The best thing that researchers can do with the presently established aging clocks, such as Phenotypic Age, is to gather as much data as possible on the relationship between the clock output and meaningful outcomes such as disease risk and mortality. Hence the existence of studies such as the one reported here. Even now, going on twenty years into the use of aging clocks, it remains unclear as to whether any of the existing, relative well-used clocks will produce a reasonable assessment of the effects of any novel potentially age-slowing or age-reversing therapy. An understanding of the links between what is measured in the clocks and the underlying processes of aging have not been established and will be very challenging to establish, and thus it is impossible to predict whether a clock will overestimate, underestimate, or just fail when it comes to assessing the quality of any given intervention in aging. This is the case even for clocks such as Phenotypic Age that use clinical chemistry rather than omics measures. In this environment, gathering more data is probably the best path forward.
Accelerated biological aging serves as a risk factor for age-related diseases, its role in the prognosis of Parkinson’s disease (PD) remains ambiguous. This study investigates the association between biological aging and the mortality in PD patients. Data were sourced from the UK Biobank. Independent prognostic factors for mortality in PD patients were assessed by Cox regression model, and a nomogram was developed to predict the survival of PD patients. A total of 569 PD patients were enrolled in this study.
Phenotypic age (PhenoAge) and PhenoAge acceleration (PhenoAgeAccel) were found to affect the survival in PD patients. Independent risk factors for PD mortality included age, male gender, smoking, underweight, depressive mood, low-density lipoprotein, and higher genetic susceptibility. The nomogram constructed based on PhenoAge showed robust prediction performance for mortality in PD patients. PhenoAge emerges as a pivotal PD mortality predictor, enabling the identification of individuals experiencing accelerated biological aging and implementing targeted interventions.
