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1. Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits.

Author

Westerman, Kenneth E, Walker, Maura E, Gaynor, Sheila M, Wessel, Jennifer, DiCorpo, Daniel, Ma, Jiantao, Alonso, Alvaro, Aslibekyan, Stella, Baldridge, Abigail S, Bertoni, Alain G, Biggs, Mary L, Brody, Jennifer A, Chen, Yii-Der Ida, Dupuis, Joseé, Goodarzi, Mark O, Guo, Xiuqing, Hasbani, Natalie R, Heath, Adam, Hidalgo, Bertha, Irvin, Marguerite R, Johnson, W Craig, Kalyani, Rita R, Lange, Leslie, Lemaitre, Rozenn N, Liu, Ching-Ti, Liu, Simin, Moon, Jee-Young, Nassir, Rami, Pankow, James S, Pettinger, Mary, Raffield, Laura M, Rasmussen-Torvik, Laura J, Selvin, Elizabeth, Senn, Mackenzie K, Shadyab, Aladdin H, Smith, Albert V, Smith, Nicholas L, Steffen, Lyn, Talegakwar, Sameera, Taylor, Kent D, de Vries, Paul S, Wilson, James G, Wood, Alexis C, Yanek, Lisa R, Yao, Jie, Zheng, Yinan, Boerwinkle, Eric, Morrison, Alanna C, Fornage, Miriam, Russell, Tracy P, Psaty, Bruce M, Levy, Daniel, Heard-Costa, Nancy L, Ramachandran, Vasan S, Mathias, Rasika A, Arnett, Donna K, Kaplan, Robert, North, Kari E, Correa, Adolfo, Carson, April, Rotter, Jerome I, Rich, Stephen S, Manson, JoAnn E, Reiner, Alexander P, Kooperberg, Charles, Florez, Jose C, Meigs, James B, Merino, Jordi, Tobias, Deirdre K, Chen, Han, and Manning, Alisa K

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Nutrition, Diabetes, Human Genome, Minority Health, Genetics, Cardiovascular, Health Disparities, Prevention, Precision Medicine, Clinical Research, Metabolic and endocrine, Good Health and Well Being, Humans, Glycated Hemoglobin, Diet, Diabetes Mellitus, Eating, Guanine Nucleotide Dissociation Inhibitors, Genome-Wide Association Study, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], -0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that >150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry.Article highlightsWe aimed to identify genetic modifiers of the dietary macronutrient-glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry-enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene-macronutrient interactions.

Published
2023

2. Nontargeted and Targeted Metabolomic Profiling Reveals Novel Metabolite Biomarkers of Incident Diabetes in African Americans

Author

Chen, Zsu-Zsu, Pacheco, Julian Avila, Gao, Yan, Deng, Shuliang, Peterson, Bennet, Shi, Xu, Zheng, Shuning, Tahir, Usman A, Katz, Daniel H, Cruz, Daniel E, Ngo, Debby, Benson, Mark D, Robbins, Jeremy M, Guo, Xiuqing, del Rocio Sevilla Gonzalez, Magdalena, Manning, Alisa, Correa, Adolfo, Meigs, James B, Taylor, Kent D, Rich, Stephen S, Goodarzi, Mark O, Rotter, Jerome I, Wilson, James G, Clish, Clary B, and Gerszten, Robert E

Subjects
Medical Biochemistry and Metabolomics, Biomedical and Clinical Sciences, Diabetes, Clinical Research, Prevention, Metabolic and endocrine, Humans, Blood Glucose, Black or African American, Metabolomics, Biomarkers, Diabetes Mellitus, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Nontargeted metabolomics methods have increased potential to identify new disease biomarkers, but assessments of the additive information provided in large human cohorts by these less biased techniques are limited. To diversify our knowledge of diabetes-associated metabolites, we leveraged a method that measures 305 targeted or "known" and 2,342 nontargeted or "unknown" compounds in fasting plasma samples from 2,750 participants (315 incident cases) in the Jackson Heart Study (JHS)-a community cohort of self-identified African Americans-who are underrepresented in omics studies. We found 307 unique compounds (82 known) associated with diabetes after adjusting for age and sex at a false discovery rate of

Published
2022

3. Polygenic Risk for Type 2 Diabetes in African Americans

Author

Irvin, Marguerite R., primary, Ge, Tian, additional, Patki, Amit, additional, Srinivasasainagendra, Vinodh, additional, Armstrong, Nicole D., additional, Davis, Brittney, additional, Jones, Alana C, additional, Perez, Emma, additional, Stalbow, Lauren, additional, Lebo, Matthew, additional, Kenny, Eimear, additional, Loos, Ruth J.F., additional, Ng, Maggie C. Y., additional, Smoller, Jordan W., additional, Meigs, James B., additional, Lange, Leslie A., additional, Karlson, Elizabeth W., additional, Limdi, Nita A., additional, and Tiwari, Hemant K., additional

Published
2024
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4. Genome-Wide Association Study of the Modified Stumvoll Insulin Sensitivity Index Identifies BCL2 and FAM19A2 as Novel Insulin Sensitivity Loci

Author

Walford, Geoffrey A, Gustafsson, Stefan, Rybin, Denis, Stančáková, Alena, Chen, Han, Liu, Ching-Ti, Hong, Jaeyoung, Jensen, Richard A, Rice, Ken, Morris, Andrew P, Mägi, Reedik, Tönjes, Anke, Prokopenko, Inga, Kleber, Marcus E, Delgado, Graciela, Silbernagel, Günther, Jackson, Anne U, Appel, Emil V, Grarup, Niels, Lewis, Joshua P, Montasser, May E, Landenvall, Claes, Staiger, Harald, Luan, Jian’an, Frayling, Timothy M, Weedon, Michael N, Xie, Weijia, Morcillo, Sonsoles, Martínez-Larrad, María Teresa, Biggs, Mary L, Chen, Yii-Der Ida, Corbaton-Anchuelo, Arturo, Færch, Kristine, Gómez-Zumaquero, Juan Miguel, Goodarzi, Mark O, Kizer, Jorge R, Koistinen, Heikki A, Leong, Aaron, Lind, Lars, Lindgren, Cecilia, Machicao, Fausto, Manning, Alisa K, Martín-Núñez, Gracia María, Rojo-Martínez, Gemma, Rotter, Jerome I, Siscovick, David S, Zmuda, Joseph M, Zhang, Zhongyang, Serrano-Rios, Manuel, Smith, Ulf, Soriguer, Federico, Hansen, Torben, Jørgensen, Torben J, Linnenberg, Allan, Pedersen, Oluf, Walker, Mark, Langenberg, Claudia, Scott, Robert A, Wareham, Nicholas J, Fritsche, Andreas, Häring, Hans-Ulrich, Stefan, Norbert, Groop, Leif, O’Connell, Jeff R, Boehnke, Michael, Bergman, Richard N, Collins, Francis S, Mohlke, Karen L, Tuomilehto, Jaakko, März, Winfried, Kovacs, Peter, Stumvoll, Michael, Psaty, Bruce M, Kuusisto, Johanna, Laakso, Markku, Meigs, James B, Dupuis, Josée, Ingelsson, Erik, and Florez, Jose C

Subjects
Biomedical and Clinical Sciences, Genetics, Prevention, Human Genome, Diabetes, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Chemokines, CC, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Insulin Receptor Substrate Proteins, Insulin Resistance, Male, Polymorphism, Single Nucleotide, Proto-Oncogene Proteins c-bcl-2, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Genome-wide association studies (GWAS) have found few common variants that influence fasting measures of insulin sensitivity. We hypothesized that a GWAS of an integrated assessment of fasting and dynamic measures of insulin sensitivity would detect novel common variants. We performed a GWAS of the modified Stumvoll Insulin Sensitivity Index (ISI) within the Meta-Analyses of Glucose and Insulin-Related Traits Consortium. Discovery for genetic association was performed in 16,753 individuals, and replication was attempted for the 23 most significant novel loci in 13,354 independent individuals. Association with ISI was tested in models adjusted for age, sex, and BMI and in a model analyzing the combined influence of the genotype effect adjusted for BMI and the interaction effect between the genotype and BMI on ISI (model 3). In model 3, three variants reached genome-wide significance: rs13422522 (NYAP2; P = 8.87 × 10(-11)), rs12454712 (BCL2; P = 2.7 × 10(-8)), and rs10506418 (FAM19A2; P = 1.9 × 10(-8)). The association at NYAP2 was eliminated by conditioning on the known IRS1 insulin sensitivity locus; the BCL2 and FAM19A2 associations were independent of known cardiometabolic loci. In conclusion, we identified two novel loci and replicated known variants associated with insulin sensitivity. Further studies are needed to clarify the causal variant and function at the BCL2 and FAM19A2 loci.

Published
2016

5. Impact of Type 2 Diabetes Susceptibility Variants on Quantitative Glycemic Traits Reveals Mechanistic Heterogeneity

Author

Dimas, Antigone S, Lagou, Vasiliki, Barker, Adam, Knowles, Joshua W, Mägi, Reedik, Hivert, Marie-France, Benazzo, Andrea, Rybin, Denis, Jackson, Anne U, Stringham, Heather M, Song, Ci, Fischer-Rosinsky, Antje, Boesgaard, Trine Welløv, Grarup, Niels, Abbasi, Fahim A, Assimes, Themistocles L, Hao, Ke, Yang, Xia, Lecoeur, Cécile, Barroso, Inês, Bonnycastle, Lori L, Böttcher, Yvonne, Bumpstead, Suzannah, Chines, Peter S, Erdos, Michael R, Graessler, Jurgen, Kovacs, Peter, Morken, Mario A, Narisu, Narisu, Payne, Felicity, Stancakova, Alena, Swift, Amy J, Tönjes, Anke, Bornstein, Stefan R, Cauchi, Stéphane, Froguel, Philippe, Meyre, David, Schwarz, Peter EH, Häring, Hans-Ulrich, Smith, Ulf, Boehnke, Michael, Bergman, Richard N, Collins, Francis S, Mohlke, Karen L, Tuomilehto, Jaakko, Quertemous, Thomas, Lind, Lars, Hansen, Torben, Pedersen, Oluf, Walker, Mark, Pfeiffer, Andreas FH, Spranger, Joachim, Stumvoll, Michael, Meigs, James B, Wareham, Nicholas J, Kuusisto, Johanna, Laakso, Markku, Langenberg, Claudia, Dupuis, Josée, Watanabe, Richard M, Florez, Jose C, Ingelsson, Erik, McCarthy, Mark I, Prokopenko, Inga, and Investigators, on behalf of the MAGIC

Subjects
Biomedical and Clinical Sciences, Genetics, Diabetes, Autoimmune Disease, Clinical Research, Prevention, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Alleles, Cluster Analysis, Diabetes Mellitus, Type 2, Female, Gene Frequency, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Insulin, Insulin Resistance, Insulin Secretion, Insulin-Secreting Cells, Male, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Risk Factors, Transcription Factors, MAGIC Investigators, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.

