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Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease

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Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease Diabetes Volume 70, August 2021 1843 Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease Susan Martin,1 Madeleine Cule,2 Nicolas Basty,3 Jessica Tyrrell,1 Robin N. Beaumont,1 Andrew R. Wood,1 Timothy M. Frayling,1 Elena Sorokin,2 Brandon Whitcher,3 Yi Liu,2 Jimmy D. Bell,3 E. Louise Thomas,3 and Hanieh Yaghootkar1,3 Diabetes 2021;70:1843–1856 | https://doi.org/10.2337/db21-0129 GENETICS/GENOMES/PROTEOMICS/METABOLOMICS To understand the causal role of adiposity and ectopic a cluster of events often referred to as the metabolic syn- fat in type 2 diabetes and cardiometabolic diseases, we drome(1).However,inthegeneralpopulation,15–40% of aimed to identify two clusters of adiposity genetic var- individuals categorized as obese do not present any obesity- iants: one with “adverse” metabolic effects (UFA) and related metabolic conditions or diseases and are the other with, paradoxically, “favorable” metabolic “metabolically benign” at the specific time point of measure- effects (FA). We performed a multivariate genome-wide ment, supporting the existence of metabolically benign obe- association study using body fat percentage and meta- sity (2,3). fi bolic biomarkers from UK Biobank and identi ed 38 Previously we showed that a genetic predisposition to UFA and 36 FA variants. Adiposity-increasing alleles storing excess fat in subcutaneous adipose tissue (SAT) is fi were associated with an adverse metabolic pro le, associated with a reduced propensity to store fat in the higher risk of disease, higher CRP, and higher fat in sub- liver, consequently reducing risk of disease (4). The iden- cutaneous and visceral adipose tissue, liver, and pan- tification of “favorable adiposity” variants, with their adi- creas for UFA and a favorable metabolic profile, lower posity-increasing alleles paradoxically associated with risk of disease, higher CRP and higher subcutaneous lower risk of type 2 diabetes, heart disease, and hyperten- adipose tissue but lower liver fat for FA. We detected no – sexual dimorphism. The Mendelian randomization stud- sion (4 7), provided genetic evidence for the paradox of fi ies provided evidence for a risk-increasing effect of UFA metabolically benign obesity. These genetic ndings sug- and protective effect of FA for type 2 diabetes, heart dis- gest that there are at least two types of variants associ- ease, hypertension, stroke, nonalcoholic fatty liver dis- ated with higher adiposity: one with favorable metabolic fi ease, and polycystic ovary syndrome. FA is distinct pro le (favorable adiposity [FA]) and the other with an from UFA by its association with lower liver fat and pro- unfavorable metabolic profile (unfavorable adiposity tection from cardiometabolic diseases; it was not asso- [UFA]). ciated with visceral or pancreatic fat. Understanding Although our previous studies suggested an important the difference in FA and UFA may lead to new insights in role for liver fat, we have been unable to determine preventing, predicting, and treating cardiometabolic whether pancreatic fat deposition or liver and pancreas vol- diseases. umes were similarly implicated due to lack of data, and it has not been possible to investigate mechanisms imposed by each variant individually. Clarification of the underlying Obesity is a significant risk factor for various conditions pathophysiologic mechanisms that link adiposity to higher including type 2 diabetes, heart disease, and hypertension— risk of type 2 diabetes and other cardiometabolic disease is 1Genetics of Complex Traits, University of Exeter Medical School, University of This article contains supplementary material online at https://doi.org/10.2337/ Exeter, Royal Devon & Exeter Hospital, Exeter, U.K. figshare.14555463. 2Calico Life Sciences LLC, South San Francisco, CA S.M., M.C., E.L.T., and H.Y. contributed equally. 3Research Centre for Optimal Health, School of Life Sciences, University of © Westminster, London, U.K. 2021 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for Corresponding author: Hanieh Yaghootkar, [email protected] profit, and the work is not altered. More information is available at https:// Received 11 February 2021 and accepted 6 May 2021 www.diabetesjournals.org/content/license. 