👤 J B Meigs

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Also published as: James B Meigs
articles
Kenneth E Westerman, Chirag J Patel, James B Meigs +2 more · 2025 · Genes & nutrition · BioMed Central · added 2026-04-24
Discovery and translation of gene-environment interactions (GxEs) influencing clinical outcomes is limited by low statistical power and poor mechanistic understanding. Molecular omics data may help ad Show more
Discovery and translation of gene-environment interactions (GxEs) influencing clinical outcomes is limited by low statistical power and poor mechanistic understanding. Molecular omics data may help address these limitations, but their incorporation into GxE testing requires principled analytic approaches. We focused on genetic modification of the established mechanistic link between dietary long-chain omega-3 fatty acid (dN3FA) intake, plasma N3FA (pN3FA), and chronic inflammation as measured by high sensitivity CRP (hsCRP). We considered an approach that decomposes the overall genetic effect modification into components upstream and downstream of a molecular mediator to increase the potential to discover gene-N3FA interactions. Simulations demonstrated improved power of the upstream and downstream tests compared to the standard approach when the molecular mediator for many biologically plausible scenarios. The approach was applied in the UK Biobank (N = 188,700) with regression models that used measures of dN3FA (based on fish and fish oil intake), pN3FA (% of total fatty acids measured by nuclear magnetic resonance), and hsCRP. Mediation analysis showed that pN3FA fully mediated the dN3FA-hsCRP main effect relationship. Next, we separately tested modification of the dN3FA-hsCRP ("standard"), dN3FA-pN3FA ("upstream"), and pN3FA-hsCRP ("downstream") associations. The known FADS1-3 locus variant rs174535 reached p = 1.6 × 10 Show less
📄 PDF DOI: 10.1186/s12263-025-00765-w
FADS1
Kenneth E Westerman, Chirag J Patel, James B Meigs +2 more · 2024 · medRxiv : the preprint server for health sciences · Cold Spring Harbor Laboratory · added 2026-04-24
Discovery and translation of gene-environment interactions (GxEs) influencing clinical outcomes is limited by low statistical power and poor mechanistic understanding. Molecular omics data may help ad Show more
Discovery and translation of gene-environment interactions (GxEs) influencing clinical outcomes is limited by low statistical power and poor mechanistic understanding. Molecular omics data may help address these limitations, but their incorporation into GxE testing requires principled analytic approaches. We focused on genetic modification of the established mechanistic link between dietary long-chain omega-3 fatty acid (dN3FA) intake, plasma N3FA (pN3FA), and chronic inflammation as measured by high sensitivity CRP (hsCRP). We considered an approach that decomposes the overall genetic effect modification into components upstream and downstream of a molecular mediator to increase the potential to discover gene-N3FA interactions. Simulations demonstrated improved power of the upstream and downstream tests compared to the standard approach when the molecular mediator for many biologically plausible scenarios. The approach was applied in the UK Biobank (N = 188,700) with regression models that used measures of dN3FA (based on fish and fish oil intake), pN3FA (% of total fatty acids measured by nuclear magnetic resonance), and hsCRP. Mediation analysis showed that pN3FA fully mediated the dN3FA-hsCRP main effect relationship. Next, we separately tested modification of the dN3FA-hsCRP ("standard"), dN3FA-pN3FA ("upstream"), and pN3FA-hsCRP ("downstream") associations. The known Show less
📄 PDF DOI: 10.1101/2024.09.09.24313018
FADS1
Danielle E Haslam, Gina M Peloso, Melanie Guirette +53 more · 2021 · Circulation. Genomic and precision medicine · added 2026-04-24
ChREBP (carbohydrate responsive element binding protein) is a transcription factor that responds to sugar consumption. Sugar-sweetened beverage (SSB) consumption and genetic variants in the Data from Show more
ChREBP (carbohydrate responsive element binding protein) is a transcription factor that responds to sugar consumption. Sugar-sweetened beverage (SSB) consumption and genetic variants in the Data from 11 cohorts from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (N=63 599) and the UK Biobank (N=59 220) were used to quantify associations of SSB consumption, genetic variants, and their interaction on HDL-C and triglyceride concentrations using linear regression models. A total of 1606 single nucleotide polymorphisms within or near In a meta-analysis, rs71556729 was significantly associated with higher HDL-C concentrations only among the highest SSB consumers (β, 2.12 [95% CI, 1.16-3.07] mg/dL per allele; Our results identified genetic variants in the Show less
