Abstract
Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in >55,000 participants from three ancestry groups. We identified ten new loci (P < 5 × 10−8) not previously associated with postchallenge insulin resistance, eight of which were shown to share their genetic architecture with type 2 diabetes in colocalization analyses. We investigated candidate genes at a subset of associated loci in cultured cells and identified nine candidate genes newly implicated in the expression or trafficking of GLUT4, the key glucose transporter in postprandial glucose uptake in muscle and fat. By focusing on postprandial insulin resistance, we highlighted the mechanisms of action at type 2 diabetes loci that are not adequately captured by studies of fasting glycemic traits.
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Data availability
GWAS summary statistics will be made available on the MAGIC investigators.
Website (https://magicinvestigators.org/downloads/) and GWAS catalog (https://www.ebi.ac.uk/gwas/home): GCST90267567, GCST90267568, GCST90267569, GCST90267570, GCST90267571, GCST90267572, GCST90267573, GCST90267574, GCST90267575, GCST90267576, GCST90267577 and GCST90267578.
Data from the Fenland cohort can be requested by bona fide researchers for specified scientific purposes via the study website (https://www.mrc-epid.cam.ac.uk/research/studies/fenland/information-for-researchers/). Data will either be shared through an institutional data-sharing agreement or arrangements will be made for analyses to be conducted remotely without the necessity for data transfer.
All data used in genetic risk score association analyses are available from the UK Biobank upon application (https://www.ukbiobank.ac.uk). All analyses in the UK Biobank in this manuscript were conducted under application 44448. Further details about the RISC study and data availability can be found here: http://www.egir.org/egirrisc/. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. The data used for the analyses described in this manuscript can be obtained from the GTEx Portal (https://www.gtexportal.org/home/) and dbGaP accession number phs000424.v8.p2. Genome regulatory annotations from ENCODE (https://www.encodeproject.org/) and Roadmap Epigenomics Consortium (https://egg2.wustl.edu/roadmap/web_portal/) were explored via UCSC Genome Browser (http://genome.ucsc.edu). Published differentiated 3T3-L1 RNA-sequencing data used in this study are available from GEO accession GSE129957 (https://www.ncbi.nlm.nih.gov/geo/). Source data are provided with this paper.
Code availability
No previously unreported custom code or algorithm was used to generate results. The following software and packages were used for data analysis: METAL v.2011-03-25 (http://csg.sph.umich.edu/abecasis/Metal/download/), random-metal v.2017-07-24 (https://github.com/explodecomputer/random-metal), linkage disequilibrium score regression v.1.0.1 (https://github.com/bulik/ldsc), R v.3.6.0 and v.4.0.3 (https://www.r-project.org/). R packages coloc v.5.1.0 (https://cran.r-project.org/web/packages/coloc/).
Hyprcoloc v.1.0 (https://github.com/jrs95/hyprcoloc).
GCTA 1.26.0 (https://yanglab.westlake.edu.cn/software/gcta/#Overview). EasyQC v.17.8 (https://www.uni-regensburg.de/medizin/epidemiologie-praeventivmedizin/genetische-epidemiologie/software/index.html). Associated code and scripts used in this manuscript are available on GitHub: https://github.com/MRC-Epid/GWAS_postchallenge_insulin (https://zenodo.org/record/7805583#.ZC7C_exBxhE).
