Associations Between 40‐Year Trajectories of BMI and Proteomic and Epigenetic Aging Clocks: Deciphering Nonlinearity and Interactions

TL;DR

This study explores how BMI changes over 40 years relate to biological aging, showing that these relationships are often nonlinear and that proteomic clocks could be useful in obesity research.

Contribution

The study reveals nonlinear associations between BMI trajectories and biological aging, emphasizing the potential of proteomic clocks in obesity research.

Findings

BMI at 18 and 60 years and BMI changes were associated with increased biological aging for most aging estimates.

Nonlinearity was observed in about one-third of significant associations, mostly in proteomic clocks.

Suggestive evidence of interactions between BMI at 18 and 60 years was found for two proteomic clocks.

Abstract

The potential of proteomic aging clocks for obesity research, and the extent of nonlinearity in longitudinal associations between body weight and biological aging, remain underexplored. We investigated how BMI at ages 18 and ~60, as well as changes in BMI from age 18 to ~60, relate to downstream epigenetic and proteomic aging. We also examined nonlinearity and interactions in these associations. Analyses were conducted in 401 Finnish twins with up to nine self‐reported or measured BMI values collected over 40 years. Olink proteomic and Illumina DNA methylation data were generated from blood drawn at the last BMI measurement. From these data, we derived four proteomic and five epigenetic age estimates and modeled BMI change over time using mixed‐effects models. Generalized additive models were then applied to examine (1) nonlinear associations between BMI trajectories and biological aging, adjusting for chronological age, and (2) interactions of baseline BMI with BMI change and BMI at ~60 years. BMI at 18 and ~60 years old and changes in BMI were associated with increased biological aging for most aging estimates. We found statistical evidence of nonlinearity for about one‐third of the significant associations, mostly observed for proteomic clocks. We further identified suggestive evidence for interactions between BMI at 18 years and BMI at ~60 years in explaining variability in two proteomic clocks (p = 0.07; p = 0.09). In conclusion, our study illustrates the potential of proteomic clocks in obesity research and highlights that assuming linearity in associations between BMI trajectories and biological aging is a critical oversight.

In twins followed up over 40 years across adulthood, we examined associations between long‐term body mass index trajectories and downstream acceleration in proteomic and epigenetic aging. We further examined whether these associations showed statistical evidence for nonlinear patterns. Our study outlines the potential of proteomic aging clocks for obesity research and indicates that assuming associations between weight trajectories and biological aging to be linear may represent an oversight in multiple scenarios.

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