Back

Longitudinal multi-omic evaluation of biomarkers of health and ageing over smoking cessation intervention

Herzog, C. M. S.; Vavourakis, C. D.; Theeuwes, B.; Redl, E.; Watschinger, C.; Knoll, G.; Hagen, M.; Haider, A.; Platzer, H.-P.; Kumar, U.; Zollner-Kiechl, S.; Knoflach, M.; Gibitz-Eisath, N.; Oehler, S.; Lindner, V.; Wimmer, A.; Greitemeyer, T.; Widschwendter, P.; Sturm, S.; Weinberger, B.; Moschen, A.; Hoeller, A.; Schobersberger, W.; Haring, C.; Widschwendter, M.

2025-09-04 systems biology
10.1101/2025.08.29.673135 bioRxiv
Show abstract

Smoking is one of the single most important preventable risk factors for cancer and other adverse health outcomes 1,2. Smoking cessation represents a key public health intervention with the potential to reduce its negative health outcomes 2-4. While epidemiological, cross-sectional, and individual longitudinal omic or biomarker studies have evaluated the impact of smoking cessation, no study to date has systematically profiled molecular and clinical changes in several organ systems or tissues longitudinally over the course of smoking cessation that could allow for more detailed assessment of response biomarkers and the identification of interindividual differences in the recovery of physiological functions. Here, we report the first human longitudinal multi-omic study of smoking cessation, evaluating 2,501 unique single or composite features from 1,094 longitudinal samples. Our comprehensive analysis, leveraging over half a million longitudinal data points, revealed a profound effect of smoking cessation on epigenetic biomarkers and microbiome features across multiple organ systems within 6 months of smoking cessation, alongside shifts in the immune and blood oxygenation system. Moreover, our multi-omic analysis provided unprecedented granularity that allows for identification of new cross-ome associations for mechanistic discovery. We anticipate that data and an interactive app from the Tyrol Lifestyle Atlas (eutops.github.io/lifestyle-atlas), comprising the current study and a parallel study arm evaluating the impact of diet on biomarkers of health and disease, will provide the basis for future discovery, biomarker benchmarking in their responsiveness to health-promoting interventions, and study of individualised response group, representing a major advance for personalised health monitoring using biomarkers.

Matching journals

The top 3 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.