Sample Size Estimation for Alzheimer’s Disease Trials from Japanese ADNI Serial Magnetic Resonance Imaging

Background: Little is known about the sample sizes required for clinical trials of Alzheimer’s disease (AD)-modifying treatments using atrophy measures from serial brain magnetic resonance imaging (MRI) in the Japanese population. Objective: The primary objective of the present study was to estimate how large a sample size would be needed for future clinical trials for AD-modifying treatments in Japan using atrophy measures of the brain as a surrogate biomarker. Methods: Sample sizes were estimated from the rates of change of the whole brain and hippocampus by the k-means normalized boundary shift integral (KN-BSI) and cognitive measures using the data of 537 Japanese Alzheimer’s Neuroimaging Initiative (J-ADNI) participants with a linear mixed-effects model. We also examined the potential use of ApoE status as a trial enrichment strategy. Results: The hippocampal atrophy rate required smaller sample sizes than cognitive measures of AD and mild cognitive impairment (MCI). Inclusion of ApoE status reduced sample sizes for AD and MCI patients in the atrophy measures. Conclusion: These results show the potential use of longitudinal hippocampal atrophy measurement using automated image analysis as a progression biomarker and ApoE status as a trial enrichment strategy in a clinical trial of AD-modifying treatment in Japanese people.

[1]  Vikas Singh,et al.  Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment , 2015, Alzheimer's & Dementia.

[2]  C. Jack,et al.  Longitudinal MRI findings from the vitamin E and donepezil treatment study for MCI , 2008, Neurobiology of Aging.

[3]  Denise C. Park,et al.  Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[4]  Chris Frost,et al.  The analysis of repeated ‘direct’ measures of change illustrated with an application in longitudinal imaging , 2004, Statistics in medicine.

[5]  Nick C. Fox,et al.  Algorithms, atrophy and Alzheimer's disease: Cautionary tales for clinical trials , 2011, NeuroImage.

[6]  K. Davis,et al.  A new rating scale for Alzheimer's disease. , 1984, The American journal of psychiatry.

[7]  Sébastien Ourselin,et al.  A symmetric block-matching framework for global registration , 2014, Medical Imaging.

[8]  Jeffrey L. Cummings,et al.  Integrating ADNI results into Alzheimer's disease drug development programs 1 1 Prepared for a special issue of Neurobiology of Aging on the Alzheimer's Disease Neuroimaging Initiative (ADNI). , 2010, Neurobiology of Aging.

[9]  Takeshi Iwatsubo,et al.  Japanese Alzheimer's Disease Neuroimaging Initiative: Present status and future , 2010, Alzheimer's & Dementia.

[10]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[11]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[12]  S. Edland,et al.  Power calculations for clinical trials in Alzheimer's disease. , 2011, Journal of Alzheimer's disease : JAD.

[13]  J. Li,et al.  Vascular risk factors promote conversion from mild cognitive impairment to Alzheimer disease , 2011, Neurology.

[14]  Brian B. Avants,et al.  A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain segmentation , 2011, NeuroImage.

[15]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[16]  R. Petersen,et al.  Development of Cognitive Instruments for Use in Clinical Trials of Antidementia Drugs: Additions to the Alzheimer's Disease Assessment Scale That Broaden Its Scope , 1997, Alzheimer disease and associated disorders.

[17]  P. Visser,et al.  Mild cognitive impairment as predictor for Alzheimer's disease in clinical practice: effect of age and diagnostic criteria , 2007, Psychological Medicine.

[18]  R. Bartha,et al.  Ventricular enlargement as a possible measure of Alzheimer's disease progression validated using the Alzheimer's disease neuroimaging initiative database. , 2008, Brain : a journal of neurology.

[19]  Dominic Holland,et al.  Neuroimaging Enrichment Strategy for Secondary Prevention Trials in Alzheimer Disease , 2010, Alzheimer disease and associated disorders.

[20]  Clifford R. Jack,et al.  Alliance for Aging Research AD Biomarkers Work Group: structural MRI , 2011, Neurobiology of Aging.

[21]  J. Morris,et al.  Clinical core of the Alzheimer's disease neuroimaging initiative: Progress and plans , 2010, Alzheimer's & Dementia.

[22]  I. Lombardo,et al.  The efficacy of RVT-101, a 5-ht6 receptor antagonist, as an adjunct to donepezil in adults with mild-to-moderate Alzheimer’s disease: Completer analysis of a phase 2b study , 2015, Alzheimer's & Dementia.

[23]  Liana G. Apostolova,et al.  Delphi definition of the EADC-ADNI Harmonized Protocol for hippocampal segmentation on magnetic resonance , 2015, Alzheimer's & Dementia.

[24]  Owen Carmichael,et al.  Standardization of analysis sets for reporting results from ADNI MRI data , 2013, Alzheimer's & Dementia.

[25]  J. Cummings,et al.  Alzheimer’s disease drug-development pipeline: few candidates, frequent failures , 2014, Alzheimer's Research & Therapy.

[26]  P. Narayana,et al.  Compensation for surface coil sensitivity variation in magnetic resonance imaging. , 1988, Magnetic resonance imaging.

[27]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease: Report of the NINCDS—ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease , 2011, Neurology.

[28]  Michael Weiner,et al.  Robust atrophy rate measurement in Alzheimer's disease using multi-site serial MRI: Tissue-specific intensity normalization and parameter selection , 2010, NeuroImage.

