Researchers have recently published an online database of biomarkers of human mortality, covering all such measures published and replicated to date. Many of these are not much more than background noise for ongoing efforts to establish a biomarker of biological age that is accurate and reliable enough to be used to assess candidate rejuvenation therapies. For example, excess fat tissue correlates very well with mortality over even fairly small study populations, but this isn't useful if the goal is to measure degree of rejuvenation following treatment. Other biomarkers might be more helpful, and taken as a whole, a database of measures of this nature allows for an easier synthesis of what is presently known about aging and mortality. Here the link points to an open access paper rather than the database itself, but you should take a look at both.
Ultimately, I think that the forms of damage outlined in the SENS vision for rejuvenation therapies will become important biomarkers. For example, senescent cell presence. As therapies for clearance of senescent cells are still in the process of clinical development, it isn't yet possible to use senescent cell counts as a biomarker of aging. Nonetheless, starting with cellular senescence, the next decade or so will see a circular process of verifying various candidate rejuvenation therapies and candidate biomarkers of biological age against one another, step by step. At the end of the day, each type of damage repaired by an effective rejuvenation therapy must be accepted as a valid biomarker of aging in and of itself. That the treatment works is proof of relevance.
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. 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. These traditional biomarkers have been joined in more recent years by many biomarkers utilizing modern assays, for example genome-wide methylation levels, cell-free DNA concentration, and leukocyte telomere length.
Biomarkers of human mortality are also centrally important to research on human aging, due largely to the long potential duration of prospective studies on human lifespan. This can be a tremendous obstacle both in terms of resources (i.e. money to support such lengthy trials) and delayed progress (i.e. each research result could take decades to obtain). Mortality biomarkers have solved similar problems in the past by providing surrogate endpoints for crucial clinical outcomes, facilitating studies that might otherwise have been prohibitively expensive or time consuming. 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. Such biomarkers have also 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. While this approach has important potential drawbacks, 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.
Abundant research on mortality biomarkers has resulted in numerous associations documented across hundreds of publications, generating an unwieldy collection of data that can be difficult for researchers or clinicians to interpret or use effectively. There have been no recent attempts to collate this data nor, to our knowledge, to provide tools for locating, organizing, or comparing data from relevant studies. In the present article, we describe an effort to facilitate a more comprehensive and effective approach to evaluating the literature in this area. We present MortalityPredictors.org, a manually curated, publicly accessible database housing published, statistically-significant relationships in humans between biomarkers and all-cause mortality in population-based or generally healthy samples. To our knowledge, this is the first publicly available resource to collect such information, and we hope it will encourage: 1) the allocation of resources to mortality biomarkers with the greatest potential for accurately predicting human all-cause mortality, 2) efforts to construct multi-biomarker models to further improve such accuracy, and 3) research on human aging and therapies that aim to slow aging or otherwise reduce mortality.