A computational neurodegenerative disease progression score: method and results with the Alzheimer's disease Neuroimaging Initiative cohort

Department of Applied Math and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA
NeuroImage (Impact Factor: 6.36). 08/2012; 63(3):1478-1486. DOI: 10.1016/j.neuroimage.2012.07.059
Source: PubMed


While neurodegenerative diseases are characterized by steady degeneration over relatively long timelines, it is widely believed that the early stages are the most promising for therapeutic intervention, before irreversible neuronal loss occurs. Developing a therapeutic response requires a precise measure of disease progression. However, since the early stages are for the most part asymptomatic, obtaining accurate measures of disease progression is difficult. Longitudinal databases of hundreds of subjects observed during several years with tens of validated biomarkers are becoming available, allowing the use of computational methods. We propose a widely applicable statistical methodology for creating a disease progression score (DPS), using multiple biomarkers, for subjects with a neurodegenerative disease. The proposed methodology was evaluated for Alzheimer's disease (AD) using the publicly available AD Neuroimaging Initiative (ADNI) database, yielding an Alzheimer's DPS or ADPS score for each subject and each time-point in the database. In addition, a common description of biomarker changes was produced allowing for an ordering of the biomarkers. The Rey Auditory Verbal Learning Test delayed recall was found to be the earliest biomarker to become abnormal. The group of biomarkers comprising the volume of the hippocampus and the protein concentration amyloid beta and Tau were next in the timeline, and these were followed by three cognitive biomarkers. The proposed methodology thus has potential to stage individuals according to their state of disease progression relative to a population and to deduce common behaviors of biomarkers in the disease itself.

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    • "The fact that subtle cognitive decline has traditionally been viewed as the last marker to be affected in preclinical AD may be due to the routine use of crude, insensitive measures of cognitive abilities (i.e., rating scales or screening measures) or variations in the way subtle cognitive decline is defined. By contrast, sensitive memory measures (e.g., Rey Auditory Verbal Learning Test) have been shown to be the earliest markers to become abnormal in the progression to AD relative to screening measures, rating scales, and biomarkers [14], and are more robust predictors of conversion from MCI to AD than most biomarkers [15] [16]. "
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    ABSTRACT: The NIA-AA criteria for "preclinical" Alzheimer's disease (AD) propose a staging method in which AD biomarkers follow an invariable temporal sequence in accordance with the amyloid cascade hypothesis. However, recent findings do not align with the proposed temporal sequence and "subtle cognitive decline," which has not been definitively operationalized, may occur earlier than suggested in preclinical AD. We aimed to define "subtle cognitive decline" using sensitive and reliable neuropsychological tests, and to examine the number and sequence of biomarker abnormalities in the Alzheimer's Disease Neuroimaging Initiative (ADNI). 570 cognitively normal ADNI participants were classified based on NIA-AA criteria and separately based on the number of abnormal biomarkers/cognitive markers associated with preclinical AD that each individual possessed. Results revealed that neurodegeneration alone was 2.5 times more common than amyloidosis alone at baseline. For those who demonstrated only one abnormal biomarker at baseline and later progressed to mild cognitive impairment/AD, neurodegeneration alonewas most common, followed by amyloidosis alone or subtle cognitive decline alone, which were equally common. Findings suggest that most individuals do not follow the temporal order proposed by NIA-AA criteria. We provide an operational definition of subtle cognitive decline that captures both cognitive and functional decline. Additionally, we offer a new approach for staging preclinical AD based on number of abnormal biomarkers, without regard to their temporal order of occurrence. This method of characterizing preclinical AD is more parsimonious than the NIA-AA staging system and does not presume that all patients follow a singular invariant expression of the disease.
    Journal of Alzheimer's disease: JAD 07/2015; 47(1):231-242. DOI:10.3233/JAD-150128 · 4.15 Impact Factor
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    • "Jedynak et al. recently proposed a method to investigate the temporal evolution of disease progression using selected cognitive and biological markers related to AD and applied this method to data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) [1]. In addition to estimating the longitudinal trajectories that best describe the biomarker data for the entire sample, the method calculates an Alzheimer's Disease Progression Score (ADPS) for each participant based on the participant's own biomarker measurements. "
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    ABSTRACT: Background The delineation of the relative temporal trajectories of specific cognitive measures associated with Alzheimer's disease (AD) is important for evaluating preclinical markers and monitoring disease progression. Methods We characterized the temporal trajectories of measures of verbal episodic memory, short-term visual memory, and mental status using data from 895 participants in the Baltimore Longitudinal Study of Aging. Results The California Verbal Learning Test (CVLT) immediate recall was the first measure to decline, followed by CVLT delayed recall. However, further along the disease progression scale, CVLT delayed recall and visual memory changed more rapidly than CVLT immediate recall. Conclusions Our findings reconcile reports of early changes in immediate recall with greater reliance on delayed recall performance in clinical settings. Moreover, the utility of cognitive markers in evaluating AD progression depends on the stage of cognitive decline, suggesting that optimal endpoints in therapeutic trials may vary across different stages of the disease process.
    Alzheimer's and Dementia 11/2014; 10(6). DOI:10.1016/j.jalz.2014.04.520 · 12.41 Impact Factor
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    • "E-mail address: tgoldber@nshs.edu of learning impairment and increased hippocampal atrophy had the highest risk of conversion to AD [4]. Jedynak and colleagues [5], using advanced statistical methods, found that inflection of a delayed memory measure preceded that of other biomarkers (cerebrospinal fluid [CSF] levels and hippocampal volumes) on the progression from MCI to AD in the ADNI database. This set of findings was recently the subject of an editorial that highlighted the otherwise often undervalued importance of cognitive measures as early markers of AD progression [6]. "
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    ABSTRACT: Background This study examined the predictive value of different classes of markers in the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) over an extended 4-year follow-up in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Methods MCI patients were assessed for clinical, cognitive, magnetic resonance imaging (MRI), positron emission tomography–fluorodeoxyglucose (PET-FDG), and cerebrospinal fluid (CSF) markers at baseline and were followed on a yearly basis for 4 years to ascertain progression to AD. Logistic regression models were fitted in clusters, including demographics, APOE genotype, cognitive markers, and biomarkers (morphometric, PET-FDG, CSF, amyloid-β, and tau). Results The predictive model at 4 years revealed that two cognitive measures, an episodic memory measure and a Clock Drawing screening test, were the best predictors of conversion (area under the curve = 0.78). Conclusions This model of prediction is consistent with the previous model at 2 years, thus highlighting the importance of cognitive measures in progression from MCI to AD. Cognitive markers were more robust predictors than biomarkers.
    Alzheimer's & dementia: the journal of the Alzheimer's Association 11/2014; 10(6). DOI:10.1016/j.jalz.2013.11.009 · 12.41 Impact Factor
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