Lifecourse progression of AD or modeling of AD biomarker trajectories: history and pitfalls Clifford R Jack Jr MD Professor of Radiology The Alexander Family Professor of Alzheimer's Disease Research Mayo Clinic, Rochester, MN

Acknowledgements Funded in part by Grant R13 AG030995 from the National Institute on Aging RO1 AG011378 RO1 AG041851 U01 AG06786 Alexander Family Professorship in Alzheimer's disease research GHR Foundation The views expressed in written conference materials or publications and by speakers and moderators do not necessarily reflect the official policies of the Department of Health and Human Services; nor does mention by trade names, commercial practices, or organizations imply endorsement by the U.S. Government.

outline AD biomarkers Motivation for 2010 Lancet Neurology model 2013 refinements of 2010 model Evidence since publication of 2010 model Non-AD pathology in elderly and SNAP Summary

AD Biomarkers are proxies for AD pathophysiology: 6 Major – “2” categories Measures of brain A deposition – amyloid plaques Amyloid PET CSF AB 42 – low Measures of Neruofibrillary tangles (tau) CSF tau (t-tau and p-tau) – high Tau PET Measures of Neurodegeneration (progressive loss of neurons or processes with corresponding impairment in neuronal function) FDG PET – AD signature hypo metabolism Structural MRI - AD signature atrophy

MRI AD-signature FDG AD-signature Jack et al Nat Neurol Rev in press

specificity for AD pathology: ranking amyloid PET > CSF Ab 42, CSF p-tau > tau PET> CSF total tau > FDG and MRI FDG and MRI sensitive markers of neurodegeneration, correlate very well with cognition but neurodegeneration not specific to AD Atrophy and hypometabolism not specific to AD

Hippocampal Volume vs CA1 neuron counts Bobinski, Neuroscience 95, 2000 Zarow, Ann Neurol, 2005

STAND algorithm for Individual Subject Diagnosis Vemuri, Neuroimage 2008; 39(3):1186-97 and Neuroimage 2008; 42(2):559-67

Hippocampal W score by diagnosis in those with a single path Dx HS = hippocampal sclerosis; DLBD = diffuse Lewy body disease; FTD = frontotemporal degeneration; NFT = Neurofibrillary tangle-only dementia Jack Neurology 2002

outline AD biomarkers Motivation for 2010 Lancet Neurology model Order of biomarker events Shape of curves 2013 refinements of 2010 model Evidence since publication of 2010 model Non-AD pathology in elderly and SNAP Summary

Dominate theory of AD pathogenesis since 1990s Sporadic AD: Failure of Ab42 clearance The Amyloid Hypothesis of Alzheimer’s Disease: Progress and Problems on the Road to Therapeutics Hardy and Selkoe, Science 2002 Dominate theory of AD pathogenesis since 1990s

Cross sectional dissociation – time and location Problems with amyloid cascade hypothesis: state of biomarker studies ~ 2008/2009 was confusing Cross sectional dissociation – time and location direct relationship between neurodegenerative biomarker magnitude/topography and symptoms indirect relationship between amyloid biomarkers and symptoms: 30% CN abnormal, topographic dissociation Longitudinal dissociation change in cognition closely coupled to rate of neurodegeneration not to rate of amyloid deposition

AD vs. Cog Normal: Topography Jack et al, Brain 2008 5 4 3 2 1 p < 0.005 (unc) MRI

Paradox Atrophy, FDG hypo metabolism, CSF tau, symptoms group together in location and time, amyloid does not Genetics all point to Ab as causative early onset AD: Down syndrome and all known autosomal-dominant mutations - increase production of Ab42 or all Ab species Late onset AD: APOE 4 facilitates Ab deposition Protective genetics: APOE 2, Icelandic mutation primary tauopathies lead to FTLD, CBD, PSP but never to pathological AD

“solution”: modified amyloid cascade b-amyloid facilitates spread of tau, effect of b-amyloid on cognition is indirect AT  N  C A is the upstream driver of TNC sequence although topology of A and TNC differ Time shifts or ordering - biomarkers become abnormal in an ordered but temporally overlapping manner

amyloid precedes tauopathy/neurodegeneration, effect of amyloid on cognition is indirect Inglesson & Hyman, Neurology 2004 Jack et al, Brain 2008 & 2009 Mormino & Jagust, Brain 2009 Perrin & Holztman, Nat Rev 2009

Lancet Neurol 2010 Ab Amyloid = CSF Ab42 or amyloid PET imaging; Tau Mediated Neuron Injury and Dysfunction = CSF tau or FDG PET; Brain Structure = structural MRI

Sigmoid shape

Rate of atrophy accelerates as approach dementia Chan and Fox, Lancet 2003

Annual change in PIB SUVR and ventricular volume by clinical diagnosis Jack et al , Brain 2009