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Acknowledgements
We are grateful to investigators, staff members and study participants for their contribution to all participating studies. A full list of individual and study acknowledgments appears in the Supplementary Note. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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A.W., X.Y., K.A.B., E.P.W., N.G., M.B., N.J.W., K.L.M., E.W., S.O’R and C.L. contributed to central analysis group. A.W., X.Y., K.A.B., A.U.J, M.W., N.J.W., K.L.M., E.W., S.O’R and C.L. contributed to follow-up analysis and interpretation. A.H.M., S.V. and K.L.M. contributed to SLC2A4 in vitro follow-up. A.W., D.M.N. and D.J.F. contributed to siRNA knockdown screen. A.W., X.Y., K.A.B., A.U.J, V.A., M.K.A., Z.A., L.L.B., S.R.B., M.P.B., T.A.B., Y-C.C., L-M.C., R-H.C., T.D.C., P.D., G.E.D., V.D.dM., J.D., O.P.D., M.R.E, L.F., T.M.F., C.G., M.O.G., X.G., S.G., L.H., U.H., G.H., S.H., K.H., K.Horn, W.A.H., Y-J.H., C-M.H., A.J., L.L.K., M.E.K., P.K., T.A.L., M.L., I-T.L., C.L., J.L., A.L., C-T.L., J’an.L., D.M., E.M., A.P. Moissl, A.P. Morris, N.N., N.P., A. Peters, R.B.P., R.N.R., K.R., C.R., C.S., K.S., M. Scholz, S. Sharma, S.E.S., S. Suleman, J. Tan, K.T., M.U., D.V., P.W., D.R.W., R.W., A.H.X., B.Z., E.A., M. Laakso, L.L., J.B.M., R.R., J.S., M.W., N.G. and N.J.W. contributed to study-level GWAS—analysis, phenotyping and genotyping. E.A., R.N.B., Y.C., F.S.C., T.F., J.C.F., A.F., H.G., L.G., T.H., H.A.K., P.K., M. Laakso, L.L., M. Loeffler, W.M., J.B.M., L.J.R., R.R., J.I.R., P.E.H.S., M. Stumvoll, J.S., A.T., T.T., J. Tuomilehto, R.W., M.W., N.G., M.B., N.J.W., K.L.M. and C.L. contributed to study-level oversight/PI. A.W., D.M.N., A.H.M., I.B., K.L.M., E.W., S.O’R, D.J.F. and C.L. contributed to writing group. All authors read, edited and approved the final version of the manuscript.
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I.B. and spouse declare stock ownership in GlaxoSmithKline, Incyte Ltd. and Inivata Ltd. J.C.F. has received consulting honoraria from Goldfinch Bio and AstraZeneca; speaker honoraria from Novo Nordisk, AstraZeneca and Merck for research lectures over which he had full control on content. M.E.K. is employed by SYNLAB Holding Deutschland GmbH. C.L. receives grants from Bayer Ag & Novo Nordisk and her husband works for Vertex. W.M. reports grants and personal fees from Siemens Diagnostics, Aegerion Pharmaceuticals, AMGEN, AstraZeneca, Danone Research, Sanofi, Pfizer, BASF and Numares; personal fees from Hoffmann LaRoche, MSD, Synageva; grants from Abbott Diagnostics, outside the submitted work. W.M. is employed by Synlab Holding Deutschland GmbH. J.B.M. serves as an Academic Associate for Quest Diagnostics. S.O’R. has undertaken remunerated consultancy work for Pfizer, AstraZeneca, GSK and ERX Pharmaceuticals. N.P. reports consulting honoraria from Bayer Vital GmbH and speaker honoraria from Novo Nordisk. J.S. is shareholder in Anagram kommunikation AB and Symptoms Europe AB, outside of the present study. D.V. has received research grants from Bayer A/S, Sanofi, Novo Nordisk A/S and Boehringer Ingelheim and holds shares in Novo Nordisk A/S. E.W. is now an employee of AstraZeneca. B.Z. is employed at the Swedish Medical Products Agency, SE-751 03 Uppsala, Sweden. The views expressed in this paper are the personal views of the authors and not necessarily the views of the Swedish government agency. All other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Overview of genetic discovery for insulin fold change and modified Stumvoll ISI, and downstream genetic and in vitro studies.
Created with BioRender.com.
Extended Data Fig. 2 Observational correlation of insulin fold change and modified Stumvoll ISI with metabolic traits in the Fenland study.
Pairwise Spearman’s rank correlation. Red shades denote positive correlation, blue shared denote negative correlation between trait pairs. X denotes no significant correlation (P > 0.05).
Extended Data Fig. 3 Observational correlation of insulin sensitivity and clearance related traits in the RISC study.
