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Approved Research

Retrospective evaluation of relationships between existing clinical biomarkers, emerging/potential biomarkers and chronic disease in populations classified as generally healthy.

Principal Investigator: Dr Eliot Siegel
Approved Research ID: 62876
Approval date: September 1st 2021

Lay summary

We plan to incorporate individualized data for known risk factors including diet, exercise behaviors, BMI, smoking status, obesity, infection status, genetics, and health/family history and others, with the commonly used clinical test data, and the data produced by an assortment of unbiased approaches, in order to generate a more accurate representation of individual and combined disease risk.   We intend to follow a proprietary analytical model that we developed for evaluating cancer risk in healthy individuals.  If successful, this will allow the use of routine medical data and commonly used tests, as well as the affordable and widely available new technologies, to detect diseases and medical disorders at an earlier stage than currently possible. 

The explicit aims of this endeavor is to conduct large scale analyses of commonly used clinical tests in people classified as "generally healthy" and to also analyze it in context of data generated by new and emerging technologies, with the aim of improving prognostic and diagnostic power of these analyses.  We will further evaluate this data in the context of diseases such as cancer or preexisting chronic diseases for the purposes of improved early detection.

Scientific rationale: Our preliminary results from meta-analyses of oncology literature suggests that population level data dramatically overestimates cancer risk for individuals with healthy behavioral profiles, and that better use of available data and commonly used medical tests may improve risk estimates for all cancers, early detection, and diagnosis.  Using the UK Biobank database, we hope to validate our existing data while also seeking to apply similar methodologies to other diseases, thus leading the way to better and earlier disease diagnostics.

Duration:  24 months, after which we plan to publish the results in scientific or medical journals.

Public health impact: Establishing more reliable metrics for the key determinants in disease risk will enable us to generate modified risk base rates, which we intend to use to prioritize test recommendations for early intervention. In addition, these same values will serve to improve each diagnostic test when applied in conjunction with a Bayesian approach for calculating an individual's post-test disease probability. Using this dual approach, it is our hope that the UK Biobank database will provide robust validation as well as new insights for improving our current screening and intervention protocols.