Lancet Neurol 2010 Ab Amyloid = CSF Ab42 or amyloid PET imaging; Tau Mediated Neuron Injury and Dysfunction = CSF tau or FDG PET; Brain Structure = structural MRI

outline AD biomarkers Motivation for 2010 Lancet Neurology model 2013 refinements of 2010 model Evidence since publication of 2010 model Non-AD pathology in elderly and SNAP Summary

Lancet Neurology, Feb, 2013

Modulators of Biomarker Temporal Relationships (Fig 5) C- = cognition in the presence of co-morbid pathologies (e.g., Lewy bodies or vascular disease) or risk amplification genes, C+ = cognition in subjects with enhanced cognitive reserve or protective genes, Co = cognition in subjects without co-morbidity or enhanced cognitive reserve. Jack et al Lancet Neurol 2010

medial temporal tauopathy often occurs without (“before”) Aβ deposition at autopsy (discussed but not incorporated into 2010 model) Isolated medial temporal tauopathy - brain stem, entorhinal cortex, hippocampus, tauopathy is common in middle age and older subjects (as young as 6yo) with no amyloid plaques - Braak 1997, 2011; Price and Morris, Annals Neurol 1999; Haroutunian, Arch Neurol 1999

Late onset AD - MTL tauopathy precedes b-amyloid Lancet Neurology, Feb, 2013 Late onset AD - MTL tauopathy precedes b-amyloid

outline AD biomarkers Motivation for 2010 Lancet Neurology model 2013 refinements of 2010 model Evidence since publication of 2010 model Non-AD pathology in elderly and SNAP Summary

AD pathology in young vs old Young – pathologically pure (except LB) Old – plaques and tangles superimposed changes of non AD pathologies CVD Non AD tauopathies – PART, grains, CTE, rarely PSP, CBD, and FTLD LB Hipp sclerosis TDP43 aging

DIAN, Bateman et al NEJM 2012 Cross sectional, years from parental age onset, difference between carriers and non-carriers

Evidence for temporal ordering of AD biomarkers model DIAN, Benzinger et al PNAS, 2012 (Mutation carries, PIB n = 121; FDG n= 116; MRI n= 137) Subcortical MRI ROI analyses – hippocampus, amygdala, N accumbens all - 10 yrs in carriers

Fleisher et al JAMA Neurol 2015

Modeling studies in elderly Support model Caroli, 2010 Jack, 2011 Buchave, 2012 Villemagne, 2013 Young, 2014 Donohue, 2014 Do not support model Jedynak, 2012

Cerebrospinal Fluid Levels of beta-Amyloid 1-42, but Not of Tau, Are Fully Changed Already 5 to 10 Years Before the Onset of Alzheimer Dementia Buchhave et al, Arch Gen Psych 2012

Brain Beta Amyloid Load Approaches a Plateau Jack et al, Neurology 2013

Brain Beta Amyloid Load Approaches a Plateau Jack et al, Neurology 2013 Relating the inverted U-shaped amyloid rates as a function of baseline SUVR to sigmoid shaped trajectory of amyloid accumulation with time – red = 195; blue = 158

Sigmoid shape - amyloid Villemange 2013 - amyloid PET Landau and Jagust 2015 - amyloid PET Shaw 2015 - CSF

outline AD biomarkers Motivation for 2010 Lancet Neurology model 2013 refinements of 2010 model Evidence since publication of 2010 model Non-AD pathology in elderly and SNAP Summary

NIA-AA Preclinical AD staging in relation to 2010 Lancet Neurol biomarker model Jack, Annals, 2012

Operationalize the NIA-AA criteria Objectives Operationalize the NIA-AA criteria How do cognitively normal subjects (n=450) in MCSA distribute in the NIA-AA scheme? Annals Neurol 2012

Distribution of 450 CN in MCSA by NIA-AA Preclinical Stage Annals, 2012 0 – 43%; 1 - 16%; 2 – 12%; 3 – 3%’; SNAP – 23%; Unclassif – 4%

