Neuropsychological Equivalence of the Clinical Diagnosis of Mild Cognitive Impairment in the National Alzheimer's Coordinating Center Uniform Data Set and Alzheimer's Disease Neuroimaging Initiative.

Introduction Our understanding of Alzheimer's disease may be improved by harmonizing data from large cohort studies of older adults. Differences in the way clinical conditions, like mild cognitive impairment (MCI), are diagnosed may lead to variability among participants that share the same diagnostic label. This variability presents a challenge for cohort harmonization and may lead to inconsistency in research findings. Little research to date has explored the equivalence of the diagnostic label of MCI across 2 of the largest and most influential cohort studies in the USA: the National Alzheimer's Coordinating Center (NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Methods Participants with MCI due to presumed Alzheimer's disease from the NACC Uniform Data Set (n = 789) and ADNI (n = 131) were compared on demographic, psychological, and functional variables, as well as on an abbreviated neuropsychological battery common to the 2 data sets. Results Though similar in terms of age, education, and functional status, the NACC sample was more diverse (17.4% non-White participants vs. 7.6% in ADNI; χ2 = 7.923, p = 0.005) and tended to perform worse on some cognitive tests. In particular, participants diagnosed with MCI in NACC were more likely to have clinically significant impairments on language measures (26.36-31.18%) than MCI participants in ADNI (16.03-19.85%). Discussion The current findings suggest important differences in cognitive performances between 2 large MCI cohorts, likely reflective of differences in diagnostic criteria used in these 2 studies, as well as differences in sample compositions. Such diagnostic heterogeneity may make harmonizing data across these cohorts challenging. However, application of shared psychometric criteria across studies may lead to closer equivalence of MCI groups. Such approaches could pave the way for cohort harmonization and enable "big data" analytic approaches to understanding Alzhei-mer's to be developed.