| Title: | Global patterns of prognostic biomarkers across disease space |
| Journal: | Scientific Reports |
| Published: | 19 Dec 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36535980/ |
| DOI: | https://doi.org/10.1038/s41598-022-25209-y |
| URL: | https://www.nature.com/articles/s41598-022-25209-y.pdf |
Publication 8020
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Abstract
There is a multitude of pathological conditions that affect human health, yet we currently lack a predictive model for most diseases, and underlying mechanisms that are shared by multiple diseases are poorly understood. We leveraged baseline clinical biomarker data and long-term disease outcomes in UK Biobank to build prognostic multivariate survival models for over 200 most common diseases. We construct a similarity map between biomarker-disease hazard ratios and demonstrate broad patterns of shared similarity in biomarker profiles across the entire disease space. Further aggregation of risk profiles through density based clustering showed that biomarker-risk profiles can be partitioned into few distinct clusters with characteristic patterns representative of broad disease categories. To confirm these risk patterns we built disease co-occurrence networks in the UK Biobank and US HCUP hospitalization databases, and compared similarity in biomarker risk profiles to disease co-occurrence. We show that proximity in the biomarker-disease space is strongly related to the occurrence of disease comorbidity, suggesting biomarker profile patterns can be used for both predicting future outcomes as well as a sensitive mechanism for detecting under-diagnosed disease states.2 Authors
- Neha Murad
- Eugene Melamud
1 Application
| Application ID | Title |
|---|---|
| 18448 | Investigation of factors contributing to Healthy Aging. |
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