📄 PDF DOI: 10.1161/CIRCGEN.120.003288
MLXIPL
Nicola M McKeown, Hassan S Dashti, Jiantao Ma +47 more · 2018 · Diabetologia · Springer · added 2026-04-24
Sugar-sweetened beverages (SSBs) are a major dietary contributor to fructose intake. A molecular pathway involving the carbohydrate responsive element-binding protein (ChREBP) and the metabolic hormon Show more
Sugar-sweetened beverages (SSBs) are a major dietary contributor to fructose intake. A molecular pathway involving the carbohydrate responsive element-binding protein (ChREBP) and the metabolic hormone fibroblast growth factor 21 (FGF21) may influence sugar metabolism and, thereby, contribute to fructose-induced metabolic disease. We hypothesise that common variants in 11 genes involved in fructose metabolism and the ChREBP-FGF21 pathway may interact with SSB intake to exacerbate positive associations between higher SSB intake and glycaemic traits. Data from 11 cohorts (six discovery and five replication) in the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium provided association and interaction results from 34,748 adults of European descent. SSB intake (soft drinks, fruit punches, lemonades or other fruit drinks) was derived from food-frequency questionnaires and food diaries. In fixed-effects meta-analyses, we quantified: (1) the associations between SSBs and glycaemic traits (fasting glucose and fasting insulin); and (2) the interactions between SSBs and 18 independent SNPs related to the ChREBP-FGF21 pathway. In our combined meta-analyses of discovery and replication cohorts, after adjustment for age, sex, energy intake, BMI and other dietary covariates, each additional serving of SSB intake was associated with higher fasting glucose (β ± SE 0.014 ± 0.004 [mmol/l], p = 1.5 × 10 In this large meta-analysis, we observed that SSB intake was associated with higher fasting glucose and insulin. Although a suggestive interaction with a genetic variant in the ChREBP-FGF21 pathway was observed in the discovery cohorts, this observation was not confirmed in the replication analysis. Trials related to this study were registered at clinicaltrials.gov as NCT00005131 (Atherosclerosis Risk in Communities), NCT00005133 (Cardiovascular Health Study), NCT00005121 (Framingham Offspring Study), NCT00005487 (Multi-Ethnic Study of Atherosclerosis) and NCT00005152 (Nurses' Health Study). Show less
📄 PDF DOI: 10.1007/s00125-017-4475-0
MLXIPL
Dajiang J Liu, Gina M Peloso, Haojie Yu +229 more · 2017 · Nature genetics · Nature · added 2026-04-24
Dajiang J Liu, Gina M Peloso, Haojie Yu, Adam S Butterworth, Xiao Wang, Anubha Mahajan, Danish Saleheen, Connor Emdin, Dewan Alam, Alexessander Couto Alves, Philippe Amouyel, Emanuele Di Angelantonio, Dominique Arveiler, Themistocles L Assimes, Paul L Auer, Usman Baber, Christie M Ballantyne, Lia E Bang, Marianne Benn, Joshua C Bis, Michael Boehnke, Eric Boerwinkle, Jette Bork-Jensen, Erwin P Bottinger, Ivan Brandslund, Morris Brown, Fabio Busonero, Mark J Caulfield, John C Chambers, Daniel I Chasman, Y Eugene Chen, Yii-der Ida Chen, Rajiv Chowdhury, Cramer Christensen, Audrey Y Chu, John M Connell, Francesco Cucca, L Adrienne Cupples, Scott M Damrauer, Gail Davies, Ian J Deary, George Dedoussis, Joshua C Denny, Anna Dominiczak, Marie-Pierre Dubé, Tapani Ebeling, Gudny Eiriksdottir, Tõnu Esko, Aliki-Eleni Farmaki, Mary F Feitosa, Marco Ferrario, Jean Ferrieres, Ian Ford, Myriam Fornage, Paul W Franks, Timothy M Frayling, Ruth Frikke-Schmidt, Lars G Fritsche, Philippe Frossard, Valentin Fuster, Santhi K Ganesh, Wei Gao, Melissa E Garcia, Christian Gieger, Franco Giulianini, Mark O Goodarzi, Harald Grallert, Niels Grarup, Leif Groop, Megan L Grove, Vilmundur Gudnason, Torben Hansen, Tamara B Harris, Caroline Hayward, Joel N Hirschhorn, Oddgeir L Holmen, Jennifer Huffman, Yong Huo, Kristian Hveem, Sehrish Jabeen, Anne U Jackson, Johanna Jakobsdottir, Marjo-Riitta Jarvelin, Gorm B Jensen, Marit E Jørgensen, J Wouter Jukema, Johanne M Justesen, Pia R Kamstrup, Stavroula Kanoni, Fredrik Karpe, Frank Kee, Amit V Khera, Derek Klarin, Heikki A Koistinen, Jaspal S Kooner, Charles Kooperberg, Kari Kuulasmaa, Johanna Kuusisto, Markku Laakso, Timo Lakka, Claudia Langenberg, Anne Langsted, Lenore J Launer, Torsten Lauritzen, David C M Liewald, Li An Lin, Allan Linneberg, Ruth J F Loos, Yingchang Lu, Xiangfeng Lu, Reedik Mägi, Anders Malarstig, Ani Manichaikul, Alisa K Manning, Pekka Mäntyselkä, Eirini Marouli, Nicholas G D Masca, Andrea Maschio, James B Meigs, Olle Melander, Andres Metspalu, Andrew P Morris, Alanna C