CSF Ab42 and tau (either ptau or ttatu) Characteristics of SNAP subjects in different study cohorts Cognitively normal subjects Source Population characteristics Biomarkers used for classification Number (%) of SNAP/ number in cohort Age in SNAP group Number (%) of men in SNAP group Number (%) of APOE4 in SNAP group Clinical outcome Follow up time across cohort reference Mayo Clinic Study of Aging population based, cognitively normal Amyloid PET, FDG PET or hipp volume 103 (23%) / 450 79 yrs (IQR 76,84) 62 (60%) 12 (13%)   Jack et al 2012 69 (23%) / 296 81 yrs 43 (62%) 8 (12%) CN to MCI or dementia: st 0 – 5%, st 1 – 11%, st 2 – 21%, st 3 – 43%, SNAP – 10% 1.3 yrs (range 1.1 – 5.1) Knopman et al 2012 Washington University community dwelling, cognitively normal CSF Ab42 and tau (either ptau or ttatu) 72 (23%) / 311 73.6 yrs (SD 5.8) 30 (40%) 22 (31%) CDR 0 to >= .5 AD dementia: st 0 – 2%, st 2 – 26%, st 3 – 56%, SNAP – 5% 3.9 yrs (range 1 – 15) Vos et al 2013 CDR 0 to >= 0.5: survival HR A-N- ref A+N- 2.58 A+N+ 8.41 SNAP 1.12 3.70 yrs (SD 1.46) Roe et al 2013 Berkeley Aging Cohort Amyloid PET, FDG PET, hipp volume and MRI cortical ROI 19 (26%) / 72 Wirth et al 2013 Harvard Aging Brain Study 38 (23%) / 166 (IQR 75,82) 248 (63%) 7 (19%) SNAP greater decline than stage 0, less decline than A+N+ 2.09 yrs (IQR 1.9 – 2.3) Mormino et al 2014 ADNI clinical trial sites, cognitively normal CSF Ab42, and either CSF tau or hipp volume 54 (23%) / 238 survival HR stage 0 ref st 1 – 2.6, st 2 – 1.8, st 3 – 11.3, SNAP – 2.4 6 yrs (IQR 3.0 – 7.0) Toledo et al 2014 Amsterdam Dementia Cohort memory clinic, subjective memory complaint CSF Ab42 and tau 31 (23%) / 132 st 0 – 3%, st 1 – 18%, st 2 – 60%, SNAP – 10% 1.8 yrs (SD 1.3) Van Harten et al 2013

Characteristics of SNAP subjects in different study cohorts Impaired subjects source Population characteristics Biomarkers used for classification Number (%) of SNAP/ number in cohort Age in SNAP group Number (%) of men in SNAP group Number (%) of APOE4 in SNAP group Clinical outcome Follow up time across cohort reference 3- site European consortium memory clinics, MCI CSF Ab42, hipp volume, FDG PET 15 (20%) / 73   MCI to dementia: A-N- 5% A+N- 27% A+N+ 100% SNAP 47% Progressor MCI 23.3 (2-76) mo. stable MCI 31.8 (12-84) mo Prestia et al 2013 Mayo Clinic Study of Aging Population based, MCI Amyloid PET, FDG PET or hipp volume 36 (29%) / 126 82 yrs (IQR 78,85) 28 (78%) 4 (11%) A-N- 8% A+N- 0 A+N+ 16% SNAP 21% 15 mo Petersen et al 2013 ADNI clinical trial sites, MCI 10 (17%) / 58 77 yrs (IQR 73,83) 7 (70%) 4 (40%) A-N- 11% A+N+ 42% SNAP 25% 12 mo clinical trial, 7 European sites memory clinics, MCI Amyloid PET, medial temporal atrophy 7 (35%) / 20 Duara et al 2013 clinical trial sites, AD dementia Amyloid PET, CSF Ab42, FDG PET or hipp volume 6 (7%) / 92 Lowe et al 2013 ADNI plus 4- site European consortium clinical trial sites and memory clinics, MCI 34(17%) / 201 70.6 yrs (SD 9.2) 23 (68%) 10 (31%) Percent progressors*: A+N- 34% A+N+ 71% SNAP 56% 26.4 months (SD 16.8) Among SNAP Caroli et al 2015 ADNI plus multi- site European consortium CSF Ab42 and tau, medial temp atrophy, or FDG PET 220 (29%) / 776 69.4 yrs (SD 8.3) 116 (53%) 62 (32%) progression at last follow up: to AD dementia 21%, to non AD dementia 10% 2.5 (SD1.3) yrs Vos et al 2015

SNAP SNAP is a biomarker based construct denoting amyloid negative neurodegeneration positive individuals Common in cognitively normal elderly (roughly 23%) and in mild cognitive impairment (roughly 25%) APOE4 is markedly underrepresented compared to amyloid positive individuals (A+N- and A+N+) individuals Suspected to be pathologically heterogeneous, composed of a variety of non-AD etiologies common in aging

Non AD pathologies in elderly CVD Non AD tauopathies – PART, grains, CTE, rarely PSP, CBD, and FTLD LB Hipp sclerosis TDP43 “aging”

outline AD biomarkers Motivation for 2010 Lancet Neurology model 2013 refinements of 2010 model Evidence since publication of 2010 model Non-AD pathology in elderly and SNAP Summary

Difficulties with empiric modeling of AD with biomarkers in elderly Can not measure full extent of disease (creates x axis and y axis problem) Non-AD pathology (SNAP) Proportional etiological substrates of neurodegeneration and cognitive impairment unknown Mixed pathology No specific biomarkers for important pathologies Neurodegeneration and its biomarkers not specific for AD Account for aging effects on cognition/neurodegeneration

Lancet Neurol 2010 Ab Amyloid = CSF Ab42 or amyloid PET imaging; Tau Mediated Neuron Injury and Dysfunction = CSF tau or FDG PET; Brain Structure = structural MRI

Mixed AD: proportional etiological substrates of neurodegeneration are unknown and vary from person to person Amyloid-first sequence Neurodegeneration-first sequence Jack and Holtzman, Neuron 2013