Published
2014

6. New Diabetes Diagnostic Threshold of Hemoglobin A1c and the 3-Year Incidence of Retinopathy

Author

Tsugawa, Yusuke, Takahashi, Osamu, Meigs, James B, Davis, Roger B, Imamura, Fumiaki, Fukui, Tsuguya, Taylor, William C, and Wee, Christina C

Subjects
Biomedical and Clinical Sciences, Clinical Research, Eye Disease and Disorders of Vision, Diabetes, Hematology, Prevention, Metabolic and endocrine, Adult, Diabetes Mellitus, Diabetic Retinopathy, Female, Glycated Hemoglobin, Humans, Logistic Models, Male, Middle Aged, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

The new diagnostic threshold of hemoglobin A(1c) was made based on evidence from cross-sectional studies, and no longitudinal study supports its validity. To examine whether hemoglobin A(1c) of 6.5% or higher defines a threshold for elevated risk of incident retinopathy, we analyzed longitudinal data of 19,897 Japanese adults who underwent a health checkup in 2006 and were followed up 3 years later. We used logistic regression models and restricted cubic spline models to examine the relationship between baseline hemoglobin A(1c) levels and the prevalence and the 3-year incidence of retinopathy. The restricted cubic spline model indicated a possible threshold for the risk of incident retinopathy at hemoglobin A(1c) levels of 6.0-7.0%. Logistic regression analysis found that individuals with hemoglobin A(1c) levels of 6.5-6.9% were at significantly higher risk of developing retinopathy at 3 years compared with those with hemoglobin A(1c) levels of 5.0-5.4% (adjusted odds ratio, 2.35 [95% CI 1.08-5.11]). Those with hemoglobin A(1c) levels between 5.5 and 6.4% exhibited no evidence of elevated risks. We did not observe a threshold in the analysis of prevalent retinopathy. Our longitudinal results support the validity of the new hemoglobin A(1c) threshold of 6.5% or higher for diagnosing diabetes.

Published
2012

7. No Interactions Between Previously Associated 2-Hour Glucose Gene Variants and Physical Activity or BMI on 2-Hour Glucose Levels

Author

Scott, Robert A, Chu, Audrey Y, Grarup, Niels, Manning, Alisa K, Hivert, Marie-France, Shungin, Dmitry, Tönjes, Anke, Yesupriya, Ajay, Barnes, Daniel, Bouatia-Naji, Nabila, Glazer, Nicole L, Jackson, Anne U, Kutalik, Zoltán, Lagou, Vasiliki, Marek, Diana, Rasmussen-Torvik, Laura J, Stringham, Heather M, Tanaka, Toshiko, Aadahl, Mette, Arking, Dan E, Bergmann, Sven, Boerwinkle, Eric, Bonnycastle, Lori L, Bornstein, Stefan R, Brunner, Eric, Bumpstead, Suzannah J, Brage, Soren, Carlson, Olga D, Chen, Han, Chen, Yii-Der Ida, Chines, Peter S, Collins, Francis S, Couper, David J, Dennison, Elaine M, Dowling, Nicole F, Egan, Josephine S, Ekelund, Ulf, Erdos, Michael R, Forouhi, Nita G, Fox, Caroline S, Goodarzi, Mark O, Grässler, Jürgen, Gustafsson, Stefan, Hallmans, Göran, Hansen, Torben, Hingorani, Aroon, Holloway, John W, Hu, Frank B, Isomaa, Bo, Jameson, Karen A, Johansson, Ingegerd, Jonsson, Anna, Jørgensen, Torben, Kivimaki, Mika, Kovacs, Peter, Kumari, Meena, Kuusisto, Johanna, Laakso, Markku, Lecoeur, Cécile, Lévy-Marchal, Claire, Li, Guo, Loos, Ruth JF, Lyssenko, Valeri, Marmot, Michael, Marques-Vidal, Pedro, Morken, Mario A, Müller, Gabriele, North, Kari E, Pankow, James S, Payne, Felicity, Prokopenko, Inga, Psaty, Bruce M, Renström, Frida, Rice, Ken, Rotter, Jerome I, Rybin, Denis, Sandholt, Camilla H, Sayer, Avan A, Shrader, Peter, Schwarz, Peter EH, Siscovick, David S, Stančáková, Alena, Stumvoll, Michael, Teslovich, Tanya M, Waeber, Gérard, Williams, Gordon H, Witte, Daniel R, Wood, Andrew R, Xie, Weijia, Boehnke, Michael, Cooper, Cyrus, Ferrucci, Luigi, Froguel, Philippe, Groop, Leif, Kao, WH Linda, Vollenweider, Peter, Walker, Mark, Watanabe, Richard M, Pedersen, Oluf, and Meigs, James B

Subjects
Biomedical and Clinical Sciences, Clinical Research, Genetics, Diabetes, Prevention, Obesity, Nutrition, 2.1 Biological and endogenous factors, Aetiology, 2.3 Psychological, social and economic factors, Metabolic and endocrine, Blood Glucose, Body Mass Index, Epigenesis, Genetic, Gene Expression Regulation, Genotype, Humans, Life Style, Motor Activity, Polymorphism, Single Nucleotide, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Gene-lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13-0.31], P = 1.63 × 10(-6)). All SNPs were associated with 2-h glucose (β = 0.06-0.12 mmol/allele, P ≤ 1.53 × 10(-7)), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene-lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.

Published
2012

8. Genome-Wide Association Identifies Nine Common Variants Associated With Fasting Proinsulin Levels and Provides New Insights Into the Pathophysiology of Type 2 Diabetes

Author

Strawbridge, Rona J, Dupuis, Josée, Prokopenko, Inga, Barker, Adam, Ahlqvist, Emma, Rybin, Denis, Petrie, John R, Travers, Mary E, Bouatia-Naji, Nabila, Dimas, Antigone S, Nica, Alexandra, Wheeler, Eleanor, Chen, Han, Voight, Benjamin F, Taneera, Jalal, Kanoni, Stavroula, Peden, John F, Turrini, Fabiola, Gustafsson, Stefan, Zabena, Carina, Almgren, Peter, Barker, David JP, Barnes, Daniel, Dennison, Elaine M, Eriksson, Johan G, Eriksson, Per, Eury, Elodie, Folkersen, Lasse, Fox, Caroline S, Frayling, Timothy M, Goel, Anuj, Gu, Harvest F, Horikoshi, Momoko, Isomaa, Bo, Jackson, Anne U, Jameson, Karen A, Kajantie, Eero, Kerr-Conte, Julie, Kuulasmaa, Teemu, Kuusisto, Johanna, Loos, Ruth JF, Luan, Jian'an, Makrilakis, Konstantinos, Manning, Alisa K, Martínez-Larrad, María Teresa, Narisu, Narisu, Nastase Mannila, Maria, Öhrvik, John, Osmond, Clive, Pascoe, Laura, Payne, Felicity, Sayer, Avan A, Sennblad, Bengt, Silveira, Angela, Stančáková, Alena, Stirrups, Kathy, Swift, Amy J, Syvänen, Ann-Christine, Tuomi, Tiinamaija, van 't Hooft, Ferdinand M, Walker, Mark, Weedon, Michael N, Xie, Weijia, Zethelius, Björn, Ongen, Halit, Mälarstig, Anders, Hopewell, Jemma C, Saleheen, Danish, Chambers, John, Parish, Sarah, Danesh, John, Kooner, Jaspal, Östenson, Claes-Göran, Lind, Lars, Cooper, Cyrus C, Serrano-Ríos, Manuel, Ferrannini, Ele, Forsen, Tom J, Clarke, Robert, Franzosi, Maria Grazia, Seedorf, Udo, Watkins, Hugh, Froguel, Philippe, Johnson, Paul, Deloukas, Panos, Collins, Francis S, Laakso, Markku, Dermitzakis, Emmanouil T, Boehnke, Michael, McCarthy, Mark I, Wareham, Nicholas J, Groop, Leif, Pattou, François, Gloyn, Anna L, Dedoussis, George V, Lyssenko, Valeriya, Meigs, James B, Barroso, Inês, Watanabe, Richard M, and Ingelsson, Erik

Subjects
Diabetes, Obesity, Human Genome, Genetics, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Adult, Diabetes Mellitus, Type 2, Fasting, Female, Genetic Variation, Genome, Human, Genotype, Humans, Insulin, Male, Polymorphism, Single Nucleotide, Proinsulin, DIAGRAM Consortium, GIANT Consortium, MuTHER Consortium, CARDIoGRAM Consortium, C4D Consortium, Medical and Health Sciences, Endocrinology & Metabolism
Abstract

ObjectiveProinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology.Research design and methodsWe have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates.ResultsNine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10(-8)). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10(-4)), improved β-cell function (P = 1.1 × 10(-5)), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10(-6)). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets.ConclusionsWe have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis.

