robust associations in population-based or large-scale clinical studies demands large numbers of subjects and therefore sample batching for gas-chromatography/mass spectrometry (GC/MS) non-targeted assays. When run over weeks or months, technical noise due to batch and run-order threatens data interpretability. Application of existing normalization methods to metabolomics is challenged by unsatisfied modeling assumptions and, notably, failure to address batch-specific truncation of low abundance compounds. RESULTS: To curtail technical noise and make GC/MS metabolomics data amenable to analyses describing biologically relevant variability, we propose mixture model normalization (mixnorm) that accommodates truncated data and estimates per-metabolite batch and run-order effects using quality control samples. Mixnorm outperforms other approaches across many metrics, including improved correlation of non-targeted and targeted measurements and superior performance when metabolite detectability varies according to batch. For some metrics, particularly when truncation is less frequent for a metabolite, mean centering and median scaling demonstrate comparable performance to mixnorm. CONCLUSIONS: When quality control samples are systematically included in batches, mixnorm is uniquely suited to normalizing non-targeted GC/MS metabolomics data due to explicit accommodation of batch effects, run order and varying thresholds of detectability. Especially in large-scale studies, normalization is crucial for drawing accurate conclusions from non-targeted GC/MS metabolomics data.