Update on the biomarker core of the Alzheimer's Disease Neuroimaging Initiative subjects

Abstract

Here, we review progress by the Penn Biomarker Core in the Alzheimer's Disease Neuroimaging Initiative (ADNI) toward developing a pathological cerebrospinal fluid (CSF) and plasma biomarker signature for mild Alzheimer's disease (AD) as well as a biomarker profile that predicts conversion of mild cognitive impairment (MCI) and/or normal control subjects to AD. The Penn Biomarker Core also collaborated with other ADNI Cores to integrate data across ADNI to temporally order changes in clinical measures, imaging data, and chemical biomarkers that serve as mileposts and predictors of the conversion of normal control to MCI as well as MCI to AD, and the progression of AD. Initial CSF studies by the ADNI Biomarker Core revealed a pathological CSF biomarker signature of AD defined by the combination of Abeta1-42 and total tau (T-tau) that effectively delineates mild AD in the large multisite prospective clinical investigation conducted in ADNI. This signature appears to predict conversion from MCI to AD. Data fusion efforts across ADNI Cores generated a model for the temporal ordering of AD biomarkers which suggests that Abeta amyloid biomarkers become abnormal first, followed by changes in neurodegenerative biomarkers (CSF tau, F-18 fluorodeoxyglucose-positron emission tomography, magnetic resonance imaging) with the onset of clinical symptoms. The timing of these changes varies in individual patients due to genetic and environmental factors that increase or decrease an individual's resilience in response to progressive accumulations of AD pathologies. Further studies in ADNI will refine this model and render the biomarkers studied in ADNI more applicable to routine diagnosis and to clinical trials of disease modifying therapies.

Figure 1

This figure shows a hypothetical time line for the onset and progression of AD neurodegeneration and cognitive impairments progressing from NC to MCI and on to AD. The only highly predictive biomarkers for AD years before disease onset are genetic mutations that are pathogenic for familial AD (FAD), and these can be detected from birth onwards to identify those individuals in FAD kindreds who will go on to develop AD later in life. However, the emphasis in this review is on promising AD biomarkers studied in ADNI for the diagnosis of AD and predicting conversion from NC and/or MCI status to AD. Age from birth onwards is indicated in the timeline at the bottom of the figure and the green, blue and red bars indicate the time points at which preventive, disease modifying and symptomatic interventions, respectively, are likely to be most effective, and the aqua bar identifies milestones in the pathobiology of AD that culminate in death and autopsy confirmation of AD. However, AD biomarkers are needed to accelerate efforts to test the efficacy of preventive and disease modifying therapies for AD. To do this, it is important to determine the temporal ordering of AD biomarkers, and the proposed ADNI model illustrating the ordering of biomarkers of AD pathology relative to stages in the clinical onset and progression of AD is shown in the insert at the upper right of the figure adjacent to a depiction to the left of the defining pathologies of AD, i.e. plaques and tangles. In the insert on the right, clinical disease is on the horizontal axis and it is divided into three stages; cognitively normal, MCI and dementia. The vertical axis indicates the range from normal to abnormal for each of the biomarkers as well as for measures of memory and functional impairments. Amyloid imaging and CSF Aβ are biomarkers of brain Aβ amyloidosis. CSF tau and FDG PET are biomarkers of neuron injury and degeneration while structural MRI is a biomarker of abnormal brain morphology.