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

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.

Conflict of interest statement

Competing Financial Interests Statement

The authors report no conflict of interest relative to this study.

Figures

Figure 1. Combined associations with detailed anthropometry and metabolic disease risk at the 53 genomic loci.

Panel A: association of the 53-SNP genetic score with anthropometric and glycaemic traits in meta-analyses of genetic association studies. Body mass index (BMI), waist-to-hip ratio (WHR), 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 (FPG), 2 hour glucose and HbA1c data are from the MAGIC consortium. Squares with error bars represent the per-allele beta coefficients in standard deviation units and their 95% confidence intervals. Panel B: association of genetic scores with compartmental body masses. Data are from 12,848 participants of the Fenland and EPIC-Norfolk studies who underwent a DEXA scan. Squares with error bars represent the per-allele beta coefficients in standard deviation units and their 95% confidence intervals.Panel C: association with lower levels of leg fat mass and higher hazard of incident type 2 diabetes by quintiles of the 53-SNP genetic risk scores. Associations are reported for individuals in the exposed category compared with the bottom quintile (reference category). Associations with leg fat mass are from 9,747 participants of the Fenland study and are reported on the left. Associations with incident type 2 diabetes are from 7,420 incident cases and 9,267 controls of the InterAct study and are reported on the right. Squares represent the beta coefficients in grams of leg fat mass (left plot) or the hazard ratio (HR) for incident type 2 diabetes (right plot) in each category compared with the lowest quintile. Error bars represent the 95% confidence intervals of these estimates. Panel D: associations of individual lead SNPs at eight loci with waist, hip circumference (left) and type 2 diabetes (right). Loci were selected on the basis of their genome-wide significant association with hip circumference or body fat percentage (i.e.PIK3R1). Waist and hip circumference analyses are from a meta-analysis of the GIANT and UK Biobank studies. Type 2 diabetes analyses are from a meta-analysis of the DIAGRAM, InterAct and UK Biobank studies. Squares with error bars represent the per-allele beta coefficients in standard deviation units of waist and hip circumference (left plot) or the per-allele odds ratio (OR) of type 2 diabetes (right plot). Error bars represent the 95% confidence intervals of these estimates. *Detailed associations at thePIK3R1 locus, which was primarily associated with lower body fat percentage, are presented in Supplementary Figure 9.

Figure 2. Associations at the 53 genomic loci with familial partial lipodystrophy type 1 (FPLD1).

Panel A: distribution of leg fat mass as a function of the fat mass of the rest of the body (from DEXA) in women of the Fenland study at the extreme quintiles (Q) of the 53-SNP genetic score and in 14 FPLD1 subjects. Q1 represents a low genetic burden, whereas Q5 a high genetic burden. Lines of fit are plotted for each group. Panel B: histograms of the distribution of risk alleles in the FPLD1 subjects and in control women from the UKHLS study. Panel C: bi-dimensional box plots of the distribution of leg fat mass as a function of the distribution of the number of risk alleles in women of the Fenland study at the extreme quintiles (Q) of the 53-SNP genetic score and in FPLD1 subjects. Q1 represents a low genetic burden, whereas Q5 a high genetic burden. Each rectangle represents a group of individuals. For each dimension, the two sides of the rectangle represent the interquartile range and the central line the median. Data for obese women from Fenland were plotted to show the relationship between genetic risk and levels of leg fat in a group of women with a similar body mass index to that of FPLD1 patients.

Figure 3. Putative effector genes, tissues and cell types.

Panel A: schematic representation of some established components of the insulin signalling pathway with stars reporting the location in the pathway of putative effector genes, with their respective lead single nucleotide polymorphism listed. Panel B: associations of gain- and loss-of-function genetic variants in the LPL gene with type 2 diabetes. The reference number in parenthesis refers to the study reporting the association with triglycerides and coronary heart disease (see reference number 38 of this manuscript).Panel C: summary of evidence about links between genetic variants, lipodystrophy, insulin resistance, and type 2 diabetes at different levels of the population phenotypic distribution. *Rare syndromes caused by autosomal dominant INSR mutations are not usually associated with lipodystrophy and the INSR rs8101064 polymorphism is not associated with body fat percentage. Panel D: overlap of the 53 loci (lead SNPs plus proxy variants in r2>0.8) with chromatin state annotations from the NIH Roadmap. Panel E: DEPICT’s annotation of cell types and tissues on the basis of expression patterns in 37,427 human microarray samples. The y-axis represents the –log10(p-value) for enrichment of signal in a cell or tissue type attributed by DEPICT. The horizontal broken line represents the multiple-test corrected threshold of statistical significance (Bonferroni p=0.00072).

Figure 4. Experimental knockdown of putative effector genes in cellular adipogenesis models and comparisons with phenotypic associations.

Panel A: results of experimental knockdown in OP9-K cells. Full circles represent the difference of the means from knockout experiments of a given gene compared with control experiments (n=4-7). Error bars represent the 95% confidence intervals of the difference of the means. Top graph: effect on mRNA levels of knockdown experiments of target genes using short interfering RNA (siRNA) in OP9-K cells. The two-tailed t-test p-values for differences in means were: IRS1, p=4.6 x 10-06; CCDC92, p=2.7 x 10-09, DNAH10, p=2.4 x 10-06;L3MBTL3, p=4.6 x 10-06; FAM13A, p=2.4 x 10-05. Bottom graph: effect on lipid accumulation in siRNA knockdown experiments. The two-tailed t-test p-values for differences in means were: IRS1, p=0.0047;CCDC92, p=1.2 x 10-05, DNAH10,p=0.0027; L3MBTL3, p=0.00013; FAM13A,p=0.92.Panel B: illustrative images showing florescence microscopy from lipid accumulation experiments. Red indicates adipored staining of neutral lipid, blue is hoechst staining of nuclei. Panel C: Association of the risk (insulin-raising) allele of the lead single nucleotide polymorphism in or near each of the putative effector genes with (a) the expression of the corresponding gene in subcutaneous adipocytes in the EUROBATS project (top graph in the panel); (b) hip circumference in a meta-analysis of GIANT and UK Biobank (mid graph); and (c) type 2 diabetes in a meta-analysis of InterAct, DIAGRAM and UK Biobank (bottom graph). Full circles represent the –log10(p-value) for the association of the insulin-raising allele multiplied by the direction of the beta coefficient (i.e. a “directional” –log10(p)). For graphic display purposes, the –log10(p-value) for the association with type 2 diabetes of the rs2943645-T allele near IRS1 is represented as 10 instead of 16.9.