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Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance

Nature Genetics volume 49, pages 17–26 (2017)Cite this article

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A Corrigendum to this article was published on 31 January 2017

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Abstract

Insulin resistance is a key mediator of obesity-related cardiometabolic disease, yet the mechanisms underlying this link remain obscure. Using an integrative genomic approach, we identify 53 genomic regions associated with insulin resistance phenotypes (higher fasting insulin levels adjusted for BMI, lower HDL cholesterol levels and higher triglyceride levels) and provide evidence that their link with higher cardiometabolic risk is underpinned by an association with lower adipose mass in peripheral compartments. Using these 53 loci, we show a polygenic contribution to familial partial lipodystrophy type 1, a severe form of insulin resistance, and highlight shared molecular mechanisms in common/mild and rare/severe insulin resistance. Population-level genetic analyses combined with experiments in cellular models implicate CCDC92, DNAH10 and L3MBTL3 as previously unrecognized molecules influencing adipocyte differentiation. Our findings support the notion that limited storage capacity of peripheral adipose tissue is an important etiological component in insulin-resistant cardiometabolic disease and highlight genes and mechanisms underpinning this link.

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Figure 1: Combined associations with detailed anthropometric traits and metabolic disease risk at the 53 genomic loci.
Figure 2: Associations at the 53 genomic loci with familial partial lipodystrophy type 1.
Figure 3: Putative effector genes, tissues and cell types.
Figure 4: Experimental knockdown of putative effector genes in cellular adipogenesis models and comparison with phenotypic associations.

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  • 05 December 2016

    In the version of this article initially published online, the middle initial of collaborator Maarten R. Soeters was inadvertently omitted. The error has been corrected for the print, PDF and HTML versions of this article.

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Acknowledgements

We are grateful for the OP9-K cells kindly shared by the laboratory of A. Kopin (Tufts Medical Center). The authors gratefully acknowledge the help of the MRC Epidemiology Unit Support Teams, including the Field Teams, the Laboratory Team and the Data Management Team, and of the staff of the Wellcome Trust Clinical Research Facility.

This study was funded by the UK Medical Research Council through grants MC_UU_12015/1, MC_PC_13046, MC_PC_13048 and MR/L00002/1. This work was supported by the MRC Metabolic Diseases Unit (MC_UU_12012/5) and the Cambridge NIHR Biomedical Research Centre and EU/EFPIA Innovative Medicines Initiative Joint Undertaking (EMIF grant 115372). Funding for the InterAct project was provided by the EU FP6 program (grant LSHM_CT_2006_037197). This work was funded, in part, through an EFSD Rising Star award to R.A.S. supported by Novo Nordisk. D.B.S. is supported by Wellcome Trust grant 107064. M.I.M. is a Wellcome Trust Senior Investigator and is supported by the following grants from the Wellcome Trust: 090532 and 098381. M.v.d.B. is supported by a Novo Nordisk postdoctoral fellowship run in partnership with the University of Oxford. I.B. is supported by Wellcome Trust grant WT098051. S.O'R. acknowledges funding from the Wellcome Trust (Wellcome Trust Senior Investigator Award 095515/Z/11/Z and Wellcome Trust Strategic Award 100574/Z/12/Z).

AUTHOR CONTRUBUTIONS

Concept and design: L.A.L., I.B., N.J.W., D.B.S., C.L., S.O'R. and R.A.S. Generation, acquisition, analysis and/or interpretation of data: all authors. Drafting of the manuscript: L.A.L., I.B., N.J.W., D.B.S., C.L., S.O'R. and R.A.S. Critical review of the manuscript for important intellectual content and approval of the final version of the manuscript: all authors.

Author information

Author notes

  1. Nicholas J Wareham, Richard Ross, Stephen O'Rahilly, David B Savage, Inês Barroso, Nicholas J Wareham, David B Savage, Claudia Langenberg, Stephen O'Rahilly and Robert A Scott: These authors contributed equally to this work

Authors and Affiliations

  1. MRC Epidemiology Unit, University of Cambridge, Cambridge, UK

    Luca A Lotta, Felix R Day, Stephen J Sharp, Jian'an Luan, Emanuella De Lucia Rolfe, Isobel D Stewart, Sara M Willems, Claudia Langenberg, Robert A Scott, Stephen J Sharp, Nita G Forouhi, Nicola D Kerrison, Matt Sims, Debora M E Lucarelli, Nicholas J Wareham, Nita G Forouhi, John R B Perry, Nicholas J Wareham, Claudia Langenberg & Robert A Scott