Morrison, Antonella Mulas, Martina Müller-Nurasyid, Patricia B Munroe, Matt J Neville, Jonas B Nielsen, Sune F Nielsen, Børge G Nordestgaard, Jose M Ordovas, Roxana Mehran, Christoper J O'Donnell, Marju Orho-Melander, Cliona M Molony, Pieter Muntendam, Sandosh Padmanabhan, Colin N A Palmer, Dorota Pasko, Aniruddh P Patel, Oluf Pedersen, Markus Perola, Annette Peters, Charlotta Pisinger, Giorgio Pistis, Ozren Polasek, Neil Poulter, Bruce M Psaty, Daniel J Rader, Asif Rasheed, Rainer Rauramaa, Dermot F Reilly, Alex P Reiner, Frida Renström, Stephen S Rich, Paul M Ridker, John D Rioux, Neil R Robertson, Dan M Roden, Jerome I Rotter, Igor Rudan, Veikko Salomaa, Nilesh J Samani, Serena Sanna, Naveed Sattar, Ellen M Schmidt, Robert A Scott, Peter Sever, Raquel S Sevilla, Christian M Shaffer, Xueling Sim, Suthesh Sivapalaratnam, Kerrin S Small, Albert V Smith, Blair H Smith, Sangeetha Somayajula, Lorraine Southam, Timothy D Spector, Elizabeth K Speliotes, John M Starr, Kathleen E Stirrups, Nathan Stitziel, Konstantin Strauch, Heather M Stringham, Praveen Surendran, Hayato Tada, Alan R Tall, Hua Tang, Jean-Claude Tardif, Kent D Taylor, Stella Trompet, Philip S Tsao, Jaakko Tuomilehto, Anne Tybjaerg-Hansen, Natalie R van Zuydam, Anette Varbo, Tibor V Varga, Jarmo Virtamo, Melanie Waldenberger, Nan Wang, Nick J Wareham, Helen R Warren, Peter E Weeke, Joshua Weinstock, Jennifer Wessel, James G Wilson, Peter W F Wilson, Ming Xu, Hanieh Yaghootkar, Robin Young, Eleftheria Zeggini, He Zhang, Neil S Zheng, Weihua Zhang, Yan Zhang, Wei Zhou, Yanhua Zhou, Magdalena Zoledziewska, Charge Diabetes Working Group, EPIC-InterAct Consortium, EPIC-CVD Consortium, GOLD Consortium, VA Million Veteran Program, Joanna M M Howson, John Danesh, Mark I McCarthy, Chad A Cowan, Goncalo Abecasis, Panos Deloukas, Kiran Musunuru, Cristen J Willer, Sekar Kathiresan Show less
We screened variants on an exome-focused genotyping array in >300,000 participants (replication in >280,000 participants) and identified 444 independent variants in 250 loci significantly associated w Show more
We screened variants on an exome-focused genotyping array in >300,000 participants (replication in >280,000 participants) and identified 444 independent variants in 250 loci significantly associated with total cholesterol (TC), high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C), and/or triglycerides (TG). At two loci (JAK2 and A1CF), experimental analysis in mice showed lipid changes consistent with the human data. We also found that: (i) beta-thalassemia trait carriers displayed lower TC and were protected from coronary artery disease (CAD); (ii) excluding the CETP locus, there was not a predictable relationship between plasma HDL-C and risk for age-related macular degeneration; (iii) only some mechanisms of lowering LDL-C appeared to increase risk for type 2 diabetes (T2D); and (iv) TG-lowering alleles involved in hepatic production of TG-rich lipoproteins (TM6SF2 and PNPLA3) tracked with higher liver fat, higher risk for T2D, and lower risk for CAD, whereas TG-lowering alleles involved in peripheral lipolysis (LPL and ANGPTL4) had no effect on liver fat but decreased risks for both T2D and CAD. Show less
📄 PDF DOI: 10.1038/ng.3977
ANGPTL4
Belinda K Cornes, Jennifer A Brody, Naghmeh Nikpoor +25 more · 2014 · Circulation. Cardiovascular genetics · added 2026-04-24
Common variation at the 11p11.2 locus, encompassing MADD, ACP2, NR1H3, MYBPC3, and SPI1, has been associated in genome-wide association studies with fasting glucose and insulin (FI). In the Cohorts fo Show more
Common variation at the 11p11.2 locus, encompassing MADD, ACP2, NR1H3, MYBPC3, and SPI1, has been associated in genome-wide association studies with fasting glucose and insulin (FI). In the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study, we sequenced 5 gene regions at 11p11.2 to identify rare, potentially functional variants influencing fasting glucose or FI levels. Sequencing (mean depth, 38×) across 16.1 kb in 3566 individuals without diabetes mellitus identified 653 variants, 79.9% of which were rare (minor allele frequency <1%) and novel. We analyzed rare variants in 5 gene regions with FI or fasting glucose using the sequence kernel association test. At NR1H3, 53 rare variants were jointly associated with FI (P=2.73×10(-3)); of these, 7 were predicted to have regulatory function and showed association with FI (P=1.28×10(-3)). Conditioning on 2 previously associated variants at MADD (rs7944584, rs10838687) did not attenuate this association, suggesting that there are >2 independent signals at 11p11.2. One predicted regulatory variant, chr11:47227430 (hg18; minor allele frequency=0.00068), contributed 20.6% to the overall sequence kernel association test score at NR1H3, lies in intron 2 of NR1H3, and is a predicted binding site for forkhead box A1 (FOXA1), a transcription factor associated with insulin regulation. In human HepG2 hepatoma cells, the rare chr11:47227430 A allele disrupted FOXA1 binding and reduced FOXA1-dependent transcriptional activity. Sequencing at 11p11.2-NR1H3 identified rare variation associated with FI. One variant, chr11:47227430, seems to be functional, with the rare A allele reducing transcription factor FOXA1 binding and FOXA1-dependent transcriptional activity. Show less
