Literature DB >> 19592311

Disease progression meta-analysis model in Alzheimer's disease.

Kaori Ito1, Sima Ahadieh, Brian Corrigan, Jonathan French, Terence Fullerton, Thomas Tensfeldt.   

Abstract

BACKGROUND: Various authors have evaluated disease progression in Alzheimer's disease (AD), using patient data from individual clinical studies or pooled data across various trials. We conducted a systematic review of public data sources from 1990 to 2008 for all available AChE inhibitor studies, as well as clinical studies that evaluated the rate of deterioration in AD patients. Unique to this analysis, we developed a model based on literature data to describe the longitudinal response in the Alzheimer's Disease Assessment Scale-Cognitive (ADAS-cog) (change from baseline) in mild to moderate severity AD patients. The model was used to estimate disease progression for both placebo-treated patients and acetylcholinesterase (AChE)-inhibitor treated patients, and factors that affected disease progression.
METHODS: We collected 576 mean ADAS-cog changes from baseline data points of 52 trials, representing data from approximately 19,972 patients and more than 84,000 individual observations. The model described the rate of disease progression, the evident placebo effect, and the symptomatic effect of AChE-inhibitors. Baseline ADAS-cog, Mini-Mental State Examination score, age, and year of publication were tested as covariates.
RESULTS: The disease progression in mild to moderate AD patients across all available and relevant literature sources was estimated as 5.5 points per year. An Emax-type model best described the symptomatic drug effect of AChE inhibitors. The rate of disease progression (underlying disease progression) was no different between placebo and AChE-inhibitors groups. Baseline ADAS-cog is a significant covariate in disease progression. Baseline age was also tested as a covariate in the rate of disease progression, but the model was unable to describe any effects of age, likely because of the narrow distribution of mean age (literature-level analysis). There was no significant impact of publication year in the model.
CONCLUSIONS: Baseline ADAS-cog is a significant covariate affecting the rate of disease progression, and it describes or at least explains the different rates of deterioration evident in early or late stages of the disease. There was no significant impact of publication year in the model, suggesting that disease progression has not slowed in more recent trials. 2010 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

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Year:  2009        PMID: 19592311     DOI: 10.1016/j.jalz.2009.05.665

Source DB:  PubMed          Journal:  Alzheimers Dement        ISSN: 1552-5260            Impact factor:   21.566


  68 in total

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Authors:  Michael W Weiner; Paul S Aisen; Clifford R Jack; William J Jagust; John Q Trojanowski; Leslie Shaw; Andrew J Saykin; John C Morris; Nigel Cairns; Laurel A Beckett; Arthur Toga; Robert Green; Sarah Walter; Holly Soares; Peter Snyder; Eric Siemers; William Potter; Patricia E Cole; Mark Schmidt
Journal:  Alzheimers Dement       Date:  2010-05       Impact factor: 21.566

Review 2.  The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception.

Authors:  Michael W Weiner; Dallas P Veitch; Paul S Aisen; Laurel A Beckett; Nigel J Cairns; Robert C Green; Danielle Harvey; Clifford R Jack; William Jagust; Enchi Liu; John C Morris; Ronald C Petersen; Andrew J Saykin; Mark E Schmidt; Leslie Shaw; Judith A Siuciak; Holly Soares; Arthur W Toga; John Q Trojanowski
Journal:  Alzheimers Dement       Date:  2011-11-02       Impact factor: 21.566

3.  Efficacy of a medical food on cognition in Alzheimer's disease: results from secondary analyses of a randomized, controlled trial.

Authors:  P J G H Kamphuis; F R J Verhey; M G M Olde Rikkert; J W R Twisk; S H N Swinkels; P Scheltens
Journal:  J Nutr Health Aging       Date:  2011-08       Impact factor: 4.075

Review 4.  Integrating ADNI results into Alzheimer's disease drug development programs.

Authors:  Jeffrey L Cummings
Journal:  Neurobiol Aging       Date:  2010-05-05       Impact factor: 4.673

5.  Longitudinal aggregate data model-based meta-analysis with NONMEM: approaches to handling within treatment arm correlation.

Authors:  Jae Eun Ahn; Jonathan L French
Journal:  J Pharmacokinet Pharmacodyn       Date:  2010-04-01       Impact factor: 2.745

Review 6.  The rationale for deep brain stimulation in Alzheimer's disease.

Authors:  Zaman Mirzadeh; Ausaf Bari; Andres M Lozano
Journal:  J Neural Transm (Vienna)       Date:  2015-10-06       Impact factor: 3.575

7.  Population pharmacodynamic modeling of various extended-release formulations of methylphenidate in children with attention deficit hyperactivity disorder via meta-analysis.

Authors:  Holly Kimko; Ekaterina Gibiansky; Leonid Gibiansky; H Lynn Starr; Joris Berwaerts; Joseph Massarella; Frank Wiegand
Journal:  J Pharmacokinet Pharmacodyn       Date:  2012-01-22       Impact factor: 2.745

8.  Organosulfur compound protects against memory decline induced by scopolamine through modulation of oxidative stress and Na+/K+ ATPase activity in mice.

Authors:  Fernanda D da Silva; Mikaela P Pinz; Renata L de Oliveira; Karline C Rodrigues; Francine R Ianiski; Mariana M Bassaco; Claudio C Silveira; Cristiano R Jesse; Silvane S Roman; Ethel A Wilhelm; Cristiane Luchese
Journal:  Metab Brain Dis       Date:  2017-07-14       Impact factor: 3.584

Review 9.  Clinical pharmacology = disease progression + drug action.

Authors:  Nick Holford
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

10.  Repetitive transcranial magnetic stimulation (rTMS) combined with cognitive training is a safe and effective modality for the treatment of Alzheimer's disease: clinical experience.

Authors:  Jose Martin Rabey; Evgenia Dobronevsky
Journal:  J Neural Transm (Vienna)       Date:  2016-09-08       Impact factor: 3.575

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