Detection of Alzheimer's disease and dementia in the preclinical phase: population based cohort study
Related Experiment Video
Updated: Sep 27, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Katie Palmer1, Lars Bäckman, Bengt Winblad
1Aging Research Center, Division of Geriatric Epidemiology and Medicine, Neurotec, Karolinska Institute, and Stockholm Gerontology Research Center, Box 6401, 11382, Stockholm, Sweden. katie.palmer@neurotec.ki.se
Objectives:
To evaluate a simple three step procedure to identify people in the general population who are in the preclinical phase of Alzheimer's disease and dementia.
Design:
Three year population based cohort study.
Setting:
Kungsholmen cohort, Stockholm, Sweden.
Participants:
1435 people aged 75-95 years without dementia.
Assessments:
Single question asking about memory complaints, assessment by mini-mental state examination, and neuropsychological testing.
Main Outcome Measure:
Alzheimer's disease and dementia at three year follow up.
Results:
None of the three instruments was sufficiently predictive of Alzheimer's disease and dementia when administered separately. After participants had been screened for memory complaints and global cognitive impairment, specific tests of word recall and verbal fluency had positive predictive values for dementia of 85-100% (95% confidence intervals range from 62% to 100%). However, only 18% of future dementia cases were identified in the preclinical phase by this three step procedure. Memory complaints were the most sensitive indicator of Alzheimer's disease and dementia in the whole population, but only half the future dementia cases reported memory problems three years before diagnosis.
Conclusion:
This three step procedure, which simulates what might occur in clinical practice, has a high positive predictive value for dementia, although only a small number of future cases can be identified.
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