|Alzheimer′s disease progression model based on integrated biomarkers and clinical measures|
|Qiu, Yue1,2; Li, Liang1,2; Zhou, Tian-yan1,2; Lu, Wei1,2; Alzheimer′ s Dis Neuroimaging|
|关键词||Alzheimer&prime s disease mild cognitive impairment A beta(42) p-tau hippocampus ADAS-cog disease progression model NONMEM|
|刊名||ACTA PHARMACOLOGICA SINICA|
|WOS标题词||Science & Technology|
|类目[WOS]||Chemistry, Multidisciplinary ; Pharmacology & Pharmacy|
|研究领域[WOS]||Chemistry ; Pharmacology & Pharmacy|
|关键词[WOS]||COGNITIVE IMPAIRMENT ; IN-VIVO ; NEURODEGENERATION ; THERAPEUTICS ; AUTOPHAGY ; MELATONIN ; DEMENTIA ; ATROPHY ; PLAQUES ; TAU|
Aim: Biomarkers and image markers of Alzheimer′s disease (AD), such as cerebrospinal fluid A beta(42) 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 A beta(42) 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 A beta(42) to p-tau (the Ratio), hippocampal volume and ADAS-cog.
Results: The most likely models for the Ratio, hippocampal volume, and ADAS-cog (logistic, E-max, 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 APOE epsilon 4 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.
|项目编号||U01 AG024904 ; P30 AG010129 ; K01 AG030514|
|资助机构||Alzheimer&prime ; s disease Neuroimaging Initiative (ADNI, National Institutes of Health Grant) ; National Institute on Aging ; National Institute of Biomedical Imaging and Bioengineering ; Canadian Institutes of Health Research ; NIH ; Dana Foundation|
|作者单位||1.Peking Univ, State Key Lab Nat & Biomimet Drugs, Beijing 100191, Peoples R China|
2.Peking Univ, Hlth Sci Ctr, Dept Pharmaceut, Sch Pharmaceut Sci, Beijing 100191, Peoples R China
|Qiu, Yue,Li, Liang,Zhou, Tian-yan,et al. Alzheimer′s disease progression model based on integrated biomarkers and clinical measures[J]. ACTA PHARMACOLOGICA SINICA,2014,35(9):1111-1120.|
|APA||Qiu, Yue,Li, Liang,Zhou, Tian-yan,Lu, Wei,Alzheimer&prime,&s Dis Neuroimaging.(2014).Alzheimer′s disease progression model based on integrated biomarkers and clinical measures.ACTA PHARMACOLOGICA SINICA,35(9),1111-1120.|
|MLA||Qiu, Yue,et al."Alzheimer′s disease progression model based on integrated biomarkers and clinical measures".ACTA PHARMACOLOGICA SINICA 35.9(2014):1111-1120.|