1844 Types of Adiposity in Cardiometabolic Disease Diabetes Volume 70, August 2021 À critical to understanding disease progression and remis- We identified 254 variants at P < 5 Â 10 8 associated sion, especially given the rising prevalence of obesity and with both our univariate GWAS of body fat percentage the rapid rise of type 2 diabetes in an aging population. (n = 620 variants previously published [4]) and our com- The availability of both metabolic markers and MRI scan posite metabolic phenotype as estimated by the above data in the UK Biobank (8) has enabled us to test in more multivariate GWAS model. This represents an increase in detail the characteristics of adiposity variants and the role 221 signals compared with the 33 previously reported of ectopic fat in disease mechanism. using a similar approach (4). This increase was largely In this study, we focused on how higher adiposity is attributable to the availability of the metabolic bio- associated with ectopic fat, metabolic derangements, and markers in 451,099 individuals of European ancestry cardiometabolic risk. Specifically, we aimed to 1)identify from UK Biobank, whereas previous studies were limited distinct clusters of FA and UFA variants, 2) investigate the to smaller separate data (e.g., 100,000 with HDL and tri- relation between FA and UFA variants and ectopic fat glycerides, 21,800 with SHBG, and 55,500 with ALT). deposition in the liver and pancreas, 3)examinehowFA and UFA variants are associated with circulating markers Step 2: Classification of Adiposity Variants of inflammation, 4) determine whether sexual dimorphism We applied a k-means algorithm on the 254 variants and is a factor in the association between the clusters and met- their effects on the values of the six phenotypes from the fi abolic biomarkers, fat distribution, and disease risk; and 5) rst step and used the parameter k = 3 to group them use Mendelian randomization (MR) to determine the into FA and UFA. We considered a third cluster of “ fl ” potential causal role of “favorable” and “unfavorable” adi- con icting to group any variants that do not belong to posity in different components of metabolic syndrome. the FA or UFA clusters and did not pursue these variants in the rest of the analyses to minimize false discovery. Within UFA and FA clusters, we inspected whether the RESEARCH DESIGN AND METHODS loci are driven by colocalization of signals from a combi- Discovery Data Set—UK Biobank nation of traits or represent a strong univariate signal. UK Biobank recruited >500,000 individuals aged 37–73 years (99.5% were between 40 and 69 years of age) Step 3: Validation of FA and UFA Variants between 2006 and 2010 from across the U.K. (8) To validate FA and UFA variants, we assessed their effects (Supplementary Table 1). The UK Biobank has approval on risk of type 2 diabetes using data from GWAS of 31 from the North West Multicenter Research Ethics Com- studies, excluding UK Biobank, which included 55,005 mittee (https://www.ukbiobank.ac.uk/ethics/), and these case and 400,308 control subjects of European ancestry ethics regulations cover the work in this study. Written (12). We expected to observe adiposity-increasing alleles informed consent was obtained from all participants. as associated with lower risk of type 2 diabetes for FA The steps performed to identify variants associated variants and higher risk of type 2 diabetes for UFA with adiposity but with different effects on metabolic variants. traits are outlined in Supplementary Fig. 1 and, briefly, are as follows. Imaging Study A subcohort of 100,000 subjects were selected for the imag- Step 1: Genetic Variants Associated With Both Body Fat ing enhancement of the UK Biobank, currently at 49,938. Percentage and Composite Metabolic Biomarkers Abdominal MRI scans were obtained with a MAGNETOM We performed a multivariate genome-wide association Aera 1.5T scanner (software version syngo MR D13) (Sie- study (GWAS) of relevant metabolic biomarkers that were mens Healthineers, Erlangen, Germany) (13). Image-derived available in individuals of European ancestry from the UK phenotypes were generated from the three-dimensional Biobank, including HDL cholesterol (HDL) (n 5 392,965), Dixon neck-to-knee acquisition, the high-resolution T1- sex hormone–binding globulin (SHBG) (n = 389,354), tri- weighted three-dimensional pancreas acquisition, and liver glycerides (n 5 429,011), AST (n 5 427,778), and ALT and pancreas single-slice multiecho acquisitions. Images for (n 5 429,203), using BOLT-LMM v2.3.4 (9) and metaCCA this study were obtained through UK Biobank application software (10) as described previously (4). Specifically, no. 44584. Following automated preprocessing of the differ- metaCCA uses canonical correlation analysis to identify ent sequences, volumes of organs of interest (including the the maximal correlation coefficient between genome-wide liver, pancreas, and SAT and visceral adipose tissue [VAT]) genetic variants and a linear combination of the above were segmented using convolutional neural networks (14). phenotypes, based on the computed phenotype-pheno- Fat content of the liver and pancreas was obtained from the type Pearson correlation matrix. We chose these specific multiecho acquisitions after preprocessing where the proton metabolic biomarkers to be consistent with our previous density fat fraction was estimated (15). approach (4). These biomarkers are used to discriminate between three monogenic forms of insulin resistance: lip- GWAS of Imaging-Derived Phenotypes odystrophy (disorders of fat storage), monogenic obesity, We used the UK Biobank Imputed Genotypes v3 (16), and insulin signaling defects (6,11). excluding single nucleotide polymorphisms with minor diabetes.diabetesjournals.org Martin and Associates 1845 allele frequency <1% and imputation quality <0.9.
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