Background/Aims: To detect cognitive decline in older adults, measures of verbal fluency and verbal memory are widely used. Less is known about performance in these measures in younger persons or according to education level and gender. We investigated cognitive performance according to age, education and gender among cognitively healthy adults aged 30–100 years. Methods: The study population comprised 4,174 cognitively healthy persons participating in the nationally representative Finnish Health 2011 survey. Cognitive assessment included verbal fluency, word list memory, word list recall and word list savings from the Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological battery. Results: Total variance in the cognitive test performance explained by age, education and gender varied from 12.3 to 31.2%. A decreasing trend in cognitive performance existed in all subtests by advancing age, with differences appearing between 50 and 55 years. Persons with the highest-education level performed best for all measures. For the participants < 55 years, education explained part of the variance, while age and gender did not. Conclusions: When assessing cognition, age and education should be accounted for in more detail in research and clinical practice. Additionally, the cohort effect and its potential impact on the renewal cycle of future normative values for cognitive tests should be considered.

<b><i>Introduction:</i></b> The educational background and size of the elderly population are undergoing significant changes in Finland during the 2020s. A similar process is likely to occur also in several European countries. For cognitive screening of early Alzheimer’s disease (AD), using outdated norms and cutoff scores may negatively affect clinical accuracy. The aim of the present study was to examine the effects of education, age, and gender on the Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological battery (CERAD-nb) in a large register-based, clinical sample of patients with mild AD and nondemented at-risk persons from the general population (controls) and to examine whether corrected cutoff scores would increase the accuracy of differentiation between the 2 groups. <b><i>Methods:</i></b> CERAD-nb scores were obtained from AD patients (<i>n</i> = 389, 58% women, mean age 74.0 years) and from controls (<i>n</i> = 1,980, 52% women, mean age 68.5 years). The differences in CERAD-nb performance were evaluated by univariate GLM. Differentiation between the 2 groups was evaluated using a receiver operating characteristic (ROC) curve, where a larger area under the ROC curve represents better discrimination. Youden’s J was calculated for the overall performance and accuracy of each of the measures. <b><i>Results:</i></b> Of the demographic factors, education was the strongest predictor of CERAD-nb performance, explaining more variation than age or gender in both the AD patients and the controls. Education corrected cutoff scores had better diagnostic accuracy in discriminating between the AD patients and controls than existing uncorrected scores. The highest level of discrimination between the 2 groups overall was found for two CERAD-nb total scores. <b><i>Conclusions:</i></b> Education-corrected cutoff scores were superior to uncorrected scores in differentiating between controls and AD patients especially for the highest level of education and should therefore be used in clinical cognitive screening, also as the proportion of the educated elderly is increasing substantially during the 2020s. Our results also indicate that total scores of the CERAD-nb are better at discriminating AD patients from controls than any single subtest score. A digital tool for calculating the total scores and comparing education-based cutoffs would increase the efficiency and usability of the test.

We aimed to evaluate the feasibility of using real-world register data for identifying persons with mild Alzheimer’s disease (AD) and to describe their cognitive performance at the time of diagnosis. Patients diagnosed with AD during 2010–2013 (aged 60–81 years) were identified from the Finnish national health registers and enlarged with a smaller private sector sample (total n = 1,268). Patients with other disorders impacting cognition were excluded. Detailed clinical and cognitive screening data (the Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological battery [CERAD-nb]) were obtained from local health records. Adequate cognitive data were available for 389 patients with mild AD (31%) of the entire AD group. The main reasons for not including patients in analyses of cognitive performance were AD diagnosis at a moderate/severe stage (n = 266, 21%), AD diagnosis given before full register coverage (n = 152, 12%), and missing CERAD-nb data (n = 139, 11%). The cognitive performance of persons with late-onset AD (n = 284), mixed cerebrovascular disease and AD (n = 51), and other AD subtypes (n = 54) was compared with that of a non-demented sample (n = 1980) from the general population. Compared with the other AD groups, patients with late-onset AD performed the worst in word list recognition, while patients with mixed cerebrovascular disease and AD performed the worst in constructional praxis and clock drawing tests. A combination of national registers and local health records can be used to collect data relevant for cognitive screening; today, the process is laborious, but it could be improved in the future with refined search algorithms and electronic data.