Disease progression model for cognitive deterioration from Alzheimer's Disease Neuroimaging Initiative database

Ito, Kaori; Corrigan, Brian; Zhao, Qinying; French, Jonathan; Miller, Raymond; Soares, Holly; Katz, Elyse; Nicholas, Timothy; Billing, Bill; Anziano, Richard J.; Fullerton, Terence

doi:10.1016/j.jalz.2010.03.018

Cited by 118 publications

(152 citation statements)

References 24 publications

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“…Since the rate of cognitive decline in AD is not linear and may differ by disease stage, 25 these findings are particularly notable and suggest that CSF biomarkers, including VILIP-1, may complement information provided by clinical assessments in guiding prognostic and therapeutic decisions in clinical practice or in trials of diseasemodifying therapies.…”

Section: Rates Of Decline In (A and B) Clinical Dementia Rating-sum Omentioning

confidence: 99%

CSF VILIP-1 predicts rates of cognitive decline in early Alzheimer disease

Tarawneh¹,

J²,

Ladenson³

et al. 2012

Neurology

105

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Objective: Measures of neuronal damage/dysfunction are likely good surrogates for disease progression in Alzheimer disease (AD). CSF markers of neuronal injury may offer utility in predicting disease progression and guiding prognostic and outcome assessments in therapeutic trials. Visinin-like protein-1 (VILIP-1) has demonstrated potential utility as a marker of neuronal injury. We here investigate the utility of VILIP-1 and VILIP-1/A␤42 in predicting rates of cognitive decline in early AD.Methods: Individuals with a clinical diagnosis of very mild or mild AD (n ϭ 60) and baseline CSF measures of VILIP-1, tau, p-tau181, and A␤42 were followed longitudinally for an average of 2.6 years. Annual assessments included the Clinical Dementia Rating (CDR), CDR-sum of boxes (CDR-SB), and global composite scores. Mixed linear models assessed the ability of CSF biomarker measures to predict rates of cognitive decline over time.Results: Baseline CSF VILIP-1 and VILIP-1/A␤42 levels predicted rates of future decline in CDR-SB and global composite scores over the follow-up period. Individuals with CSF VILIP-1 Ն560 pg/mL (corresponding to the upper tercile) progressed much more rapidly in CDR-SB (1.61 boxes/year; p ϭ 0.0077) and global scores (Ϫ0.53 points/year; p ϭ 0.0002) than individuals with lower values (0.85 boxes/year and Ϫ0.15 points/year, respectively) over the follow-up period. CSF tau, p-tau181, tau/A␤42, and p-tau181/A␤42 also predicted more rapid cognitive decline in CDR-SB and global scores over time. Conclusion:These findings suggest that CSF VILIP-1 and VILIP-1/A␤42 predict rates of global cognitive decline similarly to tau and tau/A␤42, and may be useful CSF surrogates for neurodegeneration in early AD. Neurology The aggregation and deposition of amyloid-␤ and tau, the 2 key proteins involved in Alzheimer disease (AD) pathogenesis, are estimated to begin years prior to the onset of cognitive impairment.1,2 However, it is only after a threshold of neuronal loss is reached in vulnerable brain regions that the first signs of cognitive impairment appear. Several lines of evidence suggest that neuronal and synaptic loss is the best surrogate for disease progression and cognitive decline in AD.2,4 While CSF tau and amyloid-beta 1-42 (A␤42) each predominantly reflect a specific AD pathology, neuronal injury/neurodegeneration likely represents the cumulative outcome of different pathologic substrates. Therefore, CSF markers of neuronal injury, along with CSF tau and A␤42, may offer utility in predicting disease progression and future cognitive decline in the early stages of disease.

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Section: Rates Of Decline In (A and B) Clinical Dementia Rating-sum Omentioning

confidence: 99%

CSF VILIP-1 predicts rates of cognitive decline in early Alzheimer disease

Tarawneh¹,

J²,

Ladenson³

et al. 2012

Neurology

105

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“…However, the methodology permits adding additional subject-specific covariates to the models through design matrices A i and B i , or subject-and visit-specific covariates through A ij and B ij . Such covariates could include age, ApoE-4 genotype or education, all of which were shown to be covariates of the rate of Alzheimer's disease progression [20]. Additionally, some of the markers considered here have a fixed range of possible values, data at the extremes of these markers could be censored during the model training phase [21] in order to improve model fitting.…”

