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

Abstract

Objectives: 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. Therefore, we 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: A total of 806 participants diagnosed by means of 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 three-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 apolipoprotein e4+ and have worse Alzheimer's disease 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. (JINS, 2017, 23, 564-576).

Keywords: Alzheimer’s disease; Assessment of cognitive disorders/dementia; Biomarkers; Latent profile analysis; Mild cognitive impairment (MCI); Multivariate mixture modeling; Neuropsychological profiles.

Figure 1. Neuropsychological Performance for the Latent Profile Classes

Error bars denote 99.5% confidence intervals. Abbreviations: MCI = Mild Cognitive Impairment; LPA = Latent Profile Analysis; MMSE = Mini-Mental State Examination; TMT = Trail Making Test; AVLT = Rey Auditory Verbal Learning Test

Figure 2. Mean CSF Biomarker Concentrations of Latent Profile Classes and Robust Normal Controls

Error bars denote 99.5% confidence intervals. 2a) Mean total tau (pg/mL).2b) Mean p-tau_181p (pg/mL). 2c) Mean Aβeta_1–42 (pg/mL). 2d) Mean ratio of p-tau_181p to Aβeta_1–42. Abbreviations: CSF = Cerebrospinal fluid; MCI = Mild Cognitive Impairment; LPA = Latent Profile Analysis

Figure 2. Mean CSF Biomarker Concentrations of Latent Profile Classes and Robust Normal Controls

Error bars denote 99.5% confidence intervals. 2a) Mean total tau (pg/mL).2b) Mean p-tau_181p (pg/mL). 2c) Mean Aβeta_1–42 (pg/mL). 2d) Mean ratio of p-tau_181p to Aβeta_1–42. Abbreviations: CSF = Cerebrospinal fluid; MCI = Mild Cognitive Impairment; LPA = Latent Profile Analysis

Figure 3. Progression and Reversion Rates of Latent Profile Classes

Error bars denote 99.5% confidence intervals. Abbreviations: NL = Normal; MCI = Mild Cognitive Impairment; LPA = Latent Profile Analysis