@unpublished {466, title = {Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins}, journal = {medRxiv}, year = {2023}, abstract = {

Background: The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

Methods: Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks.

Results: We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35\%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

Conclusions: Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

}, doi = {10.1101/2023.06.28.23291995}, author = {Drouard, Gabin and Hagenbeek, Fiona A and Whipp, Alyce and Pool, Ren{\'e} and Hottenga, Jouke-Jan and Jansen, Rick and Hubers, Nikki and Afonin, Aleksei and Willemsen, Gonneke and de Geus, Eco J C and Ripatti, Samuli and Pirinen, Matti and Kanninen, Katja M and Boomsma, Dorret I and van Dongen, Jenny and Kaprio, Jaakko and BIOS Consortium and BBMRI-NL Metabolomics Consortium} }