Section: Discussionmentioning

confidence: 99%

“…Detailed studies into early state longitudinal Alzheimer's disease marker trajectory dynamics, using data-driven methods, have the potential to aid the effort in the development of measures that can accurately and robustly quantify indications of the disease, even before its presymtomatic and preclinal stages. Previously, hypothetical [23,24] and experimental models [13,14,50,10,20,7,25,41,21,4,5,1,12,15,53] of disease progression based on Alzheimer's disease markers, such as cerebrospinal fluid (CSF), imaging and cognitive markers have been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, several approaches that regard the disease progression trajectory as a continuous process have been developed [13,14,50,10,20,7,25,41,21,4,5,1,12]. Many of the proposed frameworks rely on modeling cognitive scores such as the mini mental state examination (MMSE), Alzheimer's disease assessment scale (ADAS), clinical dementia rating sum of boxes (CDRSB), or Rey's audio visual learning test (RAVLT) among others, as surrogate measures of disease progression.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Delor et al [10] computed a disease onset time by adjusting subjects according to CDRSB score. Ito et al [20] developed a model to describe the longitudinal response in ADAS score, where the rate of progression was found to increase with baseline severity, age and/or ApoE-4 genotype status.…”

Section: Introductionmentioning

confidence: 99%

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Instantiated mixed effects modeling of Alzheimer's disease markers

et al. 2016

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The assessment and prediction of a subject's current and future risk of developing neurodegenerative diseases like Alzheimer's disease is of great interest in both the design of clinical trials as well as in clinical decision making. Exploring the longitudinal trajectory of markers related to neurodegeneration is an important task when selecting subjects for treatment in trials and the clinic, in the evaluation of early disease indicators and the monitoring of disease progression. Given that there is substantial intersubject variability, models that attempt to describe marker trajectories for a whole population will likely lack specificity for the representation of individual patients. Therefore, we argue here that individualized models provide a more accurate alternative that can be used for tasks such as population stratification and a subject-specific prognosis. In the work presented here, mixed effects modeling is used to derive a global and individual marker trajectories for a training population. Test subject (new patient) specific models are then instantiated using a stratified "marker signature" that defines a subpopulation of similar * Corresponding author: Ricardo Guerrero Email address: reg09@imperial.ac.uk (R. Guerrero) 1 This project was partially funded by the Innovate UK (formerly Technology Strategy Board -TSB).2 Data used in the preparation of this article was obtained from the ADNI database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how to apply/ ADNI Acknowledgement List.pdf Preprint submitted to NeuroImage June 27, 2016 cases within the training database. From this subpopulation, personalized models of the expected trajectory of several markers are subsequently estimated for unseen patients. These patient specific models of markers are shown to provide better predictions of time-to-conversion to Alzheimer's disease than population based models.

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A Longitudinal Item Response Theory Model to Characterize Cognition Over Time in Elderly Subjects

Vandemeulebroecke

Bornkamp

Krahnke³

et al. 2017

CPT Pharmacom & Syst Pharma

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For drug development in neurodegenerative diseases such as Alzheimer's disease, it is important to understand which cognitive domains carry the most information on the earliest signs of cognitive decline, and which subject characteristics are associated with a faster decline. A longitudinal Item Response Theory (IRT) model was developed for the Basel Study on the Elderly, in which the Consortium to Establish a Registry for Alzheimer's Disease – Neuropsychological Assessment Battery (with additions) and the California Verbal Learning Test were measured on 1,750 elderly subjects for up to 13.9 years. The model jointly captured the multifaceted nature of cognition and its longitudinal trajectory. The word list learning and delayed recall tasks carried the most information. Greater age at baseline, fewer years of education, and positive APOEɛ4 carrier status were associated with a faster cognitive decline. Longitudinal IRT modeling is a powerful approach for progressive diseases with multifaceted endpoints.

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Disease progression model for cognitive deterioration from Alzheimer's Disease Neuroimaging Initiative database

Cited by 118 publications

References 24 publications

CSF VILIP-1 predicts rates of cognitive decline in early Alzheimer disease

CSF VILIP-1 predicts rates of cognitive decline in early Alzheimer disease

Instantiated mixed effects modeling of Alzheimer's disease markers

A Longitudinal Item Response Theory Model to Characterize Cognition Over Time in Elderly Subjects

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