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  • Late-Life Physical and Cognitive Activities Independently Contribute to Brain and Cognitive Resilience. Journal of Alzheimer's disease : JAD Casaletto, K. B., Renteria, M. A., Pa, J., Tom, S. E., Harrati, A., Armstrong, N. M., Rajan, K. B., Mungas, D., Walters, S., Kramer, J., Zahodne, L. B. 2020

    Abstract

    BACKGROUND: Active lifestyles are related to better cognitive aging outcomes, yet the unique role of different types of activity are unknown.OBJECTIVE: To examine the independent contributions of physical (PA) versus cognitive (CA) leisure activities to brain and cognitive aging.METHODS: Independent samples of non-demented older adults from University of California, San Francisco Hillblom Aging Network (UCSF; n = 344 typically aging) and University of California, Davis Diversity cohort (UCD; n = 485 normal to MCI) completed: 1) self-reported engagement in current PA and CA (UCSF: Physical Activity Scale for the Elderly and Cognitive Activity Scale; UCD: Life Experiences Assessment Form); 2) neuropsychological batteries; and 3) neuroimaging total gray matter volume, white matter hyperintensities, and/or global fractional anisotropy. PA and CA were simultaneously entered into multivariable linear regression models, adjusting for demographic characteristics and functional impairment severity.RESULTS: Brain outcomes: In UCSF, only PA was positively associated with gray matter volume and attenuated the relationship between age and fractional anisotropy. In UCD, only CA was associated with less white matter hyperintensities and attenuated the relationship between age and gray matter volume. Cognitive outcomes: In both cohorts, greater CA, but not PA, related to better cognition, independent of age and brain structure. In UCSF, CA attenuated the relationship between fractional anisotropy and cognition. In UCD, PA attenuated the association between white matter hyperintensities and cognition.CONCLUSIONS: Although their specificity was not easily teased apart, both PA and CA are clearly related to better brain and cognitive resilience markers across cohorts with differing educational, racial, and disease statuses. PA and CA may independently contribute to converging neuroprotective pathways for brain and cognitive aging.

    View details for DOI 10.3233/JAD-191114

    View details for PubMedID 32039854

  • Lifecourse epidemiology matures: Commentary on Zhang et al. "Early-life socioeconomic status, adolescent cognitive ability, and cognition in late midlife". Social science & medicine (1982) Harrati, A., Glymour, M. M. 2019: 112645

    Abstract

    The effect of education on late life cognition has attracted substantial attention in lifecourse epidemiology, in part because of its relevance for understanding the effect of education on dementia. Although numerous studies document an association between education and later life cognition, these studies are potentially confounded by early life socioeconomic position and cognition. Good measures of these early life constructs are rarely available in data sets assessing cognition in late life. A further body of evidence has taken advantage of compulsory schooling law (CSL) instrumental variables (IV), although these estimates have been criticized based on questions about the validity of CSL IVs. In this issue of the Journal, Zhang et al. took advantage of the Wisconsin Longitudinal Study to control for both prospectively measured adolescent IQ and early life socioeconomic status in an analysis evaluating the effect of education on cognitive scores in late middle age (Zhang et al., 2019; IN THIS ISSUE). Their results indicate a moderate effect of each additional year of education on later life cognition, of approximately 0.1-0.15 standard deviations per year of schooling. These estimates are remarkably aligned with findings from prior observational designs and from the CSL IV studies. Although criticisms of any individual study are plausible, this new study complements the body of prior evidence to provide compelling evidence for the benefits of education on late life cognition.

    View details for DOI 10.1016/j.socscimed.2019.112645

    View details for PubMedID 31722818

  • Characterizing Long-Term Trajectories of Work and Disability Leave: The Role of Occupational Exposures, Health, and Personal Demographics. Journal of occupational and environmental medicine Harrati, A., Hepburn, P., Meausonne, V., Cullen, M. R. 2019

    Abstract

    This paper characterizes trajectories of work and disability leave across the tenure of a cohort of 49,595 employees in a large American manufacturing firm.We employ sequence and cluster analysis to group workers who share similar trajectories of work and disability leave. We then use multinomial logistic regression models to describe the demographic, health, and job-specific correlates of these trajectories.All workers were clustered into one of eight trajectories. Female workers (RR 1.3 - 2.1), those experiencing musculoskeletal disease (RR 1.3 - 1.5), and those whose jobs entailed exposure to high levels of air pollution (Total Particulate Matter; RR 1.9 - 2.4) were more likely to experience at least one disability episode.These trajectories and their correlates provide insight into disability processes and their relationship to demographic characteristics, health, and working conditions of employees.