  2. Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK

    Pawan Gulati, Claire Adams, Inês Barroso, Panos Deloukas, Robert K Semple, Claire Adams, Stephen O'Rahilly, David B Savage, Robert K Semple, Inês Barroso, David B Savage & Stephen O'Rahilly

  3. Wellcome Trust Sanger Institute, Hinxton, UK

    Felicity Payne, Eleanor Wheeler, Inês Barroso & Inês Barroso

  4. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland

    Halit Ongen & Emmanouil Dermitzakis

  5. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK

    Martijn van de Bunt, Mark I McCarthy & Mark I McCarthy

  6. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

    Martijn van de Bunt, Mark I McCarthy & Mark I McCarthy

  7. Department of Pediatrics, University of California at San Diego, La Jolla, California, USA

    Kyle J Gaulton

  8. Division of Intramural Research, Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA

    John D Eicher & Andrew D Johnson

  9. Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, UK

    Hanieh Yaghootkar & Timothy Frayling

  10. Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Kay-Tee Khaw

  11. Public Health Division of Gipuzkoa, San Sebastian, Spain

    Larraitz Arriola

  12. Instituto BIO-Donostia, Basque Government, San Sebastian, Spain

    Larraitz Arriola

  13. CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

    Larraitz Arriola, Aurelio Barricarte, Carmen Navarro & Elena Salamanca-Fernández

  14. INSERM, CESP, U1018, Villejuif, France

    Beverley Balkau

  15. Université Paris–Sud, UMRS 1018, Villejuif, France

    Beverley Balkau

  16. Navarre Public Health Institute (ISPN), Pamplona, Spain

    Aurelio Barricarte

  17. Navarra Institute for Health Research (IdiSNA), Pamplona, Spain

    Aurelio Barricarte

  18. German Institute of Human Nutrition, Potsdam-Rehbruecke, Nuthetal, Germany

    Heiner Boeing

  19. Lund University, Malmö, Sweden

    Paul W Franks

  20. Umeå University, Umeå, Sweden

    Paul W Franks & Olov Rolandsson

  21. Catalan Institute of Oncology (ICO), Barcelona, Spain

    Carlos Gonzalez

  22. Epidemiology and Prevention Unit, Milan, Italy

    Sara Grioni

  23. German Cancer Research Centre (DKFZ), Heidelberg, Germany

    Rudolf Kaaks

  24. Nuffield Department of Population Health, University of Oxford, Oxford, UK

    Timothy J Key

  25. Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain

    Carmen Navarro

  26. Unit of Preventive Medicine and Public Health, School of Medicine, University of Murcia, Murcia, Spain

    Carmen Navarro

  27. Department of Clinical Sciences, Lund University, Skane University Hospital, Malmö, Sweden

    Peter M Nilsson

  28. Department of Public Health, Section for Epidemiology, Aarhus University, Aarhus, Denmark

    Kim Overvad

  29. Aalborg University Hospital, Aalborg, Denmark

    Kim Overvad

  30. Cancer Research and Prevention Institute (ISPO), Florence, Italy

    Domenico Palli

  31. Dipartimento di Medicina Clinica e Chirurgia, Federico II University, Naples, Italy

    Salvatore Panico

  32. Public Health Directorate, Oviedo, Spain

    J Ramón Quirós

  33. Unit of Cancer Epidemiology, Città della Salute e della Scienza Hospital, University of Turin, and Center for Cancer Prevention (CPO), Turin, Italy