Published
2011

9. 137-OR: A Genetic Approach to Detangling the Heterogeneity of Fasting Insulin Levels

Author

MAGDALENA SEVILLA, MARK O. GOODARZI, DANIEL A. DICORPO, JEROME I. ROTTER, JAMES B. MEIGS, MIRIAM UDLER, and ALISA MANNING

Subjects
Endocrinology, Diabetes and Metabolism, Internal Medicine
Abstract

Fasting insulin (FI) is a heterogeneous trait with high variability between individuals and populations. Increased FI is associated with an increased risk of type 2 diabetes (T2D) and its complications, including coronary artery disease (CAD) and hypertension (HTN) . We hypothesize that we could use a genetic approach to cluster FI-associated variants, characterize the clusters in terms of insulin processes within the Mexican American Cardiovascular Disease Study (MACAD; N=821) , and test for association of polygenic scores (PS) created from these clusters with T2D, CAD and HTN in three cohorts: Mass General Brigham Biobank (MGBB; N=18,127) , Aspirin in Reducing Events in the Elderly (ASPREE; N=13,349) , and Framingham Heart Study (FHS; N=7,145) . We identified six clusters using a soft clustering approach with 446 FI-increasing genetic alleles and 27 FI-related traits. Associations in two of the clusters, allowed us to characterize them and show their impact on T2D and T2D-associated complications. The first cluster had alleles associated with increased subcutaneous and visceral adiposity and was characterized by reduced insulin sensitivity (from euglycemic clamp; P=0.02) , increased insulin secretion (from oral glucose tolerance test [OGTT]; P=0.04) , and increased insulin clearance (from euglycemic clamp; P=0.02) in MACAD. This cluster’s PS was associated with HTN (MGBB Odds ratio (OR) = 1.04, P=0.013) , T2D (MGBB OR = 1.04, P =0.001) and CAD (MGB OR=1.07, P =0.008; ASPREE OR=1.04, P =0.036) The second cluster had alleles associated with a lipodystrophy phenotype and was characterized by increased insulin secretion (from OGTT P =0.019) in MACAD. We observed associations of the PS with T2D (MGBB OR= 1.13, P =2.3x10-10) and HTN (MGBB OR = 1.06, P =0.001) which were replicated in ASPREE and FHS. Our results suggest the presence of two different FI-associated genetic domains and elucidate the deleterious mechanisms by which increased FI is associated with T2D and T2D complications. Disclosure M.Sevilla: None. M.O.Goodarzi: None. D.A.Dicorpo: None. J.I.Rotter: None. J.B.Meigs: Consultant; Quest Diagnostics. M.Udler: None. A.Manning: None. Funding UM1DK078616

Published
2022

10. 109-OR: Physiologic Subtypes of Gestational Glucose Intolerance and Risk of Future Prediabetes

Author

DARYL J. SELEN, TANAYOTT THAWEETHAI, CHU YU, SARAH HSU, KAITLYN JAMES, JEFFREY L. ECKER, JAMES B. MEIGS, and CAMILLE E. POWE

Subjects
Endocrinology, Diabetes and Metabolism, Internal Medicine
Abstract

Background: Gestational glucose intolerance (GGI, abnormal initial gestational diabetes [GDM] screen) conveys an increased risk of future diabetes (DM) , even when GDM criteria is not met. We previously defined subtypes of GGI/GDM according to the underlying mechanism leading to hyperglycemia (insulin resistance vs. deficiency) . We aimed to determine if GGI subtypes are at differential risk for future prediabetes/DM; we hypothesized increased risk with insulin deficient subtypes. Methods: We defined GGI as glucose loading test 1-hr glucose ≥ 140 mg/dL at > 22 weeks’ gestation. We applied homeostasis model assessment (HOMA) to fasting glucose and insulin at 16-20 weeks’ gestation and classified pregnancies with GGI without GDM into subtypes according to the presence of insulin resistance and/or deficiency. We used Cox proportional-hazards models with time-varying exposures to assess risk of preDM/DM (HbA1c ≥ 5.7% at ≥ 3 months after delivery) in each GGI subtype compared to pregnancies with normal glucose tolerance after adjustment for age, race/ethnicity, health insurance, and first trimester BMI. Women were censored at the time of last HbA1c or GDM diagnosis. Results: Of 671 women with a median 9.9 years of follow-up, 29% (n=196) developed preDM/DM. Among pregnancies in 113 women with GGI, 54% had the insulin resistant subtype (IR) , 25% had the insulin deficient subtype (ID) , and 16% had the mixed pathophysiology subtype (MP) . Subtypes with insulin deficiency (ID + MP) and insulin resistance (IR) were both associated with increased risk of preDM/DM (ID + MP hazard ratio [HR]=1.8 [1.1-2.9], p=0.02 and IR HR=1.7 [1.1-2.6], p=0.01) . Each insulin deficient subtype also appeared to carry increased risk: ID HR=1.7 (0.9-3.1, p=0.12) and MP HR=2.1 (1.0-4.2, p=0.048) . Conclusions: GGI confers an increased risk of future prediabetes/DM, regardless of the mechanism leading to glucose intolerance. A combination of insulin resistance and deficiency may convey the highest risk of future prediabetes/DM. Disclosure D.J. Selen: None. T. Thaweethai: None. K. James: None. J.L. Ecker: None. J.B. Meigs: Consultant; Quest Diagnostics. C.E. Powe: None. Funding National Institutes of Health (T32DK007028) , Massachusetts General Hospital (Physician Scientist Development Award and Claflin Distinguished Scholar’s Award)

Published
2022

14. Nontargeted and Targeted Metabolomic Profiling Reveals Novel Metabolite Biomarkers of Incident Diabetes in African Americans

Author

Zsu-Zsu Chen, Julian Avila Pacheco, Yan Gao, Shuliang Deng, Bennet Peterson, Xu Shi, Shuning Zheng, Usman A. Tahir, Daniel H. Katz, Daniel E. Cruz, Debby Ngo, Mark D. Benson, Jeremy M. Robbins, Xiuqing Guo, Magdalena del Rocio Sevilla Gonzalez, Alisa Manning, Adolfo Correa, James B. Meigs, Kent D. Taylor, Stephen S. Rich, Mark O. Goodarzi, Jerome I. Rotter, James G. Wilson, Clary B. Clish, and Robert E. Gerszten

Subjects
Blood Glucose, Endocrinology, Diabetes and Metabolism, Prevention, Diabetes, Medical and Health Sciences, Black or African American, Endocrinology & Metabolism, Clinical Research, Internal Medicine, Diabetes Mellitus, Humans, Metabolomics, Metabolic and endocrine, Biomarkers
Abstract

Nontargeted metabolomics methods have increased potential to identify new disease biomarkers, but assessments of the additive information provided in large human cohorts by these less biased techniques are limited. To diversify our knowledge of diabetes associated metabolites, we leveraged a method that measures 305 targeted or “known” and 2,342 nontargeted or “unknown” compounds in fasting plasma samples from 2,750 participants (315 incident cases) in the Jackson Heart Study (JHS)—a community cohort of self-identified African Americans (AAs), who are underrepresented in omics studies. We found 307 unique compounds (82 known) associated with diabetes after adjusting for age and sex at a false discovery rate (FDR)

Published
2022

15. Islet Autoimmunity is Highly Prevalent and Associated With Diminished β-Cell Function in Patients With Type 2 Diabetes in the Grade Study

