Showing papers by "Jose C. Florez published in 2019"

Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine.

Abstract: During the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes. As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management. In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity. We also describe the challenges that will need to be overcome if this potential is to be fully realized.

Genome-Wide Association Study of Diabetic Kidney Disease Highlights Biology Involved in Glomerular Basement Membrane Collagen.

Abstract: Background Although diabetic kidney disease demonstrates both familial clustering and single nucleotide polymorphism heritability, the specific genetic factors influencing risk remain largely unknown. Methods To identify genetic variants predisposing to diabetic kidney disease, we performed genome-wide association study (GWAS) analyses. Through collaboration with the Diabetes Nephropathy Collaborative Research Initiative, we assembled a large collection of type 1 diabetes cohorts with harmonized diabetic kidney disease phenotypes. We used a spectrum of ten diabetic kidney disease definitions based on albuminuria and renal function. Results Our GWAS meta-analysis included association results for up to 19,406 individuals of European descent with type 1 diabetes. We identified 16 genome-wide significant risk loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain (COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM). Mutations in COL4A3 are implicated in heritable nephropathies, including the progressive inherited nephropathy Alport syndrome. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of diabetic kidney disease, including albuminuria and ESKD, and demonstrated a significant association with GBM width; protective allele carriers had thinner GBM before any signs of kidney disease, and its effect was dependent on glycemia. Three other loci are in or near genes with known or suggestive involvement in this condition (BMP7) or renal biology (COLEC11 and DDR1). Conclusions The 16 diabetic kidney disease-associated loci may provide novel insights into the pathogenesis of this condition and help identify potential biologic targets for prevention and treatment.

Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls

Abstract: Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10−3) and candidate genes from knockout mice (P = 5.2 × 10−3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000–185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.Exome-sequencing analyses of a large cohort of patients with type 2 diabetes and control individuals without diabetes from five ancestries are used to identify gene-level associations of rare variants that are associated with type 2 diabetes.

Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium.

Abstract: Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P < 1 × 10−6) associated with intake of any macronutrient in 91,114 European ancestry participants. Four loci replicated and reached genome-wide significance in a combined meta-analysis including 123,659 European descent participants, unraveling two novel loci; a common variant in RARB locus for carbohydrate intake and a rare variant in DRAM1 locus for protein intake, and corroborating earlier FGF21 and FTO findings. In additional analysis of 144,770 participants from the UK Biobank, all identified associations from the two-stage analysis were confirmed except for DRAM1. Identified loci might have implications in brain and adipose tissue biology and have clinical impact in obesity-related phenotypes. Our findings provide new insight into biological functions related to macronutrient intake.

Quality of dietary fat and genetic risk of type 2 diabetes: individual participant data meta-analysis.

Abstract: Objective To investigate whether the genetic burden of type 2 diabetes modifies the association between the quality of dietary fat and the incidence of type 2 diabetes. Design Individual participant data meta-analysis. Data sources Eligible prospective cohort studies were systematically sourced from studies published between January 1970 and February 2017 through electronic searches in major medical databases (Medline, Embase, and Scopus) and discussion with investigators. Review methods Data from cohort studies or multicohort consortia with available genome-wide genetic data and information about the quality of dietary fat and the incidence of type 2 diabetes in participants of European descent was sought. Prospective cohorts that had accrued five or more years of follow-up were included. The type 2 diabetes genetic risk profile was characterized by a 68-variant polygenic risk score weighted by published effect sizes. Diet was recorded by using validated cohort-specific dietary assessment tools. Outcome measures were summary adjusted hazard ratios of incident type 2 diabetes for polygenic risk score, isocaloric replacement of carbohydrate (refined starch and sugars) with types of fat, and the interaction of types of fat with polygenic risk score. Results Of 102 305 participants from 15 prospective cohort studies, 20 015 type 2 diabetes cases were documented after a median follow-up of 12 years (interquartile range 9.4-14.2). The hazard ratio of type 2 diabetes per increment of 10 risk alleles in the polygenic risk score was 1.64 (95% confidence interval 1.54 to 1.75, I2=7.1%, τ2=0.003). The increase of polyunsaturated fat and total omega 6 polyunsaturated fat intake in place of carbohydrate was associated with a lower risk of type 2 diabetes, with hazard ratios of 0.90 (0.82 to 0.98, I2=18.0%, τ2=0.006; per 5% of energy) and 0.99 (0.97 to 1.00, I2=58.8%, τ2=0.001; per increment of 1 g/d), respectively. Increasing monounsaturated fat in place of carbohydrate was associated with a higher risk of type 2 diabetes (hazard ratio 1.10, 95% confidence interval 1.01 to 1.19, I2=25.9%, τ2=0.006; per 5% of energy). Evidence of small study effects was detected for the overall association of polyunsaturated fat with the risk of type 2 diabetes, but not for the omega 6 polyunsaturated fat and monounsaturated fat associations. Significant interactions between dietary fat and polygenic risk score on the risk of type 2 diabetes (P>0.05 for interaction) were not observed. Conclusions These data indicate that genetic burden and the quality of dietary fat are each associated with the incidence of type 2 diabetes. The findings do not support tailoring recommendations on the quality of dietary fat to individual type 2 diabetes genetic risk profiles for the primary prevention of type 2 diabetes, and suggest that dietary fat is associated with the risk of type 2 diabetes across the spectrum of type 2 diabetes genetic risk.