📄 PDF DOI: 10.1161/CIRCGENETICS.113.000169
ACP2
Aldi T Kraja, Daniel I Chasman, Kari E North +76 more · 2014 · Molecular genetics and metabolism · Elsevier · added 2026-04-24
Metabolic syndrome (MetS) has become a health and financial burden worldwide. The MetS definition captures clustering of risk factors that predict higher risk for diabetes mellitus and cardiovascular Show more
Metabolic syndrome (MetS) has become a health and financial burden worldwide. The MetS definition captures clustering of risk factors that predict higher risk for diabetes mellitus and cardiovascular disease. Our study hypothesis is that additional to genes influencing individual MetS risk factors, genetic variants exist that influence MetS and inflammatory markers forming a predisposing MetS genetic network. To test this hypothesis a staged approach was undertaken. (a) We analyzed 17 metabolic and inflammatory traits in more than 85,500 participants from 14 large epidemiological studies within the Cross Consortia Pleiotropy Group. Individuals classified with MetS (NCEP definition), versus those without, showed on average significantly different levels for most inflammatory markers studied. (b) Paired average correlations between 8 metabolic traits and 9 inflammatory markers from the same studies as above, estimated with two methods, and factor analyses on large simulated data, helped in identifying 8 combinations of traits for follow-up in meta-analyses, out of 130,305 possible combinations between metabolic traits and inflammatory markers studied. (c) We performed correlated meta-analyses for 8 metabolic traits and 6 inflammatory markers by using existing GWAS published genetic summary results, with about 2.5 million SNPs from twelve predominantly largest GWAS consortia. These analyses yielded 130 unique SNPs/genes with pleiotropic associations (a SNP/gene associating at least one metabolic trait and one inflammatory marker). Of them twenty-five variants (seven loci newly reported) are proposed as MetS candidates. They map to genes MACF1, KIAA0754, GCKR, GRB14, COBLL1, LOC646736-IRS1, SLC39A8, NELFE, SKIV2L, STK19, TFAP2B, BAZ1B, BCL7B, TBL2, MLXIPL, LPL, TRIB1, ATXN2, HECTD4, PTPN11, ZNF664, PDXDC1, FTO, MC4R and TOMM40. Based on large data evidence, we conclude that inflammation is a feature of MetS and several gene variants show pleiotropic genetic associations across phenotypes and might explain a part of MetS correlated genetic architecture. These findings warrant further functional investigation. Show less
📄 PDF DOI: 10.1016/j.ymgme.2014.04.007
MACF1
C-T Liu, M C Y Ng, D Rybin +37 more · 2012 · Diabetologia · Springer · added 2026-04-24
Hyperglycaemia disproportionately affects African-Americans (AfAs). We tested the transferability of 18 single-nucleotide polymorphisms (SNPs) associated with glycaemic traits identified in European a Show more
Hyperglycaemia disproportionately affects African-Americans (AfAs). We tested the transferability of 18 single-nucleotide polymorphisms (SNPs) associated with glycaemic traits identified in European ancestry (EuA) populations in 5,984 non-diabetic AfAs. We meta-analysed SNP associations with fasting glucose (FG) or insulin (FI) in AfAs from five cohorts in the Candidate Gene Association Resource. We: (1) calculated allele frequency differences, variations in linkage disequilibrium (LD), fixation indices (F(st)s) and integrated haplotype scores (iHSs); (2) tested EuA SNPs in AfAs; and (3) interrogated within ± 250 kb around each EuA SNP in AfAs. Allele frequency differences ranged from 0.6% to 54%. F(st) exceeded 0.15 at 6/16 loci, indicating modest population differentiation. All iHSs were <2, suggesting no recent positive selection. For 18 SNPs, all directions of effect were the same and 95% CIs of association overlapped when comparing EuA with AfA. For 17 of 18 loci, at least one SNP was nominally associated with FG in AfAs. Four loci were significantly associated with FG (GCK, p = 5.8 × 10(-8); MTNR1B, p = 8.5 × 10(-9); and FADS1, p = 2.2 × 10(-4)) or FI (GCKR, p = 5.9 × 10(-4)). At GCK and MTNR1B the EuA and AfA SNPs represented the same signal, while at FADS1, and GCKR, the EuA and best AfA SNPs were weakly correlated (r(2) <0.2), suggesting allelic heterogeneity for association with FG at these loci. Few glycaemic SNPs showed strict evidence of transferability from EuA to AfAs. Four loci were significantly associated in both AfAs and those with EuA after accounting for varying LD across ancestral groups, with new signals emerging to aid fine-mapping. Show less
