Dementia Risk and Protective Factors Differ in the Context of Memory Trajectory Groups

Abstract

Background Previous research has identified multiple risk and protective factors for late onset Alzheimer’s disease (LOAD). However, it is not known whether these risk and protective factors differ for individuals who are cognitively stable versus those already experiencing declines. Objective This study examined how dementia risk factors differ across subgroups of older adults defined by memory trajectory. This line of research may lead to more individualized risk profiles. Methods Risk factors for incident LOAD were compared across previously-validated groups of older adults exhibiting different memory trajectories (“Stable-High,” “Stable-Low,” “Decliner,” “Rapid Decliner”) using stratified Cox regressions. Participants included 2,593 racially/ethnically diverse older adults (mean age of 76 at study entry) in the Washington Heights-Inwood Columbia Aging Project. Results Predictors of incident dementia differed across trajectory groups: older age only incurred independent risk in stable groups, education did not incur independent risk in the rapidly declining group, depression only incurred independent risk in the stable-low group, stroke incurred independent risk in the two extreme groups, and APOE-ε4 only incurred independent risk in the rapidly declining group. Conclusion The finding that different risk factors for LOAD were associated with specific memory trajectories may reflect the existence of resilience or vulnerability factors that modify the individual influences of risk/protective factors. This study highlights the utility of considering interactions between dementia risk factors and a patient’s unique cognitive history.

Stroke and dementia risk: A systematic review and meta‐analysis

“…Thirty four prospective cohort studies [19–52] (including three cohort studies of patients with MCI [19,24,28] and one diabetic cohort [22]) and two observational analyses of cohorts recruited for randomized controlled trials (RCTs) [53,54] investigated the association between prevalent stroke and incident all-cause dementia (around 1.9 million participants and 240,471 stroke events; Appendix B, Table B1). Most studies included older adults with an analytic sample size ranging from 52 [28] to 486,640 [25].…”

“…Eight studies [21–23,33,35,46,51,52] reported adjusted ORs instead of HRs (11,336 participants and 1,001 stroke events). The pooled estimate indicated increased odds of incident dementia in those with prevalent stroke compared to no prevalent stroke (pooled OR = 1.53, 95% CI: 1.30–1.80, p<0.00001, I 2 = 0%; 95% PI: 1.22 – 1.84; Fig.…”

Distinct Cognitive Trajectories in Late Life and Associated Predictors and Outcomes: A Systematic Review

“…Two studies analyzed the interaction between a number of factors and trajectory class in predicting health outcomes. Zahodne et al observed that predictors of the risk of dementia differed across the four classes identified [43]. Similarly, Marioni et al found that being female and higher social engagement decreased the risk of mortality in all classes, but higher education counterintuitively increased the risk of mortality in the best class [44].…”

Episodic memory performance in a multi-ethnic longitudinal study of 13,037 elderly

“…Although there are several methodological approaches used in developmental cognitive neuroscience, growth curve models (GCM) represent a powerful analytical framework to model individual differences in cognitive change over time, as well as the variability of patterns of cognitive change between individuals [ 14 ]. In cognitive neuroscience GCMs have been derived using linear mixed effects model (LMEM) or latent curve models (LCM) [ 1 – 4 , 6 – 11 , 15 – 23 ]. LCM uses factor analysis and structural equation models for unobserved outcomes [ 14 , 24 ] and are best suited for complex models with straightforward large data structures [ 25 ].…”

“…The majority of previous studies using trajectories of cognitive performance have investigated non-Hispanic white (NHW) populations [ 1 – 4 , 6 – 11 , 15 , 18 , 19 , 22 , 23 ] and have relied on the Mini-Mental Examination (MMSE) as the measure of cognitive performance [ 17 , 20 , 21 ]. Although MMSE is one of the most widely used cognitive screening tests in clinical and epidemiological research, it is limited by sensitivity to practice effects, large ceiling and floor effects [ 27 , 28 ] and insufficient assessment of specific cognitive domains.…”