Roberts, Rosebud O.

2067 Background: An estimated 2% of the general population has a meningioma (Vernooij et al. 2007), which accounts for about 36% of all primary intracranial tumors (Ostrom et al. 2015). The most established risk factors are older age and female gender. One small study identified gender but no other risk factors with meningioma (Krampla et al 2004). A larger study using the Iowa Women’s Health study data found lower levels of physical activity, greater body mass index (BMI), greater height and uterine fibroids were associated with meningioma (Johnson et al. 2011). We sought to replicate these findings and to identify additional risk factors related to meningioma in a large population-based sample. Methods: Study participants were enrolled in the Mayo Clinic Study of Aging (MCSA), a population-based sample of Olmsted County, Minnesota residents used to study prevalence, incidence, and risk-factors for Mild Cognitive Impairment and dementia and includes a variety of medical factors. Using a text search of radiologists’ notes of 2,402 MCSA individuals, mean age 77±8 years and scanned between 2004-2014.We identified 52 subjects who had at least one meningioma. We estimated the association of selected potential risk factors with presence of meningioma using odds ratios and 95% confidence intervals from logistic regression models adjusted for age and gender, which informed the multivariable models. Results: In the initial models, significant risk factors identified included BMI (as a continuous variable) (OR = 1.06 95%CI 1.01 to 1.12), taking NSAIDS (OR = 2.11, 95%CI 1.13 to 3.95), aspirin (OR = 1.90, 95%CI 1.04 to 3.46), and blood pressure lowering medication (OR = 2.06, 95%CI 1.07 to 3.99). Protective factors included male gender (OR = 0.51, 95%CI 0.29 to 0.90), coronary artery disease (CAD; OR = 0.46, 95%CI 0.22 to 0.97) and higher Beck Anxiety Inventory (BAI) total score (OR = 0.88, 95%CI 0.78 to 0.98). Simultaneous adjustment for these factors in a multivariable model did not attenuate these associations. Conclusions: Findings reveal gender and BMI as risk factors for meningioma. Additionally, certain medications such as NSAIDS and BP lowering medications warrant follow up as potential factors related to development of meningioma.

Background: Statins have been proposed to reduce the risk of Alzheimer’s disease (AD). Objective: Assess whether long-term statin use was associated with neuroimaging biomarkers of aging and dementia. Methods: Methods: We analyzed neuroimaging biomarkers in 1,160 individuals aged 65+ from the Mayo Clinic Study of Aging, a population-based prospective longitudinal study of cognitive aging. Results: Statin-treated (5+ years of therapy) individuals had greater burden of mid-and late-life cardiovascular disease ( p < 0.001) than statin-untreated (≤3 months) individuals. Lower fractional anisotropy in the genu of the corpus callosum, an early marker of cerebrovascular disease, was associated with long-term statin exposure ( p < 0.035). No significant associations were identified between long-term statin exposure and cerebral amyloid or tau burden, AD pattern neurodegeneration, or white matter hyperintensity burden. Conclusions: Long-term statin therapy was not associated with differences in AD biomarkers. Individuals with long-term statin exposure had worse white matter integrity in the genu of the corpus callosum, consistent with the coexistence of higher cerebrovascular risk factor burden in this group.

