Abstract
Biomarkers of all-cause mortality are of tremendous clinical and research interest. Because of the long potential duration of prospective human lifespan studies, such biomarkers can play a key role in quantifying human aging and quickly evaluating any potential therapies. Decades of research into mortality biomarkers have resulted in numerous associations documented across hundreds of publications. Here, we present MortalityPredictors.org, a manually-curated, publicly accessible database, housing published, statistically-significant relationships between biomarkers and all-cause mortality in population-based or generally healthy samples. To gather the information for this database, we searched PubMed for appropriate research papers and then manually curated relevant data from each paper. We manually curated 1,576 biomarker associations, involving 471 distinct biomarkers. Biomarkers ranged in type from hematologic (red blood cell distribution width) to molecular (DNA methylation changes) to physical (grip strength). Via the web interface, the resulting data can be easily browsed, searched, and downloaded for further analysis. MortalityPredictors.org provides comprehensive results on published biomarkers of human all-cause mortality that can be used to compare biomarkers, facilitate meta-analysis, assist with the experimental design of aging studies, and serve as a central resource for analysis. We hope that it will facilitate future research into human mortality and aging.
Highlights
Mortality biomarkers are of great clinical and research interest
A number of aging-related databases exist as well, including Human Aging Genomic Resources (HAGR; http://genomics.senescence.info/; [16]), GeroProtectors database, and the JenAge Aging Factor Database (AgeFactDB; http://agefactdb.jenage.de/; [18]), but none focus on mortality biomarkers and the curation of reported associations
If a therapy causes positive changes in those mortality biomarkers, it might warrant the additional expense and effort of a long-term study to directly assess its true effect on mortality
Summary
Mortality biomarkers are of great clinical and research interest. General clinical applications include identifying high-risk patient groups, prognosticating for individual patients, and helping healthcare providers decide among treatment options [1]. Examples of very well-studied mortality biomarkers include blood pressure, cholesterol, and waist circumference, which have well-established relationships with mortality in various populations documented in dozens of studies, some with thousands or millions of participants [2,3,4]. Biomarkers of human mortality are centrally important to research on human aging, due largely to the long potential duration of prospective studies on human lifespan. Blood pressure and cholesterol are two of many markers that have played this role in the past, by facilitating cardiovascular research aimed at reducing morbidity and mortality [10] Such biomarkers have gained clinical importance as surrogate markers in clinical practice, where treatments are often initiated with the explicit goal of changing a patient’s biomarker value [11,12,13]. While this approach has important potential drawbacks [8, 10], it is certainly more practical for a patient to track how a new intervention affects her blood pressure or serum cholesterol, rather than how it affects her lifespan, which is unknown until death
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