📄 PDF DOI: 10.1007/s00125-012-2656-4
FADS1
Guillaume Lettre, Cameron D Palmer, Taylor Young +57 more · 2011 · PLoS genetics · PLOS · added 2026-04-24
Coronary heart disease (CHD) is the leading cause of mortality in African Americans. To identify common genetic polymorphisms associated with CHD and its risk factors (LDL- and HDL-cholesterol (LDL-C Show more
Coronary heart disease (CHD) is the leading cause of mortality in African Americans. To identify common genetic polymorphisms associated with CHD and its risk factors (LDL- and HDL-cholesterol (LDL-C and HDL-C), hypertension, smoking, and type-2 diabetes) in individuals of African ancestry, we performed a genome-wide association study (GWAS) in 8,090 African Americans from five population-based cohorts. We replicated 17 loci previously associated with CHD or its risk factors in Caucasians. For five of these regions (CHD: CDKN2A/CDKN2B; HDL-C: FADS1-3, PLTP, LPL, and ABCA1), we could leverage the distinct linkage disequilibrium (LD) patterns in African Americans to identify DNA polymorphisms more strongly associated with the phenotypes than the previously reported index SNPs found in Caucasian populations. We also developed a new approach for association testing in admixed populations that uses allelic and local ancestry variation. Using this method, we discovered several loci that would have been missed using the basic allelic and global ancestry information only. Our conclusions suggest that no major loci uniquely explain the high prevalence of CHD in African Americans. Our project has developed resources and methods that address both admixture- and SNP-association to maximize power for genetic discovery in even larger African-American consortia. Show less
📄 PDF DOI: 10.1371/journal.pgen.1001300
FADS1
Rona J Strawbridge, Josée Dupuis, Inga Prokopenko +105 more · 2011 · Diabetes · added 2026-04-24
Rona J Strawbridge, Josée Dupuis, Inga Prokopenko, Adam Barker, Emma Ahlqvist, Denis Rybin, John R Petrie, Mary E Travers, Nabila Bouatia-Naji, Antigone S Dimas, Alexandra Nica, Eleanor Wheeler, Han Chen, Benjamin F Voight, Jalal Taneera, Stavroula Kanoni, John F Peden, Fabiola Turrini, Stefan Gustafsson, Carina Zabena, Peter Almgren, David J P Barker, Daniel Barnes, Elaine M Dennison, Johan G Eriksson, Per Eriksson, Elodie Eury, Lasse Folkersen, Caroline S Fox, Timothy M Frayling, Anuj Goel, Harvest F Gu, Momoko Horikoshi, Bo Isomaa, Anne U Jackson, Karen A Jameson, Eero Kajantie, Julie Kerr-Conte, Teemu Kuulasmaa, Johanna Kuusisto, Ruth J F Loos, Jian'an Luan, Konstantinos Makrilakis, Alisa K Manning, María Teresa Martínez-Larrad, Narisu Narisu, Maria Nastase Mannila, John Ohrvik, Clive Osmond, Laura Pascoe, Felicity Payne, Avan A Sayer, Bengt Sennblad, Angela Silveira, Alena Stancáková, Kathy Stirrups, Amy J Swift, Ann-Christine Syvänen, Tiinamaija Tuomi, Ferdinand M van 't Hooft, Mark Walker, Michael N Weedon, Weijia Xie, Björn Zethelius, DIAGRAM Consortium, GIANT Consortium, MuTHER Consortium, CARDIoGRAM Consortium, C4D Consortium, Halit Ongen, Anders Mälarstig, Jemma C Hopewell, Danish Saleheen, John Chambers, Sarah Parish, John Danesh, Jaspal Kooner, Claes-Göran Ostenson, Lars Lind, Cyrus C Cooper, Manuel Serrano-Ríos, Ele Ferrannini, Tom J Forsen, Robert Clarke, Maria Grazia Franzosi, Udo Seedorf, Hugh Watkins, Philippe Froguel, Paul Johnson, Panos Deloukas, Francis S Collins, Markku Laakso, Emmanouil T Dermitzakis, Michael Boehnke, Mark I McCarthy, Nicholas J Wareham, Leif Groop, François Pattou, Anna L Gloyn, George V Dedoussis, Valeriya Lyssenko, James B Meigs, Inês Barroso, Richard M Watanabe, Erik Ingelsson, Claudia Langenberg, Anders Hamsten, Jose C Florez Show less
Proinsulin 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 diab Show more
Proinsulin 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. We 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. Nine 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. We 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. Show less
no PDF DOI: 10.2337/db11-0415
VPS13C
Erik Ingelsson, Claudia Langenberg, Marie-France Hivert +65 more · 2010 · Diabetes · added 2026-04-24
OBJECTIVE Recent genome-wide association studies have revealed loci associated with glucose and insulin-related traits. We aimed to characterize 19 such loci using detailed measures of insulin process Show more