Mendelian Randomization Analysis of Hemoglobin A1c as a Risk Factor for Coronary Artery Disease

Abstract: OBJECTIVE Observational studies show that higher hemoglobin A1c (A1C) predicts coronary artery disease (CAD). It remains unclear whether this association is driven entirely by glycemia. We used Mendelian randomization (MR) to test whether A1C is causally associated with CAD through glycemic and/or nonglycemic factors. RESEARCH DESIGN AND METHODS To examine the association of A1C with CAD, we selected 50 A1C-associated variants (log10 Bayes factor ≥6) from an A1C genome-wide association study (GWAS; n = 159,940) and performed an inverse-variance weighted average of variant-specific causal estimates from CAD GWAS data (CARDIoGRAMplusC4D; 60,801 CAD case subjects/123,504 control subjects). We then replicated results in UK Biobank (18,915 CAD case subjects/455,971 control subjects) and meta-analyzed all results. Next, we conducted analyses using two subsets of variants, 16 variants associated with glycemic measures (fasting or 2-h glucose) and 20 variants associated with erythrocyte indices (e.g., hemoglobin [Hb]) but not glycemic measures. In additional MR analyses, we tested the association of Hb with A1C and CAD. RESULTS Genetically increased A1C was associated with higher CAD risk (odds ratio [OR] 1.61 [95% CI 1.40, 1.84] per %-unit, P = 6.9 × 10−12). Higher A1C was associated with increased CAD risk when using only glycemic variants (OR 2.23 [1.73, 2.89], P = 1.0 × 10−9) and when using only erythrocytic variants (OR 1.30 [1.08, 1.57], P = 0.006). Genetically decreased Hb, with concomitantly decreased mean corpuscular volume, was associated with higher A1C (0.30 [0.27, 0.33] %-unit, P = 2.9 × 10−6) per g/dL and higher CAD risk (OR 1.19 [1.04, 1.37], P = 0.02). CONCLUSIONS Genetic evidence supports a causal link between higher A1C and higher CAD risk. This relationship is driven not only by glycemic but also by erythrocytic, glycemia-independent factors.

Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium

Abstract: Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P < 1 × 10−6) associated with intake of any macronutrient in 91,114 European ancestry participants. Four loci replicated and reached genome-wide significance in a combined meta-analysis including 123,659 European descent participants, unraveling two novel loci; a common variant in RARB locus for carbohydrate intake and a rare variant in DRAM1 locus for protein intake, and corroborating earlier FGF21 and FTO findings. In additional analysis of 144,770 participants from the UK Biobank, all identified associations from the two-stage analysis were confirmed except for DRAM1. Identified loci might have implications in brain and adipose tissue biology and have clinical impact in obesity-related phenotypes. Our findings provide new insight into biological functions related to macronutrient intake.

Metabolite Profiles of Incident Diabetes and Heterogeneity of Treatment Effect in the Diabetes Prevention Program

Abstract: Novel biomarkers of type 2 diabetes (T2D) and response to preventative treatment in individuals with similar clinical risk may highlight metabolic pathways that are important in disease development. We profiled 331 metabolites in 2,015 baseline plasma samples from the Diabetes Prevention Program (DPP). Cox models were used to determine associations between metabolites and incident T2D, as well as whether associations differed by treatment group (i.e., lifestyle [ILS], metformin [MET], or placebo [PLA]), over an average of 3.2 years of follow-up. We found 69 metabolites associated with incident T2D regardless of treatment randomization. In particular, cytosine was novel and associated with the lowest risk. In an exploratory analysis, 35 baseline metabolite associations with incident T2D differed across the treatment groups. Stratification by baseline levels of several of these metabolites, including specific phospholipids and AMP, modified the effect that ILS or MET had on diabetes development. Our findings highlight novel markers of diabetes risk and preventative treatment effect in individuals who are clinically at high risk and motivate further studies to validate these interactions.