Background: There is accumulating evidence suggesting that diet may play a role in preventing or delaying cognitive decline and dementia, but the underlying biological mechanisms are not well understood. Objectives: To examine the cross-sectional associations of the Mediterranean diet (MeDi) and its components with 11 C-PiB-PET scan measures of amyloid-β (Aβ) deposition. Methods: The study consisted of 278 Mayo Clinic Study of Aging participants 70+ years old, who were cognitively unimpaired (CU) at the time of completion of the Food Frequency Questionnaire (FFQ) and when they underwent PET imaging. Adherence to the MeDi was assessed by computing the MeDi score for each participant. All scans were performed after the FFQ completion; median [IQR] time between FFQ and Aβ PET was 3.5 (1.4) years. Z-scores were created for component, macro- and micronutrients measured. Linear and logistic regression models were adjusted for age, sex, education, apolipoprotein E (APOE) ɛ4 allele carrier status, time interval between the FFQ completion and PET scan, and total energy intake. Results: Participants’ median age at FFQ was 77.7 years (55.8% men; 26.6% with an APOE ɛ4 allele). Higher MeDi score (linear regression slope (beta):–0.035, p = 0.012; per standard deviation increase), vegetable intake (beta:–0.043, p = 0.002), intake of vitamin A (beta:–0.041, p = 0.003) or β-carotene (beta: –0.039, p = 0.005) from food sources and moderate alcohol consumption (beta: –0.074, p = 0.03) were associated with lower 11 C-PiB standardized uptake value ratio. Conclusion: Findings are consistent with previous studies suggesting that higher adherence to a MeDi pattern and higher vegetable consumption are associated with better neuroimaging biomarker profile. Prospective studies are needed to validate current findings.

ObjectiveTo determine the cross‐sectional and longitudinal associations between diabetes treatment type and cognitive outcomes among type II diabetics.MethodsWe examined the association between metformin use, as compared to other diabetic treatment (ie, insulin, other oral medications, and diet/exercise) and cognitive test performance and mild cognitive impairment (MCI) diagnosis among 508 cognitively unimpaired at baseline type II diabetics enrolled in the Mayo Clinic Study of Aging. We created propensity scores to adjust for treatment effects. We used multivariate linear and logistic regression models to investigate the cross‐sectional association between treatment type and cognitive test z scores, respectively. Mixed effects models and competing risk regression models were used to determine the longitudinal association between treatment type and change in cognitive test z scores and risk of developing incident MCI.ResultsIn linear regression analyses adjusted for age, sex, education, body mass index, APOE ε4, insulin treatment, medical comorbidities, number of medications, duration of diabetes, and propensity score, we did not observe an association between metformin use and cognitive test performance. Additionally, we did not observe an association between metformin use and cognitive test performance over time (median = 3.7‐year follow‐up). Metformin was associated with an increased risk of MCI (subhazard ratio (SHR) = 2.75; 95% CI = 1.64, 4.63, P < .001). Similarly, other oral medications (SHR = 1.96; 95% CI = 1.19, 3.25; P = .009) and insulin (SHR = 3.17; 95% CI = 1.27, 7.92; P = .014) use were also associated with risk of MCI diagnosis.ConclusionsThese findings suggest that metformin use, as compared to management of diabetes with other treatments, is not associated with cognitive test performance. However, metformin was associated with incident MCI diagnosis.

Background: Etiologic differences in mild cognitive impairment (MCI) subtypes may impact mortality. Objective: To assess the rate of death in MCI overall, and by subtype, in the population-based Mayo Clinic Study of Aging. Methods: Participants aged 70–89 years at enrollment were clinically evaluated at baseline and 15-month intervals to assess diagnoses of MCI and dementia. Mortality in MCI cases versus cognitively normal (CN) individuals was estimated using Cox proportional hazards models. Results: Over a median follow-up of 5.8 years, 331 of 862 (38.4%) MCI cases and 224 of 1,292 (17.3%) cognitively normal participants died. Compared to CN individuals, mortality was elevated in persons with MCI (hazard ratio [HR] = 2.03; 95% CI: 1.61 to 2.55), and was higher for non-amnestic MCI (naMCI; HR = 2.47; 95% CI: 1.80 to 3.39) than for amnestic MCI (aMCI; HR = 1.89; 95% CI: 1.48 to 2.41) after adjusting for confounders. Mortality varied significantly by sex, education, history of heart disease, and engaging in moderate physical exercise (p for interaction <0.05 for all). Mortality rate estimates were highest in MCI cases who were men, did not exercise, had heart disease, and had higher education versus CN without these factors, and for naMCI cases versus aMCI cases without these factors. Conclusions: These findings suggest stronger impact of etiologic factors on naMCI mortality. Prevention of heart disease, exercise vigilance, may reduce MCI mortality and delayed MCI diagnosis in persons with higher education impacts mortality.