Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates

Abstract

We compared two methods of diagnosing mild cognitive impairment (MCI): conventional Petersen/Winblad criteria as operationalized by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and an actuarial neuropsychological method put forward by Jak and Bondi designed to balance sensitivity and reliability. 1,150 ADNI participants were diagnosed at baseline as cognitively normal (CN) or MCI via ADNI criteria (MCI: n = 846; CN: n = 304) or Jak/Bondi criteria (MCI: n = 401; CN: n = 749), and the two MCI samples were submitted to cluster and discriminant function analyses. Resulting cluster groups were then compared and further examined for APOE allelic frequencies, cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarker levels, and clinical outcomes. Results revealed that both criteria produced a mildly impaired Amnestic subtype and a more severely impaired Dysexecutive/Mixed subtype. The neuropsychological Jak/Bondi criteria uniquely yielded a third Impaired Language subtype, whereas conventional Petersen/Winblad ADNI criteria produced a third subtype comprising nearly one-third of the sample that performed within normal limits across the cognitive measures, suggesting this method's susceptibility to false positive diagnoses. MCI participants diagnosed via neuropsychological criteria yielded dissociable cognitive phenotypes, significant CSF AD biomarker associations, more stable diagnoses, and identified greater percentages of participants who progressed to dementia than conventional MCI diagnostic criteria. Importantly, the actuarial neuropsychological method did not produce a subtype that performed within normal limits on the cognitive testing, unlike the conventional diagnostic method. Findings support the need for refinement of MCI diagnoses to incorporate more comprehensive neuropsychological methods, with resulting gains in empirical characterization of specific cognitive phenotypes, biomarker associations, stability of diagnoses, and prediction of progression. Refinement of MCI diagnostic methods may also yield gains in biomarker and clinical trial study findings because of improvements in sample compositions of 'true positive' cases and removal of 'false positive' cases.

Keywords: Alzheimer's Disease Neuroimaging Initiative; Alzheimer's disease; biomarker; cluster analysis; dementia; mild cognitive impairment; neuropsychology; progression.

Figures

Fig. 1

Mean z-scores for the three MCI subtypes on neuropsychological measures included in cluster analyses of conventional Petersen/Winblad ADNI criteria (A) and neuropsychological Jak/Bondi criteria (B). Error bars denote standard deviations. TMT, Trail Making Test.

Fig. 2

Individual scores on discriminant functions for MCI participants classified according to (A) the conventional criteria and (B) the neuropsychological criteria.

Fig. 3

CSF biomarker levels of (A) Aβ1–42, (B) total tau, and (C) p-tau181 for the cluster subgroups and CN participants according to the conventional criteria and actuarial neuropsychological criteria. Error bars denote standard deviations.

Fig. 3

CSF biomarker levels of (A) Aβ1–42, (B) total tau, and (C) p-tau181 for the cluster subgroups and CN participants according to the conventional criteria and actuarial neuropsychological criteria. Error bars denote standard deviations.