The SLC16A11 risk haplotype is associated with decreased insulin action, higher transaminases and large-size adipocytes.

Abstract: Objective A haplotype at chromosome 17p13 that reduces expression and function of the solute carrier transporter SLC16A11 is associated with increased risk for type 2 diabetes in Mexicans. We aim to investigate the detailed metabolic profile of SLC16A11 risk haplotype carriers to identify potential physiological mechanisms explaining the increased type 2 diabetes risk. Design Cross-sectional study. Methods We evaluated carriers (n = 72) and non-carriers (n = 75) of the SLC16A11 risk haplotype, with or without type 2 diabetes. An independent sample of 1069 subjects was used to replicate biochemical findings. The evaluation included euglycemic-hyperinsulinemic clamp, frequently sampled intravenous glucose tolerance test (FSIVGTT), dual-energy X-ray absorptiometry (DXA), MRI and spectroscopy and subcutaneous abdominal adipose tissue biopsies. Results Fat-free mass (FFM)-adjusted M value was lower in carriers of the SLC16A11 risk haplotype after adjusting for age and type 2 diabetes status (β = -0.164, P = 0.04). Subjects with type 2 diabetes and the risk haplotype demonstrated an increase of 8.76 U/L in alanine aminotransferase (ALT) (P = 0.02) and of 7.34 U/L in gamma-glutamyltransferase (GGT) (P = 0.05) compared with non-carriers and after adjusting for gender, age and ancestry. Among women with the risk haplotype and normal BMI, the adipocyte size was higher (P < 0.001). Conclusions Individuals carrying the SLC16A11 risk haplotype exhibited decreased insulin action. Higher serum ALT and GGT levels were found in carriers with type 2 diabetes, and larger adipocytes in subcutaneous fat in the size distribution in carrier women with normal weight.

Characterising the loss-of-function impact of 5’ untranslated region variants in whole genome sequence data from 15,708 individuals

Abstract: Upstream open reading frames (uORFs) are important tissue-specific cis-regulators of protein translation. Although isolated case reports have shown that variants that create or disrupt uORFs can cause disease, genetic sequencing approaches typically focus on protein-coding regions and ignore these variants. Here, we describe a systematic genome-wide study of variants that create and disrupt human uORFs, and explore their role in human disease using 15,708 whole genome sequences collected by the Genome Aggregation Database (gnomAD) project. We show that 14,897 variants that create new start codons upstream of the canonical coding sequence (CDS), and 2,406 variants disrupting the stop site of existing uORFs, are under strong negative selection. Furthermore, variants creating uORFs that overlap the CDS show signals of selection equivalent to coding loss-of-function variants, and uORF-perturbing variants are under strong selection when arising upstream of known disease genes and genes intolerant to loss-of-function variants. Finally, we identify specific genes where perturbation of uORFs is likely to represent an important disease mechanism, and report a novel uORF frameshift variant upstream of NF2 in families with neurofibromatosis. Our results highlight uORF-perturbing variants as an important and under-recognised functional class that can contribute to penetrant human disease, and demonstrate the power of large-scale population sequencing data to study the deleteriousness of specific classes of non-coding variants.

Tissue-Specific Alteration of Metabolic Pathways Influences Glycemic Regulation

Abstract: bioRxiv preprint doi: https://doi.org/10.1101/790618; this version posted October 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations

