Unraveling the heterogeneity in Alzheimer's disease progression across multiple cohorts and the implications for data-driven disease modeling.

Alzheimer's & Dementia : the Journal of the Alzheimer's Association
Colin BirkenbihlAlzheimer's Disease Neuroimaging Initiative

Abstract

Given study-specific inclusion and exclusion criteria, Alzheimer's disease (AD) cohort studies effectively sample from different statistical distributions. This heterogeneity can propagate into cohort-specific signals and subsequently bias data-driven investigations of disease progression patterns. We built multi-state models for six independent AD cohort datasets to statistically compare disease progression patterns across them. Additionally, we propose a novel method for clustering cohorts with regard to their progression signals. We identified significant differences in progression patterns across cohorts. Models trained on cohort data learned cohort-specific effects that bias their estimations. We demonstrated how six cohorts relate to each other regarding their disease progression. Heterogeneity in cohort datasets impedes the reproducibility of data-driven results and validation of progression models generated on single cohorts. To ensure robust scientific insights, it is advisable to externally validate results in independent cohort datasets. The proposed clustering assesses the comparability of cohorts in an unbiased, data-driven manner.

References

Dec 3, 2011·Science Translational Medicine·Reisa A SperlingPaul S Aisen
Jul 12, 2012·Archives of Neurology·Jennifer L WhitwellUNKNOWN Alzheimer Disease Neuroimaging Initiative
Apr 11, 2017·Hippocampus·Daniel FerreiraUNKNOWN Australian Imaging Biomarkers and Lifestyle Study of Ageing (AIBL) research group
Feb 6, 2018·Alzheimer's & Dementia : the Journal of the Alzheimer's Association·Annie RobitailleGraciela Muniz Terrera
Apr 15, 2018·Alzheimer's & Dementia : the Journal of the Alzheimer's Association·Clifford R JackUNKNOWN Contributors
May 14, 2018·Alzheimer's & Dementia : the Journal of the Alzheimer's Association·Takeshi IwatsuboUNKNOWN Alzheimer's Disease Neuroimaging Initiative
May 29, 2018·Alzheimer's & Dementia : the Journal of the Alzheimer's Association·Ron Brookmeyer, Nada Abdalla
Jun 6, 2018·Journal of Alzheimer's Disease : JAD·David A BennettJulie A Schneider
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Jul 25, 2018·Statistical Methods in Medical Research·Liangliang ZhangUNKNOWN and for the Alzheimer’s Disease Neuroimaging Initiative
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Oct 31, 2018·Alzheimer Disease and Associated Disorders·Lilah BesserUNKNOWN Neuropsychology Work Group, Directors, and Clinical Core leaders of the National Institute on Aging-funded US Alzheimer’s Di
Dec 18, 2018·Alzheimer's & Dementia : the Journal of the Alzheimer's Association·Ganesh M BabulalUNKNOWN International Society to Advance Alzheimer's Research and Treatment, Alzheimer's Association
Jun 6, 2019·Alzheimer's & Dementia : the Journal of the Alzheimer's Association·Lisa VermuntUNKNOWN ICTUS/DSA study groups
Jan 30, 2020·Frontiers in Molecular Biosciences·Sepehr Golriz KhatamiMartin Hofmann-Apitius
Jun 15, 2020·Alzheimer's Research & Therapy·Christoforos HadjichrysanthouUNKNOWN Alzheimer’s Disease Neuroimaging Initiative
Dec 9, 2020·Journal of Alzheimer's Disease : JAD·Colin BirkenbihlMartin Hofmann-Apitius

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Citations

Aug 24, 2021·Journal of Alzheimer's Disease : JAD·Andrea ZangrossiSara Mondini

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