    View details for DOI 10.1097/JOM.0000000000001705

    View details for PubMedID 31490897

  • To the freezers! Stored biospecimens from human randomized trials are an important new direction for studies of biological aging. The journals of gerontology. Series A, Biological sciences and medical sciences Belsky, D. W., Harrati, A. 2019; 74 (1): 89–90

    View details for PubMedID 30561553

  • Predictors and Implications of Accelerated Cognitive Aging. Biodemography and social biology Levine, M. E., Harrati, A., Crimmins, E. M. 2018; 64 (2): 83–101

    Abstract

    Aging is a major risk factor for both normal and pathological cognitive decline. However, individuals vary in their rate of age-related decline. We developed an easily interpretable composite measure of cognitive age, and related both the level of cognitive age and cognitive slope to sociodemographic, genetic, and disease indicators and examine its prediction of dementia transition. Using a sample of 19,594 participants from the Health and Retirement Study, cognitive age was derived from a set of performance tests administered at each wave. Our findings reveal different conclusions as they relate to levels versus slopes of cognitive age, with more pronounced differences by sex and race/ethnicity for absolute levels of cognitive decline rather than for rates of declines. We also find that both level and slope of cognitive age are inversely related to education, as well as increased for persons with APOE ε4 and/or diabetes. Finally, results show that the slope in cognitive age predicts subsequent dementia among non-demented older adults. Overall, our study suggests that this measure is applicable to cross-sectional and longitudinal studies on cognitive aging, decline, and dementia with the goal of better understanding individual differences in cognitive decline.

    View details for PubMedID 31007841

    View details for PubMedCentralID PMC6469682

  • Predictors and implications of accelerated cognitive aging BIODEMOGRAPHY AND SOCIAL BIOLOGY Levine, M. E., Harrati, A., Crimmins, E. M. 2018; 64 (2): 83–101
  • Mortality selection in a genetic sample and implications for association studies. International journal of epidemiology Domingue, B. W., Belsky, D. W., Harrati, A., Conley, D., Weir, D. R., Boardman, J. D. 2017

    Abstract

    Mortality selection occurs when a non-random subset of a population of interest has died before data collection and is unobserved in the data. Mortality selection is of general concern in the social and health sciences, but has received little attention in genetic epidemiology. We tested the hypothesis that mortality selection may bias genetic association estimates, using data from the US-based Health and Retirement Study (HRS).We tested mortality selection into the HRS genetic database by comparing HRS respondents who survive until genetic data collection in 2006 with those who do not. We next modelled mortality selection on demographic, health and social characteristics to calculate mortality selection probability weights. We analysed polygenic score associations with several traits before and after applying inverse-probability weighting to account for mortality selection. We tested simple associations and time-varying genetic associations (i.e. gene-by-cohort interactions).We observed mortality selection into the HRS genetic database on demographic, health and social characteristics. Correction for mortality selection using inverse probability weighting methods did not change simple association estimates. However, using these methods did change estimates of gene-by-cohort interaction effects. Correction for mortality selection changed gene-by-cohort interaction estimates in the opposite direction from increased mortality selection based on analysis of HRS respondents surviving through 2012.Mortality selection may bias estimates of gene-by-cohort interaction effects. Analyses of HRS data can adjust for mortality selection associated with observables by including probability weights. Mortality selection is a potential confounder of genetic association studies, but the magnitude of confounding varies by trait.

    View details for DOI 10.1093/ije/dyx041

    View details for PubMedID 28402496

  • Characterizing the Genetic Influences on Risk Aversion BIODEMOGRAPHY AND SOCIAL BIOLOGY Harrati, A. 2014; 60 (2): 185–98

    Abstract

    Risk aversion has long been cited as an important factor in retirement decisions, investment behavior, and health. Some of the heterogeneity in individual risk tolerance is well understood, reflecting age gradients, wealth gradients, and similar effects, but much remains unexplained. This study explores genetic contributions to heterogeneity in risk aversion among older Americans. Using over 2 million genetic markers per individual from the U.S. Health and Retirement Study, I report results from a genome-wide association study (GWAS) on risk preferences using a sample of 10,455 adults. None of the single-nucleotide polymorphisms (SNPs) are found to be statistically significant determinants of risk preferences at levels stricter than 5 × 10(-8). These results suggest that risk aversion is a complex trait that is highly polygenic. The analysis leads to upper bounds on the number of genetic effects that could exceed certain thresholds of significance and still remain undetected at the current sample size. The findings suggest that the known heritability in risk aversion is likely to be driven by large numbers of genetic variants, each with a small effect size.

    View details for DOI 10.1080/19485565.2014.951986

    View details for Web of Science ID 000343911700006

    View details for PubMedID 25343366