Abstract: The increasing size and diversity of genome-wide association studies provide an exciting opportunity to study how the genetics of complex traits vary among diverse populations. Here, we introduce covariate-adjusted LD score regression (cov-LDSC), a method to accurately estimate genetic heritability and its enrichment in both homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the GWAS samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates by 10% − 60% in admixed populations; in contrast, cov-LDSC is robust to all simulation parameters. We apply cov-LDSC to genotyping data from approximately 170,000 Latino, 47,000 African American and 135,000 European individuals. We estimate and detect heritability enrichment in three quantitative and five dichotomous phenotypes respectively, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals. Our results show that most traits have high concordance of and consistent tissue-specific heritability enrichment among different populations. However, for age at menarche, we observe population-specific heritability estimates of . We observe consistent patterns of tissue-specific heritability enrichment across populations; for example, in the limbic system for BMI, the per-standardized-annotation effect size τ* is 0.16 ± 0.04, 0.28 ± 0.11 and 0.18 ± 0.03 in Latino, African American and European populations respectively. Our results demonstrate that our approach is a powerful way to analyze genetic data for complex traits from underrepresented populations. Author summary Admixed populations such as African Americans and Hispanic Americans bear a disproportionately high burden of disease but remain underrepresented in current genetic studies. It is important to extend current methodological advancements for understanding the genetic basis of complex traits in homogeneous populations to individuals with admixed genetic backgrounds. Here, we develop a computationally efficient method to answer two specific questions. First, does genetic variation contribute to the same amount of phenotypic variation (heritability) across diverse populations? Second, are the genetic mechanisms shared among different populations? To answer these questions, we use our novel method to conduct the first comprehensive heritability-based analysis of a large number of admixed individuals. We show that there is a high degree of concordance in total heritability and tissue-specific enrichment between different ancestral groups. However, traits such as age at menarche show a noticeable differences among populations. Our work provides a powerful way to analyze genetic data in admixed populations and may contribute to the applicability of genomic medicine to admixed population groups.

A Polygenic Lipodystrophy Genetic Risk Score Characterizes Risk Independent of BMI in the Diabetes Prevention Program.

Abstract: Context There is substantial heterogeneity in insulin sensitivity, and genetics may suggest possible mechanisms by which common variants influence this trait. Objectives We aimed to evaluate an 11-variant polygenic lipodystrophy genetic risk score (GRS) for association with anthropometric, glycemic and metabolic traits in the Diabetes Prevention Program (DPP). In secondary analyses, we tested the association of the GRS with cardiovascular risk factors in the DPP. Design In 2713 DPP participants, we evaluated a validated GRS of 11 common variants associated with fasting insulin-based measures of insulin sensitivity discovered through genome-wide association studies that cluster with a metabolic profile of lipodystrophy, conferring high metabolic risk despite low body mass index (BMI). Results At baseline, a higher polygenic lipodystrophy GRS was associated with lower weight, BMI, and waist circumference measurements, but with worse insulin sensitivity index (ISI) values. Despite starting at a lower weight and BMI, a higher GRS was associated with less weight and BMI reduction at one year and less improvement in ISI after adjusting for baseline values but was not associated with diabetes incidence. A higher GRS was also associated with more atherogenic low-density lipoprotein peak-particle-density at baseline but was not associated with coronary artery calcium scores in the Diabetes Prevention Program Outcomes Study. Conclusions In the DPP, a higher polygenic lipodystrophy GRS for insulin resistance with lower BMI was associated with diminished improvement in insulin sensitivity and potential higher cardiovascular disease risk. This GRS helps characterize insulin resistance in a cohort of individuals at high risk for diabetes, independent of adiposity.

Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic loci and establishes causal relationships between educational attainment and healthy eating

Abstract: Unhealthy dietary habits are leading risk factors for life-altering diseases and mortality. Large-scale biobanks now enable genetic analysis of traits with modest heritability, such as diet. We performed genomewide association on 85 single food intake and 85 principal component-derived dietary patterns from food frequency questionnaires in UK Biobank. We identified 814 associated loci, including olfactory receptor associations with fruit and tea intake; 136 associations were only identified using dietary patterns. Mendelian randomization suggests a Western vs. prudent dietary pattern is causally influenced by factors correlated with education but is not strongly causal for coronary artery disease or type 2 diabetes.

Modeling Snyder-Robinson Syndrome in multipotent stromal cells reveals impaired mitochondrial function as a potential cause for deficient osteogenesis

Abstract: Patients with Snyder-Robinson Syndrome (SRS) exhibit deficient Spermidine Synthase (SMS) gene expression, which causes neurodevelopmental defects and osteoporosis, often leading to extremely fragile bones. To determine the underlying mechanism for impaired bone formation, we modelled the disease by silencing SMS in human bone marrow - derived multipotent stromal cells (MSCs) derived from healthy donors. We found that silencing SMS in MSCs led to reduced cell proliferation and deficient bone formation in vitro, as evidenced by reduced mineralization and decreased bone sialoprotein expression. Furthermore, transplantation of MSCs in osteoconductive scaffolds into immune deficient mice shows that silencing SMS also reduces ectopic bone formation in vivo. Tag-Seq Gene Expression Profiling shows that deficient SMS expression causes strong transcriptome changes, especially in genes related to cell proliferation and metabolic functions. Similarly, metabolome analysis by mass spectrometry, shows that silencing SMS strongly impacts glucose metabolism. This was consistent with observations using electron microscopy, where SMS deficient MSCs show high levels of mitochondrial fusion. In line with these findings, SMS deficiency causes a reduction in glucose consumption and increase in lactate secretion. Our data also suggests that SMS deficiency affects iron metabolism in the cells, which we hypothesize is linked to deficient mitochondrial function. Altogether, our studies suggest that SMS deficiency causes strong transcriptomic and metabolic changes in MSCs, which are likely associated with the observed impaired osteogenesis both in vitro and in vivo.