    Carlotta Sacerdote

  34. Human Genetics Foundation (HuGeF), Turin, Italy

    Carlotta Sacerdote

  35. Andalusian School of Public Health, Granada, Spain

    Elena Salamanca-Fernández

  36. Instituto de Investigación Biosanitaria de Granada (Granada.ibs), Granada, Spain

    Elena Salamanca-Fernández

  37. International Agency for Research on Cancer, Lyon, France

    Nadia Slimani

  38. Danish Cancer Society Research Center, Copenhagen, Denmark

    Anne Tjonneland

  39. ASP Ragusa, Ragusa, Italy

    Rosario Tumino

  40. National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands

    Annemieke M W Spijkerman & Daphne L van der A

  41. University Medical Center Utrecht, Utrecht, the Netherlands

    Yvonne T van der Schouw

  42. School of Public Health, Imperial College London, London, UK

    Elio Riboli

  43. Wolfson Diabetes and Endocrine Clinic, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK

    Anna Stears, Ellie Gurnell & Amanda Adler

  44. East and North Herts NHS Trust, Lister Hospital, Herts, UK

    Stella George

  45. Institute of Cellular Medicine (Diabetes), Newcastle University Medical School, Newcastle-upon-Tyne, UK

    Mark Walker

  46. Harrogate and District Hospital, Harrogate, UK

    Deirdre Maguire

  47. James Cook University Hospital, Middlesborough, UK

    Rasha Mukhtar & Sath Nag

  48. Department of Endocrinology and Metabolism, Internal Medicine, Academic Medical Center, Amsterdam, the Netherlands

    Maarten R Soeters

  49. St Richard's Hopsital, Chichester, UK

    Ken Laji

  50. North Devon District Hospital, Raleigh Park, Barnstaple, UK

    Alistair Watt

  51. King's College Hospital, London, UK

    Simon Aylwin

  52. Department of Diabetes and Endocrinology, Southmead Hospital, Bristol, UK

    Andrew Johnson

  53. Ipswich Hospital, Ipswich, UK

    Gerry Rayman

  54. University Hospital of North Midlands NHS Trust, Royal Stoke University Hospital, Stoke-on-Trent, UK

    Fahmy Hanna

  55. Royal Devon and Exeter NHS Foundation Trust, Exeter, UK

    Sian Ellard

  56. Medical School, University of Sheffield, Sheffield, UK

    Richard Ross

  57. Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Zagreb, Croatia

    Kristina Blaslov & Lea Smirčić Duvnjak

Authors
  1. Luca A Lotta
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  2. Pawan Gulati
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  3. Felix R Day
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  4. Felicity Payne
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  5. Halit Ongen
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  6. Martijn van de Bunt
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  7. Kyle J Gaulton
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  8. John D Eicher
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  9. Stephen J Sharp
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  10. Jian'an Luan
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  11. Emanuella De Lucia Rolfe
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  12. Isobel D Stewart
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  13. Eleanor Wheeler
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  14. Sara M Willems
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  15. Claire Adams
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  16. Hanieh Yaghootkar
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  17. Nita G Forouhi
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  18. Kay-Tee Khaw
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  19. Andrew D Johnson
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  20. Robert K Semple
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  21. Timothy Frayling
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  22. John R B Perry
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  23. Emmanouil Dermitzakis
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  24. Mark I McCarthy
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  25. Inês Barroso
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  26. Nicholas J Wareham
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  27. David B Savage
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  28. Claudia Langenberg
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  29. Stephen O'Rahilly
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  30. Robert A Scott
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Consortia

EPIC-InterAct Consortium

  • Claudia Langenberg
  • , Robert A Scott
  • , Stephen J Sharp
  • , Nita G Forouhi
  • , Nicola D Kerrison
  • , Matt Sims
  • , Debora M E Lucarelli
  • , Inês Barroso
  • , Panos Deloukas
  • , Mark I McCarthy
  • , Larraitz Arriola
  • , Beverley Balkau
  • , Aurelio Barricarte
  • , Heiner Boeing
  • , Paul W Franks
  • , Carlos Gonzalez
  • , Sara Grioni
  • , Rudolf Kaaks
  • , Timothy J Key
  • , Carmen Navarro
  • , Peter M Nilsson
  • , Kim Overvad
  • , Domenico Palli
  • , Salvatore Panico
  • , J Ramón Quirós
  • , Olov Rolandsson
  • , Carlotta Sacerdote
  • , Elena Salamanca-Fernández
  • , Nadia Slimani
  • , Anne Tjonneland
  • , Rosario Tumino
  • , Annemieke M W Spijkerman
  • , Daphne L van der A
  • , Yvonne T van der Schouw
  • , Elio Riboli
  •  & Nicholas J Wareham

Cambridge FPLD1 Consortium

  • Robert K Semple
  • , Claire Adams
  • , Anna Stears
  • , Stella George
  • , Mark Walker
  • , Ellie Gurnell
  • , Deirdre Maguire
  • , Rasha Mukhtar
  • , Sath Nag
  • , Amanda Adler
  • , Maarten R Soeters
  • , Ken Laji
  • , Alistair Watt
  • , Simon Aylwin
  • , Andrew Johnson
  • , Gerry Rayman
  • , Fahmy Hanna
  • , Sian Ellard
  • , Richard Ross
  • , Kristina Blaslov
  • , Lea Smirčić Duvnjak
  • , Stephen O'Rahilly
  •  & David B Savage

Corresponding authors

Correspondence to Inês Barroso, Nicholas J Wareham, David B Savage, Claudia Langenberg, Stephen O'Rahilly or Robert A Scott.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Design and scope of the study.

