In today’s open access paper, researchers report on correlations between phenotypic age and cognitive function in older adults. Phenotypic age is an aging clock that uses a small number of blood chemistry measures as its inputs, such as portions of a complete blood count, creatine, C-reactive protein, and so forth. The big advantage of this approach over epigenetic clocks is that one can look at what changed following an intervention and theorize a little about what that means. Did C-reactive protein levels go down in the course of a reduction in phenotypic age, for example? That indicates positive effects on the chronic inflammation characteristic of later life. This sort of reasoning remains impossible for epigenetic clocks at the present time. One can see what changed, which CpG sites on the genome are differently methylated in the sample, but there is no connection from there to the rest of our biochemistry. It is a dead end.
The most interesting outcome reported in today’s study is that chronological age fails to correlate with cognitive function. We might take this as hopeful. Cognitive decline is not inevitable in a normal human life span, even given the universal operation of mechanisms of degenerative aging, and even given the paucity of interventions to slow aging beyond exercise and lifestyle choice. Accelerated phenotypic age does correlate with a decline in cognitive function in the study population, and we might take this as the usual cautionary tale about taking better care of one’s long-term health. The data suggests that these differences are largely a matter of exercise and physical fitness.
Cognitive decline in older adults is a growing public health concern, and traditional measures such as chronological age are insufficient for accurately assessing cognitive function. Phenotypic age (PhenoAge) and phenotypic age acceleration (PhenoAgeAccel), which reflect biological age and aging acceleration, may be better predictors of cognitive decline. Additionally, physical activity (PA) has been recognized for its protective effects on aging and cognitive health. This study explored the role of PhenoAge and PhenoAgeAccel in cognitive performance and investigated whether PA moderates this relationship.
We used data from the National Health and Nutrition Examination Survey, which analyzed 1,298 participants aged 60 years and older. PhenoAge was calculated using 10 biomarkers, and PhenoAgeAccel was derived as the difference between chronological age and PhenoAge. Cognitive performance was assessed using the Digit Symbol Substitution Test. The relationship between PhenoAge, PhenoAgeAccel, and low cognitive performance was analyzed using weighted logistic regression models. Subgroup and sensitivity analyses were conducted, and the interactions between PhenoAgeAccel and PA were evaluated.
Both PhenoAge and PhenoAgeAccel scores were significantly associated with low cognitive performance. The highest quartiles of PhenoAge (odds ratio = 3.22) and PhenoAgeAccel (odds ratio = 2.31) were associated with higher odds of low cognitive performance. By contrast, chronological age did not show a significant relationship with cognitive performance. PA was found to moderate the association between PhenoAgeAccel and cognitive performance. Higher levels of PA attenuated the impact of PhenoAgeAccel on cognitive decline. Receiver operating characteristic curve analysis showed that PhenoAge (area under the curve [AUC] = 0.562), PhenoAgeAccel (AUC = 0.589), and chronological age (AUC = 0.513) were significantly different. In conclusion, PhenoAgeAccel and PA are significant predictors of cognitive decline, with PA offering a protective effect against the impact of accelerated aging on cognition.
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