Alzheimer's disease progression model based on integrated biomarkers and clinical measures
Abstract
Yue QIU1, 2, Liang LI1, 2, Tian-yan ZHOU1, 2, Wei LU1, 2, *, Alzheimer’s Disease Neuroimaging Initiative3
1State Key Laboratory of Natural and Biomimetic Drugs (Peking University), Beijing 100191, China; 2Department of Pharmaceutics, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing 100191, China; 3Data used to prepare this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). Investigators within the ADNI contributed to the design and implementation of ADNI, and/or provided data, but did not participate in the analysis or writing of this report
Aim: Biomarkers and image markers of Alzheimer’s disease (AD), such as cerebrospinal fluid Aβ42 and p-tau, are effective predictors of cognitive decline or dementia. The aim of this study was to integrate these markers with a disease progression model and to identify their abnormal ranges.
Methods: The data of 395 participants, including 86 normal subjects, 108 early mild cognitive impairment (EMCI) subjects, 120 late mild cognitive impairment (LMCI) subjects, and 81 AD subjects were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. For the participants, baseline and long-term data on cerebrospinal fluid Aβ42 and p-tau, hippocampal volume, and ADAS-cog were available. Various linear and nonlinear models were tested to determine the associations among the ratio of Aβ42 to p-tau (the Ratio), hippocampal volume and ADAS-cog.
Results: The most likely models for the Ratio, hippocampal volume, and ADAS-cog (logistic, Emax, and linear models, respectively) were used to construct the final model. Baseline disease state had an impact on all the 3 endpoints (the Ratio, hippocampal volume, and ADAS-cog), while APOEε4 genotype and age only influence the Ratio and hippocampal volume.
Conclusion: The Ratio can be used to identify the disease stage for an individual, and clinical measures integrated with the Ratio improve the accuracy of mild cognitive impairment (MCI) to AD conversion forecasting.
Keywords: Alzheimer’s disease; mild cognitive impairment; Aβ42; p-tau; hippocampus; ADAS-cog; disease progression model; NONMEM
Data collection and sharing for this paper was provided by the Alzheimer’s disease Neuroimaging Initiative (ADNI, National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Amorfix Life Sciences Ltd; Astra Zeneca; Bayer Health Care; Bio Clinica, Inc; Biogen Idec Inc; Bristol-Myers Squibb Company; Eisai Inc; Elan Pharmaceuticals Inc; Eli Lilly and Company; F Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc; GE Healthcare; Innogenetics, NV; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Medpace, Inc; Merck & Co, Inc; Meso Scale Diagnostics, LLC; Novartis Pharmaceuticals Corporation; Pfizer Inc; Servier; Synarc Inc; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego, USA. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles, USA. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation.
* To whom correspondence should be addressed.
E-mail luwei_pk@bjmu.edu.cn
Received 2014-02-21 Accepted 2014-05-16
Keywords:
1State Key Laboratory of Natural and Biomimetic Drugs (Peking University), Beijing 100191, China; 2Department of Pharmaceutics, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing 100191, China; 3Data used to prepare this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). Investigators within the ADNI contributed to the design and implementation of ADNI, and/or provided data, but did not participate in the analysis or writing of this report
Aim: Biomarkers and image markers of Alzheimer’s disease (AD), such as cerebrospinal fluid Aβ42 and p-tau, are effective predictors of cognitive decline or dementia. The aim of this study was to integrate these markers with a disease progression model and to identify their abnormal ranges.
Methods: The data of 395 participants, including 86 normal subjects, 108 early mild cognitive impairment (EMCI) subjects, 120 late mild cognitive impairment (LMCI) subjects, and 81 AD subjects were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. For the participants, baseline and long-term data on cerebrospinal fluid Aβ42 and p-tau, hippocampal volume, and ADAS-cog were available. Various linear and nonlinear models were tested to determine the associations among the ratio of Aβ42 to p-tau (the Ratio), hippocampal volume and ADAS-cog.
Results: The most likely models for the Ratio, hippocampal volume, and ADAS-cog (logistic, Emax, and linear models, respectively) were used to construct the final model. Baseline disease state had an impact on all the 3 endpoints (the Ratio, hippocampal volume, and ADAS-cog), while APOEε4 genotype and age only influence the Ratio and hippocampal volume.
Conclusion: The Ratio can be used to identify the disease stage for an individual, and clinical measures integrated with the Ratio improve the accuracy of mild cognitive impairment (MCI) to AD conversion forecasting.
Keywords: Alzheimer’s disease; mild cognitive impairment; Aβ42; p-tau; hippocampus; ADAS-cog; disease progression model; NONMEM
Data collection and sharing for this paper was provided by the Alzheimer’s disease Neuroimaging Initiative (ADNI, National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Amorfix Life Sciences Ltd; Astra Zeneca; Bayer Health Care; Bio Clinica, Inc; Biogen Idec Inc; Bristol-Myers Squibb Company; Eisai Inc; Elan Pharmaceuticals Inc; Eli Lilly and Company; F Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc; GE Healthcare; Innogenetics, NV; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Medpace, Inc; Merck & Co, Inc; Meso Scale Diagnostics, LLC; Novartis Pharmaceuticals Corporation; Pfizer Inc; Servier; Synarc Inc; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego, USA. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles, USA. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation.
* To whom correspondence should be addressed.
E-mail luwei_pk@bjmu.edu.cn
Received 2014-02-21 Accepted 2014-05-16