Key Points Question At what age do individuals with higher genetic risk of Alzheimer disease first show cognitive differences from individuals with lower genetic risk, and which of 32 cognitive measures show the earliest difference? Findings In this cross-sectional study of 405 050 individuals, higher genetic risk of Alzheimer disease significantly modified the association of age with 13 of 32 cognitive measures. Best-fitting models suggested that higher genetic risk of Alzheimer disease was associated with changes in cognitive scores of individuals older than 56 years for all 13 measures and older than 47 years for 9 measures. Meaning These findings suggest that by early midlife, subtle differences in cognitive measures may emerge among individuals with higher genetic risk of Alzheimer disease.

INTRODUCTIONThe challenge of accounting for practice effects (PEs) when modeling cognitive change was amplified by the COVID‐19 pandemic, which introduced period and mode effects that may bias the estimation of cognitive trajectory.METHODSIn three Kaiser Permanente Northern California prospective cohorts, we compared predicted cognitive trajectories and the association of grip strength with cognitive decline using three approaches: (1) no acknowledgment of PE, (2) inclusion of a wave indicator, and (3) constraining PE based on a preliminary model (APM) fit using a subset of the data.RESULTSAPM‐based correction for PEs based on balanced, pre‐pandemic data, and with current age as the timescale produced the smallest discrepancy between within‐person and between‐person estimated age effects. Estimated associations between grip strength and cognitive decline were not sensitive to the approach used.DISCUSSIONConstraining PEs based on a preliminary model is a flexible, pragmatic approach allowing for meaningful interpretation of cognitive change.Highlights The magnitude of practice effects (PEs) varied widely by study. When PEs were present, the three PE approaches resulted in divergent estimated age‐related cognitive trajectories. Estimated age‐related cognitive trajectories were sometimes implausible in models that did not account for PEs. The associations between grip strength and cognitive decline did not differ by the PE approach used. Constraining PEs based on estimates from a preliminary model allows for a meaningful interpretation of cognitive change.

IntroductionEpidemiological studies have identified an inverse association between cancer and dementia. Underlying methodological biases such as confounding, diagnostic bias, and selective survival have been postulated, yet no studies have systematically investigated the potential for each source of bias within a single dataset. We used the UK Biobank to generate and compare estimates for the cancer-dementia association using different analytical specifications designed to address different sources of bias.MethodsWe included 140,959 UK Biobank participants aged ≥ 55 without dementia before enrollment, and with linked primary care data. We used cancer registry data to identify cases of prevalent cancer before UK Biobank enrollment and incident cancer after enrollment. We used multivariable-adjusted Cox models to evaluate the associations of prevalent and incident cancer with incidence of all-cause dementia, Alzheimer’s disease (AD), and vascular dementia. And we systematically evaluated each potential source of bias.ResultsThe cohort accumulated 3,310 incident dementia diagnoses over a median of 12.3 years of follow-up. All-site incident cancer was positively associated with all-cause dementia risk (hazard ratio [HR]=1.14, 95% CI: 1.02-1.29). The adjusted HR for prevalent cancer was 1.03 (95% CI: 0.92-1.17). Results were similar for vascular dementia. AD dementia was not associated with prevalent or incident cancer. Dementia diagnosis was substantially elevated in the first year after cancer diagnosis (HR=1.83, 95% CI: 1.48-2.36), suggesting diagnostic bias.ConclusionIncident cancer diagnoses were associated with a higher risk of subsequent dementia diagnoses. Increased chance of dementia diagnosis associated with increased health care utilization after a cancer diagnosis may be a source of bias in electronic health records-based studies.

We evaluated overall and race-specific relationships between social integration and cognition in older adults. Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) cohort participants included 1343 Asian, Black, Latino, or non-Latino White Kaiser Permanente Northern California members. We estimated the effect of social integration on verbal episodic memory, semantic memory, and executive function derived from the Spanish and English Neuropsychological Assessment (SENAS) Scales. Social integration scores included marital status; volunteer activity; and contact with children, relatives, friends, and confidants. We estimated covariate-adjusted linear mixed-effects models for baseline and 17-month follow-up cognition. Social integration was associated with higher baseline cognitive scores (average [Formula: see text] = 0.066 (95% confidence interval: 0.040, 0.092)) overall and in each racial/ethnic group. The association did not vary by race/ethnicity. Social integration was not associated with the estimated rate of cognitive change. In this cohort, more social integration was similarly associated with better late-life cognition across racial/ethnic groups.

Background: Efforts to explain the burden of cardiovascular disease (CVD) often focus on genetic factors or social determinants of health. There is little evidence on the comparative predictive value of each, which could guide clinical and public health investments in measuring genetic versus social information. We compared the variance in CVDrelated outcomes explained by genetic versus socioeconomic predictors. Methods: Data were drawn from the Health and Retirement Study (N = 8,720). We examined self-reported diabetes, heart disease, depression, smoking, and body mass index, and objectively measured total and high-density lipoprotein cholesterol. For each outcome, we compared the variance explained by demographic characteristics, socioeconomic position (SEP), and genetic characteristics including a polygenic score for each outcome and principal components (PCs) for genetic ancestry. We used R-squared values derived from race-stratified multivariable linear regressions to evaluate the variance explained. Results:The variance explained by models including all predictors ranged from 3.7% to 14.3%. Demographic characteristics explained more than half this variance for most outcomes. SEP explained comparable or greater variance relative to the combination of the polygenic score and PCs for most conditions among both white and Black participants. The combination of SEP, polygenic score, and PCs performed substantially better, suggesting that each set of characteristics may independently contribute to the prediction of CVD-related outcomes.