Journal
NEUROLOGY
Volume 84, Issue 14, Pages 1433-1442Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1212/WNL.0000000000001437
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Funding
- NIH [P50 AG016574, U01 AG006786, K01 MH068351, K01 AG028573]
- Robert Wood Johnson Foundation
- Robert H. and Clarice Smith and Abigail Van Buren Alzheimer's Disease Research Program
- Rochester Epidemiology Project [R01 AG034676]
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Objective: We sought to develop risk scores for the progression from cognitively normal (CN) to mild cognitive impairment (MCI). Methods: We recruited into a longitudinal cohort study a randomly selected, population-based sample of Olmsted County, MN, residents, aged 70 to 89 years on October 1, 2004. At baseline and subsequent visits, participants were evaluated for demographic, clinical, and neuropsychological measures, and were classified as CN, MCI, or dementia. Using baseline demographic and clinical variables in proportional hazards models, we derived scores that predicted the risk of progressing from CN to MCI. We evaluated the ability of these risk scores to classify participants for MCI risk. Results: Of 1,449 CN participants, 401 (27.7%) developed MCI. A basic model had a C statistic of 0.60 (0.58 for women, 0.62 for men); an augmented model resulted in a C statistic of 0.70 (0.69 for women, 0.71 for men). Both men and women in the highest vs lowest sex-specific quartiles of the augmented model's risk scores had an approximately 7-fold higher risk of developing MCI. Adding APOE epsilon 4 carrier status improved the model (p = 0.002). Conclusions: We have developed MCI risk scores using variables easily assessable in the clinical setting and that may be useful in routine patient care. Because of variability among populations, validation in independent samples is required. These models may be useful in identifying patients who might benefit from more expensive or invasive diagnostic testing, and can inform clinical trial design. Inclusion of biomarkers or other risk factors may further enhance the models.
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