Ching-Wen Lee

Age and education effects and norms on a cognitive test battery from a population-based cohort: The Monongahela–Youghiogheny Healthy Aging Team

Objectives: Performance on cognitive tests can be affected by age, education, and also selection bias. We examined the distribution of scores on several cognitive screening tests by age and educational levels in a population-based cohort. Method: An age-stratified random sample of individuals aged 65+ years was drawn from the electoral rolls of an urban US community. Those obtaining age and education-corrected scores ≥21/30 on the Mini-Mental State Examination (MMSE) were designated as cognitively normal or only mildly impaired, and underwent a full assessment including a battery of neuropsychological tests. Participants were also rated on the Clinical Dementia Rating (CDR) scale. The distribution of neuropsychological test scores within demographic strata, among those receiving a CDR of 0 (no dementia), are reported here as cognitive test norms. After combining individual test scores into cognitive domain composite scores, multiple linear regression models were used to examine associations of cognitive test performance with age and education. Results: In this cognitively normal sample of older adults, younger age and higher education were associated with better performance in all cognitive domains. Age and education together explained 22% of the variation of memory, and less of executive function, language, attention, and visuospatial function. Conclusion: Older age and lesser education are differentially associated with worse neuropsychological test performance in cognitively normal older adult representatives of the community at large. The distribution of scores in these participants can serve as population-based norms for these tests, and can be especially useful to clinicians and researchers assessing older adults outside specialty clinic settings.

Prevalence of Mild Cognitive Impairment by Multiple Classifications: The Monongahela-Youghiogheny Healthy Aging Team (MYHAT) Project

OBJECTIVES To estimate and compare the frequency and prevalence of mild cognitive impairment (MCI) and related entities using different classification approaches at the population level. DESIGN Cross-sectional epidemiologic study of population-based cohort recruited by age-stratified random sampling from electoral rolls. SETTING Small-town communities in western Pennsylvania. PARTICIPANTS Of 2,036 individuals aged 65 years and older, 1,982 participants with normal or mildly impaired cognition (age-education-corrected Mini-Mental State scores ≥ 21). MEASUREMENTS Demographics, neuropsychological assessment expressed as cognitive domains, functional ability, and subjective reports of cognitive difficulties; based on these measurements, operational criteria for the Clinical Dementia Rating (CDR) scale, the 1999 criteria for amnestic MCI, the 2004 Expanded criteria for MCI, and new, purely cognitive criteria for MCI. RESULTS A CDR rating of 0.5 (uncertain/very mild dementia) was obtained by 27.6% of participants, whereas 1.2% had CDR ≥ 1 (mild or moderate dementia). Among those with CDR <1, 2.27% had amnestic MCI and 17.66% had expanded MCI, whereas 35.17% had MCI by purely cognitive classification. Isolated executive function impairment was the least common, whereas impairment in multiple domains including executive function was the most common. Prevalence estimates weighted against the U.S. Census are also provided. CONCLUSIONS The manner in which criteria for MCI are operationalized determines the proportion of individuals who are thus classified and the degree of overlap with other criteria. Prospective follow-up is needed to determine progression from MCI to dementia and thus empirically develop improved MCI criteria with good predictive value.

Smoking, death, and Alzheimer disease: a case of competing risks.

If smoking is a risk factor for Alzheimer disease (AD) but a smoker dies of another cause before developing or manifesting AD, smoking-related mortality may mask the relationship between smoking and AD. This phenomenon, referred to as competing risk, complicates efforts to model the effect of smoking on AD. Typical survival regression models assume that censorship from analysis is unrelated to an individual’s probability for developing AD (ie, censoring is noninformative). However, if individuals who die before developing AD are younger than those who survive long enough to develop AD, and if they include a higher percentage of smokers than nonsmokers, the incidence of AD will appear to be higher in older individuals and in nonsmokers. Further, age-specific mortality rates are higher in smokers because they die earlier than nonsmokers. Therefore, if we fail to take into account the competing risk of death when we estimate the effect of smoking on AD, we bias the results and are in fact only comparing the incidence of AD in nonsmokers with that in the healthiest smokers. In this study, we demonstrate that the effect of smoking on AD differs in models that are and are not adjusted for competing risks.

Rates and risk factors for progression to incident dementia vary by age in a population cohort

Objective: To estimate rate of progression from normal cognition or mild impairment to dementia, and to identify potential risk and protective factors for incident dementia, based on age at dementia onset in a prospective study of a population-based cohort (n = 1,982) aged 65 years and older. Methods: Following the cohort annually for up to 5 years, we estimated incidence of dementia (Clinical Dementia Rating ≥1) among individuals previously normal or mildly impaired (Clinical Dementia Rating 0 or 0.5). In the whole cohort, and also stratified by median onset age, we examined several vascular, metabolic, and inflammatory variables as potential risk factors for developing dementia, using interval-censored survival models. Results: Based on 67 incident cases of dementia, incidence rate (per 1,000 person-years) was 10.0 overall, 5.8 in those with median onset age of 87 years or younger, and 31.5 in those with onset age after 87 years. Adjusting for demographics, the risk of incident dementia with onset age of 87 years or younger (n = 33) was significantly increased by baseline smoking, stroke, low systolic blood pressure, and APOE*4 genotype, and reduced by current alcohol use. Among those with dementia with onset after 87 years (n = 34), no risk or protective factor was significant. Conclusion: Risk and protective factors were only found for incident dementia with onset before the median onset age of 87 years, and not for those with later onset. Either unexplored risk factors explain the continued increase in incidence with age, or unknown protective factors are allowing some individuals to delay onset into very old age.

