Multi-time-point data preparation robustly reveals MCI and dementia risk factors

Daman Kaur, Magda Bucholc, David Finn, Stephen Todd, KongFatt Wong-Lin, Paula McClean

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4 Citations (Scopus)
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INTRODUCTION: Conflicting results on dementia risk factors have been reported across studies. We hypothesize that variation in data preparation methods may partially contribute to this issue.
METHODS: We propose a comprehensive data preparation approach comparing individuals with stable diagnosis over time, to those who progress to mild cognitive impairment (MCI)/dementia. This was compared to the often-used ‘baseline’ analysis. Multivariate logistic regression was employed to evaluate both methods.
RESULTS: The results obtained from sensitivity analyses were consistent with those from our multi-time-point data preparation approach, exhibiting its robustness. Compared to analysis using only baseline data, the number of significant risk factors identified in progression analyses was substantially lower. Additionally, we found that moderate depression increased Healthy-to-MCI/Dementia risk, while hypertension reduced MCI-to-Dementia risk.
DISCUSSION: Overall, multi-time-point based data preparation approaches may pave the way for a better understanding of dementia risk factors, and address some of the reproducibility issues in the field.
Original languageEnglish
Article numbere12116
Pages (from-to)1-13
Number of pages13
JournalAlzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM)
Issue number1
Early online date14 Oct 2020
Publication statusPublished online - 14 Oct 2020

Bibliographical note

Funding Information:
This project was supported by the European Union’s INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB [Centre for Personalised Medicine, IVA 5036]), with additional support by the Northern Ireland Functional Brain Mapping Project Facility (1303/101154803), funded by invest Northern Ireland and the University of Ulster (KongFatt Wong-Lin), Alzheimer’s Research UK (ARUK) NI Pump Priming (Magda Bucholc, Stephen Todd, KongFatt Wong-Lin, Paula L. McClean), Ulster University Research Challenge Fund (Magda Bucholc, Stephen Todd, KongFatt Wong-Lin), the Dr George Moore Endowment for Data Science at Ulster University (Magda Bucholc), and the COST Action Open Multiscale Systems Medicine (OpenMultiMed) supported by COST (European Cooperation in Science and Technology; KongFatt Wong-Lin). The views and opinions expressed in this article do not necessarily reflect those of the European Commission or the SEUPB.

Funding Information:
The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA-funded ADCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI M. Marsel Mesulam, MD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG005131 (PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerd-low, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD).

Publisher Copyright:
© 2020 The Authors. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring.

Copyright 2021 Elsevier B.V., All rights reserved.


  • National Alzheimer's Coordinating Center data
  • baseline
  • cardiometabolic risk factors
  • dementia progression
  • longitudinal data
  • mild cognitive impairment (MCI)
  • multivariate logistic regression
  • multi‐time‐point data preparation


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