Proteomic Signatures Capture Environmental Exposures Across Diverse Populations
- 1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA;Program in Medical and Population Genetics, Broad Ins
Abstract
Environmental exposures shape disease risk, but the generalizability of circulating protein-based readouts of exposures across diverse populations remains unknown. Using plasma proteomic profiles of 5,416 proteins in 10,043 participants from the All of Us Research Program, we evaluated associations with and composite scores of 100+ exposures, spanning adiposity, air pollution, ambient temperature, and substance use. We find that proteomic exposure signatures are largely consistent across populations and socioeconomic strata, with <1% of proteins showing significant cross-population heterogeneity, which included BMI as an exception, with leptin being the most heterogeneous association. Population-specific leptin trajectories adiposity, together with residual leptin-type 2 diabetes associations after BMI adjustment, revealed sex-specific metabolic risk not captured by BMI alone. Aggregated protein scores were largely exposure-specific, but substance-use scores showed cross-prediction and SES scores were broadly associated. PM2.5 protein scores captured within-region exposure variation and outperformed geographic and socioeconomic correlates, but spatial confounders remain embedded within protein-environment associations. Scores trained in UK Biobank transferred to All of Us for biologically proximal exposures, supporting cross-biobank portability. These findings establish plasma proteomics as a scalable framework for exposure measurement across diverse populations, while underscoring that responsible applications require careful consideration of biological context, social patterning, and cohort structure.
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BibTeX
@misc{jee2026proteomic,
title = {{Proteomic Signatures Capture Environmental Exposures Across Diverse Populations}},
author = {Y. H. Jee and K. Tsuo and S. Parsa and M. A. Argentieri and S. Isaac and A. Elliott and R. Walters and E. Padhi and E. Kiernan and L. Lichtenstein and M. E. Talkowski and S. B. Montgomery and N. J. Lennon and C. Patel and K. J. Karczewski and M. J. Daly and A. R. Martin},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.06.26364311},
url = {https://www.medrxiv.org/content/10.64898/2026.10.06.26364311v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Proteomic Signatures Capture Environmental Exposures Across Diverse Populations
AU - Y. H. Jee
AU - K. Tsuo
AU - S. Parsa
AU - M. A. Argentieri
AU - S. Isaac
AU - A. Elliott
AU - R. Walters
AU - E. Padhi
AU - E. Kiernan
AU - L. Lichtenstein
AU - M. E. Talkowski
AU - S. B. Montgomery
AU - N. J. Lennon
AU - C. Patel
AU - K. J. Karczewski
AU - M. J. Daly
AU - A. R. Martin
PY - 2026
DA - 2026/10/06
PB - medRxiv
DO - 10.64898/2026.10.06.26364311
UR - https://www.medrxiv.org/content/10.64898/2026.10.06.26364311v1
AB - Environmental exposures shape disease risk, but the generalizability of circulating protein-based readouts of exposures across diverse populations remains unknown. Using plasma proteomic profiles of 5,416 proteins in 10,043 participants from the All of Us Research Program, we evaluated associations with and composite scores of 100+ exposures, spanning adiposity, air pollution, ambient temperature, and substance use. We find that proteomic exposure signatures are largely consistent across populations and socioeconomic strata, with <1% of proteins showing significant cross-population heterogeneity, which included BMI as an exception, with leptin being the most heterogeneous association. Population-specific leptin trajectories adiposity, together with residual leptin-type 2 diabetes associations after BMI adjustment, revealed sex-specific metabolic risk not captured by BMI alone. Aggregated protein scores were largely exposure-specific, but substance-use scores showed cross-prediction and SES scores were broadly associated. PM2.5 protein scores captured within-region exposure variation and outperformed geographic and socioeconomic correlates, but spatial confounders remain embedded within protein-environment associations. Scores trained in UK Biobank transferred to All of Us for biologically proximal exposures, supporting cross-biobank portability. These findings establish plasma proteomics as a scalable framework for exposure measurement across diverse populations, while underscoring that responsible applications require careful consideration of biological context, social patterning, and cohort structure.
ER -