Pairwise Spearman’s rank correlation. Red shades denote positive correlation, blue shared denote negative correlation between trait pairs, legend along bottom of heatmap shows color scale relative to rho value. X denotes no significant correlation (P > 0.05). Abbreviations denote: IFC_OGTT – IFC calculated using OGTT measures, ISI_OGTT – Modified Stumvoll ISI calculated using OGTT measures. M_I – M/I index of insulin sensitivity measured by clamp. InsC0c – insulin measured at 0 min during clamp. InsO0c—insulin measured at 0 min during OGTT. InsC120c—insulin measured at 120 min during clamp. InsO120c—insulin measured at 120 min during OGTT. InsC_20c—insulin measured at 20 min before clamp. EGP_B—basal glucose production, EGP_SS—glucose production during clamp, GCR_B—basal glucose clearance, ml/min/kg lean body mass, GCR_SS—steady state glucose clearance, ml min−1 kg−1 lean body mass, icl_clamp—peripheral insulin clearance (1 min−1 m−2), icl_OGTT—endogenous ‘pre-hepatic’ clearance during the OGTT, hie_0—hepatic insulin extraction during clamp, OGIS—oral glucose insulin sensitivity index (ml min−1 m−2). ISR5dr—insulin secretion 5 mM glucose, beta cell dose response (pmol min−1 m−2). ISR0 basal insulin secretion (pmol min-1m-2). ISRtot—total insulin secretion (nmol m−2).
Extended Data Fig. 4 Meta-analysis workflow for genetic discovery analyses.
Analysis workflow for the meta-analysis of study-level GWAS results for Insulin fold change and modified Stumvoll ISI. Created with BioRender.com.
Extended Data Fig. 5 Two independent signals were identified at PPP1R3B for insulin fold change.
Conditional analyses identify a second independent signal at PPP1R3B for insulin fold change adjusted for BMI. The regional association plot shows the primary signal in red and the secondary signal in blue for marginal summary statistics for insulin fold change adjusted for BMI. Shade of point indicates pairwise linkage disequilibrium (R2) with indicated lead variant.
Extended Data Fig. 6 Forest plot of beta estimates for the association of rs60453193 with insulin fold change in individual cohorts.
Labels on the right-hand side indicate the ancestry of the study and study name. EUR- European ancestry, HIS-AMR—Hispanic American ancestry, EAS—East Asian ancestry. Left-hand side values are beta estimate and 95% confidence interval. Error bars denote a 95% confidence interval. X-axis denotes the beta estimate of associations with insulin fold change in BMI adjusted analyses.
Extended Data Fig. 7 Regional association plot at BICC1 (rs60453193) for insulin fold change in meta-analysis of non-European cohorts.
Unadjusted -log10 p-values are indicated on the y axis. Lead variant indicated by purple diamond.
Extended Data Fig. 8 rs117643180 exhibits allelic differences in transcription factor binding.
An EMSA using 6 µg per lane of nuclear extract from undifferentiated LHCN-M2 cells shows protein–DNA interactions for probes centered around each both alleles of rs117643180. The probe containing rs117643180-A shows allele-specific protein binding (arrow A, lane 6), relative to the probe containing rs117643180-C (lane 2). A 25-fold excess unlabeled probe containing the A allele competed away A-specific bands more effectively (lane 7) than 25-fold excess unlabeled probe containing the C allele (lane 8). Arrow B shows a biotinylated free probe (200 fmol per lane). Uncropped image is available in Source Data.
Extended Data Fig. 9 Confirmation of knockdown of positive control genes in wildtype 3T3-L1 adipocytes by western blot.
Representative blot from N = 2. Marker indicates protein size in kDa is outlined on the right-hand side of the blot. siGenome and OT+ represent siRNA pools with their corresponding targets indicated below (see Methods) and NT denotes non-targeting control. Antibodies are outlined on the left-hand side of the blot with Tubulin and B-actin used as loading controls. Uncropped blots are provided in Source Data.
Supplementary information
Supplementary Information
Supplementary Note—Study and Individual Acknowledgments, Supplementary Methods, Supplementary Results and Discussion and Supplementary Figs. 1–25.
Supplementary Tables
Supplementary Tables 1–33.
Source data
Source Data Extended Data Fig. 8 and Source Data Extended Data Fig. 9
Unprocessed ESMA blot corresponding to extended data figure 8. Unprocessed western blots corresponding to extended data figure 9
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Williamson, A., Norris, D.M., Yin, X. et al. Genome-wide association study and functional characterization identifies candidate genes for insulin-stimulated glucose uptake. Nat Genet 55, 973–983 (2023). https://doi.org/10.1038/s41588-023-01408-9
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DOI: https://doi.org/10.1038/s41588-023-01408-9
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