Author

Brooks-Worrell, Barbara, Hampe, Christiane S., Hattery, Erica G., Palomino, Brenda, Zangeneh, Sahar Z., Utzschneider, Kristina, Kahn, Steven E., Larkin, Mary E., Johnson, Mary L., Mather, Kieren J., Younes, Naji, Rasouli, Neda, Desouza, Cyrus, Cohen, Robert M., Park, Jean Y., Florez, Hermes J., Valencia, Willy Marcos, Shojaie, Ali, Palmer, Jerry P., Balasubramanyam, Ashok, Crandall, Jill P., McKee, Melissa Diane, Brown-Friday, Janet, Xhori, Entila, Ballentine-Cargill, Keisha, Duran, Sally, Lukin, Jennifer, Beringher, Stephanie, Gonzalez de la Torre, Susana, Phillips, Lawrence, Burgess, Elizabeth, Olson, Darin, Rhee, Mary, Wilson, Peter, Raines, Tasha Stephanie, Costello, Julie, Gullett, Chona, Maher-Albertelli, Maxine, Morehead, Folayan, Mungara, Radhika, Person, Saranjit, Savoye, Louise, Sibymon, Mabil, Tanukonda, Sridhar, White, Carol Ann, Holloway, Leah, Adams, Cynthia, Ross, April, Gonzalez Hattery, Erica, Gaba, Ruchi, Montes, Graciela, Wright, Charlyne, Hollander, Priscilla, Roe, Erin, Uy, Analyn, Burt, Polly, Estrada, Lorie, Chionh, Kris, Ismail-Beigi, Faramarz, Falck-Ytter, Corinna, Sayyed Kassem, Laure, Sood, Ajay, Tiktin, Margaret, Cramer, Bethany, Iacoboni, Jacalyn, Kononets, Maria V., Kulow, Tanya, Newman, Cynthia, Stancil, Katherine A., Sanders, Cristina, Tucker, Lisa, Werner, Amanda, Krol, Adrienne, McPhee, Gloria, Patel, Christine, Colosimo, Linda, Goland, Robin, Pring, James, Kringas, Patricia, Tejada, Jessica, Hausheer, Camille, Schneier, Harvey, Gumpel, Kelly, Kirpitch, Amanda, Green, Jennifer B., AbouAssi, Hiba, Chatterjee, Ranee, Feinglos, Mark N., English Jones, Jennifer, Khan, Shubi A., Kimpel, Jeanne B., Zimmer, Ronna P., Furst, Mary, Satterwhite, Barbara M., Thacker, Connie, Evans Kreider, Kathryn, Lteif, Amale, Hamilton, Tonya, Patel, Nick, Riera, Gabriela, Jackson, Marcia, Pirics, Vivian, Howard, Devin, Aguillar, Danielle, Hurt, Sloan, Bergenstal, Richard, Carlson, Anders, Martens, Thomas, Johnson, Mary, Hill, Renae, Hyatt, Jamie, Jensen, Connie, Madden, Marcia, Martin, Dianna, Willis, Holly, Konerza, Wanda, Passi, Rebecca, Kleeberger, Kathleen, Fortmann, Stephen, Herson, Michael, Mularski, Karen, Glauber, Harry, Prihoda, James, Ash, Britt, Carlson, Christina, Ramey, Phyllis Anne, Schield, Emily, Torgrimson-Ojerio, Britta, Arnold, Kathy, Kauffman, Bryan, Panos, Elease, Sahnow, Samantha, Bays, Kristi, Cook, Jennifer, Gluth, Jennifer, Sasaki, Debra, Schell, Katrina, Criscola, Jennifer, Friason, Camille, Jones, Suzi, Nazarov, Sergey, Barzilay, Joshua, Rassouli, Negah, Puttnam, Rachel, Curtis, Michelle, Stokes, Kia, Hollis, Bonita, Sanders-Jones, Cynthia, Nelson, Roslin, El-Haqq, Zakiah, Kolli, Abby, Tran, Tu, Wexler, Deborah, Larkin, Mary, Meigs, James, Dushkin, Amy, Rocchio, Gianna, Chambers, Brittany, Yepes, Mike, Steiner, Barbara, Dulin, Hilary, Cayford, Melody, DeManbey, Andrea, Gurry, Lindsey, Hillard, Mallory, Martin, Kimberly, Stevens, Christine, Thangthaeng, Nopporn, Kochis, Raquel, Raymond, Elyse, Ripley, Valerie, Park, Jean, Aroda, Vanita, Ghazi, Adline, Loveland, Amy, Hurtado, Maria, Kuhn, Alexander, Mofor, Florence, Marks, Jennifer, Oropesa-Gonzalez, Lisset, Riccio Veliz, Ana K., Nieto-Martinez, Ramfis, Gutt, Miriam, Ahmann, Andrew, Aby-Daniel, Diana, Joarder, Farahnaz, Morimoto, Victoria, Sprague, Carol, Yamashita, Daisuke, Cady, Nancy, Kirchhoff, Patricia, Rivera-Eschright, Nadia, Adducci, Joseph, Morales Gomez, Brianna, Goncharova, Alina, Hox, Sophia H., Petrovitch, Helen, Matwichyna, Michael, Jenkins, Victoria, Bermudez, Nina O., Ishii, Renée R., Hsia, Daniel S., Cefalu, William T., Greenway, Frank L., Waguespack, Celeste, King, Erin, Haynes, Natalie, Thomassie, Amy, Bourgeois, Brandi, Hazlett, Claire, Henry, Robert, Mudaliar, Sunder, Boeder, Schafer, Pettus, Jeremy, Diaz, Elsa, DeLue, Catherine, Castro, Erick, Hernandez, Sylvia, Krakoff, Jonathan, Curtis, Jeffrey M., Killean, Tina, Joshevama, Erica, Diaz, Enrique, Martin, Denelle, Karshner, Tracey, Albu, Jeanine, Pi-Sunyer, F. Xavier, Frances, Sylvaine, Maggio, Carol, Ellis, Emily, Bastawrose, Joseph, Gong, Xiuqun, Banerji, Mary Ann, August, Phyllis, Lorber, Daniel, Brown, Necole M., Josephson, Debra H., Thomas, Lorraine L., Tsovian, Mari, Cherian, Ajini, Jacobson, Marlo H., Mishko, Motria M., Kirkman, M. Sue, Bergamo, Katherine, Buse, John B., Dostou, Jean, Young, Laura, Goley, April, Kerr, Jeffrey, Largay, Joseph F., Guarda, Sonia, Cuffee, Juanita, Culmer, Dawn, Fraser, Rachael, Almeida, Hope, Coffer, Samantha, Debnam, Elizabeth, Kiker, Lauren, Morton, Sarah, Josey, Kim, Fuller, Gail, Garvey, W. Timothy, Cherrington, Andrea, Golson, Dana, Griffith, Olivia, Robertson, Mary Catherine, Agne, April, McCullars, Steve, Craig, Jacqueline, Kersey, Kimberly, Rogge, M. Colleen, Wilson, Carla, Burton, Kathryn, Lipp, Sonia, Vonder Meulen, Mary Beth, Schroeder, Emily, Steiner, Stephanie, Baker, Chelsea, Underkofler, Chantal, Douglass, Sara, Sivitz, William, Cline, Erin, Knosp, Laura, McConnell, Jennifer, Lowe, Tamara, Herman, William H., Pop-Busui, Rodica, Tan, Meng H., Martin, Catherine, Waltje, Andrea, Goodhall, Lynn, Eggleston, Rebecca, Kuo, Shihchen, Bule, Stephanie, Kessler, Nancy, LaSalle, Elizabeth, Seaquist, Elizabeth R., Bantle, Anne, Kumar, Anjali, Redmon, Bruce, Bantle, John, Harindhanavudhi, Tasma, Coe, Mary, Mech, Michael, Taddese, Abdisa, Lesne, Lesia, Smith, Shannon, Kuechenmeister, Lisa, Shivaswamy, Vijay, Morales, Ana Laura, Rodriguez, Maria Grace, Seipel, Kris, Alfred, Alissa, Eggert, Jenna, Lord, Grace, Taylor, William, Tillson, Renee, Schade, David S., Adolphe, Allen, Burge, Mark, Duran-Valdez, Elizabeth, Martinez, Janae, Hernandez McGinnis, Doris, Pucchetti, Benjamin, Scripsick, Elizabeth, DeFronzo, Ralph A., Cersosimo, Eugenio, Abdul-Ghani, Muhammad, Triplitt, Curtis, Verastiqui, Hector, Garza, Rosa Irene, Wright, Kathryn, Puckett, Curtiss, Raskin, Philip, Rhee, Chanhaeng, Abraham, Soma, Jordan, Lin Fan, Sao, Serey, Morton, Luisa, Smith, Oralenda, Osornio Walker, Laura, Schnurr-Breen, Laura, Ayala, Rosa, Kraymer, Robert, Sturgess, Daytheon, Utzschneider, Kristina M., Alarcon-Casas Wright, Lorena, Boyko, Edward, Tsai, Elaine C., Trence, Dace L., Fattaleh, Basma N., Montgomery, Brenda K., Atkinson, Karen M., Concepcion, Tessa, Kozedub, Alexandra, Moak, Cameron, Rhothisen, Samantha, Elasy, Tom A., Martin, Stephanie, Shackelford, Laura, Goidel, Rita, Hinkle, Nina, Lipps Hogan, Janie, Lovell, Cynthia, Myers, Janet, McGill, Janet B., Salam, Maamoun, Kissel, Sarah, Schweiger, Toni, Recklein, Carol, Tamborlane, William, Gatcomb, Patricia, Camp, Anne, Gulanski, Barbara, Inzucchi, Silvio, Pham, Kim, Alguard, Michele, Lessard, Katarzyna, Perez, Magalys, Magenheimer, Elizabeth, Montoza, Abmaridel, Nathan, David M., Lachin, John, Krause-Steinrauf, Heidi, Burch, Henry, Linder, Barbara, Bremer, Andrew, Backman, Michael, Bebu, Ionut, Buys, C.J., Fagan Murphy, Anna, Gao, Yuping, Gramzinski, Michaela, Hall, Stephanie, Legowski, Elizabeth, Arey, Alyssa, Bethepu, Joel, Lund, Claire, Mangat Dhaliwal, Pam, McGee, Paula, Mesimer, Emily, Ngo, Lisa, Steffes, Michael, Seegmiller, Jesse, Saenger, Amy, Arends, Valerie, Gabrielson, Deanna, Conner, Todd, Stuart, Warren, Day, Jolene, Scrymgeour, Alexandra, Soliman, Elsayed Z., Zhang, Zhu-Ming, Campbell, Charles, Hu, Julie, Keasler, Lisa, Hensley, Susan, Li, Yabing, Herman, William, Mihalcea, Rada, Perez-Rosas, Veronica, Prosser, Lisa, Resnicow, Kenneth, Ye, Wen, Shao, Hui, Zhang, Ping, Luchsinger, Jose, Sanchez, Danurys, Burch, Henry B., Fradkin, Judith, Groessl, Erik, Chong, Helen, Hillery, Naomi, Abdouch, Ivan, Brantley, Paula, Broyles, Frances E., Canaris, Gay, Copeland, Paul, Craine, Jeri J., Fein, Warren L., Lee, Melissa S., Meiners, Rebecca, Meiners, Vaughn, O’Neal, Hollis, Park, James E., Sledge, Edward, Steppel-Resnick, Jeanne, Turchin, Alexander, Higgins, John, Fischer, Lawrence, Golden, Sherita, Gonzalez, Jeffrey, Naik, Aanand, Walker, Elizabeth, Doner Lotenberg, Lynne, Gallivan, Joanne M., Lim, Joanne, Tuncer, Diane M., and Behringer-Massera, Stephanie

Subjects
endocrine system diseases, Diabetes Mellitus, Type 2, Endocrinology, Diabetes and Metabolism, Smoke, T-Lymphocytes, Internal Medicine, Humans, Pathophysiology, Fires
Abstract

Islet autoimmunity may contribute to β-cell dysfunction in type 2 diabetes (T2D). Its prevalence and clinical significance have not been rigorously determined. In this ancillary study to the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE), we investigated the prevalence of cellular and humoral islet autoimmunity in patients with T2D duration of 4.0 ± 3.0 years (HbA1c 7.5 ± 0.5% on metformin alone). We measured T-cell autoreactivity against islet proteins, islet autoantibodies against 65-kDa GAD antigen, IA-2, and zinc transporter-8, and β-cell function. Cellular islet autoimmunity was present in 41.3%, humoral islet autoimmunity in 13.5%, and both in 5.3%. β-Cell function calculated as incremental area under the curve of glucose from 0–120 min (iAUC-CG) and ΔC-peptide(0–30)/Δglucose(0–30) from an oral glucose tolerance test was lower among T-cell–positive (T+) than T-cell–negative (T−) individuals using two different adjustments for insulin sensitivity (iAUC-CG: 13.2% [95% CI 0.3, 24.4] or 11.4% [95% CI 0.4, 21.2] lower; ΔC-peptide[0–30]/Δglucose[0–30]: 19% [95% CI 3.1, 32.3] or 17.7% [95% CI 2.6, 30.5%] lower). T+ patients had 17% higher HbA1c (95% CI 0.07, 0.28) and 7.7 mg/dL higher fasting plasma glucose levels (95% CI 0.2, 15.3) than T− patients. We conclude that islet autoimmunity is much more prevalent in patients with T2D than previously reported. T-cell–mediated autoimmunity is associated with diminished β-cell function and worse glycemic control.

