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Contribution of genetics to visceral adiposity and its relation to cardiovascular and metabolic disease

Nature Medicine volume 25, pages 1390–1395 (2019)Cite this article

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Abstract

Visceral adipose tissue (VAT)—fat stored around the internal organs—has been suggested as an independent risk factor for cardiovascular and metabolic disease1,2,3, as well as all-cause, cardiovascular-specific and cancer-specific mortality4,5. Yet, the contribution of genetics to VAT, as well as its disease-related effects, are largely unexplored due to the requirement for advanced imaging technologies to accurately measure VAT. Here, we develop sex-stratified, nonlinear prediction models (coefficient of determination = 0.76; typical 95% confidence interval (CI) = 0.74–0.78) for VAT mass using the UK Biobank cohort. We performed a genome-wide association study for predicted VAT mass and identified 102 novel visceral adiposity loci. Predicted VAT mass was associated with increased risk of hypertension, heart attack/angina, type 2 diabetes and hyperlipidemia, and Mendelian randomization analysis showed visceral fat to be a causal risk factor for all four diseases. In particular, a large difference in causal effect between the sexes was found for type 2 diabetes, with an odds ratio of 7.34 (95% CI = 4.48–12.0) in females and an odds ratio of 2.50 (95% CI = 1.98–3.14) in males. Our findings bolster the role of visceral adiposity as a potentially independent risk factor, in particular for type 2 diabetes in Caucasian females. Independent validation in other cohorts is necessary to determine whether the findings can translate to other ethnicities, or outside the UK.

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Fig. 1: Tissue enrichment for the most significant GWA signals, and results from functional experiments for HMBS.
Fig. 2: VAT^ in relation to cardiovascular and metabolic diseases.
Fig. 3: Polynomial logistic regression and the change in OR with increasing VAT^.

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Data availability

The data on which this study is based (application number 15152) are available for bona fide researchers from the UKBB Resource (http://www.ukbiobank.ac.uk/about-biobank-uk/), on filing an application to the UKBB. The data for VAT^ can be accessed via the UKBB Resource, while the summary statistics of the GWAS are available for download from the GWAS Catalog (https://www.ebi.ac.uk/gwas/). Relevant additional data will be available from the authors on request.

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Acknowledgements

We acknowledge all of the participants and staff involved in UKBB for their valuable contribution. This research was conducted using the UKBB Resource under application number 15152, following the restrictions on data availability set up by the UKBB. The computations were performed on resources provided by SNIC through the Uppsala Multidisciplinary Center for Advanced Computational Science under projects b2016021 and sens2017538. The research was funded by the Swedish Society for Medical Research (M.R.-A. and Å.J.), Swedish Research Council (Å.J., 2015-03327), Kjell and Märta Beijers Foundation (Å.J.), Göran Gustafssons Foundation (Å.J.), Marcus Borgström Foundation (Å.J.), Åke Wiberg Foundation (Å.J., M16-0210), Swedish Heart and Lung Foundation (Å.J., 20170484), Swedish Diabetes Foundation (C.W.) and Science for Life Laboratory (Å.J.).

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Authors and Affiliations

  1. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden

    Torgny Karlsson, Mathias Rask-Andersen, Gang Pan, Julia Höglund, Claes Wadelius, Weronica E. Ek & Åsa Johansson

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  1. Torgny Karlsson
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Contributions

T.K., M.R.-A. and Å.J. designed the study and performed the data analysis. T.K. developed all of the models, performed the statistical analysis and generated the figures. G.P. and C.W. performed the functional study. T.K., M.R.-A., G.P., J.H., C.W., W.E.E. and Å.J. interpreted the data and wrote the manuscript.

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Correspondence to Torgny Karlsson or Åsa Johansson.

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Peer review information: Kate Gao and Brett Benedetti were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended Data

Extended Data Fig. 1 Study selection.

Instances 0, 1 and 2 denote different data collection time periods (instance 0, 2006–2010; instance 1, 2012–2013; instance 2, 2014 to the present). At instance 2, VAT mass was measured by DXA. For the MR analysis, the cohort was split into two nonoverlapping subsets. IV, instrumental variable.

Extended Data Fig. 2 Correlations between predictors and measured VAT mass.

a,b, The length of each bar denotes the strength of the pairwise Pearson’s product–moment correlation between the regression predictor (specified on the left) and measured VAT mass, for the female (a) and male (b) training datasets. The strength of the correlation is also visualized by color (dark blue, lowest correlation; yellow, highest correlation). The regression predictors are ordered from largest positive to largest negative correlation in females. Error bars denote 95% asymptotic CIs based on Fisher’s Z transform. Sample sizes are n = 2,010 for females and n = 2,188 for males.

Extended Data Fig. 3 Bias and 95% CIs of full prediction models, as a function of measured VAT.

a,b, VAT^, as predicted from the leave-one-out cross-validation, plotted against measured VAT mass for females (a; black circles) and males (b; red circles). Also plotted is VAT^ against measured VAT mass for the out-of-sample data (green dots in a and blue dots in b). The out-of-sample datasets constitute Irish and other white individuals from the UKBB, excluding white British. The sample sizes are n = 2,010 females and n = 2,188 males for the training datasets, and n = 119 females and n = 102 males for the out-of-sample datasets. The long-dashed, gray lines denote the linear fits (ordinary least squares regression) to the leave-one-out cross-validation data, and the gray (a) and red shaded areas (b) denote the corresponding CIs of the estimated slopes. Green (a) and blue shaded areas (b) denote the CIs of the linear fits (not plotted) to the out-of-sample data. Thin black lines denote the one-to-one relation. A slope below the one-to-one relation indicates that a small bias is present in the data. However, note that the attenuation is exaggerated due to measurement errors also in the measured VAT mass. c,d, VAT prediction residuals plotted against measured VAT mass for females (c) and males (d). The long-dashed lines correspond to the fitted regression lines in a and b. The gray, solid lines denote the conditional 95% CIs. These lines become dashed at high VAT mass, to indicate an increasing uncertainty in the CIs. Otherwise, symbols are as in a and b.

