Harmonizing the preclinical Alzheimer cognitive composite for multi‐cohort studies

Abstract

AbstractBackgroundVersions of the Preclinical Alzheimer Cognitive Composite (PACC) and within cohort standardization models have been implemented in multiple observational and clinical trials. Harmonizing PACC data across studies, however, requires modern psychometric approaches to reflect the psychometric, testing, and demographic nuances between cohorts and enable meta‐analyses. We present a method for harmonizing PACC across four cohorts: the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s disease (A4), Harvard Aging Brain Study (HABS), and Australian Imaging, Biomarker & Lifestyle Study of Ageing (AIBL).MethodTables 1 and 2 show the demographic and neuropsychological test data used to form each PACC. The ADNI dataset was defined as the anchor for harmonization. The final visit data were used to set factor score loadings for each PACC (except A4) to maximize cognitive variability in the sample for calibration purposes. First, we used confirmatory factor analysis (CFA) on the ADNI dataset to ascertain raw loadings and thresholds of each test onto a latent PACC factor (lPACC). These loadings were then applied to the longitudinal ADNI dataset (Fig.1). For the other studies, common items (MMSE and Logical Memory) were set as “anchors” to freely estimate unique items. Common items in HABS were set as anchors for A4 (FCsrt and DSST). Longitudinal lPACC scores were generated once all item parameters were estimated for all cohorts. We performed validation analyses to assess the quality of integration using lPACC scores versus within‐cohort standardized PACC (zPACC).ResultLPACC scores largely aligned with zPACC, particularly when examining within‐cohort relationships with baseline demographic variables (Table3) and comparing slope distributions across cohorts (Fig2B). Notable differences between the scores include: a) shifted baseline distributions for lPACC relative to zPACC, reflecting cohort demographic heterogeneity (Fig.2A), and b) constricted slope distribution for lPACC slopes compared with zPACC slopes (Fig.2B), suggesting reduced variability (Fig.2B/C). When the cohorts were combined, the point estimate for strength of association between baseline amyloid and longitudinal cognitive change was higher using the lPACC than the zPACC (Fig.3).ConclusionLPACC scores reflect within cohort patterns, while reducing slope variability. Using advanced psychometrics, a harmonized PACC can be pooled across studies, facilitating robust multi‐cohort analyses.