Supplementary Figure 2 Design of the study, investigated phenotypes, sources of data and sample size.

The reported sample size is the maximum available for a given trait or set of traits in this study. *In the study by Knowles and colleagues (PubMed ID: 25798622), insulin sensitivity was measured by euglycemic clamp or insulin suppression test in 2,764 European individuals from four cohorts. IR, insulin resistance; FIadjBMI, fasting insulin levels adjusted for body mass index; TG, triglyceride levels; HDL, high-density lipoprotein cholesterol levels; ISI, insulin sensitivity index; DEXA, dual-energy X-ray absorptiometry; BF%, body fat percentage; FPLD1, familial partial lipodystrophy type 1; MAGIC, Meta-Analyses of Glucose- and Insulin-related traits Consortium; GLGC, Global Lipids Genetics Consortium; GIANT, Genetic Investigation of ANthropometric Traits; DIAGRAM, DIAbetes Genetics Replication And Meta-analysis; CARDIOGRAM, Coronary ARtery DIsease Genome-wide Replication and Meta-analysis; C4D, Coronary Artery Disease Genetics consortium.

Supplementary Figure 3 Combined directional Manhattan plots of the association with insulin-resistance-related phenotypes.

(a–c) Manhattan plots of the association of SNPs with fasting insulin adjusted for body mass index (FIadjBMI) (a), triglycerides (b) and HDL cholesterol (c). We plotted only variants with FIadjBMI, triglycerides and HDL cholesterol (P < 0.005 for each phenotype). All associations are represented for the FIadjBMI-raising allele. The 630 alleles associated with higher FIadjBMI, higher triglycerides and lower HDL cholesterol are plotted in dark red. The graph also plots 21 variants that met the P-value threshold for the three phenotypes but were not associated in the required direction (gray). For graphic display purposes, P values below 10−20 are represented as 10−20.

Supplementary Figure 4 Flowchart of the identification of insulin resistance loci.

Numbers refer to SNPs. FIadjBMI, fasting insulin adjusted for body mass index; HDL, high-density lipoprotein cholesterol; TG, triglycerides.

Supplementary Figure 5 Associations with insulin resistance phenotypes in an independent data set.

The figure reports associations of the genetic scores comprising the 53 or 43 SNPs with fasting insulin adjusted for body mass index, triglycerides and HDL cholesterol in up to 6,101 participants of the Fenland study who were not included in any of the discovery efforts used for identification of the 53 loci. Squares indicate the central estimate of the β coefficient; error bars represent 95% confidence intervals. N, number of participants; FIadjBMI, fasting insulin adjusted for body mass index; HDL, high-density lipoprotein; SD, standard deviation.

Supplementary Figure 6 Associations with glycemic and anthropometric traits and with disease endpoints at the 53 genomic loci.

The heat map represents z scores for the association of the lead insulin-raising allele at each locus. Loci are ranked on the basis of their z scores for fasting insulin (largest to smallest). With the exception of fasting insulin, none of the association analyses were adjusted for body mass index. N, maximum sample size; FIadjBMI, fasting insulin adjusted for body mass index; HDL, high-density lipoprotein cholesterol; BMI, body mass index; WHR, waist–hip ratio; CHD, coronary heart disease; T2D, type 2 diabetes. Color scale: red indicates positive associations for the insulin-raising allele at each locus, while blue indicates negative associations. Asterisks indicate known loci for the traits, i.e., those for which our lead SNP is within 500 kb on either side of a lead SNP from the largest GWAS for that trait.

Supplementary Figure 7 Associations of the genetic scores comprising the 53 or 43 SNPs with glycemic and anthropometric traits in large-scale meta-analyses and in the Fenland study.

(a) Association of the genetic scores with anthropometric and glycemic traits in meta-analyses of genetic association studies. Body mass index, waist–hip ratio, and waist and hip circumference data are from the GIANT consortium and the UK Biobank study. Body fat percentage data are from the UK Biobank, EPIC-Norfolk and Fenland studies. Fasting plasma glucose, 2-h glucose and HbA1c data are from the MAGIC consortium. Leg fat mass data are from the EPIC-Norfolk and Fenland studies. Squares with error bars represent the per-allele β coefficients in standard deviation units and their 95% confidence intervals. (b) Association with the same traits in participants of the Fenland study not included in the discovery efforts that contributed to the identification of the 53 loci. Because HbA1c has been measured only in a subset of Fenland participants, the HbA1c analysis also includes individuals from the InterAct study subcohort who did not take part in the discovery efforts that contributed to the identification of the 53 loci. Squares with error bars represent the per-allele β coefficients in standard deviation units and their 95% confidence intervals. Red and blue squares represent the results of the 53-SNP and 43-SNP genetic scores, respectively. None of the results presented in the figure were adjusted for body mass index. N, number of participants; SD, standard deviation; BMI, body mass index; WHR, waist–hip ratio; FPG, fasting plasma glucose.

