Human biomarkers database is game-changer for research on aging and mortality
Understanding the biomarkers that cause death (mortality) are of great clinical and research interest. These biomarkers enable clinicians to identify high-risk patient groups, provide a forecast of the likely course of a disease or ailment (prognosis) for individual patients, and help healthcare providers decide which treatment options will be most effective. Because the average human lifespan is long, study of these biomarkers can provide real insight into the aging process and play a key role in evaluating potential therapies.
As a result of decades of research across hundreds of publications, researchers have created a publicly accessible database called MortalityPredictors.org to store published, statistically-significant information on the relationships between biomarkers and cause of death in samples from certain groups or generally healthy samples.
As described in Aging Volume 9, Issue 8, “MortalityPredictors.org: a manually-curated database of published biomarkers of human all-cause mortality,” researchers searched PubMed for appropriate research papers and then manually selected and organized relevant data from each paper to gather the information for the database. The researchers manually curated 1,576 biomarker associations, involving 471 distinct biomarkers. Biomarkers ranged in type based on their distribution in red blood cells, to changes in their molecular DNA, to physical attributes. Through the web interface, the resulting data can be easily browsed, searched, and downloaded for further analysis.
The image below shows the five top most commonly studied human biomarkers of mortality by the number of publications that featured the studies. The bar height indicates the number of publications associated with each, and the actual number is shown in white near the top of each bar.
MortalityPredictors.org provides comprehensive results on published biomarkers of human death that can be used to compare biomarkers, facilitate combined research from multiple studies (meta-analysis), assist with the experimental design of aging studies, and serve as a central resource for analysis.
Given the aging population in many countries, as well as the rise of frailty and age-related deterioration as major public health concerns, researchers are actively looking for new reliable predictors of mortality to improve therapies for age-related disease. It is hoped that the database will make it easier to conduct future research into the relationship of these biomarkers with human mortality and aging.