Genes and cognitive aging

The search for genetic factors related to late life cognitive change initially focused on cognitive diseases, particularly Alzheimer’s disease (AD) and now increasingly employs the method of genome-wide association studies. This approach has repeatedly confirmed the major importance of the Apolipoprotein E polymorphism as a risk factor for AD, but no consensus has emerged concerning other genes. The reasons for this situation likely include factors such as the large number of potential candidate genes with small effects, interactions between genes, and the difficulty of establishing a reliable and valid phenotypic marker of both disease and normality.

The field of cognitive aging is also grappling with the genetic determinants of cognitive change, using different approaches in the face of a unique set of challenges. Because cognitive aging is probably even more heterogeneous than a cognitive disorder like AD, definition of the precise phenotype is problematic. Factors that can define this phenotype include the exact cognitive process and a host of demographic factors. In addition, the small effect sizes and potential secular or cohort effects would seem to necessitate a longitudinal approach.

Rising to these challenges, Erickson et al. (2008) have produced an interesting set of findings that examined polymorphic alleles in the catechol-O-methyl transferase (COMT) gene and the brain-derived neurotrophic factor (BDNF) gene. Both genes have functional single nucleotide polymorphisms. In the case of COMT a valine to methionine substitution at codon 158 results in a met/met form with low thermostability, less enzymatic degradation of prefrontal dopamine (DA) and thus higher prefrontal DA function than the val/val form. While many studies of both young and old individuals find that met homozygotes perform better on tests of prefrontal function than val homozygotes, these results are not consistent. BDNF is characterized by a met/val substitution at codon 66 in which the met form of the allele results in impaired BDNF trafficking that appears to coincide with reductions in both cognitive performance and brain volumes in young people although, again, results disagree. Erickson and colleagues used a longitudinal, within-subjects design to evaluate how these polymorphisms affected change in performance on a task-switching paradigm in a group of 53 individuals studied at baseline at an average age of 65 and then again 10 years later. In short, the authors found no effect of the COMT polymorphism on the task, but they did find an effect of the BDNF polymorphism. Interestingly, the effect, revealed as a time × genotype interaction, such that those homozygous for the val allele had greater decline in performance than those with a met allele.

In the same issue of this journal, Nagel et al. (2008) used a cross-sectional approach and looked at two different cognitive tasks (the Wisconsin Card Sorting Test and a spatial working memory task) and found strong evidence of both an age × COMT genotype (such that older subjects with the Val allele were poorer performers than Met homozygotes) and a three-way interaction with the BDNF allele in which the Met allele in COMT Val carriers was detrimental.

These results follow on previous studies that have been inconsistent in many ways. Neither of these studies noted effects of the COMT genotype on performance in young people, despite the fact that a benefit of the Met allele has been frequently seen on a variety of frontal and executive tasks including the WCST, as well as on brain activation during such tasks in both young and old (Egan et al., 2001 ; Goldberg and Weinberger, 2004 ; Malhotra et al., 2002 ; Starr et al., 2007 ). However, COMT genotype has not invariably shown this effect in either young or older individuals, and effects are often small, vary by gender or the precise cognitive task, and may differ between Met homozygotes and heterozygotes (Barnett et al., 2007 ; Harris et al., 2005 ; Holtzer et al., 2008 ; O’Hara et al., 2006 ). A key question is whether longitudinal studies of older people show different results. Reports have noted beneficial effects of the Met/Met genotype (Starr et al., 2007 ) and greater declines in those with Val/Val (de Frias et al., 2004 , 2005 ). However, these effects also vary according to the specific tests, and the specific age of the subjects. With regard to BDNF, some studies of older people show better cognitive performance in Met homozygotes (Harris et al., 2006 ), and others worse (Raz et al., 2009 ).

Why are results of these studies so inconsistent? One possibility is that these genes have relatively small effects so that the precise specification of the phenotype is crucial. For example, the cognitive tasks assessed in these studies have involved either tests of visuospatial ability, executive function (and very different measures of this broad category), episodic memory, reasoning ability, or speed of processing. It does not necessarily make any more sense to compare genetic influences on these processes to one another than it does to compare the genetics of Alzheimer’s and Parkinson’s disease. In addition, findings differ by gender and age, and age itself is a complicated variable that may be measured cross-sectionally, longitudinally, and at many different time points and ranges. Furthermore, evidence suggests that the polymorphisms not only interact with basic demographic features, but with comorbid disease such that, for example, individuals with high blood glucose show poorer memory function only if they carry the Met BDNF allele (Raz et al., 2008 ). And, of course there are gene–gene interactions. These issues are not limited to genes with small effects, as the Apolipoprotein E4 allele, repeatedly demonstrated as a risk for cognitive decline and dementia in older people, may be beneficial in younger individuals (Mondadori et al., 2007 ).

The precise specification of a demographic and behavioral phenotype in association with genetic variations is clearly one way the field is moving forwards. Another way is through the use of other phenotypic markers such as brain imaging (Pezawas et al., 2004 ) that may be a reliable endophenotype. Here again, if applied carefully, associations between changes in brain structure and function may help to explain not only genetic associations, but behavioral mechanisms. It seems reasonable that only by precisely specifying a cognitive or imaging phenotype and performing studies in well characterized individuals will the genetics of cognitive aging advance.