Supplementary Figure 8 Associations of the 53-SNP genetic score with detailed anthropometric variables from dual-energy X-ray absorptiometry.

The figure represents the association of quintiles of the 53-SNP genetic score with the absolute values of compartmental and total fat mass. Data are from 9,747 participants of the Fenland study. The Fenland population was divided into quintiles of the distribution of the genetic score, and each quintile was compared with the bottom (reference category). Squares with error bars represent the β coefficients in grams for individuals in the exposure category as compared with the reference category and their 95% confidence intervals.

Supplementary Figure 9 Associations of the rs4976033[G] allele near PIK3R1 with continuous metabolic traits and cardiometabolic disease endpoints.

(a) Associations with continuous traits. (b) Associations with disease endpoints. Squares with error bars represent the β coefficients (a) or odds ratios (b) and their 95% confidence intervals. HDL, high-density lipoprotein; LDL, low-density lipoprotein; BMI, body mass index; WHR, waist–hip ratio; FIadjBMI, fasting insulin adjusted for BMI; FPG, fasting plasma glucose; 2hr glucose, glucose at 2 h during an oral glucose challenge; SD, standard deviation; OR, odds ratio.

Supplementary Figure 10 Associations of functional variants in LPL with cardiometabolic traits and disease endpoints.

(a) Association of the gain-of-function p.Ser447* (rs328; left) and the loss-of-function p.Asp36Asn (rs1801177; right) variants in LPL with lipid levels, anthropometric traits, liver markers and glycemic traits. (b) Association of the two variants with the risk of coronary heart disease (from the Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators; PubMed ID: 26934567) and that of type 2 diabetes. Squares with error bars represent the β coefficients (a) or odds ratios (b) and their 95% confidence intervals. HDL, high-density lipoprotein; LDL, low-density lipoprotein; BMI, body mass index; WHR, waist–hip ratio; VAT, visceral adipose tissue; ALT, alanine aminotransferase; GGT, γ-glutamyltransferase; FIadjBMI, fasting insulin adjusted for BMI; FPG, fasting plasma glucose; 2hr, glucose at 2 h during an oral glucose challenge; SD, standard deviation; OR, odds ratio.

Supplementary Figure 11 Mechanistic hypothesis for the implication of putative effector genes in the observed associations and selection of genes for experimental validation in cellular models of adipogenesis.

(a) Mechanistic hypothesis. (b) Selection criteria to prioritize genes for experimental validation. (c) Selection flowchart. Numbers in c refer to loci meeting certain selection criteria. *We did not take forward the KLF14 gene to experimental validation because previous studies on the role of this gene in metabolic disease suggest complex etiological mechanisms at this locus, including a possible parent-of-origin effect.

Supplementary Figure 12 Associations with fasting insulin adjusted for body mass index, body mass index or fasting insulin of the 53 polymorphisms identified in this study.

(a) Association of the 53 lead polymorphisms from our study with FIadjBMI as a function of the association with BMI. There was no clear bias in the association with FIadjBMI (linear regression between the β coefficients of the 53 polymorphisms, P = 0.26). (b) Association with FI (unadjusted for BMI) of the lead 53 polymorphisms as a function of the association with FIadjBMI. The line of fit was aligned with the line of equality consistent with no bias. In a, the dark red line and surrounding areas represent the line of fit with 95% confidence areas. The dashed gray line in b represents the line of equality. Data on fasting insulin associations are from the MAGIC consortium; data on BMI associations are from the GIANT consortium.

Supplementary Figure 13 Scatterplot matrix of the top ten genetic principal components in women with FPLD1 and control women from UKHLS.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13, Supplementary Tables 1–3, 5–8 and 12–14, and Supplementary Note. (PDF 2691 kb)

Supplementary Tables 4 and 9–11

Supplementary Tables 4 and 9–11. (XLSX 266 kb)

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Lotta, L., Gulati, P., Day, F. et al. Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance. Nat Genet 49, 17–26 (2017). https://doi.org/10.1038/ng.3714

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