Background: The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains 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 4 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 in 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. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by 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) applying mixed-effects 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 7 and 3 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 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.
| Original language | English |
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| Article number | 508 |
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| Journal | BMC Medicine |
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| Volume | 21 |
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| Issue number | 1 |
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| DOIs | |
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| Publication status | Published - 1 Dec 2023 |
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Phenotype and genotype data collection in FinnTwin12 cohort has been supported by the Wellcome Trust Sanger Institute, the Broad Institute, ENGAGE – European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007, grant agreement number 201413, National Institute of Alcohol Abuse and Alcoholism (grants AA-12502, AA-00145, and AA-09203 to R J Rose; AA15416 and K02AA018755 to D M Dick; R01AA015416 to Jessica Salvatore) and the Academy of Finland (grants 100,499, 205,585, 118,555, 141,054, 264,146, 308,248 to JK, and the Centre of Excellence in Complex Disease Genetics (grants 312,073, 336,823, and 352,792 to JKaprio). This research was partly funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No 874724 (Equal-Life). Equal-Life is part of the European Human Exposome Network.
We gratefully acknowledge the contribution of the Turku Proteomics Facility team supported by Biocenter Finland for mass spectrometry. We warmly thank the participants involved in this study. Biobank-based Integrative Omics Study Consortium and BBMRI-NL Metabolomics Consortium lists of authors and their affiliations appear in the supplementary material (Additional file 1).
For the Netherlands Twin Register, funding was obtained from the Netherlands Organization for Scientific Research (NWO) and The Netherlands Organisation for Health Research and Development (ZonMW) grants 904–61-090, 985–10-002, 912–10-020, 904–61-193,480–04-004, 463–06-001, 451–04-034, 400–05-717, Addiction-31160008, 016–115-035, 481–08-011, 400–07-080, 056–32-010, Middelgroot-911–09-032, OCW_NWO Gravity program –024.001.003, NWO-Groot 480–15-001/674, Center for Medical Systems Biology (CSMB, NWO Genomics), NBIC/BioAssist/RK(2008.024), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI –NL, 184.021.007 and 184.033.111), X-Omics 184–034-019; Spinozapremie (NWO- 56–464-14,192), KNAW Academy Professor Award (PAH/6635) and University Research Fellow grant (URF) to DIB; Amsterdam Public Health research institute (former EMGO +), Neuroscience Amsterdam research institute (former NCA); the European Community’s Fifth and Seventh Framework Program (FP5- LIFE QUALITY-CT-2002–2006, FP7- HEALTH-F4-2007–2013, grant 01254: GenomEUtwin, grant 01413: ENGAGE and grant 602,768: ACTION); the European Research Council (ERC Starting 284,167, ERC Consolidator 771,057, ERC Advanced 230,374), Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06), the National Institutes of Health (NIH, R01D0042157-01A1, R01MH58799-03, MH081802, DA018673, R01 DK092127-04, Grand Opportunity grants 1RC2 MH089951, and 1RC2 MH089995); the Avera Institute for Human Genetics, Sioux Falls, South Dakota (USA). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health as well as the US National Institute of Mental Health (RC2 MH089951). Computing was supported by NWO through grant 2018/EW/00408559, BiG Grid, the Dutch e-Science Grid and SURFSARA.
Open Access funding provided by University of Helsinki (including Helsinki University Central Hospital). The Biobank-based Integrative Omics Study (BIOS) Consortium and the BBMRI Metabolomics Consortium are funded by BBMRI-NL, a Research Infrastructure financed by NWO, project nos. 184.021.007 and 184033111. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
GD has received funding for his doctoral studies from the Doctoral Programme in Population Health (DOCPOP), University of Helsinki, Finland. JK acknowledges support by the Academy of Finland (grants 265,240, 263,278) and the Sigrid Juselius Foundation.
- Netherlands Twin Register (NTR)