Genetic subtyping of obesity reveals biological insights into the uncoupling of adiposity from its cardiometabolic comorbidities

Abstract

Obesity is a highly heterogeneous disease that cannot be captured by one single adiposity trait. Here, we performed a multi-trait analysis to study obesity in the context of its common cardiometabolic comorbidities, acknowledging that not all individuals with obesity suffer from cardiometabolic comorbidities and that not all those with normal weight clinically present without them. We leveraged individual-level genotype-phenotype data of 452,768 individuals from the UK Biobank and designed uncoupling phenotypes that are continuous and range from high adiposity with a healthy cardiometabolic profile to low adiposity with an unhealthy cardiometabolic profile. Genome-wide association analyses of these uncoupling phenotypes identified 266 independent variants across 205 genomic loci where the adiposity-increasing allele is also associated with a lower cardiometabolic risk. Consistent with the individual variant effects, a genetic score (GRS_uncoupling) that aggregates the uncoupling effects of the 266 variants was associated with lower risk of cardiometabolic disorders, including dyslipidemias (OR=0.92, P=1.4×10^-89), type 2 diabetes (OR=0.94, P=6×10^-21), and ischemic heart disease (OR=0.96, P=7×10^-11), despite a higher risk of obesity (OR=1.16, P=4×10^-108), which is in sharp contrast to the association profile observed for the adiposity score (GRS_BFP). Nevertheless, a higher GRS_uncoupling score was also associated with a higher risk of other, mostly weight-bearing disorders, to the same extent as the GRS_BFP. The 266 variants clustered into eight subsets, each representing a genetic subtype of obesity with a distinct cardiometabolic risk profile, characterized by specific underlying pathways. Association of GRS_uncoupling and GRS_BFP with levels of 2,920 proteins in plasma found 208 proteins to be associated with both scores. The majority (85%) of these overlapping GRS-protein associations were directionally consistent, suggesting adiposity-driven effects. In contrast, levels of 32 (15%) proteins (e.g. IGFBP1, IGFBP2, LDLR, SHBG, MSTN) had opposite directional effects between GRS_BFP and GRS_uncoupling, suggesting that cardiometabolic health, and not adiposity, associated with their levels. Follow-up analyses provide further support for adipose tissue expandability, insulin secretion and beta-cell function, beiging of white adipose tissue, inflammation and fibrosis. They also highlight mechanisms not previously implicated in uncoupling, such as hepatic lipid accumulation, hepatic control of glucose homeostasis, and skeletal muscle growth and function. Taken together, our findings contribute new insights into the mechanisms that uncouple adiposity from its cardiometabolic comorbidities and illuminate some of the heterogeneity of obesity, which is critical for advancing precision medicine.

Fig. 1:. Study overview.

Overall steps and traits analyzed in the study. Bi-traits are obtained by subtracting standardized values of a cardiometabolic trait from an adiposity trait. BMI: body mass index; BFP: body fat percentage; WHR: waist-to-hip ratio; TC: total cholesterol; LDL-C: LDL cholesterol; HDL-C: HDL cholesterol; TG: triglycerides; SBP: systolic blood pressure; DBP: diastolic blood pressure.

Fig. 2:. Associations of genetic risk scores with anthropometric and cardiometabolic traits in the UK Biobank.

A. Estimated per 10 allele change effect sizes of GRS–trait associations in UK Biobank European ancestry population for GRS_uncoupling (in magenta) and GRS_BFP (in blue). B-C. Estimated per 10 allele change effect sizes of GRS–trait associations in UK Biobank European ancestry population for each cluster specific GRS (GRS 1–8, in red) and GRS_BFP (in gray). Dashed circles indicate Beta=0, indicating no association between each GRS and the trait. Points outside the circle represent positive GRS–trait associations, while those inside represent negative associations. The effect and reference alleles of GRS₂, a cluster associated with lower WHR and higher blood pressure, were flipped in order to reflect a profile of higher adiposity and facilitate comparison with the other clusters.

Fig. 3:. Association of genetic risk scores with disease outcomes in the UK Biobank.

Phenome-wide association results of disease outcomes and GRS_BFP (in blue) and GRS_uncoupling (in magenta) performed using PHESANT in the European population. Odds ratios represent effect size estimates per 10 risk-allele increments.

Fig. 4:. Heatmap of the association of the lead variants with adiposity and cardiometabolic traits.

Clustering of the 266 uncoupling lead variants using the NAvMix algorithm identified eight clusters. The color coding represents beta values ranging from negative (blue) to positive (red). Associations are expressed by the BFP-increasing allele as the effect allele to enable comparison across traits.

Fig. 5:. Associations of genetic risk scores with anthropometric and cardiometabolic traits in the ARIC study.

A. Estimated per 10 allele change effect sizes of GRS–trait associations in 9,240 unrelated European ancestry participants of the ARIC study for GRS_uncoupling (in magenta) and GRS_BFP (in blue). B-C. Estimated per 10 allele change effect sizes of GRS–trait associations in UK Biobank European ancestry population for each cluster specific GRS (GRS 1–8, in red) and GRS_BFP (in gray). Dashed circles indicate Beta=0, indicating no association between each GRS and the trait. Points outside the circle represent positive GRS–trait associations, while those inside represent negative associations. The effect and reference alleles of GRS₂, a cluster associated with lower WHR and higher blood pressure, were flipped in order to reflect a profile of higher adiposity and facilitate comparison with the other clusters.

Fig. 6:. Physiological systems, tissue and cell-type enrichment analyses for uncoupling loci (top) and general body fat percentage loci (bottom).

DEPICT results were based on summary statistics generated from the association analyses of the 266 lead SNPs with body fat percentage. We restricted our analyses to the Gene Ontology, KEGG, and REACTOME pathways terms. Results that passed the FDR corrected significance threshold (FDR < 0.05) are highlighted in bright blue. The dashed line represents the nominal significance level (P<0.05).

Fig. 7:. Plasma proteins (n=32) with directionally opposing associations with the body fat percentage- and uncoupling genetic risk scores in the UK Biobank.

Estimated per 10 allele change effect sizes of GRS-protein associations in UK Biobank European ancestry population for GRS_uncoupling (in magenta) and GRS_BFP (in blue), for rank-based inverse-normal transformed Olink-derived plasma protein concentrations.