Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

• Design: A longitudinal multi-omics twin study (NTR and FinnTwin12 cohorts) examining the association between plasma protein levels and changes in BMI (trajectories) over approximately a decade.
• Key Finding: The association between the plasma proteome and BMI trajectories is largely explained by shared genetic factors and common environmental influences, pointing to a common underlying etiology.
• Causal Link: Mendelian Randomization analysis identified Apolipoprotein B (ApoB) as a key protein, providing evidence that genetically determined ApoB levels have a causal effect on BMI, but not the reverse.

PubMed: 38129841 DOI: 10.1186/s12916-023-03198-7 Overview generated by: Gemini 2.5 Flash, 28/11/2025

Key Findings: Shared Etiology of Plasma Proteome and BMI Trajectories

This longitudinal multi-omics study utilized a twin design to dissect the influence of genetic and environmental factors on the association between the plasma proteome and Body Mass Index (BMI) trajectories during adolescence and young adulthood.

The central finding is that the observed associations between protein levels and BMI trajectories (changes in BMI over time) are largely attributable to common etiological factors, specifically:

1. Shared Genetic Factors (A): Genetic effects explained a significant portion of the covariance between plasma proteins and BMI, suggesting that heritable factors simultaneously influence both protein expression and adiposity development.
2. Shared Environmental Factors (C): Common environmental influences also played a role in explaining the protein-BMI association.

Identification of Key Proteins and Causal Links

The study identified 38 plasma proteins whose levels were significantly associated with an individual’s BMI trajectory. Clustering analysis showed these proteins grouped into pathways related to lipid transport/metabolism (e.g., Apolipoproteins) and immune/inflammatory response.

Methods and Study Design

The study employed a rigorous, longitudinal twin design spanning approximately 10 years of follow-up.

Cohorts and Data

• Participants: Two independent cohorts of twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665).
• Phenotypes: BMI was measured four times per individual over the follow-up period to model BMI trajectories.
• Omics Data: Plasma proteomics (Olink Proximity Extension Assays), metabolomics, and genotype data were utilized.

Statistical Analysis

• Twin Modeling: Structural equation modeling (using ACE models) was applied to estimate the proportion of variance and covariance in protein levels and BMI trajectories explained by additive genetics (A), common environment (C), and unique environment (E).
• Mendelian Randomization (MR): Two-sample MR was performed using public GWAS and pQTL data to investigate potential causal effects between genetically determined protein levels and BMI, and vice-versa.

Results: Focus on Apolipoprotein B

A key protein highlighted by the analysis was Apolipoprotein B (ApoB).

• Genetic Correlation: ApoB showed a strong genetic correlation with BMI, suggesting the genetic architecture underlying ApoB concentration substantially overlaps with the genetic architecture of BMI.
• Causality: The MR analysis provided evidence for a causal effect of ApoB on BMI, meaning genetically predicted higher ApoB levels lead to higher BMI. Conversely, there was no evidence suggesting that genetically predicted BMI causally affects ApoB levels.
• Metabolite Connection: The ApoB-BMI association was also linked to other metabolic markers, reinforcing ApoB’s role as a central, genetically mediated biomarker in adiposity.

Conclusions and Recommendations

The study concludes that the association between the plasma proteome and BMI trajectories is predominantly driven by shared genetic and common environmental factors, providing a strong biological basis for the co-occurrence of these traits. The findings prioritize the ApoB-coding gene (APOB) as a genetically determined factor with a likely causal influence on BMI, supporting its role as a potential therapeutic target in the early development of adiposity. The multi-omics approach, especially the use of longitudinal twin data, offers a powerful framework for dissecting the complex etiology of chronic diseases.