Clusters of cognitive performance predict long-term cognitive impairment in elderly patients with subjective memory complaints and healthy controls

Abstract

Introduction: Patients with subjective memory complaints (SMC) may include subgroups with different neuropsychological profiles and risks of cognitive impairment.

Methods: Cluster analysis was performed on two datasets (n: 630 and 734) comprising demographic and neuropsychological data from SMC and healthy controls (HC). Survival analyses were conducted on clusters. Bayesian model averaging assessed the predictive utility of clusters and other biomarkers.

Results: Two clusters with higher and lower than average cognitive performance were detected in SMC and HC. Assignment to the lower performance cluster increased the risk of cognitive impairment in both datasets (hazard ratios: 1.78 and 2.96; P_log-rank: 0.04 and <0.001) and was associated with lower hippocampal volumes and higher tau/amyloid beta 42 ratios in cerebrospinal fluid. The effect of SMC was small and confounded by mood.

Discussion: This study provides evidence of the presence of cognitive clusters that hold biological significance and predictive value for cognitive decline in SMC and HC.

Highlights: Patients with subjective memory complaints include two cognitive clusters. Assignment to the lower performance cluster increases risk of cognitive impairment. This cluster shows a pattern of biomarkers consistent with incipient Alzheimer's disease pathology. The same cognitive cluster structure is found in healthy controls. The effect of memory complaints on risk of cognitive decline is small and confounded.

Keywords: Alzheimer's disease; Bayesian model averaging; biomarkers; cluster analysis; cognitive impairment; neuropsychological profiles; subjective memory complaints.

FIGURE 1

Heatmap displaying the correlation‐based distance matrices of the clinical and neuropsychological variables used for clustering in the CUN (Clínica Universidad de Navarra) and ADNI (Alzheimer's Disease Neuroimaging Initiative) datasets.

FIGURE 2

Mean standardized (Z) scores of the variables used for clustering in the CUN and ADNI datasets. The sign of TMT‐A and TMT‐B was reversed to align with the direction of the other variables. ADNI, Alzheimer's Disease Neuroimaging Initiative; CUN, Clínica Universidad de Navarra; MMSE, Mini‐Mental State Examination; TMT‐A, Trail Making Test, Part A; TMT‐B, Trail Making Test, Part B.

FIGURE 3

Kaplan–Meier survival curves stratified by cognitive cluster in the CUN dataset, and stratified by cluster, initial diagnostic classification (SMC vs. CN) or both in the ADNI dataset. The P values correspond to the log‐rank test. ADNI, Alzheimer's Disease Neuroimaging Initiative; CN, cognitively normal; CUN, Clínica Universidad de Navarra; SMC, subjective memory complaints.

FIGURE 4

Posterior probabilities of the coefficients that represent the variables analyzed by Bayesian model averaging. High values at 0 indicate a low probability to be included in the models. APOE, apolipoprotein E; FDG, fluorodeoxyglucose; GDS, Geriatric Depression Scale; SMC, subjective memory complaints; TauAbeta, tau/Aβ42 ratio in cerebrospinal fluid.