OBJECTIVE Recent genome-wide association studies have revealed loci associated with glucose and insulin-related traits. We aimed to characterize 19 such loci using detailed measures of insulin processing, secretion, and sensitivity to help elucidate their role in regulation of glucose control, insulin secretion and/or action. RESEARCH DESIGN AND METHODS We investigated associations of loci identified by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) with circulating proinsulin, measures of insulin secretion and sensitivity from oral glucose tolerance tests (OGTTs), euglycemic clamps, insulin suppression tests, or frequently sampled intravenous glucose tolerance tests in nondiabetic humans (n = 29,084). RESULTS The glucose-raising allele in MADD was associated with abnormal insulin processing (a dramatic effect on higher proinsulin levels, but no association with insulinogenic index) at extremely persuasive levels of statistical significance (P = 2.1 x 10(-71)). Defects in insulin processing and insulin secretion were seen in glucose-raising allele carriers at TCF7L2, SCL30A8, GIPR, and C2CD4B. Abnormalities in early insulin secretion were suggested in glucose-raising allele carriers at MTNR1B, GCK, FADS1, DGKB, and PROX1 (lower insulinogenic index; no association with proinsulin or insulin sensitivity). Two loci previously associated with fasting insulin (GCKR and IGF1) were associated with OGTT-derived insulin sensitivity indices in a consistent direction. CONCLUSIONS Genetic loci identified through their effect on hyperglycemia and/or hyperinsulinemia demonstrate considerable heterogeneity in associations with measures of insulin processing, secretion, and sensitivity. Our findings emphasize the importance of detailed physiological characterization of such loci for improved understanding of pathways associated with alterations in glucose homeostasis and eventually type 2 diabetes. Show less
📄 PDF DOI: 10.2337/db09-1568
GIPR
Richa Saxena, Marie-France Hivert, Claudia Langenberg +153 more · 2010 · Nature genetics · Nature · added 2026-04-24
Richa Saxena, Marie-France Hivert, Claudia Langenberg, Toshiko Tanaka, James S Pankow, Peter Vollenweider, Valeriya Lyssenko, Nabila Bouatia-Naji, Josée Dupuis, Anne U Jackson, W H Linda Kao, Man Li, Nicole L Glazer, Alisa K Manning, Jian'an Luan, Heather M Stringham, Inga Prokopenko, Toby Johnson, Niels Grarup, Trine W Boesgaard, Cécile Lecoeur, Peter Shrader, Jeffrey O'Connell, Erik Ingelsson, David J Couper, Kenneth Rice, Kijoung Song, Camilla H Andreasen, Christian Dina, Anna Köttgen, Olivier Le Bacquer, François Pattou, Jalal Taneera, Valgerdur Steinthorsdottir, Denis Rybin, Kristin Ardlie, Michael Sampson, Lu Qi, Mandy van Hoek, Michael N Weedon, Yurii S Aulchenko, Benjamin F Voight, Harald Grallert, Beverley Balkau, Richard N Bergman, Suzette J Bielinski, Amelie Bonnefond, Lori L Bonnycastle, Knut Borch-Johnsen, Yvonne Böttcher, Eric Brunner, Thomas A Buchanan, Suzannah J Bumpstead, Christine Cavalcanti-Proença, Guillaume Charpentier, Yii-der Ida Chen, Peter S Chines, Francis S Collins, Marilyn Cornelis, Gabriel J Crawford, Jerome Delplanque, Alex Doney, Josephine M Egan, Michael R Erdos, Mathieu Firmann, Nita G Forouhi, Caroline S Fox, Mark O Goodarzi, Jürgen Graessler, Aroon Hingorani, Bo Isomaa, Torben Jørgensen, Mika Kivimaki, Peter Kovacs, Knut Krohn, Meena Kumari, Torsten Lauritzen, Claire Lévy-Marchal, Vladimir Mayor, Jarred B McAteer, David Meyre, Braxton D Mitchell, Karen L Mohlke, Mario A Morken, Narisu Narisu, Colin N A Palmer, Ruth Pakyz, Laura Pascoe, Felicity Payne, Daniel Pearson, Wolfgang Rathmann, Annelli Sandbaek, Avan Aihie Sayer, Laura J Scott, Stephen J Sharp, Eric Sijbrands, Andrew Singleton, David S Siscovick, Nicholas L Smith, Thomas Sparsø, Amy J Swift, Holly Syddall, Gudmar Thorleifsson, Anke Tönjes, Tiinamaija Tuomi, Jaakko Tuomilehto, Timo T Valle, Gérard Waeber, Andrew Walley, Dawn M Waterworth, Eleftheria Zeggini, Jing Hua Zhao, GIANT Consortium, MAGIC Investigators, Thomas Illig, H Erich Wichmann, James F Wilson, Cornelia van Duijn, Frank B Hu, Andrew D Morris, Timothy M Frayling, Andrew T Hattersley, Unnur Thorsteinsdottir, Kari Stefansson, Peter Nilsson, Ann-Christine Syvänen, Alan R Shuldiner, Mark Walker, Stefan R Bornstein, Peter Schwarz, Gordon H Williams, David M Nathan, Johanna Kuusisto, Markku Laakso, Cyrus Cooper, Michael Marmot, Luigi Ferrucci, Vincent Mooser, Michael Stumvoll, Ruth J F Loos, David Altshuler, Bruce M Psaty, Jerome I Rotter, Eric Boerwinkle, Torben Hansen, Oluf Pedersen, Jose C Florez, Mark I McCarthy, Michael Boehnke, Inês Barroso, Robert Sladek, Philippe Froguel, James B Meigs, Leif Groop, Nicholas J Wareham, Richard M Watanabe Show less