Multi-trait genome-wide association meta-analysis of dietary intake identifies new loci and genetic and functional links with metabolic traits

Abstract: Dietary intake, a major contributor to the global obesity epidemic1–5, is a complex phenotype partially affected by innate physiological processes.6–11 However, previous genome-wide association studies (GWAS) have only implicated a few loci in variability of dietary composition.12–14 Here, we present a multi-trait genome-wide association meta-analysis of inter-individual variation in dietary intake in 283,119 European-ancestry participants from UK Biobank and CHARGE consortium, and identify 96 genome-wide significant loci. Dietary intake signals map to different brain tissues and are enriched for genes expressed in β1-tanycytes and serotonergic and GABAergic neurons. We also find enrichment of biological pathways related to neurogenesis. Integration of cell-line and brain-specific epigenomic annotations identify 15 additional loci. Clustering of genome-wide significant variants yields three main genetic clusters with distinct associations with obesity and type 2 diabetes (T2D). Overall, these results enhance biological understanding of dietary composition, highlight neural mechanisms, and support functional follow-up experiments.

Tissue-Specific Alteration of Metabolic Pathways Influences Glycemic Regulation

Abstract: Summary Metabolic dysregulation in multiple tissues alters glucose homeostasis and influences risk for type 2 diabetes (T2D). To identify pathways and tissues influencing T2D-relevant glycemic traits (fasting glucose [FG], fasting insulin [FI], two-hour glucose [2hGlu] and glycated hemoglobin [HbA1c]), we investigated associations of exome-array variants in up to 144,060 individuals without diabetes of multiple ancestries. Single-variant analyses identified novel associations at 21 coding variants in 18 novel loci, whilst gene-based tests revealed signals at two genes, TF (HbA1c) and G6PC (FG, FI). Pathway and tissue enrichment analyses of trait-associated transcripts confirmed the importance of liver and kidney for FI and pancreatic islets for FG regulation, implicated adipose tissue in FI and the gut in 2hGlu, and suggested a role for the non-endocrine pancreas in glucose homeostasis. Functional studies demonstrated that a novel FG/FI association at the liver-enriched G6PC transcript was driven by multiple rare loss-of-function variants. The FG/HbA1c-associated, islet-specific G6PC2 transcript also contained multiple rare functional variants, including two alleles within the same codon with divergent effects on glucose levels. Our findings highlight the value of integrating genomic and functional data to maximize biological inference. Highlights 23 novel coding variant associations (single-point and gene-based) for glycemic traits 51 effector transcripts highlighted different pathway/tissue signatures for each trait The exocrine pancreas and gut influence fasting and 2h glucose, respectively Multiple variants in liver-enriched G6PC and islet-specific G6PC2 influence glycemia

Genome-wide Association Study of Change in Fasting Glucose over time in 13,807 non-diabetic European Ancestry Individuals

Abstract: Type 2 diabetes (T2D) affects the health of millions of people worldwide. The identification of genetic determinants associated with changes in glycemia over time might illuminate biological features that precede the development of T2D. Here we conducted a genome-wide association study of longitudinal fasting glucose changes in up to 13,807 non-diabetic individuals of European descent from nine cohorts. Fasting glucose change over time was defined as the slope of the line defined by multiple fasting glucose measurements obtained over up to 14 years of observation. We tested for associations of genetic variants with inverse-normal transformed fasting glucose change over time adjusting for age at baseline, sex, and principal components of genetic variation. We found no genome-wide significant association (P < 5 × 10−8) with fasting glucose change over time. Seven loci previously associated with T2D, fasting glucose or HbA1c were nominally (P < 0.05) associated with fasting glucose change over time. Limited power influences unambiguous interpretation, but these data suggest that genetic effects on fasting glucose change over time are likely to be small. A public version of the data provides a genomic resource to combine with future studies to evaluate shared genetic links with T2D and other metabolic risk traits.