Published
2021

16. 73-OR: Clonal Hematopoiesis of Indeterminate Potential (CHIP), Glycemic Traits, and Type 2 Diabetes (T2D) Risk

Author

DEIRDRE K. TOBIAS, ALISA MANNING, JENNIFER WESSEL, KENNETH E. WESTERMAN, SRIDHARAN RAGHAVAN, LAURA RAFFIELD, JOSEE DUPUIS, ALEXANDER BICK, PEITAO WU, DANIEL A. DICORPO, BRUCE M. PSATY, ALANNA C. MORRISON, VASAN S. RAMACHANDRAN, L. ADRIENNE CUPPLES, LESLIE LANGE, ADOLFO CORREA, CHARLES L. KOOPERBERG, LESLEY F. TINKER, BARBARA V. HOWARD, ALEX REINER, JEROME I. ROTTER, JASON M. COLLINS, JAMES B. MEIGS, and JOANN E. MANSON

Subjects
Endocrinology, Diabetes and Metabolism, Internal Medicine
Abstract

Objective: CHIP is an aging-related accumulation of somatic mutations in hematopoietic stem cells, leading to clonal expansion. CHIP presence has been implicated in elevated atherosclerotic heart disease (CHD) and all-cause mortality, but its association with incident T2D is unknown. We hypothesize CHIP is adversely associated with glycemic traits and T2D. Methods: We included N=16,054 participants in NHLBI Trans-omics for Precision Medicine (TOPMed), without prior T2D or chronic disease at blood draw. CHIP was derived from blood DNA whole genome sequencing using the GATK MuTECT2 somatic variant caller in pre-specified leukemogenic driver mutations. We estimated CHIP overall and candidate CHD-related mutations (DNMT3A, TET2, ASXL1, JAK2) in relation to baseline glycemic traits (fasting glucose, insulin, HOMA-IR) with linear regression. We evaluated CHIP with incident T2D using Cox regression to estimate hazard ratios and 95% confidence intervals (HR [CI]). Models adjusted for age, sex, body mass index, smoking, and family history of T2D. We combined cohort-specific estimates via random effects meta-analysis with inverse variance weighting. Results: CHIP was present in n=1,088 (7%) of participants. We observed 1,853 incident T2D cases over mean=10y follow-up. Baseline fasting glucose, insulin, and HOMA-IR did not differ by CHIP status. Participants with CHIP had a higher risk of incident T2D than those without CHIP (1.27 [1.08, 1.51]), with a significant dose-response per additional CHIP mutation (p-trend=0.005). Analyses of CHD-related CHIP vs. no CHIP mutations suggested associations with higher T2D risk for DNMT3A (1.21 [0.97, 1.51]) and TET2 (1.35 [0.93, 1.97]), and ASXL1 (1.92 [0.71, 5.13]), although analyses of individual mutations were underpowered. Conclusions: CHIP is associated with a higher T2D risk. This relationship may be unrelated to baseline glycemic traits. CHIP-T2D association suggests a potential role for CHD-related pathology in T2D. Disclosure D. K. Tobias: None. D. A. Dicorpo: None. B. M. Psaty: None. A. C. Morrison: None. V. S. Ramachandran: None. L. Cupples: None. L. Lange: None. A. Correa: None. C. L. Kooperberg: None. L. F. Tinker: None. B. V. Howard: None. A. Manning: Employee; Spouse/Partner; Emulate, Inc, Stock/Shareholder; Spouse/Partner; Invitae. A. Reiner: None. J. I. Rotter: None. J. M. Collins: None. J. B. Meigs: Consultant; Self; Quest Diagnostics. J. E. Manson: None. J. Wessel: Employee; Spouse/Partner; Eli Lilly and Company. K. E. Westerman: None. S. Raghavan: None. L. Raffield: None. J. Dupuis: None. A. Bick: None. P. Wu: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (U01DK078616 to J.B.M., J.D., D.A.D., P.W.), (U01DK105554 to J.D., D.A.D.)

Published
2021

17. 24-OR: Time-to-Event GWAS for Incident Cardiovascular Disease in Type 2 Diabetes Patients

Author

Soo Heon Kwak, Peitao Wu, and James B. Meigs

Subjects
medicine.medical_specialty, business.industry, Endocrinology, Diabetes and Metabolism, Internal medicine, Event (relativity), Internal Medicine, medicine, Cardiology, Genome-wide association study, Type 2 diabetes, Disease, medicine.disease, business
Abstract

Patients with type 2 diabetes (T2D) are at two-fold increased risk of cardiovascular disease (CVD), which is the leading cause of T2D morbidity and mortality. Here, we conducted a time-to-event genome-wide association study (GWAS) to identify genetic variants associated with incident CVD among patients with T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Incident CVD was defined based on a composite of myocardial infarction, stroke, and cardiovascular death that occurred at least one year after the diagnosis of T2D. In each participating prospective cohort, we performed GWAS using a Cox proportional hazard survival model adjusting for age, and sex to identify genetic variants associated with incident CVD, and cohort-level estimated effect sizes were combined using inverse variance weighted fixed effects meta-analysis. A total of 37,327 participants were included in the analyses (26,928 European ancestry and 10,399 non-European ancestry) which comprised 6,012 primary event cases. We identified three distinct genetic loci associated with incident CVD among individuals with T2D that reached the threshold for genome-wide significance: rs231964 (intronic variant in NOX3, hazard ratio [HR] 1.4, P=8.3x10-9), rs139753656 (intronic variant in DCC, HR 2.0, P=2.4x10-8), and rs77202398 (intronic variant in TNS3, HR 1.4, P=4.4x10-8). Among 204 known coronary artery disease risk loci, 27 were associated with incident CVD with P Disclosure S. Kwak: None. P. Wu: None. J. B. Meigs: Consultant; Self; Quest Diagnostics. On behalf of the charge diabetes working group: n/a. Funding National Institutes of Health (1R01HL151855-01)

Published
2021

18. 1100-P: A Four-Humanitarian Agency Survey of Diabetes Care in 83 Humanitarian Settings

Author

Sylvia Kehlenbrink, James B. Meigs, Omar Mahboob, Ahmad H. Alani, Stephanie Kayden, Michael Woodman, Philippa Boulle, Bianca Porneala, Heiko Hering, Lilian Kiapi, Sigiriya Aebischer Perone, Sara A. Al-Zubi, and Karen Donelan

Subjects
education.field_of_study, business.industry, Endocrinology, Diabetes and Metabolism, Refugee, Population, medicine.disease, Care provision, Diabetes mellitus, Internally displaced person, Agency (sociology), Internal Medicine, medicine, Natural disaster, education, business, Demography, Patient education
Abstract

Background: We are currently facing unprecedented humanitarian crises. With diabetes at record-high levels and projected increases in humanitarian crises globally, data on the burden and management of DM in humanitarian crises is needed to stop unnecessary disability and death. Methods: We surveyed data on diabetes care provision in humanitarian medical services in 2018 across 4 humanitarian agencies (Doctors Without Borders, International Committee of the Red Cross, International Rescue Committee, United Nations High Commissioner for Refugees) with 83 randomly selected sites across 27 countries in 5 global regions. Of 83 sites, 65 (78%) reported collecting DM care data and were used for cross-sectional analysis of rates and proportions. Results: Of 65 sites, most were in the Eastern Mediterranean (n=29, 45%) and Africa (35%), with 20% elsewhere; 34% were refugee camps, 34% rural non-camp sites, 21% urban non-camp sites, 11% internally displaced persons (IDP) camps. Populations were mostly a mix of refugees, IDPs and the general population (n=46/65, 71%), with refugees only at 23% and IDPs only at 6% of sites. Of 65 sites, 58 were affected by conflict (89%), 5% epidemics, 1% natural disasters and 5% multiple crisis types. Most sites (n=49) were in protracted crises (75%), with 23% in recovery stages and 2% in acute crisis. Of 65 sites, 46 (71%) reported providing clinical DM management. However, only 66% had insulin available, 71% had capillary glucose testing, 55% urine dipstick glucose, 19% hemoglobin A1c testing, 22% home glucose monitoring, 35% community outreach, 58% patient education, 32% training of staff and 52% continuity of care systems. Conclusions: DM services were mostly provided in protracted humanitarian settings. Services were widespread but often rudimentary and delivered to the general population as well as refugees and IDPs. Improving DM care for crisis-affected populations is urgently needed. Disclosure S. Kehlenbrink: None. S. Kayden: None. K. Donelan: None. B. Porneala: None. J. B. Meigs: Consultant; Self; Quest Diagnostics. O. Mahboob: None. S. A. Al-zubi: None. P. Boulle: None. S. Aebischer perone: None. L. Kiapi: None. A. H. Alani: None. H. Hering: None. M. Woodman: None.