[29]  Margaret A. Pericak-Vance,et al.  Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease , 1997 .

[30]  Norbert Schuff,et al.  Mapping Alzheimer's Disease Progression in 1309 Mri Scans: Power Estimates for Different Inter-scan Intervals ☆ ⁎ and the Alzheimer's Disease Neuroimaging Initiative , 2022 .

[31]  H. Arrighi,et al.  Rate of Conversion from Prodromal Alzheimer's Disease to Alzheimer's Dementia: A Systematic Review of the Literature , 2013, Dementia and Geriatric Cognitive Disorders Extra.

[32]  A. Dale,et al.  Subregional neuroanatomical change as a biomarker for Alzheimer's disease , 2009, Proceedings of the National Academy of Sciences.

[33]  C. Jack,et al.  MRI of hippocampal volume loss in early Alzheimer's disease in relation to ApoE genotype and biomarkers , 2008, Brain : a journal of neurology.

[34]  Sébastien Ourselin,et al.  Consistent multi-time-point brain atrophy estimation from the boundary shift integral , 2012, NeuroImage.

[35]  Sébastien Ourselin,et al.  Global image registration using a symmetric block-matching approach , 2014, Journal of medical imaging.

[36]  Tetsuya Yuasa,et al.  Improved volumetric measurement of brain structure with a distortion correction procedure using an ADNI phantom. , 2013, Medical physics.

[37]  Paul A. Yushkevich,et al.  Multi-Atlas Segmentation with Joint Label Fusion , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Nick C Fox,et al.  Effects of Aβ immunization (AN1792) on MRI measures of cerebral volume in Alzheimer disease , 2005, Neurology.

[39]  J. Jolles,et al.  Affective symptoms as predictors of Alzheimer's disease in subjects with mild cognitive impairment: a 10-year follow-up study , 2009, Psychological Medicine.

[40]  Michael Weiner,et al.  Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials , 2013, NeuroImage.

[41]  M. Weiner,et al.  Magnetic resonance imaging and neuropsychological results from a trial of memantine in Alzheimer’s disease , 2011, Alzheimer's & Dementia.

[42]  Sebastien Ourselin,et al.  Imaging endpoints for clinical trials in Alzheimer’s disease , 2014, Alzheimer's Research & Therapy.

[43]  Nick C Fox,et al.  Interactive algorithms for the segmentation and quantitation of 3-D MRI brain scans. , 1997, Computer methods and programs in biomedicine.

[44]  J. Ware,et al.  Applied Longitudinal Analysis , 2004 .

[45]  W. Thies,et al.  2013 Alzheimer's disease facts and figures , 2013, Alzheimer's & Dementia.

[46]  Martin Styner,et al.  A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes , 2009, NeuroImage.

[47]  J. Haines,et al.  Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. , 1993, Science.

[48]  Emma B. Lewis,et al.  Correction of differential intensity inhomogeneity in longitudinal MR images , 2004, NeuroImage.

[49]  Bruce Fischl,et al.  Within-subject template estimation for unbiased longitudinal image analysis , 2012, NeuroImage.

[50]  N. Schuff,et al.  An MRI substudy of a donepezil clinical trial in mild cognitive impairment , 2011, Neurobiology of Aging.

[51]  A. M. Saunders,et al.  Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease , 1994, Nature Genetics.

[52]  J. Haines,et al.  Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. , 1997, JAMA.

[53]  J. Morris The Clinical Dementia Rating (CDR) , 1993, Neurology.

[54]  J. Haines,et al.  Effects of Age, Sex, and Ethnicity on the Association Between Apolipoprotein E Genotype and Alzheimer Disease: A Meta-analysis , 1997 .

[55]  Lars Bäckman,et al.  Accelerated Progression From Mild Cognitive Impairment to Dementia in People With Diabetes , 2010, Alzheimer's & Dementia.

[56]  Norbert Schuff,et al.  Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer's disease , 2010, NeuroImage.

[57]  Nick C Fox,et al.  The effect of galantamine on brain atrophy rate in subjects with mild cognitive impairment is modified by apolipoprotein E genotype: post-hoc analysis of data from a randomized controlled trial , 2014, Alzheimer's Research & Therapy.

[58]  Nick C Fox,et al.  Amyloid-related imaging abnormalities in patients with Alzheimer's disease treated with bapineuzumab: a retrospective analysis , 2012, The Lancet Neurology.

[59]  T. Goldberg,et al.  Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer's disease neuroimaging initiative. , 2011, Archives of general psychiatry.

[60]  C. Jack,et al.  Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade , 2010, The Lancet Neurology.

[61]  C. Jack,et al.  Alzheimer's Disease Neuroimaging Initiative (ADNI) , 2010, Neurology.

[62]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[63]  Nick C Fox,et al.  The clinical use of structural MRI in Alzheimer disease , 2010, Nature Reviews Neurology.

[64]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease , 1984, Neurology.

[65]  T. Iwatsubo [Alzheimer's disease Neuroimaging Initiative (ADNI)]. , 2011, Nihon rinsho. Japanese journal of clinical medicine.

[66]  Nick C Fox,et al.  The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods , 2008, Journal of magnetic resonance imaging : JMRI.

[67]  Frederik Barkhof,et al.  Hippocampal volume change measurement: Quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST , 2014, NeuroImage.