Genome-wide Association Study of Change in Fasting Glucose over time in 13,807 non-diabetic European Ancestry Individuals.

Abstract: Type 2 diabetes (T2D) affects the health of millions of people worldwide. The identification of genetic determinants associated with changes in glycemia over time might illuminate biological features that precede the development of T2D. Here we conducted a genome-wide association study of longitudinal fasting glucose changes in up to 13,807 non-diabetic individuals of European descent from nine cohorts. Fasting glucose change over time was defined as the slope of the line defined by multiple fasting glucose measurements obtained over up to 14 years of observation. We tested for associations of genetic variants with inverse-normal transformed fasting glucose change over time adjusting for age at baseline, sex, and principal components of genetic variation. We found no genome-wide significant association (P < 5 × 10−8) with fasting glucose change over time. Seven loci previously associated with T2D, fasting glucose or HbA1c were nominally (P < 0.05) associated with fasting glucose change over time. Limited power influences unambiguous interpretation, but these data suggest that genetic effects on fasting glucose change over time are likely to be small. A public version of the data provides a genomic resource to combine with future studies to evaluate shared genetic links with T2D and other metabolic risk traits.

SAT-123 Burden of Type 2 Diabetes Genetic Risk Alleles Differs Among Physiologic Subtypes of Gestational Diabetes Mellitus

Abstract: Abstract Background: Gestational diabetes mellitus (GDM) is associated with an increased risk of adverse perinatal outcomes and future maternal type 2 diabetes (T2D). We previously demonstrated that women with GDM can be classified into subtypes according to the predominant late pregnancy physiologic defect leading to hyperglycemia (i.e. predominant insulin secretion or sensitivity defect, defined using oral glucose tolerance test-based indices); the risk of adverse perinatal outcomes differs among subtypes. In the present analysis, we tested whether women with different GDM subtypes carry different burdens of genetic risk alleles known to be associated with T2D and related glycemic traits. Methods: We built genetic risk scores (GRSs) using variant alleles known to be associated with T2D and glycemic traits in non-pregnant Europeans (T2D: 85 variants, fasting glucose: 38, fasting insulin: 18, reduced insulin secretion: 24, reduced insulin sensitivity: 14). In the Genetics of Glucose Regulation in Gestation and Growth cohort, we compared these GRSs among previously defined GDM subtypes and pregnant women with normal glucose tolerance (NGT) using the Kruskal-Wallis test. We performed post-hoc comparisons using Dunn’s test with Bonferroni-adjusted P-values. Results: Of 550 women, 43 (7.8%) developed GDM. Of women with GDM, 23 (53%) had a predominant insulin sensitivity defect, 13 (30%) had a predominant insulin secretion defect, and 6 (14%) had mixed defects. The insulin secretion and T2D GRSs were associated with GDM (insulin secretion: unadjusted odds ratio [95% confidence interval] 1.12 [1.02, 1.24]; T2D: 1.06 [1.01, 1.11]); the other GRSs showed consistent trends toward association (fasting glucose: 1.07 [1.00, 1.15], P=0.06; fasting insulin: 1.09 [0.97, 1.23], P=0.10; insulin sensitivity: 1.14 [0.99, 1.32], P=0.08). The fasting insulin, T2D, and insulin secretion GRSs differed by GDM subtype. Compared to women with NGT, women with GDM due to an insulin sensitivity defect had higher mean [SD] fasting insulin GRS (21.5[2.0] vs 20.2[2.7], P=0.01) and similar T2D GRS (95.0 [7.9] vs. 95.0[6.1], P>0.99). Women with GDM due to an insulin secretion defect had higher mean T2D GRS (99.6[6.1] P=0.04 vs. NGT) and a trend toward higher mean insulin secretion GRS (24.1[2.0] vs 22.4[3.1] in NGT, P=0.10). Women with mixed defects had higher mean T2D GRS (104.6[7.4], P=0.01) and insulin secretion GRS (27.4[4.1], P=0.01), compared to women with NGT. Discussion: Physiologic subtypes of GDM differ genetically. Women with GDM due to a predominant insulin secretion defect or a mixed secretion-sensitivity defect carry an increased burden of T2D-associated genetic risk alleles, while women with GDM due to a predominant insulin sensitivity defect do not. Future research should test whether certain physiologic subtypes of GDM have an increased risk of progression to T2D.