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. 2023 May;37(4):436-449.
doi: 10.1037/neu0000833. Epub 2022 Jul 21.

Harmonizing the preclinical Alzheimer cognitive composite for multicohort studies

Olivia L Hampton  1 , Shubhabrata Mukherjee  2 , Michael J Properzi  1 , Aaron P Schultz  1 , Paul K Crane  2 , Laura E Gibbons  2 , Timothy J Hohman  3 , Paul Maruff  4 , Yen Ying Lim  5 , Rebecca E Amariglio  1 , Kathryn V Papp  1 , Keith A Johnson  6 , Dorene M Rentz  1 , Reisa A Sperling  1 , Rachel F Buckley  1
Affiliations
Free PMC article

Harmonizing the preclinical Alzheimer cognitive composite for multicohort studies

Olivia L Hampton et al. Neuropsychology. 2023 May.
Free PMC article

Abstract

Objectives: Studies are increasingly examining research questions across multiple cohorts using data from the preclinical Alzheimer cognitive composite (PACC). Our objective was to use modern psychometric approaches to develop a harmonized PACC.

Method: We used longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Harvard Aging Brain Study (HABS), and Australian Imaging, Biomarker and Lifestyle Study of Ageing (AIBL) cohorts (n = 2,712). We further demonstrated our method with the Anti-Amyloid Treatment of Asymptomatic Alzheimer's Disease (A4) Study prerandomized data (n = 4,492). For the harmonization method, we used confirmatory factor analysis (CFA) on the final visit of the longitudinal cohorts to determine parameters to generate latent PACC (lPACC) scores. Overlapping tests across studies were set as "anchors" that tied cohorts together, while parameters from unique tests were freely estimated. We performed validation analyses to assess the performance of lPACC versus the common standardized PACC (zPACC).

Results: Baseline (BL) scores for the zPACC were centered on zero, by definition. The harmonized lPACC did not define a common mean of zero and demonstrated differences in baseline ability levels across the cohorts. Baseline lPACC slightly outperformed zPACC in the prediction of progression to dementia. Longitudinal change in the lPACC was more constrained and less variable relative to the zPACC. In combined-cohort analyses, longitudinal lPACC slightly outperformed longitudinal zPACC in its association with baseline β-amyloid status.

Conclusions: This study proposes procedures for harmonizing the PACC that make fewer strong assumptions than the zPACC, facilitating robust multicohort analyses. This implementation of item response theory lends itself to adapting across future cohorts with similar composites. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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Figures

Figure 1
Figure 1. Bifactor CFA Model Structure at Last Visit
Note. CFA = confirmatory factor analysis; FCSRT = Free and Cued Selective Recall Test trials 1–3; MMSE = Mini-Mental State Examination; LMDR = logical memory delayed recall; lPACC = latent PACC; f1/f2 = secondary latent structures for FCSRT and categories (granular data); each arrow indicates loading of each neuropsychological test onto the latent factors f1, f2, and lPACC.
Figure 2
Figure 2. Harmonization Workflow Diagram
Note. CFA = confirmatory factor analysis; ADNI = Alzheimer’s Disease Neuroimaging Initiative; HABS = Harvard Aging Brain Study; AIBL = Australian Imaging, Biomarker and Lifestyle Study of Ageing; FCSRT = Free and Cued Selective Recall Test; lPACC = latent PACC; CN = cognitively normal.
Figure 3
Figure 3. zPACC and LPACC Score Distributions Across Cohorts
Note. The left column displays zPACC scores, and the right column displays lPACC scores. (A, B) The baseline distribution of the scores by cohort, (C, D) longitudinal slopes (extracted from linear mixed-effects model) by cohort, and (E, F) a spaghetti plot of PACC performance per participant by cohort. The lPACC scores are shifted from zero at the baseline and show a constrained variance over time compared to the zPACC scores, and (G) smoothed estimates of standard errors of measurement (inverse square root of the total information across test items in each cohort) as a function of the estimated latent trait (from −3 to 3). PACC = preclinical Alzheimer cognitive composite; lPACC = latent PACC; zPACC = standardized PACC; SEM = standard error of measurement; ADNI = Alzheimer’s Disease Neuroimaging Initiative; HABS = Harvard Aging Brain Study; AIBL = Australian Imaging, Biomarker and Lifestyle Study of Ageing; A4 = Anti-Amyloid Treatment of Asymptomatic Alzheimer’s Disease.
Figure 4
Figure 4. Baseline Aβ Status on Longitudinal PACC Change With All Cohorts
Note. High Aβ is in red, while low Aβ is in blue. The lPACC (right panel) compared with zPACC (left panel). PACC = preclinical Alzheimer cognitive composite; lPACC = latent PACC; zPACC = standardized PACC.
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References

    1. Aisen PS, Petersen RC, Donohue MC, Gamst A, Raman R, Thomas RG, Walter S, Trojanowski JQ, Shaw LM, Beckett LA, Jack CR Jr., Jagust W, Toga AW, Saykin AJ, Morris JC, Green RC, Weiner MW, & the Alzheimer’s Disease Neuroimaging Initiative. (2010). Clinical core of the Alzheimer’s disease neuroimaging Initiative: Progress and plans. Alzheimer’s and Dementia, 6(3), 239–246. 10.1016/j.jalz.2010.03.006 - DOI - PMC - PubMed
    1. Betthauser TJ, Koscik RL, Jonaitis EM, Allison SL, Cody KA, Erickson CM, Rowley HA, Stone CK, Mueller KD, Clark LR, Carlsson CM, Chin NA, Asthana S, Christian BT, & Johnson SC (2020). Amyloid and tau imaging biomarkers explain cognitive decline from late middle-age. Brain: A Journal of Neurology, 143(1), 320–335. 10.1093/brain/awz378 - DOI - PMC - PubMed
    1. Borsboom D (2005). Measuring the mind: Conceptual issues in contemporary psychometrics. Cambridge University Press. 10.1017/CBO9780511490026 - DOI
    1. Buckley RF, Mormino EC, Amariglio RE, Properzi MJ, Rabin JS, Lim YY, Papp KV, Jacobs HIL, Burnham S, Hanseeuw BJ, Doré V, Dobson A, Masters CL, Waller M, Rowe CC, Maruff P, Donohue MC, Rentz DM, Kirn D, … the Alzheimer’s Disease Neuroimaging Initiative, the Australian Imaging, Biomarker and Lifestyle Study of Ageing, the Harvard Aging Brain Study. (2018). Sex, amyloid, and APOE ε4 and risk of cognitive decline in preclinical Alzheimer’s disease: Findings from three well-characterized cohorts. Alzheimer’s and Dementia, 14(9), 1193–1203. 10.1016/j.jalz.2018.04.010 - DOI - PMC - PubMed
    1. Burnham SC, Bourgeat P, Doré V, Savage G, Brown B, Laws S, Maruff P, Salvado O, Ames D, Martins RN, Masters CL, Rowe CC, Villemagne VL, & the AIBL Research Group. (2016). Clinical and cognitive trajectories in cognitively healthy elderly individuals with suspected non-Alzheimer’s disease pathophysiology (SNAP) or Alzheimer’s disease pathology: A longitudinal study. Lancet Neurology, 15(10), 1044–1053. 10.1016/S1474-4422(16)30125-9 - DOI - PubMed