Estimating Likelihood of Dementia in the Absence of Diagnostic Data : A Latent Dementia Index in 10 Genetically Informed Studies

BACKGROUND: Epidemiological research on dementia is hampered by differences across studies in how dementia is classified, especially where clinical diagnoses of dementia may not be available. OBJECTIVE: We apply structural equation modeling to estimate dementia likelihood across heterogeneous samples within a multi-study consortium and use the twin design of the sample to validate the results. METHODS: Using 10 twin studies, we implement a latent variable approach that aligns different tests available in each study to assess cognitive, memory, and functional ability. The model separates general cognitive ability from components indicative of dementia. We examine the validity of this continuous latent dementia index (LDI). We then identify cut-off points along the LDI distributions in each study and align them across studies to distinguish individuals with and without probable dementia. Finally, we validate the LDI by determining its heritability and estimating genetic and environmental correlations between the LDI and clinically diagnosed dementia where available. RESULTS: Results indicate that coordinated estimation of LDI across 10 studies has validity against clinically diagnosed dementia. The LDI can be fit to heterogeneous sets of memory, other cognitive, and functional ability variables to extract a score reflective of likelihood of dementia that can be interpreted similarly across studies despite diverse study designs and sampling characteristics. Finally, the same genetic sources of variance strongly contribute to both the LDI and clinical diagnosis. CONCLUSION: This latent dementia indicator approach may serve as a model for other research consortia confronted with similar data integration challenges.
CC BY-NC 4.0© 2022 – The authors. Published by IOS Press.Correspondence to: Margaret Gatz, Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA. Tel.: +1 213 740 2212; E-mail: gatz@usc.edu.Membership of the IGEMS consortium is provided at https://dornsife.usc.edu/labs/IGEMSThis work was supported by US National Institutes of Health (NIH) grants R01 AG060470 to MG and NLP and RF1 AG058068 to Pike, LaDu, and MG and a grant from the Alzheimer’s Association (AARF-17-505302) to CRB. HARMONY was supported by NIH grant R01AG08724 to MG. SATSA was supported by grants NIH R01 AG04563, R01 AG10175, the John D. and Catherine T. MacArthur Foundation Research Network on Successful Aging, the Swedish Council For Working Life and Social Research (FAS) (97:0147:1B, 2009-0795), and the Swedish Research Council (825-2007-7460, 825-2009-6141) to NLP. GENDER was supported by the MacArthur Foundation Research Network on Successful Aging to McClearn and The Axel and Margaret Ax:son Johnson’s Foundation, The Swedish Council for Social Research, and the Swedish Foundation for Health Care Sciences and Allergy Research to Malmberg. OCTO-Twin was supported by grant NIH R01 AG08861 to McClearn. OATS was funded by a National Health & Medical Research Council and Australian Research Council Strategic Award Grant of the Ageing Well, Ageing Productively Program (ID No. 401162), and NHMRC Project Grants (ID 1045325 and 1085606) to PS. OATS participant recruitment was facilitated through Twins Research Australia, a national resource in part supported by a Centre for Research Excellence Grant (ID: 1079102), from the National Health and Medical Research Council. We also acknowledge the contribution to this study of the OATS research team listed at https://cheba.unsw.edu.au/project/older-australian-twins-study. The Danish Twin Registry was supported by grants from The National Program for Research Infrastructure 2007 from th