Glucose levels 2 h after an oral glucose challenge are a clinical measure of glucose tolerance used in the diagnosis of type 2 diabetes. We report a meta-analysis of nine genome-wide association studi Show more
Glucose levels 2 h after an oral glucose challenge are a clinical measure of glucose tolerance used in the diagnosis of type 2 diabetes. We report a meta-analysis of nine genome-wide association studies (n = 15,234 nondiabetic individuals) and a follow-up of 29 independent loci (n = 6,958-30,620). We identify variants at the GIPR locus associated with 2-h glucose level (rs10423928, beta (s.e.m.) = 0.09 (0.01) mmol/l per A allele, P = 2.0 x 10(-15)). The GIPR A-allele carriers also showed decreased insulin secretion (n = 22,492; insulinogenic index, P = 1.0 x 10(-17); ratio of insulin to glucose area under the curve, P = 1.3 x 10(-16)) and diminished incretin effect (n = 804; P = 4.3 x 10(-4)). We also identified variants at ADCY5 (rs2877716, P = 4.2 x 10(-16)), VPS13C (rs17271305, P = 4.1 x 10(-8)), GCKR (rs1260326, P = 7.1 x 10(-11)) and TCF7L2 (rs7903146, P = 4.2 x 10(-10)) associated with 2-h glucose. Of the three newly implicated loci (GIPR, ADCY5 and VPS13C), only ADCY5 was found to be associated with type 2 diabetes in collaborating studies (n = 35,869 cases, 89,798 controls, OR = 1.12, 95% CI 1.09-1.15, P = 4.8 x 10(-18)). Show less
📄 PDF DOI: 10.1038/ng.521
GIPR
Josée Dupuis, Claudia Langenberg, Inga Prokopenko +305 more · 2010 · Nature genetics · Nature · added 2026-04-24
Josée Dupuis, Claudia Langenberg, Inga Prokopenko, Richa Saxena, Nicole Soranzo, Anne U Jackson, Eleanor Wheeler, Nicole L Glazer, Nabila Bouatia-Naji, Anna L Gloyn, Cecilia M Lindgren, Reedik Mägi, Andrew P Morris, Joshua Randall, Toby Johnson, Paul Elliott, Denis Rybin, Gudmar Thorleifsson, Valgerdur Steinthorsdottir, Peter Henneman, Harald Grallert, Abbas Dehghan, Jouke Jan Hottenga, Christopher S Franklin, Pau Navarro, Kijoung Song, Anuj Goel, John R B Perry, Josephine M Egan, Taina Lajunen, Niels Grarup, Thomas Sparsø, Alex Doney, Benjamin F Voight, Heather M Stringham, Man Li, Stavroula Kanoni, Peter Shrader, Christine Cavalcanti-Proença, Meena Kumari, Lu Qi, Nicholas J Timpson, Christian Gieger, Carina Zabena, Ghislain Rocheleau, Erik Ingelsson, Ping An, Jeffrey O'Connell, Jian'an Luan, Amanda Elliott, Steven A McCarroll, Felicity Payne, Rosa Maria Roccasecca, François Pattou, Praveen Sethupathy, Kristin Ardlie, Yavuz Ariyurek, Beverley Balkau, Philip Barter, John P Beilby, Yoav Ben-Shlomo, Rafn Benediktsson, Amanda J Bennett, Sven Bergmann, Murielle Bochud, Eric Boerwinkle, Amélie Bonnefond, Lori L Bonnycastle, Knut Borch-Johnsen, Yvonne Böttcher, Eric Brunner, Suzannah J Bumpstead, Guillaume Charpentier, Yii-der Ida Chen, Peter Chines, Robert Clarke, Lachlan J M Coin, Matthew N Cooper, Marilyn Cornelis, Gabe Crawford, Laura Crisponi, Ian N M Day, Eco J C de Geus, Jerome Delplanque, Christian Dina, Michael R Erdos, Annette C Fedson, Antje Fischer-Rosinsky, Nita G Forouhi, Caroline S Fox, Rune Frants, Maria Grazia Franzosi, Pilar Galan, Mark O Goodarzi, Jürgen Graessler, Christopher J Groves, Scott Grundy, Rhian Gwilliam, Ulf Gyllensten, Samy Hadjadj, Göran Hallmans, Naomi Hammond, Xijing Han, Anna-Liisa Hartikainen, Neelam Hassanali, Caroline Hayward, Simon C Heath, Serge Hercberg, Christian Herder, Andrew A Hicks, David R Hillman, Aroon D Hingorani, Albert Hofman, Jennie Hui, Joe Hung, Bo Isomaa, Paul R V Johnson, Torben Jørgensen, Antti Jula, Marika Kaakinen, Jaakko Kaprio, Y Antero Kesaniemi, Mika Kivimaki, Beatrice Knight, Seppo Koskinen, Peter Kovacs, Kirsten Ohm Kyvik, G Mark Lathrop, Debbie A Lawlor, Olivier Le Bacquer, Cécile Lecoeur, Yun Li, Valeriya Lyssenko, Robert Mahley, Massimo Mangino, Alisa K Manning, María Teresa Martínez-Larrad, Jarred B McAteer, Laura J McCulloch, Ruth McPherson, Christa Meisinger, David Melzer, David Meyre, Braxton D Mitchell, Mario A Morken, Sutapa Mukherjee, Silvia Naitza, Narisu Narisu, Matthew J Neville, Ben A Oostra, Marco Orrù, Ruth Pakyz, Colin N A Palmer, Giuseppe Paolisso, Cristian Pattaro, Daniel Pearson, John F Peden, Nancy L Pedersen, Markus Perola, Andreas F H Pfeiffer, Irene Pichler, Ozren Polasek, Danielle Posthuma, Simon C Potter, Anneli Pouta, Michael A Province, Bruce M Psaty, Wolfgang Rathmann, Nigel W Rayner, Kenneth Rice, Samuli Ripatti, Fernando Rivadeneira, Michael Roden, Olov Rolandsson, Annelli Sandbaek, Manjinder Sandhu, Serena Sanna, Avan Aihie Sayer, Paul Scheet, Laura J Scott, Udo Seedorf, Stephen J Sharp, Beverley Shields, Gunnar Sigurethsson, Eric J G Sijbrands, Angela Silveira, Laila Simpson, Andrew Singleton, Nicholas