Published
2021

19. 73-OR: Clonal Hematopoiesis of Indeterminate Potential (CHIP), Glycemic Traits, and Type 2 Diabetes (T2D) Risk

Author

TOBIAS, DEIRDRE K., primary, MANNING, ALISA, additional, WESSEL, JENNIFER, additional, WESTERMAN, KENNETH E., additional, RAGHAVAN, SRIDHARAN, additional, RAFFIELD, LAURA, additional, DUPUIS, JOSEE, additional, BICK, ALEXANDER, additional, WU, PEITAO, additional, DICORPO, DANIEL A., additional, PSATY, BRUCE M., additional, MORRISON, ALANNA C., additional, RAMACHANDRAN, VASAN S., additional, CUPPLES, L. ADRIENNE, additional, LANGE, LESLIE, additional, CORREA, ADOLFO, additional, KOOPERBERG, CHARLES L., additional, TINKER, LESLEY F., additional, HOWARD, BARBARA V., additional, REINER, ALEX, additional, ROTTER, JEROME I., additional, COLLINS, JASON M., additional, MEIGS, JAMES B., additional, and MANSON, JOANN E., additional

Published
2021
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21. 1100-P: A Four-Humanitarian Agency Survey of Diabetes Care in 83 Humanitarian Settings

Author

KEHLENBRINK, SYLVIA, primary, MAHBOOB, OMAR, additional, AL-ZUBI, SARA A., additional, BOULLE, PHILIPPA, additional, PERONE, SIGIRIYA AEBISCHER, additional, KIAPI, LILIAN, additional, ALANI, AHMAD H., additional, HERING, HEIKO, additional, WOODMAN, MICHAEL, additional, KAYDEN, STEPHANIE, additional, DONELAN, KAREN, additional, PORNEALA, BIANCA, additional, and MEIGS, JAMES B., additional

Published
2021
Full Text
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24. 1649-P: Effect of Fasting Insulin-Derived Gene Variant Clusters on Cardiometabolic Traits

Author

Alisa K. Manning, Miriam S. Udler, Sarah Hsu, Magdalena Sevilla, Hyunkyung Kim, and James B. Meigs

Subjects
medicine.medical_specialty, Endocrinology, Diabetes and Metabolism, Insulin, medicine.medical_treatment, Renal function, Genome-wide association study, Biology, medicine.disease, IRS1, Endocrinology, Insulin resistance, Internal medicine, Internal Medicine, medicine, Allele, Gene, Proinsulin
Abstract

Background: Fasting insulin concentrations reflect insulin resistance, which increase is associated with subsequent risk of heterogeneous cardiometabolic traits, including coronary artery disease (CAD), blood pressure (BP), estimated glomerular filtration rate (eGFR), BMI and lipid traits (TG, HDL, and LDL). We clustered insulin-associated genetic variants to define heterogeneous insulin resistance domains and tested whether these were associated with different cardiometabolic traits. Methods: We studied subjects of European Ancestry with genetic and clinical data available (N=18,127) in the Partners Biobank. We applied a soft clustering to 56 genome-wide association study (GWAS) fasting insulin genetic variants and 25 fasting insulin-related traits. We generated weighted genetic risk scores (GRS) for each cluster (alleles were aligned in the direction of increased fasting insulin effect). Then we aimed to identify clinical consequences between the clusters and cardiometabolic outcomes including CAD, SBP, DBP, eGFR, BMI, TG, HDL, and LDL. We tested the hypothesis that different genetic insulin domains will be cross-sectionally associated with some or all of these cardiometabolic traits. Results: We obtained three novel clusters of genetic variants. Each cluster was represented by a set of traits and a set of loci. The first represented VF and SAT, with loci near DLG2, FAM169B and, RGS12 genes, and its GRS was positive associated with CAD (p=0.004). The second cluster represents insulin sensitivity and lipid traits with loci near IRS1, ARRDC4 and, SNTG1, and its GRS was associated with lower TG (p=0.00001), and higher HDL(p=0.007). The last cluster represents proinsulin with loci near FABP2 and, PCAT5. No associations were found with this cluster. Conclusion: These results reveal different genetic insulin domains indicating different mechanisms for cardiometabolic traits associated with fasting insulin. Disclosure M. Sevilla: None. M. Udler: None. H. Kim: None. S. Hsu: None. J.B. Meigs: Consultant; Self; Quest Diagnostics. A. Manning: None.

Published
2020

25. 239-OR: Genome-Wide Association Study (GWAS) of Hypoglycemia in the Million Veteran Program (MVP)

Author

Sridharan Raghavan, Lawrence S. Phillips, Lauren Costa, Adriana M. Hung, Peter D. Reaven, Peter W.F. Wilson, Mary K. Rhee, Yan V. Sun, Million Veteran Program, Aaron Leong, James B. Meigs, Elizabeth Litkowski, Zeyuan Wang, and Brian Charest

Subjects
Type 1 diabetes, medicine.medical_specialty, business.industry, Endocrinology, Diabetes and Metabolism, Odds ratio, Type 2 diabetes, Hypoglycemia, medicine.disease, Internal medicine, Diabetes mellitus, Internal Medicine, medicine, Diagnosis code, business, Veterans Affairs, Pharmacogenetics
Abstract

Hypoglycemia is a preventable adverse effect of treatment in diabetes patients, but actionable pharmacogenetic evidence to minimize hypoglycemia risk is lacking. We used the MVP biospecimen and clinical biobank to perform a case/control GWAS of hypoglycemia in 96,406 diabetes patients receiving care in the U.S. Veterans Health Administration. Cases had an outpatient random plasma glucose (RPG) 1%. GWAS were conducted in 72,244 participants (22,045 cases) of European ancestry [EUR] and 24,162 (10,441 cases) of African ancestry [AFR]), adjusted for age at MVP enrollment, sex, and 10 genetic principal components. We identified a single locus associated with hypoglycemia in EUR individuals (rs1064173, HLA-DQB1 locus, odds ratio [OR] 1.15, p=5.6E-19, MAF 0.20) and in AFR individuals (rs12712928, SIX3 locus, OR 0.87, p=2.7E-8, MAF 0.17). In a phenomewide association study (PheWAS) in MVP EUR diabetes patients, rs1064173 was associated with type 1 diabetes (T1D, OR 1.41, p=7.8E-25) and diabetes complications (retinopathy, OR 1.24, p=9E-24; neuropathy, OR 1.16, p=1.3E-5). To address the known association of the HLA locus with T1D, we performed a sensitivity analysis excluding patients with any use of T1D diagnosis codes. In this sample enriched for type 2 diabetes, rs1064173 remained nominally associated with hypoglycemia (OR 1.23, p=0.02) and retinopathy (OR 1.10, p=0.003). Rs12712928 - previously associated with glucose and hemoglobin A1c in an East Asian sample - was not associated with any diagnoses in a PheWAS of MVP AFR diabetes patients. In conclusion, a GWAS of hypoglycemia in a clinical biobank found ancestry-specific genetic associations that may have treatment implications. Disclosure S. Raghavan: None. Z. Wang: None. A.M. Hung: None. B.R. Charest: None. L. Costa: None. E.M. Litkowski: None. A. Leong: None. J.B. Meigs: Consultant; Self; Quest Diagnostics. P.W. Wilson: None. P. Reaven: Consultant; Self; Boston Heart Diagnostics, Intercept Pharmaceuticals, Inc. Research Support; Self; Bristol-Myers Squibb, Lysulin. M.K. Rhee: Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc. Y.V. Sun: None. L.S. Phillips: Research Support; Self; AbbVie Inc., Diasome Pharmaceuticals, Inc., Eli Lilly and Company, GlaxoSmithKline plc., Novartis Pharmaceuticals Corporation, Novo Nordisk Inc., Pfizer Inc., Sanofi-Aventis. Other Relationship; Self; Diasyst, Janssen Pharmaceuticals, Inc. Funding U.S. Department of Veterans Affairs (I01-CX001737, IK2-CX001907-01)

Published
2020

26. 1655-P: Genetic Risk Factors for Incident Cardiovascular Disease in Type 2 Diabetes Patients

Author

Peitao Wu, Joanne B. Cole, Josep M. Mercader, Nam H. Cho, Jose C. Florez, Jordi Merino, James B. Meigs, Bianca Porneala, Soo Heon Kwak, Kyong Soo Park, Aaron Leong, and Josée Dupuis

Subjects
medicine.medical_specialty, education.field_of_study, business.industry, Endocrinology, Diabetes and Metabolism, Population, Genome-wide association study, Type 2 diabetes, Disease, medicine.disease, Biobank, Internal medicine, Epidemiology, Internal Medicine, medicine, business, education, Stroke, Genetic association
Abstract

Patients with type 2 diabetes (T2D) are at two-fold increased risk of cardiovascular disease (CVD). CVD is the leading cause of T2D morbidity and mortality. The aim of this study was to identify genetic risk factors associated with incident CVD in patients with T2D. We performed survival genome-wide association study (GWAS) with patients with T2D in the UK Biobank (UKBB), Partners HealthCare Biobank (PBB), and Korean Genome and Epidemiology (KoGES) studies. The primary outcome of the study was a composite of myocardial infarction and stroke that occurred at least one year after the diagnosis of T2D. We conducted survival GWAS using ’GWASTools’ R package adjusting for age, and sex. The results of the three cohorts were meta-analyzed using inverse variance weighted method. A total of 19,121 participants were included in the analyses (15,643 from UKBB, 1,161 from PBB and 2,317 from KoGES). The incidence rate of CVD was higher in the hospital-based study of PBB (33.5%) compared to population-based studies of UKBB (9.5%), and KoGES (8.0%). Although there was no genetic association reaching genome-wide significance, there were four genomic loci that had suggestive association evidence for incident CVD with P In summary, we have implemented survival GWAS analysis to investigate genetic risk factors for incident CVD in patients with T2D. However, further increase in sample size is required to identify molecular features leading to increased CVD risk among individuals with T2D. Disclosure S. Kwak: None. J.M. Mercader: None. A. Leong: None. B. Porneala: None. P. Wu: None. J.B. Cole: None. J. Merino: None. K. Park: None. N.H. Cho: None. J. Dupuis: Consultant; Self; Merck Sharp & Dohme Corp. J.C. Florez: Advisory Panel; Self; Doris Duke Charitable Foundation. Other Relationship; Self; Novo Nordisk Inc., Park Street School. J.B. Meigs: Consultant; Self; Quest Diagnostics.