WHO WANTS A FREE BRAIN SCAN? ASSESSING AND CORRECTING FOR RECRUITMENT BIASES IN A POPULATION-BASED sMRI PILOT STUDY

Neuroimaging research is usually conducted in volunteers who meet a priori selection criteria. Selection/volunteer bias is assumed but cannot be assessed. During an ongoing population-based cohort study of 1982 older adults, we asked 1702 active participants about their interest in undergoing a research brain scan. Compared with those not interested, the 915 potentially interested individuals were significantly younger, more likely to be male, better educated, generally healthier, and more likely to be cognitively intact and dementia-free. In 48 of the interested individuals, we conducted a previously reported pilot structural magnetic resonance imaging (sMRI) study modelling mild cognitive impairment (MCI) vs. normal cognition, and Clinical Dementia Rating (CDR) = 0.5 vs. CDR = 0, as a function of sMRI atrophy ratings. We now compare these 48 individuals (1) with all interested participants, to assess selection bias; (2) with all who had been asked about their interest, to assess volunteer bias; and (3) with the entire study cohort, to assess attrition bias from those who had dropped out before the question was asked. Using these data in propensity score models, we generated weights which we applied to logistic regression models reanalyzing the data from the pilot sMRI study. These weighted models adjusted, in turn, for selection bias, interest/volunteer bias, and attrition bias. They show fewer regions of interest to be associated with MCI/ CDR than were in the original unweighted models. When study participants are drawn from a well-characterized population, they can be compared with non-participants, and the information used to correct study results for potential bias and thus provide more generalizable estimates.

Independent and combined effects of cognitive and physical activity on incident MCI

The objective of this study was to examine the independent and combined influences of late‐life cognitive activity (CA) and physical activity (PA) on the risk of incident mild cognitive impairment (MCI).

How well do MCI criteria predict progression to severe cognitive impairment and dementia

Background:The International Working Group (IWG) criteria for mild cognitive impairment have variable utility in predicting progression to dementia, partly depending on the setting. We explored an empiric approach to optimize the criteria and cutoff points in a population study. Methods:In a cohort of adults aged 65 years or older, we identified 1129 individuals with normal or only mildly impaired cognition by cognitive classification, and 1146 individuals without dementia (Clinical Dementia Rating <1). Operationally defining the IWG criterion set, we examined its sensitivity and specificity for the development of severe cognitive impairment and dementia (Clinical Dementia Rating ≥1) over 4 years. We then disaggregated the criteria and used Classification and Regression Tree analyses to identify the optimal predictive model. Results:The operational IWG criteria had 49% sensitivity and 86% specificity for the outcome of severe cognitive impairment, and 40% sensitivity and 84% specificity for the outcome of dementia. Classification and Regression Tree modeling improved sensitivity to 82% for the cognitive outcome and 76% for the dementia outcome; specificity remained high. Memory scores were the most important predictors for both outcomes. The optimal cutoff points were around 1.0 SD below the age-education mean. The best fit was observed when prediction was modeled separately for each age-education group. Conclusions:Objective cognitive measurements contributed more to the prediction of dementia than subjective and functional measures. Those with less education only required memory testing, whereas those with more education required assessment of several cognitive domains. In cases in which only overall norms are available, the appropriate threshold will vary according to the individual’s age and education.

Cross-Validation of Brain Structural Biomarkers and Cognitive Aging in a Community-Based Study

BACKGROUND Population-based studies face challenges in measuring brain structure relative to cognitive aging. We examined the feasibility of acquiring state-of-the-art brain MRI images at a community hospital, and attempted to cross-validate two independent approaches to image analysis. METHODS Participants were 49 older adults (29 cognitively normal and 20 with mild cognitive impairment (MCI)) drawn from an ongoing cohort study, with annual clinical assessments within one month of scan, without overt cerebrovascular disease, and without dementia (Clinical Dementia Rating (CDR) < 1). Brain MRI images, acquired at the local hospital using the Alzheimers Disease Neuroimaging Initiative protocol, were analyzed using (1) a visual atrophy rating scale and (2) a semi-automated voxel-level morphometric method. Atrophy and volume measures were examined in relation to cognitive classification (any MCI and amnestic MCI vs. normal cognition), CDR (0.5 vs. 0), and presumed etiology. RESULTS Measures indicating greater atrophy or lesser volume of the hippocampal formation, the medial temporal lobe, and the dilation of the ventricular space were significantly associated with cognitive classification, CDR = 0.5, and presumed neurodegenerative etiology, independent of the image analytic method. Statistically significant correlations were also found between the visual ratings of medial temporal lobe atrophy and the semi-automated ratings of brain structural integrity. CONCLUSIONS High quality MRI data can be acquired and analyzed from older adults in population studies, enhancing their capacity to examine imaging biomarkers in relation to cognitive aging and dementia.

The association between pulse pressure change and cognition in late life: Age and where you start matters

Variations across studies in the association between blood pressure (BP) and cognition might be explained partly by duration of exposure to hypertension and partly by nonrandom attrition over time. Pulse pressure (PP) reflects arterial stiffness which may better reflect chronicity of hypertension.