Clinical Clocks for Biological Age
Clocks to measure biological age can be constructed from any sufficiently large set of biological data that changes with age. The first such clocks used DNA methylation at a range of CpG sites on the genome. The primary challenge in the use of these clocks is that there is no well established link between specific mechanisms of aging and the clock data. For epigenetic clocks built on DNA methylation data, for example, it is not well understood how the methylation status of specific CpG sites on the genome is determined, so it is presently impossible to understand exactly why the clock gives the result that it does. An alternative approach is to construct clocks from clinical measures that are already well connected to specific mechanisms and conditions of aging. This will at least provide more insight into why a given individual is assessed with a higher or lower biological age.
Biological age (BA) is the most important risk factor determining individual risk of morbidity and mortality, with true BA of individuals generally different from chronological age (CA). Attempts to construct biological aging clocks, inferring BA from observable physical features (biomarkers), have a long history. BA clocks have been constructed based on different classes of biological features, including clinical parameters, DNA methylation (DNAm) and many types of omics data. Historically, BA is defined as the age at which the test subject's physiology (as determined by its position in feature space) would be approximately normal for the reference cohort. First-generation DNAm clocks follow this approach. Although such clocks have attained impressive accuracy in determining CA, they are not optimized to predict future morbidity and mortality.
Second-generation BA clocks aim to directly predict future mortality from biological parameters. These clocks define true BA as 'Gompertz age', or the age commensurate with an individual's future risk of dying from all intrinsic causes. Second-generation clocks share some similarities with traditional clinical risk markers, such as the atherosclerosis cardiovascular disease (ASCVD) score, but differ in that they predict all-cause mortality, better reflecting the high degree of interconnectivity between organ system and disease etiology. Successful aging is more than the absence of specific diseases. Unlike existing clinical risk markers, BA clocks can identify individuals likely to remain free from age-dependent dysfunction, morbidity, and mortality for years to come. BA clocks can, therefore, provide normative targets for clinical intervention and individual guidance to promote healthy aging.
Second-generation BA clocks require large-scale cohort data comprising data on biological features combined with long disease and mortality follow-up. For standard clinical chemistry and physiological features, datasets meeting these criteria are available, enabling construction of second-generation 'clinical clocks' (CCs), designed to predict future mortality and morbidity directly from clinical features and biomarkers. In settings where the relevant clinical features and blood markers are readily accessible, CCs have distinct advantages. The features on which CCs are built often have intrinsic well-established biological and pathophysiological meaning, making their findings comparatively easy to interpret and act upon clinically. The development and validation of more powerful CCs, as well as tools facilitating their clinical interpretation and application, should, therefore, be a priority.