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IntroductionEpidemiological studies have identified an inverse association between cancer and dementia. Underlying methodological biases 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 compare estimates for the cancer-dementia association using different analytical specifications designed to sequentially address multiple sources of bias, including competing risk of death, selective survival, confounding bias, and diagnostic 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 diagnosed after enrollment. We used Cox models to evaluate associations of prevalent and incident cancer with all-cause dementia, Alzheimer’s disease (AD), and vascular dementia. We used time-varying models to evaluate diagnostic 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 incidence (hazard ratio [HR]=1.15, 95% CI: 1.02-1.29), but prevalent cancer was not (HR=1.04, 95% CI: 0.92-1.17). Results were similar for vascular dementia. AD 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: 1.42-2.36), after which the association attenuated to null, suggesting diagnostic bias.ConclusionHealth care utilization after a cancer diagnosis may increase chance of a dementia diagnosis, creating potential diagnostic bias in electronic health records-based studies.

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 CVD-related 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. Philip R. Lee Institute for Health Policy Studies, Department of Family & Community Medicine, UCSF. CONCLUSIONS Focusing on genetic inputs into personalized medicine predictive models, without considering measures of social context that have clear predictive value, needlessly ignores relevant information that is more feasible and affordable to collect on patients in clinical settings. See video abstract at, http://links.lww.com/EDE/B879.

BackgroundHigher education consistently predicts improved late-life cognition. Racial differences in educational attainment likely contribute to inequities in dementia risk. However, few studies of education and cognition have controlled for prospectively measured early-life confounders or evaluated whether the education late-life cognition association is modified by race/ethnicity.MethodsAmong 2343 Black and White Project Talent Aging Study participants who completed telephone cognitive assessments, we evaluated whether the association between years of education and cognition (verbal fluency, memory/recall, attention, and a composite cognitive measure) differed by race, and whether these differences persisted when adjusting for childhood factors, including the cognitive ability.ResultsIn fully adjusted linear regression models, each additional year of education was associated with higher composite cognitive scores for Black [β=0.137; 95% confidence interval (CI)=0.068, 0.206] and White respondents (β=0.056; CI=0.034, 0.078) with an interaction with race ( P =0.03). Associations between education and memory/recall among Black adults (β=0.036; CI=-0.037, 0.109) and attention among White adults (β=0.022; CI=-0.002, 0.046) were nonsignificant. However, there were significant race-education interactions for the composite ( P =0.03) and attention measures ( P

Identifying the youngest age when Alzheimer disease (AD) influences cognition and the earliest affected cognitive domains will improve understanding of the natural history of AD and approaches to early diagnosis.To evaluate the age at which cognitive differences between individuals with higher compared with lower genetic risk of AD are first apparent and which cognitive assessments show the earliest difference.This cross-sectional study used data from UK Biobank participants of European genetic ancestry, aged 40 years or older, who contributed genotypic and cognitive test data from January 1, 2006, to December 31, 2015. Data analysis was performed from March 10, 2020, to January 4, 2022.The AD genetic risk score (GRS), which is a weighted sum of 23 single-nucleotide variations.Seven cognitive tests were administered via touchscreen at in-person visits or online. Cognitive domains assessed included fluid intelligence, episodic memory, processing speed, executive functioning, and prospective memory. Multiple cognitive measures were derived from some tests, yielding 32 separate measures. Interactions between age and AD-GRS for each of the 32 cognitive measures were tested with linear regression using a Bonferroni-corrected P value threshold. For cognitive measures with significant evidence of age by AD-GRS interaction, the youngest age of interaction was assessed with new regression models, with nonlinear specification of age terms. Models with youngest age of interaction from 40 to 70 years, in 1-year increments, were compared, and the best-fitting model for each cognitive measure was chosen. Results across cognitive measures were compared to determine which cognitive indicators showed earliest AD-related change.A total of 405 050 participants (mean [SD] age, 57.1 [7.9] years; 54.1% female) were included. Sample sizes differed across cognitive tests (from 12 455 to 404 682 participants). The AD-GRS significantly modified the association with age on 13 measures derived from the pairs matching (range in difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 2.5%-11.5%), symbol digit substitution (range in difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 2.0%-5.8%), and numeric memory tests (difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 8.8%) (P = 1.56 × 10-3). Best-fitting models suggested that cognitive scores of individuals with a high vs low AD-GRS began to diverge by 56 years of age for all 13 measures and by 47 years of age for 9 measures.In this cross-sectional study, by early midlife, subtle differences in memory and attention were detectable among individuals with higher genetic risk of AD.