Physical inactivity is a strong risk factor for stroke in the oldest old: Findings from a multi-ethnic population (the Northern Manhattan Study).

Background The fastest growing segment of the population is those age ≥80 who have the highest stroke incidence. Risk factor management is complicated by polypharmacy-related adverse events. Aims To characterize the impact of physical inactivity for stroke by age in a multi-ethnic prospective cohort study (NOMAS, n = 3298). Methods Leisure time physical activity was assessed by a validated questionnaire and our primary exposure was physical inactivity (PI). Participants were followed annually for incident stroke. We fit Cox-proportional hazard models to calculate hazard ratios and 95% confidence intervals (HR 95% CI) for the association of PI and other risk factors with risk of stroke including two-way interaction terms between the primary exposures and age (<80 vs. ≥80). Results The mean age was 69 ± 10.3 years and 562 (17%) were ≥80 at enrolment. PI was common in the cohort (40.8%). Over a median of 14 years, we found 391 strokes. We found a significant interaction of age ≥80 on the risk of stroke with PI ( p = 0.03). In stratified models, PI versus any activity (adjusted HR 1.60, 95%CI 1.05-2.42) was associated with an increased risk of stroke among those ≥80. Conclusion Physical inactivity is a treatable risk factor for stroke among those older than age 80. Improving activity may reduce the risk of stroke in this segment of the population.

Leisure-time physical activity associates with cognitive decline: The Northern Manhattan Study.

OBJECTIVE: Because leisure-time physical activity (LTPA) is protective against incident dementia, we hypothesized that LTPA is protective against decline in domain-specific cognitive performance. METHODS: As part of the Northern Manhattan Study, LTPA was ascertained at enrollment using a validated in-person questionnaire. We assessed cognition in participants in the Northern Manhattan Study MRI substudy using a standard neuropsychological examination (NPE) (n = 1,228), and a repeat examination was performed 5 years later (n = 876). LTPA was summarized as the maximum intensity of any activity performed, classified as none to light intensity (physical inactivity) (90%) vs moderate to heavy intensity (10%). The NPE was subcategorized using standardized z scores over validated domains: processing speed, semantic memory, episodic memory, and executive function. We used multivariable linear regression models to examine the association of LTPA with initial and change in cognitive performance. Analyses were adjusted for sociodemographics, cardiovascular disease risk factors, and MRI findings (white matter hyperintensity volume, silent brain infarcts, cerebral volume). RESULTS: No/low levels of LTPA were associated with worse executive function, semantic memory, and processing speed scores on the first NPE. The associations were slightly attenuated and no longer significant after adjusting for vascular risk factors. Cognitively unimpaired participants reporting no/low LTPA vs moderate/high levels declined more over time in processing speed (ß = -0.231 ± 0.112, p = 0.040) and episodic memory (ß = -0.223 ± 0.117, p = 0.057) adjusting for sociodemographic and vascular risk factors. CONCLUSIONS: A low level of LTPA is independently associated with greater decline in cognitive performance over time across domains.

Physical activity and cognition in the northern Manhattan study.

BACKGROUND: To test the hypothesis that leisure time physical activity (PA) is associated with cognitive status. METHODS: We assessed cognition using the Mini-Mental Status Examination (MMSE) at enrollment and using the modified Telephone Interview for Cognitive Status (TICS-m) administered annually since 2001 in the Northern Manhattan Study. Baseline measures of leisure time PA were collected via in-person questionnaires. Total PA was categorized into 3 groups based on the metabolic equivalent (MET) score, a composite of total reported intensity and time. We used linear regression models to examine the association of PA with MMSE, and generalized estimating equations for change in TICS-m over time. RESULTS: There were 3,298 stroke-free participants with MMSE data (mean MMSE 26.0 ± 3.8) and 2,279 with TICS-m scores available. Compared to no PA, those with the upper quartile of MET scores had greater baseline MMSE scores (adjusted ß = 0.4, p = 0.01) but no association with change in TICS-m over time. There were interactions (p < 0.05) between PA and both insurance and education; compared to no PA, those in the upper quartile of MET scores had a greater MMSE score only among those with Medicaid/no insurance (adjusted ß = 0.83, p = 0.0005) and those who did not complete high school (adjusted ß = 0.68, p = 0.001). CONCLUSIONS: Increased levels of PA were associated with better baseline MMSE, particularly among those with socioeconomic disadvantages, but not with cognitive decline.