Extended Data Fig. 4 Bias and 95% CIs of reduced prediction models, as a function of measured VAT.

Symbols and sample sizes as in Extended Data Fig. 3.

Extended Data Fig. 5 Overview of the genomic locations of the selected SNPs in the functional study.

The locations of the SNPs are indicated by vertical lines. a, The HMBS/VPS11 region. Region 1 contains two SNPs (rs2509121 and rs11217133). Region 2 contains five SNPs (rs1784461, rs1786141, rs1784460, rs1784459 and rs1786684). Region 3 contains two SNPs (rs1799993 and rs1006195). b, The PKD1 region. Region 1 contains one SNP (rs13337177). Region 2 contains one SNP (rs36232). c, The DPYSL4 region. Region 1 contains three SNPs (rs881347, rs61865793 and rs11146233).

Extended Data Fig. 6 Results from the luciferase assay in HepG2 cells.

For each set of alleles, the box plots represent the median, interquartile range, and minimum and maximum values of all replicates, except for outliers, which are represented as individual points. The total number of replicates of each plasmid is given by the number of independent plasmid extractions multiplied by the number of independent transfections. The P values (two-sided t-test) represent pairwise differences in means, either for the control plasmid (pGL4.10 or pGL4.23) without any insert versus the same plasmid with one of the fragments inserted, or for the two plasmids with fragments, with different alleles inserted. In the names, the subscripts _E and _P indicate whether the fragment was cloned as an enhancer (_E) or promoter (_P) element, with _P1 and _P2 representing two different fragments in the same promoter region. The last part of the names represents the alleles of the SNPs that were targeted by each fragment (see Supplementary Table 14). Two to three independent plasmid extractions and transfections were performed, with each transfection being replicated three times.

Extended Data Fig. 7 Effect of VAT^ on the risk of developing type 2 diabetes for subgroups with specified medical complications.

a,b, Estimated ORs for females (a) and males (b). The solid, gray lines and 95% CIs (dashed gray lines) correspond to the OR estimated for all female (a) or male cases (b) (see bold text to the left; see also Fig. 2). The black (a) and red vertical lines (b) (with error bars denoting 95% CIs) denote the ORs for the various subgroups with specific medical complications. Note the difference in scale between the two panels. All ORs refer to an increase of 1 kg in VAT^.

Extended Data Fig. 8 Polynomial logistic regression models for heart attack/angina and hyperlipidemia.

a–f, Models for heart attack/angina (a–c) and hyperlipidemia (d–f), showing the probability of disease for the raw, unadjusted data (a and d), the predicted probability of disease for each individual (b and e), given the adopted polynomial model (see Supplementary Table 17), and 95% (basic bootstrap) confidence bands (shaded areas) of the ORs per one-unit increase (1 kg) in VAT^ (c and f), each as a function of VAT^. In a and d, error bars indicate 95% CIs, based on the Poisson statistics. The total numbers of cases and controls for heart attack/angina and hyperlipidemia are given in Fig. 2 for females and males separately. In all panels, black and gray denote females while red and pink denote males.

Extended Data Fig. 9 Sensitivity test for the polynomial logistic regression models.

Each panel shows the log[OR] for five different models that are polynomial in VAT^ to different degrees: dashed lines denote second-degree polynomials (q = 2); dotted lines denote third-degree polynomials (q = 3); long-dashed lines denote fourth-degree polynomials (q = 4); dot-dashed lines denote fifth-degree polynomials (q = 5); and two-dashed lines denote sixth-degree polynomials (q = 6). The lines are also color-coded from light gray (q = 2) to black (q = 6). All models also include age, smoking behavior and 15 principal components as covariates. The models are polynomial in age, with degrees of the polynomials as indicated in Supplementary Table 17. Shaded areas indicate regions of large model uncertainty. Note that for each disease, all models show a very similar functional form of the log[OR], independent of the degree of the polynomial.

Extended Data Fig. 10 Relationship between the effects of the genetic instruments on VAT^ and their effects on disease.

Each panel shows the relationship between the effects on VAT^ and the effects on disease of the n = 44 nearly independent genetic instruments (Supplementary Table 21). Error bars denote the 95% CI (normal approximation) of each effect estimate. Females are denoted in black, while males are denoted in red. Pleiotropic outliers identified by the GSMR analysis that were removed before estimation of the causal effects are shown in gray (Supplementary Table 22). An observed slope is indicative of a causal relationship between VAT^ and disease that is unbiased by confounding. The dashed lines denote the estimated log[OR] values by the gsmr package (Table 1). Note the different scales of the y axes.

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Karlsson, T., Rask-Andersen, M., Pan, G. et al. Contribution of genetics to visceral adiposity and its relation to cardiovascular and metabolic disease. Nat Med 25, 1390–1395 (2019). https://doi.org/10.1038/s41591-019-0563-7

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