Skin biomarker have been identified as predictive of a number of chronic diseases [1]. Amongst these chronic diseases, some are aging related, such as cognitive impairment [2]. However, molecular mechanisms at work in this association remains poorly described, despite already identified association between plasma proteins and aging [3,4].
Here we propose to fill the gap between molecular mechanisms in prediction of age-related diseases and clinical skin evaluation, by putting together a first model using severe skin disease. Thus, predictive performances of skin-based models for age related conditions could be improved by pertinent molecular variables such as plasma-based proteomics or DNA variants.
This project aims to identify biomarkers of severe age-related conditions in participants with skin diseases to enhance and improve prediction of age-related diseases using clinical assessment of skin.
To ensure such prediction can be biologically relevant, the biomarkers identified in this project should be supporting the molecular mechanisms by which the skin and its conditions can reflect aging.
The following research questions will be addressed:
1) For a given list of skin diseases, is it possible to characterize participants groups that are at risk of experiencing aging-related outcomes (senility, hypertension, increase in overall prescriptions or medical procedures, etc) based on high performance biomarkers (including skin biomarkers as well as molecular biomarkers)?
2) Is it possible to rank, discuss and select these biomarkers using an explainable AI tool?
[1] Zhao, Yue, Zhang et al, 2022, doi.org/10.1186/s12902-022-00997-6
[2] Wen et al, 2022, doi.org/10.1111/jdv.18360
[3] Oh, Rutledge, et al, 2023, doi.org/10.1038/s41586-023-06802-1
[4] Goeminne et al, 2025, doi.org/10.1016/j.cmet.2024.10.005