Association Between Lifetime Marijuana Use and Cognitive Function in Middle Age: The Coronary Artery Risk Development in Young Adults (CARDIA) Study.

Marijuana use is increasingly common in the United States. It is unclear whether it has long-term effects on memory and other domains of cognitive function. To study the association between cumulative lifetime exposure to marijuana use and cognitive performance in middle age. We used data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, a cohort of 5115 black and white men and women aged 18 to 30 years at baseline from March 25, 1985, to June 7, 1986 (year 0), and followed up over 25 years from June 7, 1986, to August 31, 2011, to estimate cumulative years of exposure to marijuana (1 year = 365 days of marijuana use) using repeated measures and to assess associations with cognitive function at year 25. Linear regression was used to adjust for demographic factors, cardiovascular risk factors, tobacco smoking, use of alcohol and illicit drugs, physical activity, depression, and results of the mirror star tracing test (a measure of cognitive function) at year 2. Data analysis was conducted from June 7, 1986, to August 31, 2011. Three domains of cognitive function were assessed at year 25 using the Rey Auditory Verbal Learning Test (verbal memory), the Digit Symbol Substitution Test (processing speed), and the Stroop Interference Test (executive function). Among 3385 participants with cognitive function measurements at the year 25 visit, 2852 (84.3%) reported past marijuana use, but only 392 (11.6%) continued to use marijuana into middle age. Current use of marijuana was associated with worse verbal memory and processing speed; cumulative lifetime exposure was associated with worse performance in all 3 domains of cognitive function. After excluding current users and adjusting for potential confounders, cumulative lifetime exposure to marijuana remained significantly associated with worse verbal memory. For each 5 years of past exposure, verbal memory was 0.13 standardized units lower (95% CI, -0.24 to -0.02; P = .02), corresponding to a mean of 1 of 2 participants remembering 1 word fewer from a list of 15 words for every 5 years of use. After adjustment, we found no associations with lower executive function (-0.03 [95% CI, -0.12 to 0.07]; P = .56) or processing speed (-0.04 [95% CI, -0.16 to 0.08]; P = .51). Past exposure to marijuana is associated with worse verbal memory but does not appear to affect other domains of cognitive function

Stroke disparities in older americans: Is wealth a more powerful indicator of risk than income and education?

BACKGROUND AND PURPOSE-: This study examines the independent effect of wealth, income, and education on stroke and how these disparities evolve throughout middle and old age in a representative cohort of older Americans. METHODS-: Stroke-free participants in the Health and Retirement Study (n=19 565) were followed for an average of 8.5 years. Total wealth, income, and education assessed at baseline were used in Cox proportional hazards models to predict time to stroke. Separate models were estimated for 3 age-strata (50 to 64, 65 to 74, and ≥75), and incorporating risk factor measures (smoking, physical activity, body mass index, hypertension, diabetes, and heart disease). RESULTS-: 1542 subjects developed incident stroke. Higher education predicted reduced stroke risk at ages 50 to 64, but not after adjustment for wealth and income. Wealth and income were independent risk factors for stroke at ages 50 to 64. Adjusted hazard ratios comparing the lowest decile with the 75th-90th percentiles were 2.3 (95% CI 1.6, 3.4) for wealth and 1.8 (95% CI 1.3, 2.6) for income. Risk factor adjustment attenuated these effects by 30% to 50%, but coefficients for both wealth (HR=1.7, 95% CI 1.2, 2.5) and income (HR=1.6, 95% CI 1.2, 2.3) remained significant. Wealth, income, and education did not consistently predict stroke beyond age 65. CONCLUSIONS-: Wealth and income are independent predictors of stroke at ages 50 to 64 but do not predict stroke among the elderly. This age patterning might reflect buffering of the negative effect of low socioeconomic status by improved access to social and health care programs at old ages, but may also be an artifact of selective survival

Erratum: Changes in Memory before and after Stroke Differ by Age and Sex, but Not by Race

<b><i>Background:</i></b> Post-stroke memory impairment is more common among older adults, women and blacks. It is unclear whether post-stroke differences reflect differential effects of stroke per se or differences in prestroke functioning. We compare memory trajectories before and after stroke by age, sex and race. <b><i>Methods:</i></b> Health and Retirement Study participants aged ≥50 years (n = 17,341), with no stroke history at baseline, were interviewed biennially up to 10 years for first self- or proxy-reported stroke (n = 1,574). Segmented linear regression models were used to compare annual rates of memory change before and after stroke among 1,169 stroke survivors, 405 stroke decedents and 15,767 stroke-free participants. Effect modification was evaluated with analyses stratified by baseline age (≤70 vs. >70), sex and race (white vs. nonwhite), and using interaction terms between age/sex/race indicators and annual memory change. <b><i>Results:</i></b> Older (>70 years) adults experienced a faster memory decline before stroke (-0.19 vs. -0.10 points/year for survivors, -0.24 vs. -0.13 points/year for decedents, p < 0.001 for both interactions), and among stroke survivors, larger memory decrements (-0.64 vs. -0.26 points, p < 0.001) at stroke and faster memory decline (-0.15 vs. -0.07 points/year, p = 0.003) after stroke onset, compared to younger adults. Female stroke survivors experienced a faster prestroke memory decline than male stroke survivors (-0.14 vs. -0.10 points/year, p < 0.001). However, no sex differences were seen for other contrasts. Although whites had higher post-stroke memory scores than nonwhites, race was not associated with rate of memory decline during any period of time; i.e. race did not significantly modify the rate of decline before or after stroke or the immediate effect of stroke on memory. <b><i>Conclusions:</i></b> Older age predicted worse memory change before, at and after stroke onset. Sex and race differences in post-stroke memory outcomes might be attributable to prestroke disparities, which may be unrelated to cerebrovascular disease

Supplementary Material for: Estimating the Cognitive Effects of Prevalent Diabetes, Recent Onset Diabetes, and the Duration of Diabetes among Older Adults

<b><i>Background:</i></b> Little evidence is available on the effects of incident diabetes or diabetes duration on cognitive aging. <b><i>Methods:</i></b> We evaluated the effects of prevalent and incident diabetes on deteriorations in cognitive function, based on participants (n = 8,671) aged 65+ in the Health and Retirement Study in 2000. Inverse probability weighting was used to account for selective attrition and time-varying confounding of incident diabetes. <b><i>Results:</i></b> Prevalent diabetes predicted higher odds of dementia [odds ratio 1.27; 95% confidence interval (CI) 1.03-1.58] and worse memory (-0.06 in z-score units; 95% CI -0.10 to -0.02), but incident diabetes or diabetes duration up to 8 years of follow-up was not predictive. <b><i>Conclusion:</i></b> Prevalent diabetes predicted lower cognition but not recent onset diabetes

Guidelines for performing Mendelian randomization investigations: update for summer 2023.

This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months