Physiological aging is unidirectional — none of us, as the saying goes, is getting any younger — but occurs at different rates, both over the course of an individual’s lifespan and between individuals. Nowhere is this more noticeable than in the rate of cognitive decline. While almost everyone slows down mentally over time, starting sometime in their late teens, there is tremendous interpersonal variation in the pace of the slowdown: some individuals are severely compromised by 60, whereas others celebrate their 90th birthday by blazing through the Sunday NYT crossword puzzle.
What is the source of such wide variation? Lifestyle and environment doubtless play a significant role; we know that psychological stress, via the action of stress hormones, can significantly impact the cells of the aging brain. It is also conceivable that a significant share of the difference is due to genetics, both in the sense that genes control intrinsic cellular processes and in the sense that genes control our physiological responses to many lifestyle and environmental stressors, from tobacco smoke to chronic anxiety.
A recent study by Zubenko et al. starts us down the road toward potentially identifying genes that contribute to a long, cognitively intact life:
Objective: A systematic genome survey was initiated to identify loci that affect the likelihood of reaching age 90 with preserved cognition (successful aging). Methods: The genome survey was conducted at 10-cM resolution for simple sequence tandem repeat polymorphisms (SSTRPs) that identify genes for Successful AGing (SAG loci) by virtue of linkage disequilibrium. Efficiency was enhanced by genotyping pools of DNA from 100 cognitively intact elders and 100 young (18-25 years) adults. … Results: Our genome survey identified nine SAG candidate loci that may influence the likelihood of reaching age 90 or more with preserved cognition. … Conclusions: The results of our study suggest that loci with differential effects on the successful aging of men and women may be common. The majority of the SAG candidate loci detected in this study overlap with regions previously reported to show linkage to susceptibility genes for cardiovascular disorders, psychiatric disorders, and the accumulation of tissue damage resulting from oxidative stress.
My enthusiasm for the conclusions is tempered (as probably indicated by my awkward clause immediately preceding the abstract) by two issues.
The first is methodological. The authors studied two cohorts — cognitively intact 90-year-olds, and (presumably also mentally fit) 25-year-olds — and identified loci where particular variants appeared to influence the likelihood of reaching 90 years of age with high mental acuity. Presumably, the loci identified are involved in reaching age 90, maintaining cognitive intactness over a long life, or both — but from the setup described it’s impossible to deconvolute the three possibilities. One wishes to see the results for a cohort of 90-year-olds whose mental function is impaired (or perhaps simply everage for their age) in order to evaluate whether the authors have truly identified loci that contribute specifically to successful cognitive aging, or merely to long life.
Second, the results are rather far from identifying specific genes (notwithstanding the authors’ excitement about the overlap of their loci with others involved in heart disease, psychiatric illness, or stress response; a quick jog through OMIM convinced me that it’s not hard to strike by chance a locus linked to a trait in one of these categories). 10-cM resolution is fairly broad. Here, for instance, is the 10-cM window around one of the loci identified in the study. There are, to understate the case somewhat, a lot of genes in this region; it might take a while to sort through them, especially given the technical challenge of genetic studies of this kind: since it’s impractical to wait for the offspring of cognitively intact 90-year-olds to survive to age 90 (or not) and become themeselves cognitively intact (or not) nonagenarians, these linkages can’t be refined by pedigree analysis, which would (if it were practically possible) nicely complement the pooled linkage disequilibrium approach used here.
Qualifications aside, this paper is a good start, and provides a foundation for future studies, hopefully with larger and better-controlled cohorts. Via the recently completed HapMap and related projects, we now have tremendous resources for the study of human variation. The identification of gene variants that underlie the diversity of age-related change is an eminently suitable application for our new toolkit.