English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

For more than two decades, advances in personalised medicine and precisionhealthcare have largely been based on genomics and other omics data. Thesestrategies aim to tailor interventions to individual patient profiles,promising greater treatment efficacy and more efficient allocation ofhealthcare resources. Here, we show that widely collected common haematologicmarkers can reliably predict and discriminate individual chronological age andhealth status from even noisy sources. Our analysis includes synthetic and realretrospective patient data, including medically relevant and extreme cases, anddraws on more than 100\,000 complete blood count records over 13 years from theUnited States Centers for Disease Control and Prevention's National Health andNutrition Examination Survey (CDC NHANES). We combine fully explainable riskassessment scores with machine and deep learning techniques to focus onclinically significant patterns and characteristics without functioning purelyas a ''black-box model allowing interpretation and control. We validated theresults with the UK Biobank, a larger cohort independent of the CDC NHANES andwith very different collection techniques, the former a survey and the second alongitudinal study. Unlike current biological ageing indicators, this approachmay offer rapid, and scalable implementations of personalised, precision andpredictive approaches to healthcare and medicine without or before requiringother specialised, uncommon or costly tests.

Routine haematological markers can predict and discriminate health status and biological age even from noisy sources