The benefits of staying active in old age: Physical activity counteracts the negative influence of PICALM, BIN1, and CLU risk alleles on episodic memory functioning.

Abstract

PICALM, BIN1, CLU, and APOE are top candidate genes for Alzheimer's disease, and they influence episodic memory performance in old age. Physical activity, however, has been shown to protect against age-related decline and counteract genetic influences on cognition. The aims of this study were to assess whether (a) a genetic risk constellation of PICALM, BIN1, and CLU polymorphisms influences cognitive performance in old age; and (b) if physical activity moderates this effect. Data from the SNAC-K population-based study were used, including 2,480 individuals (age range = 60 to 100 years) free of dementia at baseline and at 3- to 6-year follow-ups. Tasks assessing episodic memory, perceptual speed, knowledge, and verbal fluency were administered. Physical activity was measured using self-reports. Individuals who had engaged in frequent health- or fitness-enhancing activities within the past year were compared with those who were inactive. Genetic risk scores were computed based on an integration of risk alleles for PICALM (rs3851179 G allele, rs541458 T allele), BIN1 (rs744373 G allele), and CLU (rs11136000 T allele). High genetic risk was associated with reduced episodic memory performance, controlling for age, education, vascular risk factors, chronic diseases, activities of daily living, and APOE gene status. Critically, physical activity attenuated the effects of genetic risk on episodic memory. Our findings suggest that participants with high genetic risk who maintain a physically active lifestyle show selective benefits in episodic memory performance.

Potential contribution of the Alzheimer׳s disease risk locus BIN1 to episodic memory performance in cognitively normal Type 2 diabetes elderly

“…rs6733839 has stronger effect size (odds ratio of 1.22) compared to other SNPs reported by the meta-analysis, rendering better statistical power for detecting the association. Further, in agreement with our findings, BIN1 was previously shown to be associated with episodic memory performance (in the context of a genotyping pattern involving combination with additional AD genes) (Barral et al, 2012; Ferencz et al, 2014). This is also in line with recent studies reporting BIN1 associations with several cognitive functions in healthy, non-demented elderly.…”

“…Episodic memory is highly influenced by genetic contribution (Wilson et al, 2011), and this sub-phenotype is frequently used as primary outcome measure in genetic studies aiming to evaluate the impact of AD related variants on cognitive performance among cognitively normal and impaired populations. Among GWAS confirmed AD risk loci that were associated with this intermediate phenotype are CR1, BIN1, CLU, PICALM , and APOE (Barnes et al, 2013; Barral et al, 2012; Ferencz et al, 2014; Keenan et al, 2012; Sweet et al, 2012). …”

Genetics and Functional Imaging: Effects of APOE, BDNF, COMT, and KIBRA in Aging

“…However, one study reported that older APOE ε4 carriers who are more physically active had higher activity in posterior temporal and parietal regions during an episodic memory task than non-carriers or those with lower physical activity levels. These data suggest that physical activity may circumvent negative effects of carrying a disadvantageous genotype on brain functioning (see also Erickson et al 2013;Ferencz et al 2014). Similar to lifestyle factors, behavior-genetic studies indicate that different diseases may lower resources and make it easier to disclose genetic effects.…”

A Network of Genetic Effects on Non-Demented Cognitive Aging: Alzheimer’s Genetic Risk (CLU + CR1 + PICALM) Intensifies Cognitive Aging Genetic Risk (COMT + BDNF) Selectively for APOE ɛ4 Carriers

“…Research on genetic risk approaches have used several procedures for calculating risk scores. For example, prior studies have used an explained variance-weighted genetic risk score [60], an odds ratio weighted risk score [53,54], large number of SNPs to create a polygenic risk score [61,62], weighted sum of the top risk scores [56], and an additive allelic risk score [11,53,63]. …”