L Smith, Ulla Sovio, Amy Swift, Holly Syddall, Ann-Christine Syvänen, Toshiko Tanaka, Barbara Thorand, Jean Tichet, Anke Tönjes, Tiinamaija Tuomi, André G Uitterlinden, Ko Willems Van Dijk, Mandy van Hoek, Dhiraj Varma, Sophie Visvikis-Siest, Veronique Vitart, Nicole Vogelzangs, Gérard Waeber, Peter J Wagner, Andrew Walley, G Bragi Walters, Kim L Ward, Hugh Watkins, Michael N Weedon, Sarah H Wild, Gonneke Willemsen, Jaqueline C M Witteman, John W G Yarnell, Eleftheria Zeggini, Diana Zelenika, Björn Zethelius, Guangju Zhai, Jing Hua Zhao, M Carola Zillikens, DIAGRAM Consortium, GIANT Consortium, Global BPgen Consortium, Ingrid B Borecki, Ruth J F Loos, Pierre Meneton, Patrik K E Magnusson, David M Nathan, Gordon H Williams, Andrew T Hattersley, Kaisa Silander, Veikko Salomaa, George Davey Smith, Stefan R Bornstein, Peter Schwarz, Joachim Spranger, Fredrik Karpe, Alan R Shuldiner, Cyrus Cooper, George V Dedoussis, Manuel Serrano-Ríos, Andrew D Morris, Lars Lind, Lyle J Palmer, Frank B Hu, Paul W Franks, Shah Ebrahim, Michael Marmot, W H Linda Kao, James S Pankow, Michael J Sampson, Johanna Kuusisto, Markku Laakso, Torben Hansen, Oluf Pedersen, Peter Paul Pramstaller, H Erich Wichmann, Thomas Illig, Igor Rudan, Alan F Wright, Michael Stumvoll, Harry Campbell, James F Wilson, Anders Hamsten on behalf of Procardis Consortium, MAGIC Investigators, Richard N Bergman, Thomas A Buchanan, Francis S Collins, Karen L Mohlke, Jaakko Tuomilehto, Timo T Valle, David Altshuler, Jerome I Rotter, David S Siscovick, Brenda W J H Penninx, Dorret I Boomsma, Panos Deloukas, Timothy D Spector, Timothy M Frayling, Luigi Ferrucci, Augustine Kong, Unnur Thorsteinsdottir, Kari Stefansson, Cornelia M Van Duijn, Yurii S Aulchenko, Antonio Cao, Angelo Scuteri, David Schlessinger, Manuela Uda, Aimo Ruokonen, Marjo-Riitta Jarvelin, Dawn M Waterworth, Peter Vollenweider, Leena Peltonen, Vincent Mooser, Goncalo R Abecasis, Nicholas J Wareham, Robert Sladek, Philippe Froguel, Richard M Watanabe, James B Meigs, Leif Groop, Michael Boehnke, Mark I McCarthy, Jose C Florez, Inês Barroso Show less
Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, Show more
Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes. Show less
📄 PDF DOI: 10.1038/ng.520
FADS1
G T Russo, J B Meigs, L A Cupples +9 more · 2001 · Atherosclerosis · Elsevier · added 2026-04-24
Apolipoprotein (apo) CIII participates in the regulation of the metabolism of triglyceride-rich lipoproteins and it is a major component of chylomicrons and VLDL. The APOC3 gene is on chromosome 11q23 Show more
Apolipoprotein (apo) CIII participates in the regulation of the metabolism of triglyceride-rich lipoproteins and it is a major component of chylomicrons and VLDL. The APOC3 gene is on chromosome 11q23 and is highly polymorphic. The less common allele (S2) of the SstI polymorphism on the 3' untranslated region of the APOC3 gene has been previously associated with increased triglycerides, total cholesterol (TC), and apoCIII levels and cardiovascular risk on several, but not all, studies. The aim of this study was to examine the association of this polymorphism with plasma lipid levels, lipoprotein subfractions and coronary heart disease (CHD) risk in a population-based study: The Framingham Offspring Study. The frequency of the S2 allele was 0.086, consistent with previous reports in Caucasian populations. In men, the S2 allele was associated with lower concentrations of high-density lipoprotein cholesterol (HDL-C; P<0.04) and HDL2-C (P<0.02) and a significant increase in apoCIII non-HDL (P<0.05). TG levels were higher in men carriers of the S2 allele, but this association did not reach statistical significance (P=0.30). Conversely, in women, the S2 allele was associated with increased TC (P<0.03), low-density lipoprotein cholesterol (LDL-C; P<0.03), and ApoB levels (P<0.04). Lipoproteins subfractions were also examined using nuclear magnetic resonance (NMR) spectroscopy. S2 male carriers had significantly lower concentrations of large LDL and a significant reduction in LDL particle size (P<0.04). In women, there was a significant increase in intermediate LDL particles (P<0.05) with no significant effect on lipoprotein diameters. We also examined the associations between the S2 allele and biochemical markers of glucose metabolism. In men, the S2 allele was associated with elevated fasting insulin concentrations (P<0.04), whereas no significant associations were observed in women. Despite the described associations with lipid and glucose metabolism related risk factors, we did not find any significant increase in CHD risk associated with the S2 allele in this population. Show less
no PDF DOI: 10.1016/s0021-9150(01)00409-9
APOC3