Statistically Derived Subtypes and Associations with Cerebrospinal Fluid and Genetic Biomarkers in Mild Cognitive Impairment: A Latent Profile Analysis

Abstract

Objective Research demonstrates heterogeneous neuropsychological profiles among individuals with mild cognitive impairment (MCI). However, few studies have included visuoconstructional ability or used latent mixture modeling to statistically identify MCI subtypes. We therefore examined whether unique neuropsychological MCI profiles could be ascertained using latent profile analysis (LPA), and subsequently investigated cerebrospinal fluid (CSF) biomarkers, genotype, and longitudinal clinical outcomes between the empirically-derived classes. Methods 806 participants diagnosed via the Alzheimer’s Disease Neuroimaging Initiative (ADNI) MCI criteria received a comprehensive neuropsychological battery assessing visuoconstructional ability, language, attention/executive function, and episodic memory. Test scores were adjusted for demographic characteristics using standardized regression coefficients based on “robust” normal control performance (n=260). Calculated z-scores were subsequently used in the LPA, and CSF-derived biomarkers, genotype, and longitudinal clinical outcome were evaluated between the LPA-derived MCI classes. Results Statistical fit indices suggested a 3-class model was the optimal LPA solution. The 3-class LPA consisted of a mixed impairment MCI class (n=106), an amnestic MCI class (n=455), and an LPA-derived normal class (n=245). Additionally, the amnestic and mixed classes were more likely to be APOE e4+ and have worse AD CSF biomarkers than LPA-derived normal subjects. Conclusions Our study supports significant heterogeneity in MCI neuropsychological profiles using LPA and extends prior work (Edmonds et al., 2015) by demonstrating a lower rate of progression in the approximately one-third of ADNI MCI individuals who may represent “false-positive” diagnoses. Our results underscore the importance of using sensitive, actuarial methods for diagnosing MCI, as current diagnostic methods may be over-inclusive.

Early versus late MCI: Improved MCI staging using a neuropsychological approach

“…We have studied this FP group extensively in previous studies and found them to have normal CSF and imaging biomarkers [3][4][5][6], as well as intact cognitive performance and functional independence over time [8,9]. Consistent with this, the current study showed no differences in progression to AD, CSF biomarkers, or cortical thickness between the FP and CN participants.…”

“…Cluster analysis of the six neuropsychological scores from the 336 MCI participants resulted in three groups: 1) NP-EMCI with impaired memory and below average naming (n=147); 2) NP-LMCI with deficits across all three cognitive domains (n=49); and 3) a "false positive" (FP) group that performed within normal limits on the more extensive neuropsychological testing despite their original MCI diagnosis (n=140), consistent with our previous studies [3,4,16]; see Fig. 1.…”

“…This conventional diagnostic method, which is standard procedure for clinical trials and large-scale studies of MCI, has been shown to be limited given its high susceptibility to false positive diagnostic errors [2,3]. Within the ADNI MCI cohort, we have employed statistical methods such as cluster analysis and latent profile analysis and found that a large proportion of participants diagnosed with MCI (up to one-third of ADNI's MCI sample) demonstrate intact performance on a more extensive neuropsychological test battery and show a low rate of progression to dementia [3,4]. Examination of AD biomarkers in this presumptive "false positive" group has revealed normal CSF concentrations of beta-amyloid and phosphorylated tau [3], normal levels of cortical amyloid burden [5], and normal cortical thickness profiles [6] as compared to a sample of robust normal controls.…”

A Latent Transition Analysis Model to Assess Change in Cognitive States over Three Occasions: Results from the Rush Memory and Aging Project

“…Individuals differ in their cognitive abilities [1], and although individuals who typically perform well on one task also perform well on other tasks at cross-section and over time [1,2], sometimes, they don't follow this pattern [3,4]. For this reason, it can be useful to consider cognitive aging as a stage-sequential process [5][6][7]. This conceptualization allows us to identify particular stages of cognitive impairment and distinguish amongst pathways of transitions across latent classes over time.…”

Discrepancy-Based Evidence for Loss of Thinking Abilities (DELTA): Development and Validation of a Novel Approach to Identifying Cognitive Changes

“…Predicted scores for each relevant test component came from regression equations using coefficients (B-weights) corresponding to the effects of age, gender (male or female), years of education, and word-reading ability on test scores in the RCN subgroup (Eppig et al, 2017). Word-reading scores came from the American National Adult Reading Test (ANART number of errors), which estimates general intelligence (i.e., IQ) and informs expected premorbid cognitive abilities (McGurn et al, 2004).…”