EDITORIAL
A new biomarker classification system for AD, independent of cognition Agnosticism is a start
Alison D. Murray, MBChB, FRCR, PhD
Correspondence to Prof. Murray:
[email protected] Neurology® 2016;87:456–457
Clinicians and researchers interested in Alzheimer disease (AD) are confronted with an expanding number of biomarkers that can be derived from brain imaging and CSF analysis. But how do we make sense of specific and nonspecific disease indicators that increasingly coexist in normal and diseased older people? In this issue of Neurology®, Jack et al.1 present us with a scheme that indicates the binary presence or absence of 7 biomarkers in 3 categories of amyloid (A), tau (T), and neurodegeneration (N): the A/T/N classification system. The rationale for such a new descriptive system for categorizing multidomain biomarkers is 4-fold: (1) the advent of tau PET tracers providing an imaging biomarker of tau neuropathology; (2) the uncertainty regarding the temporal relationships among current b-amyloid (Ab) and tau biomarkers; (3) independence from diagnostic classification schemes that are not directly related to biomarkers, particularly independence from diagnostic classification based on cognition; and (4) the need to include all biomarker profiles that are found in all members of the population. The rationale is laudable, but how far does the proposed A/T/N classification scheme go toward achieving this? In a longitudinal study using amyloid (A) and neurodegeneration (N) biomarkers published earlier this year, Jack et al.2 found that imaging biomarkers of neurodegeneration are useful in predicting progression from normal to abnormal and abnormal to dementia states with increasing age. However, positive amyloid PET using Pittsburgh compound B increased to age 75 then plateaued. Like Ossenkoppele et al.,3 Jack et al. found that older people most often have mixed pathology and that the likelihood of amyloid positivity declines with age. The system proposed here goes further to include biomarkers of tau neuropathology and to include CSF biomarkers as well as brain MRI biomarkers. Positive amyloid PET or reduced CSF Ab42 are indicators of brain amyloid and designated A1, positive tau PET or raised CSF phosphorylated tau are indicators of tau neuropathology and designated T1, while temporoparietal deficits on [18F]-fluorodeoxyglucose–PET, temporal atrophy on MRI, or raised CSF total tau are indicators of neurodegeneration and
designated N1. Thus, an individual patient or participant’s classification can range from A2/T2/N2 to A1/T1/N1 or any of the 6 intervening states. Additional refinements include “u,” for unavailable, and “c” for conflicting, or perhaps confusing. A major advantage of the proposed A/T/N classification system is that it is distinct from traditional classification systems based on degree of cognitive impairment. The A/T/N system is agnostic of whether the person is asymptomatic or has severe dementia and seeks simply to classify based on biomarkers in 3 domains. Thus, it is equally suited to classification of normal older people, presymptomatic participants at increased risk of AD based on family history or genetic profile, or those with established AD. This is important as factors that contribute to cognitive reserve, the most established of which is education, will always confound classification based on severity of cognitive impairment.4 Cognitive reserve theory predicts that higher levels of education are associated with more severe measures of neurodegeneration than would otherwise be expected for a given level of cognitive impairment. Education and early-life socioeconomic circumstance, which are closely interrelated, can also influence disease biomarkers. Vemuri et al.5 found that highly educated APOE e4 carriers with high midlife cognitive activity had lower amyloid burdens. Staff et al.6 found poverty in childhood was associated with smaller hippocampal volumes in the seventh decade, all other factors being equal. In addition to being free from the influence of early and midlife brain experience, a further advantage is that the proposed system moves away from esteemed opinion-based guidelines to more objective biomarkers. However, the A/T/N system is not a panacea for AD research. It falls short in 3 important aspects. The major disadvantage is lack of inclusion of a biomarker of cerebrovascular disease.7 Cerebral small vessel disease is ubiquitous in the population at risk of late-onset AD and is itself an important cause of dementia. A binary classification system is limited, as it can only account for the presence or absence of a biomarker. Unless a sequential pathologic path is hypothesized, severity of atrophy or level of CSF biomarker, this system cannot be used to measure disease progression without inclusion
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of a measure of extent of Ab or tau PET positivity. And what about Lewy body disease? We already have a perfectly good imaging biomarker with I123 ioflupane single photon emission tomography, and it would not be unreasonable, at least in clinical practice, to have extended the system to include L, particularly in those who are amyloid negative and might be referred to as SNAP, or suspected non-AD pathophysiology.8 Beyond these 3 limitations, it would also have been informative and illustrative to see the A/T/N system applied to a dataset with which we are familiar, such as the Alzheimer’s Disease Neuroimaging Initiative. Overall, this is a novel take on how to report biomarkers in cognitive aging and dementia research. The authors discuss advantages and disadvantages of binary biomarker cutpoints in the context of AD research with common sense and clarity, and predict how the system could evolve with quantitative and topographic modification. With this system, one may classify an individual on the basis of CSF alone—a concept that neuroradiologists may wish to ponder. Of note, the proposed classification system moves away from esteemed opinion-based guidelines to factual reporting of relevant biomarkers in a simple system that, provided the gray areas and omissions I have described are appropriately dealt with, can only be helpful. STUDY FUNDING No targeted funding reported.
DISCLOSURE Prof. Murray receives research support from Engineering and Physical Sciences Research Council, Economic and Social Research Council,
Wellcome Trust, NHS Grampian Endowments, Scottish Funding Council, Roland Sutton Academic Trust, and the European Commission, and has received publishing royalties for Mosby’s Atlas and Text of Clinical Imaging and Practical Nuclear Medicine, 3rd ed. Go to Neurology.org for full disclosures.
REFERENCES 1. Jack CR Jr, Bennett DA, Blennow K, et al. A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 2016;87:539–547. 2. Jack CR Jr, Therneau TM, Wiste HJ, et al. Transition rates between amyloid and neurodegeneration biomarker states and to dementia: a population-based, longitudinal cohort study. Lancet Neurol 2016;15:56–64. 3. Ossenkoppele R, Jansen WJ, Rabinovici GD, et al. Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis. JAMA 2015;313:1939–1949. 4. Murray AD, Staff RT, McNeil CJ, et al. The balance between cognitive reserve and brain imaging biomarkers of cerebrovascular and Alzheimer’s diseases. Brain 2011; 134:3687–3696. 5. Vemuri P, Lesnick TG, Przybelski SA, et al. Effect of intellectual enrichment on AD biomarker trajectories: longitudinal imaging study. Neurology 2016;86:1128– 1135. 6. Staff RT, Murray AD, Ahearn TS, Mustafa N, Fox HC, Whalley LJ. Childhood socioeconomic status and adult brain size: childhood socioeconomic status influences adult hippocampal size. Ann Neurol 2012;71:653– 660. 7. Wardlaw JM, Smith EE, Biessels GJ, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013;12:822–838. 8. Jack CR, Knopman DS, Chetelat G, et al. Suspected nonAlzheimer disease pathophysiology—concept and controversy. Nat Rev Neurol 2016;12:117–124.
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