Back

Air pollution exposure in Generation Scotland: molecular fingerprints and health outcomes

Robertson, J. A.; Krätschmer, I.; Richmond, A.; McCartney, D. L.; Bajzik, J.; Vernardis, S.; Corley, J.; Tomlinson, S. J.; Vieno, M.; Chybowska, A. D.; Grauslys, A.; Smith, H. M.; Brigden, C.; Messner, C. B.; Zelezniak, A.; Ralser, M.; Russ, T. C.; Pearce, J.; Cox, S. R.; Robinson, M. R.; Marioni, R. E.

2026-03-04 epidemiology
10.64898/2026.03.04.26347573 medRxiv
Show abstract

Ambient air pollution has been associated with increased incidence of chronic disease and is estimated to contribute towards 4.2 million early deaths annually. Whilst the health impacts are well described, less is understood about the underlying biological mechanisms, particularly when considering the co-occurrence of multiple pollutants. Using an atmospheric chemistry transportation model (EMEP4UK), we generate pre-baseline sampling pollution exposure estimates for eight pollutants in Generation Scotland (N = 22,071, recruited between 2006 - 2011). Cox-proportional hazard models reveal associations between pollution exposure and all-cause dementia (PM2.5) and myocardial infarction (NO3_Coarse) over 18 years of follow-up. We perform Bayesian multivariate epigenome-wide (N = 18,512, Illumina EPIC v.1) and proteomic (N = 15,314, 133 mass-spectrometry proteins) association studies, revealing 11 pollutant-methylation associations and 140 pollutant-protein associations. We identify positive associations between exposure (PM2.5 and NO3_Fine) and epigenetic age-acceleration (PhenoAge epigenetic clock). Furthermore, we explore the development of pollutant EpiScores, assessing these in holdout and independent test sets. Our results enhance knowledge of molecular correlates of air pollution exposure, whilst providing further evidence of contributions of air pollutants to chronic disease.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.