Structural MRI as a Biomarker of Disease Progression in AD Department of Diagnostic Radiology and MRI Research Lab Presented by Clifford Jack, M.D. at the November 18, 2002 Peripheral and Central Nervous System Drugs Advisory Committee Meeting

Indirect measures of disease can be valid biomarkers of progression provided a plausible biologic link exists between change in the marker and progression of the disease itself changes in the marker are empirically proven to track with independent measures of progression

Applicable MR Measurements Structural MRI (link=cell loss to atrophy) MR Spectroscopy Functional MRI Proton Diffusion Perfusion Relaxometry Magnetization Transfer Amyloid Plaque Imaging

The Rate of Medial Temporal Lobe Atrophy in Typical Aging and Alzheimer’s Disease Neurology 1998;51:

Objectives To determine the annualized rates of volume change of the hippocampus and temporal horn in cognitively normal elderly control subjects and individually matched AD patients To test the hypothesis that these rates were different

Controls(n=24)Cases (n=24)Mean ± SD Age81.04 ± 3.78 yrs80.42 ± 4.02 yrs Education14.75 ± 2.51 yrs13.21 ± 2.83 yrs MMSE28.79 ± ± 4.60 DRS ± ± Interval Between MRI 1.96 ± 0.75 yrs1.89 ± 0.68 yrs Studies Characterization Of Subjects

Annual Percent Volume Change Controls (n=24) Cases (n-24) P-value* Mean  SD Mean  SD Hippocampal -1.6   1.9 <0.001 Temporal Horn 6.2   *Rank sum test of difference between cases and controls

Conclusion Reasonable 1st step: expected differences in rates between AD and controls were observed, but it did not prove that changes in imaging tracked with changes in independent measures of disease progression Rates were approximately 2.5 times greater in AD than in individually age and gender matched control

Rates of Hippocampal Atrophy in Normal Aging, Mild Cognitive Impairment, and Alzheimer's Disease Neurology, 2000;55:

Objective:Transition Analysis To test the hypothesis that change on imaging (rates of hippocampal atrophy) match clinical change Use clinical transition (or lack of) as gold standard independent measures of progression

Methods 129 subjects from the ADRC/ADPR who met established criteria for normal controls, mild cognitive impairment (MCI), or probable AD at entry Controls and MCI patients could either remain cognitively stable or could decline MRI at initial & FU clinical assessment

Age at 1 st MMSEDuration between MRI baseline and followup MRI in years Normal-Stable 80.4 ± ± ± 0.5 (N=48) (62, 97) (23, 30) (2.0, 3.9) Normal-Decliner82.3 ± ± ± 0.4 (N=10) (76, 95) (25, 30) (2.7, 4.0) MCI-Stable77.9 ± ± ± 0.5 (N=25) (60, 92) (23, 30) (2.1, 4.0) MCI-Decliner 77.3 ± ± ± 0.6 (N=18) (64, 94) (18, 30) (2.1, 3.9) AD 73.8 ± ± ± 0.5 (N=28) (51, 93) (14, 29) (2.1, 3.9) Descriptive Information

PERCENT ANNUAL CHANGE IN HIPPOCAMPAL VOLUME BY FOLLOWUP CLINICAL GROUP Normal-Stable (N = 48)-1.7 ± 0.9 Normal-Decliner (N = 10)-2.8 ± 1.7 MCI-Stable (N = 25)-2.5 ± 1.5 MCI-Decliner (N = 18)-3.7 ± 1.5 AD (N = 28)-3.5 ± 1.8 Normal-Stable (N = 48)-1.7 ± 0.9 Normal-Decliner (N = 10)-2.8 ± 1.7 MCI-Stable (N = 25)-2.5 ± 1.5 MCI-Decliner (N = 18)-3.7 ± 1.5 AD (N = 28)-3.5 ± 1.8 Values in table represent mean ± SD (range)

Conclusion Rates of hippocampal atrophy match the change in cognitive status (or lack of) over time in elderly persons who lie along the cognitive continuum from normal to MCI to AD Validation of change in MRI volume as a biomarker of Dz progression

Rates of Atrophy by Technique and by Clinical Group

Objective Are some techniques better measures of progression than others at different disease stages? To compare the annualized rates of atrophy by technique among clinical groups (normal -stable, normal- converter, MCI -stable, MCI-converter, AD-slow progressor, and AD-fast progressor)

Structures Measured: Rates of Change Hippocampus Entorhinal Cortex (ERC) Whole Brain Ventricle

Whole Brain Ventricle GROUP Ann% ch GMM GMM HF ERC Normal Stable Mean Normal Converter Mean MCI Stable Mean MCI Converter Mean AD Slow Progressor Mean AD Fast ProgressorMean SDSD

(Mean1-Mean2) Whole Brain Ventricle (SD1*SD1)+(SD2*SD2) GMM GMM HF ERC Normal Stable vs Normal Converter MCI Stable vs MCI Converter AD Slow Progressor vs AD Fast Progressor Normal Stable vs AD Fast Progressor

Conclusions Structural MRI rates consistently follow expected correlations with clinical status and clinical transition = support for use as biomarker of Dz progression Appears to be some stage specific Dx sensitivity

Multi-Site Studies MilamileneMilamilene ObjectiveObjective: To assess the technical feasibility of using MRI measurements as a surrogate end point for disease progression in a therapeutic trial of Milamilene for AD

Methods 52 week controlled trial of Milameline, a muscarinic receptor agonist, N=450 therapeutic trial itself was not completed MRI arm of the study was continued 192 subjects from 38 different centers underwent 2 MRI with 1 yr interval hippocampal and temporal horn volume

Annual Raw Annual % Percent Change ChangeDecliners (N=192) ADAS-Cog MMSE GDS Total Hippocampal mm Total Temporal Horn Volume mm Change from Baseline in Behavioral/Cognitive and MRI Variables

Power Calculations Per arm for 50% effect size (rate reduction over 1 yr.)Per arm for 50% effect size (rate reduction over 1 yr.) ADAS-Cog 320 MMSE 241 hippocampal volume 21 temporal horn volume 54

Conclusions Technical feasibility documented Decline over time was more consistently seen with imaging measures than behavioral/cognitive measures (p<0.001) Power calculations: sample sizes imaging<< behavioral/cognitive

Structural MRI as a Biomarker In the absence of a positive therapeutic trial that incorporated imaging, the best available evidence supporting the validity of MRI as a biomarker of progression would be multiple natural history studies that consistently demonstrate concordant MRI and clinical changes

Acknowledgments R01 AG11378 R01 AG19142 AG16574 ADRC AG06786 ADPR

Mayo ADRC and ADPR Ronald C. Petersen, M.D., Ph.D. Dorla Burton Ruth H. Cha, M.S. Dianne Fitch Peter C. O’Brien, Ph.D. Nancy Haukom Steven D. Edland, Ph.D. Kris Johnson Robert Ivnik Ph.D. Martha Mandarino Glenn E. Smith, Ph.D. Joan McCormick Bradly F. Boeve, M.D. Sheryl Ness Eric G. Tangalos, M.D. Kathy Wytaske David Knopman MD

MILAMILENE: Parke-Davis M. Slomkowski, Pharm.D. S. Gracon, D.V.M. T. M. Hoover, Ph.D.

MR LAB Maria Shiung Kejal Kantarci Jeff Gunter Yuecheng Xu Mira Senkacova Kelly Stewart Marina Davtian