Published
2020

27. 240-OR: Combining Type 2 Diabetes Polygenic Scores Improves Prediction in 36,000 European and Non-European Participants in the Partners Healthcare Biobank

Author

James B. Meigs, Bianca Porneala, Aaron Leong, Josep M. Mercader, Jose C. Florez, Varinderpal Kaur, and Miriam S. Udler

Subjects
American diabetes association, Linkage disequilibrium, business.industry, Endocrinology, Diabetes and Metabolism, Genome-wide association study, Type 2 diabetes, medicine.disease, Biobank, Health equity, Health care, Internal Medicine, medicine, business, Demography, Genetic association
Abstract

Polygenic scores constructed by the aggregation of effect sizes of associated genetic variants have been shown to predict type 2 diabetes (T2D). Polygenic scores can be defined as “restricted-to-significant polygenic scores” (rsPS), and “globally expanded polygenic scores” (gePS), which can include millions of variants beyond those that are genome-wide significant. Most polygenic scores have been generated based on genome-wide association studies (GWAS) conducted predominantly in participants of European ancestry (EU); how these polygenic scores perform in non-European individuals in the healthcare system has not been thoroughly evaluated. We applied the rsPS and gePS for T2D in 30,240 EU participants, 3,029 Latinos (LA), and 2,201 African Americans (AA) from the Partners HealthCare Biobank, which maintains blood and DNA samples from patients seen at Partners HealthCare-affiliated hospitals in Boston. We compared rsPS based on the DIAMANTE consortium meta-analysis, with 398 variants (rsPS_398), rsPS based on the latest Million Veterans Program meta-analysis, with 682 variants (rsPS_682), and a gePS based on ldpred, including 6.9M variants adjusted for their effects based on linkage disequilibrium. rsPS_682 was most strongly associated with T2D in all ancestries (OR per SD in EU=1.73 p=7×10-142, OR per SD in LA =1.88, p=7×10-19, OR per SD in AA =1.47, p=1×10-8 compared to the other polygenic scores. We observed that rsPS_682 and the gePS provided both complementary information, and that combining them into the model improved the prediction in all ancestries, with 16%, 22%, and 16% increase in accuracy in EU, LA, and AA, respectively. We show the advantage of combining rsPS and gePS to improve prediction of T2D in multi-ethnic populations within a healthcare system. The results suggest that larger and multi-ethnic T2D GWAS are needed to avoid major health disparities. Disclosure J.M. Mercader: None. J.C. Florez: Advisory Panel; Self; Doris Duke Charitable Foundation. Other Relationship; Self; Novo Nordisk Inc., Park Street School. A. Leong: None. V. Kaur: None. M. Udler: None. J.B. Meigs: Consultant; Self; Quest Diagnostics. B. Porneala: None. Funding American Diabetes Association (1-19-ICTS-068 to J.M.M.)

Published
2020

28. 2256-PUB: Clonal Hematopoiesis of Indeterminate Potential (CHIP), Glycemic Traits, and Type 2 Diabetes (T2D) Risk

Author

Charles Kooperberg, Josée Dupuis, JoAnn E. Manson, Peitao Wu, Jerome I. Rotter, Deirdre K Tobias, James B. Meigs, Sridharan Raghavan, Leslie A. Lange, Alexander G. Bick, Daniel DiCorpo, and Laura M. Raffield

Subjects
Oncology, medicine.medical_specialty, business.industry, Endocrinology, Diabetes and Metabolism, Type 2 diabetes, Disease, medicine.disease, Framingham Heart Study, Internal medicine, Diabetes mellitus, Cohort, Internal Medicine, Medicine, Family history, business, Body mass index, Glycemic
Abstract

Background and Objective: CHIP is the aging-related accumulation of genetic mutations in the hematopoiesis-associated genes of stem cells. CHIP was recently associated with total mortality and atherosclerotic cardiovascular disease. We tested the hypothesis that CHIP is associated with adverse glycemic traits and incident T2D risk. Methods: This study includes a subset of n=9,704 individuals in the NHLBI Trans-omics for Precision Medicine (TOPMed) project from the Framingham Heart Study (FHS), Jackson Heart Study (JHS), Multi-Ethnic Study of Atherosclerosis (MESA) and Women’s Health Initiative (WHI). We analyzed individuals without a history of T2D, cancer, or cardiovascular disease, and collected blood for fasting glycemic traits and CHIP carrier status. CHIP was recently determined from blood DNA-derived whole genome sequencing using the CATK MuTECT2 somatic variant caller in pre-specified leukemogenic driver mutations in candidate clonal expansion genes. We are conducting multivariable linear models examining both cross-sectional associations of CHIP with glycemic traits and survival models for baseline CHIP with incident T2D, adjusting for age, sex, body mass index, smoking, cohort, and family history of diabetes. Results: Mean (SD) age was FHS = 50 (14), JHS = 55 (13), MESA = 60 (10), and WHI = 66 (7) years. We identified n=460 (4.7%) as CHIP carriers. The most common variant was located in DNMT3A gene (60% of carriers). Among CHIP carriers, the prevalence of having CHIP present in 1 of the 4 genes previously implicated in atherosclerotic heart disease (DNMT3A, TET2, ASXL1, JAK2; Jaiswal, et al, NEJM 2017) was lowest in FHS (75%) and highest in JHS (96%). Results for CHIP in relation to glycemic traits and incident T2D (∼1,500 cases) will be presented in detail. We will additionally present the associations for gene-specific CHIP mutations. Conclusions: These cutting-edge TOPMed data will elucidate the role of CHIP, a novel aging biomarker, in relation to T2D risk. Disclosure D.K. Tobias: None. S. Raghavan: None. A. Bick: None. L. Raffield: None. P. Wu: None. D.A. DiCorpo: None. J. Dupuis: Consultant; Self; Merck Sharp & Dohme Corp. L. Lange: None. J.I. Rotter: None. C.L. Kooperberg: None. J.B. Meigs: Consultant; Self; Quest Diagnostics. J.E. Manson: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (U01DK078616 to J.B.M., J.D., D.A.D., P.W.), (U01DK105554 to J.D., D.A.D.)

Published
2020

29. Mendelian Randomization Studies Do Not Support a Causal Role for Reduced Circulating Adiponectin Levels in Insulin Resistance and Type 2 Diabetes

Author

Yaghootkar, Hanieh, Lamina, Claudia, Scott, Robert A., Dastani, Zari, Hivert, Marie-France, Warren, Liling L., Stancáková, Alena, Buxbaum, Sarah G., Lyytikäinen, Leo-Pekka, Henneman, Peter, Wu, Ying, Cheung, Chloe Y.Y., Pankow, James S., Jackson, Anne U., Gustafsson, Stefan, Zhao, Jing Hua, Ballantyne, Christie M., Xie, Weijia, Bergman, Richard N., Boehnke, Michael, el Bouazzaoui, Fatiha, Collins, Francis S., Dunn, Sandra H., Dupuis, Josee, Forouhi, Nita G., Gillson, Christopher, Hattersley, Andrew T., Hong, Jaeyoung, Kähönen, Mika, Kuusisto, Johanna, Kedenko, Lyudmyla, Kronenberg, Florian, Doria, Alessandro, Assimes, Themistocles L., Ferrannini, Ele, Hansen, Torben, Hao, Ke, Häring, Hans, Knowles, Joshua W., Lindgren, Cecilia M., Nolan, John J., Paananen, Jussi, Pedersen, Oluf, Quertermous, Thomas, Smith, Ulf, Lehtimäki, Terho, Liu, Ching-Ti, Loos, Ruth J.F., McCarthy, Mark I., Morris, Andrew D., Vasan, Ramachandran S., Spector, Tim D., Teslovich, Tanya M., Tuomilehto, Jaakko, van Dijk, Ko Willems, Viikari, Jorma S., Zhu, Na, Langenberg, Claudia, Ingelsson, Erik, Semple, Robert K., Sinaiko, Alan R., Palmer, Colin N.A., Walker, Mark, Lam, Karen S.L., Paulweber, Bernhard, Mohlke, Karen L., van Duijn, Cornelia, Raitakari, Olli T., Bidulescu, Aurelian, Wareham, Nick J., Laakso, Markku, Waterworth, Dawn M., Lawlor, Debbie A., Meigs, James B., Richards, J. Brent, and Frayling, Timothy M.

Published
2013
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30. Transferability and Fine Mapping of Type 2 Diabetes Loci in African Americans: The Candidate Gene Association Resource Plus Study

Author

Ng, Maggie C.Y., Saxena, Richa, Li, Jiang, Palmer, Nicholette D., Dimitrov, Latchezar, Xu, Jianzhao, Rasmussen-Torvik, Laura J., Zmuda, Joseph M., Siscovick, David S., Patel, Sanjay R., Crook, Errol D., Sims, Mario, Chen, Yii-Der I., Bertoni, Alain G., Li, Mingyao, Grant, Struan F.A., Dupuis, Josée, Meigs, James B., Psaty, Bruce M., Pankow, James S., Langefeld, Carl D., Freedman, Barry I., Rotter, Jerome I., Wilson, James G., and Bowden, Donald W.

