Effects of body mass index on the human proteome: Mendelian randomization study using individual level data

Research output: Contribution to conferenceConference Abstractpeer-review


Variation in body mass index (BMI) is associated with cardiometabolic health outcomes such as diabetes and hypertension, but the mechanisms leading from BMI to disease risk are unclear. This study used proteomic data measured by SomaLogic from 2,737 healthy adults from the INTERVAL study to explore the effect of self-reported BMI on 3,622 unique plasma proteins using observational and genetically informed methods. Linear regression models were used, complemented by one-sample Mendelian randomization (MR) analyses. A BMI genetic risk score (GRS) comprised of 654 SNPs from a recent genome-wide association study (GWAS) of adult BMI was used in both observational and MR analysis. Observationally, BMI was associated with 1,576 proteins at p <1.4x10-5 including leptin and sex hormone binding globulin (SHBG). The BMI-GRS was positively associated with BMI (R2=0.028) but not with reported confounders. MR analysis indicated a causal association between each standard deviation increase in BMI and eight unique proteins at p <1.4x10-5, including leptin (0.63 SD, 95% CI 0.48-0.79, p = 1.6x10-15) and SHBG (-0.45 SD, 95% CI -0.65 to -0.25, p = 1.4x10-5). In addition, there was strong agreement in the direction and magnitude of observational and MR estimates (R2 = 0.33). Finally, there was evidence that the genes which showed associations with BMI were enriched for involvement in cardiovascular disease. Altogether, this study provides evidence for a profound impact of higher adiposity on the human proteome and suggests that such protein alterations could be important mechanistic drivers of obesity-related cardiometabolic diseases.
Original languageEnglish
Number of pages486
Publication statusPublished - 23 May 2020


Dive into the research topics of 'Effects of body mass index on the human proteome: Mendelian randomization study using individual level data'. Together they form a unique fingerprint.

Cite this