The MMSE is used to screen for cognitive impairment and estimate dementia severity. In clinical settings, conventional cut-off scores have been used to distinguish between dementia stages. However, these scores have not been validated for different populations. This study maps scores from the modified version of the MMSE to dementia stages delineated by the Diagnostic and Statistical Manual of Mental Disorders-3rd revised edition (DSM-III-R), the Clinical Dementia Rating (CDR) and Functional Assessment Staging Test (FAST). We used cross-sectional data from a tertiary hospital memory clinic. Subjects were stratified into “primary education and below” (PE) or “secondary education and above” (SE). Receiving operating characteristic (ROC) analyses and Cohen's κ were performed to determine MMSE cut-off scores for dementia stages. Our derived cut-off scores were lower compared to the conventional scores. Scores also differed between subjects of different education levels. MMSE cut-off scores were 19, 15, and 9 for CDR stages 1, 2, and 3 respectively in PE subjects, and 23, 17, and 10 for SE subjects. Cut-off scores were comparable for staging by DSM-III-R Criteria and FAST. There is a need for locally derived stage-specific MMSE cut-off scores for the Asian population adjusted for education.

Major advances in understanding the physiology and genetics of circadian rhythm in the past decade challenge the researcher of sleep/wake disorders in Alzheimer's disease (AD) to distinguish patient characteristics stable across the course of illness (“traits”) from characteristics that vary with stage of illness (“states”). A components-of-variance approach with a repeated measures model was used to examine the between-subjects variance over time (“trait”) vs. within-subjects (“state”) variance in 42 patients with probable AD followed, on average, over 2 years on actigraphic sleep/wake measures. Mental status scores indexed stage of illness. Actigraphic measures of sleep efficiency and circadian rhythmicity appeared predominantly “trait,” with between-individual differences accounting for over 55% of variance compared to the less than 5% of variance related to stage of cognitive impairment. We discuss how “state-trait” analyses can be helpful in identifying areas of assessment most likely to be fruitful objectives of physiologic and genetic research on sleep/wake disturbance in AD.