Published
2013
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31. Impact of Common Variation in Bone-Related Genes on Type 2 Diabetes and Related Traits

Author

Billings, Liana K., Hsu, Yi-Hsiang, Ackerman, Rachel J., Dupuis, Josée, Voight, Benjamin F., Rasmussen-Torvik, Laura J., Hercberg, Serge, Lathrop, Mark, Barnes, Daniel, Langenberg, Claudia, Hui, Jennie, Fu, Mao, Bouatia-Naji, Nabila, Lecoeur, Cecile, An, Ping, Magnusson, Patrik K., Surakka, Ida, Ripatti, Samuli, Christiansen, Lene, Dalgård, Christine, Folkersen, Lasse, Grundberg, Elin, Eriksson, Per, Kaprio, Jaakko, Ohm Kyvik, Kirsten, Pedersen, Nancy L., Borecki, Ingrid B., Province, Michael A., Balkau, Beverley, Froguel, Philippe, Shuldiner, Alan R., Palmer, Lyle J., Wareham, Nick, Meneton, Pierre, Johnson, Toby, Pankow, James S., Karasik, David, Meigs, James B., Kiel, Douglas P., and Florez, Jose C.

Published
2012
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32. Consistent Directions of Effect for Established Type 2 Diabetes Risk Variants Across Populations: The Population Architecture using Genomics and Epidemiology (PAGE) Consortium

Author

Haiman, Christopher A., Fesinmeyer, Megan D., Spencer, Kylee L., Bůžková, Petra, Voruganti, V. Saroja, Wan, Peggy, Haessler, Jeff, Franceschini, Nora, Monroe, Kristine R., Howard, Barbara V., Jackson, Rebecca D., Florez, Jose C., Kolonel, Laurence N., Buyske, Steven, Goodloe, Robert J., Liu, Simin, Manson, JoAnn E., Meigs, James B., Waters, Kevin, Mukamal, Kenneth J., Pendergrass, Sarah A., Shrader, Peter, Wilkens, Lynne R., Hindorff, Lucia A., Ambite, Jose Luis, North, Kari E., Peters, Ulrike, Crawford, Dana C., Le Marchand, Loic, and Pankow, James S.

Published
2012
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33. Total Zinc Intake May Modify the Glucose-Raising Effect of a Zinc Transporter (SLC30A8) Variant: A 14-Cohort Meta-analysis

Author

Kanoni, Stavroula, Nettleton, Jennifer A., Hivert, Marie-France, Ye, Zheng, van Rooij, Frank J.A., Shungin, Dmitry, Sonestedt, Emily, Ngwa, Julius S., Wojczynski, Mary K., Lemaitre, Rozenn N., Gustafsson, Stefan, Anderson, Jennifer S., Tanaka, Toshiko, Hindy, George, Saylor, Georgia, Renstrom, Frida, Bennett, Amanda J., van Duijn, Cornelia M., Florez, Jose C., Fox, Caroline S., Hofman, Albert, Hoogeveen, Ron C., Houston, Denise K., Hu, Frank B., Jacques, Paul F., Johansson, Ingegerd, Lind, Lars, Liu, Yongmei, McKeown, Nicola, Ordovas, Jose, Pankow, James S., Sijbrands, Eric J.G., Syvänen, Ann-Christine, Uitterlinden, André G., Yannakoulia, Mary, Zillikens, M. Carola, Wareham, Nick J., Prokopenko, Inga, Bandinelli, Stefania, Forouhi, Nita G., Cupples, L. Adrienne, Loos, Ruth J., Hallmans, Goran, Dupuis, Josée, Langenberg, Claudia, Ferrucci, Luigi, Kritchevsky, Stephen B., McCarthy, Mark I., Ingelsson, Erik, Borecki, Ingrid B., Witteman, Jacqueline C.M., Orho-Melander, Marju, Siscovick, David S., Meigs, James B., Franks, Paul W., and Dedoussis, George V.

Published
2011
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36. Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity

Author

Yaghootkar, Hanieh, primary, Zhang, Yiying, additional, Spracklen, Cassandra N., additional, Karaderi, Tugce, additional, Huang, Lam Opal, additional, Bradfield, Jonathan, additional, Schurmann, Claudia, additional, Fine, Rebecca S., additional, Preuss, Michael H., additional, Kutalik, Zoltan, additional, Wittemans, Laura B.L., additional, Lu, Yingchang, additional, Metz, Sophia, additional, Willems, Sara M., additional, Li-Gao, Ruifang, additional, Grarup, Niels, additional, Wang, Shuai, additional, Molnos, Sophie, additional, Sandoval-Zárate, América A., additional, Nalls, Mike A., additional, Lange, Leslie A., additional, Haesser, Jeffrey, additional, Guo, Xiuqing, additional, Lyytikäinen, Leo-Pekka, additional, Feitosa, Mary F., additional, Sitlani, Colleen M., additional, Venturini, Cristina, additional, Mahajan, Anubha, additional, Kacprowski, Tim, additional, Wang, Carol A., additional, Chasman, Daniel I., additional, Amin, Najaf, additional, Broer, Linda, additional, Robertson, Neil, additional, Young, Kristin L., additional, Allison, Matthew, additional, Auer, Paul L., additional, Blüher, Matthias, additional, Borja, Judith B., additional, Bork-Jensen, Jette, additional, Carrasquilla, Germán D., additional, Christofidou, Paraskevi, additional, Demirkan, Ayse, additional, Doege, Claudia A., additional, Garcia, Melissa E., additional, Graff, Mariaelisa, additional, Guo, Kaiying, additional, Hakonarson, Hakon, additional, Hong, Jaeyoung, additional, Ida Chen, Yii-Der, additional, Jackson, Rebecca, additional, Jakupović, Hermina, additional, Jousilahti, Pekka, additional, Justice, Anne E., additional, Kähönen, Mika, additional, Kizer, Jorge R., additional, Kriebel, Jennifer, additional, LeDuc, Charles A., additional, Li, Jin, additional, Lind, Lars, additional, Luan, Jian’an, additional, Mackey, David A., additional, Mangino, Massimo, additional, Männistö, Satu, additional, Martin Carli, Jayne F., additional, Medina-Gomez, Carolina, additional, Mook-Kanamori, Dennis O., additional, Morris, Andrew P., additional, de Mutsert, Renée, additional, Nauck, Matthias, additional, Prokic, Ivana, additional, Pennell, Craig E., additional, Pradhan, Arund D., additional, Psaty, Bruce M., additional, Raitakari, Olli T., additional, Scott, Robert A., additional, Skaaby, Tea, additional, Strauch, Konstantin, additional, Taylor, Kent D., additional, Teumer, Alexander, additional, Uitterlinden, Andre G., additional, Wu, Ying, additional, Yao, Jie, additional, Walker, Mark, additional, North, Kari E., additional, Kovacs, Peter, additional, Ikram, M. Arfan, additional, van Duijn, Cornelia M., additional, Ridker, Paul M., additional, Lye, Stephen, additional, Homuth, Georg, additional, Ingelsson, Erik, additional, Spector, Tim D., additional, McKnight, Barbara, additional, Province, Michael A., additional, Lehtimäki, Terho, additional, Adair, Linda S., additional, Rotter, Jerome I., additional, Reiner, Alexander P., additional, Wilson, James G., additional, Harris, Tamara B., additional, Ripatti, Samuli, additional, Grallert, Harald, additional, Meigs, James B., additional, Salomaa, Veikko, additional, Hansen, Torben, additional, Willems van Dijk, Ko, additional, Wareham, Nicholas J., additional, Grant, Struan F.A., additional, Langenberg, Claudia, additional, Frayling, Timothy M., additional, Lindgren, Cecilia M., additional, Mohlke, Karen L., additional, Leibel, Rudolph L., additional, Loos, Ruth J.F., additional, and Kilpeläinen, Tuomas O., additional

Published
2020
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40. 2256-PUB: Clonal Hematopoiesis of Indeterminate Potential (CHIP), Glycemic Traits, and Type 2 Diabetes (T2D) Risk

Author

TOBIAS, DEIRDRE K., primary, RAGHAVAN, SRIDHARAN, additional, BICK, ALEXANDER, additional, RAFFIELD, LAURA, additional, WU, PEITAO, additional, DICORPO, DANIEL A., additional, DUPUIS, JOSEE, additional, LANGE, LESLIE, additional, ROTTER, JEROME I., additional, KOOPERBERG, CHARLES L., additional, MEIGS, JAMES B., additional, and MANSON, JOANN E., additional

Published
2020
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42. Detailed Physiologic Characterization Reveals Diverse Mechanisms for Novel Genetic Loci Regulating Glucose and Insulin Metabolism in Humans

Author

Ingelsson, Erik, Langenberg, Claudia, Hivert, Marie-France, Prokopenko, Inga, Lyssenko, Valeriya, Dupuis, Josée, Mägi, Reedik, Sharp, Stephen, Jackson, Anne U., Assimes, Themistocles L., Shrader, Peter, Knowles, Joshua W., Zethelius, Björn, Abbasi, Fahim A., Bergman, Richard N., Bergmann, Antje, Berne, Christian, Boehnke, Michael, Bonnycastle, Lori L., Bornstein, Stefan R., Buchanan, Thomas A., Bumpstead, Suzannah J., Böttcher, Yvonne, Chines, Peter, Collins, Francis S., Cooper, Cyrus C., Dennison, Elaine M., Erdos, Michael R., Ferrannini, Ele, Fox, Caroline S., Graessler, Jürgen, Hao, Ke, Isomaa, Bo, Jameson, Karen A., Kovacs, Peter, Kuusisto, Johanna, Laakso, Markku, Ladenvall, Claes, Mohlke, Karen L., Morken, Mario A., Narisu, Narisu, Nathan, David M., Pascoe, Laura, Payne, Felicity, Petrie, John R., Sayer, Avan A., Schwarz, Peter E., Scott, Laura J., Stringham, Heather M., Stumvoll, Michael, Swift, Amy J., Syvänen, Ann-Christine, Tuomi, Tiinamaija, Tuomilehto, Jaakko, Tönjes, Anke, Valle, Timo T., Williams, Gordon H., Lind, Lars, Barroso, Inês, Quertermous, Thomas, Walker, Mark, Wareham, Nicholas J., Meigs, James B., McCarthy, Mark I., Groop, Leif, Watanabe, Richard M., and Florez, Jose C.

Published
2010
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