Beta amyloid, tau, neuroimaging, and cognition: sequence modeling of biomarkers for Alzheimer’s Disease

Alzheimer’s disease (AD) is associated with a cascade of pathological events involving formation of amyloid-based neuritic plaques and tau-based neurofibrillary tangles, changes in brain structure and function, and eventually, cognitive impairment and functional disability. The precise sequence of when each of these disease markers becomes abnormal is not yet clearly understood. The present study systematically tested the relationship between classes of biomarkers according to a proposed model of temporal sequence by Jack et al. (Lancet Neurology 9:119–128, 2010). We examined temporal relations among four classes of biomarkers: CSF Aβ, CSF tau, neuroimaging variables (hippocampal volume, ventricular volume, FDG PET), and cognitive variables (memory and executive function). Random effects modeling of longitudinal data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to test hypotheses that putative earlier markers of AD predicted change in later markers, and that intervening markers reduced effects of earlier on later markers. Specifically, we hypothesized that CSF tau would explain CSF Aβ’s relation to neuroimaging and cognitive variables, and neuroimaging variables would explain tau’s relation to cognitive variables. Consistent with hypotheses, results indicated that CSF Aβ effects on cognition change were substantially attenuated by CSF tau and measures of brain structure and function, and CSF tau effects on cognitive change were attenuated by neuroimaging variables. Contrary to hypotheses, CSF Aβ and CSF tau were observed to have independent effects on neuroimaging and CSF tau had a direct effect on baseline cognition independent of brain structure and function. These results have implications for clarifying the temporal sequence of AD changes and corresponding biomarkers.

[1]  Charles DeCarli,et al.  Heterogeneity of cognitive trajectories in diverse older persons. , 2010, Psychology and aging.

[2]  Denise C. Park,et al.  Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[3]  Binbing Yu,et al.  Joint Modeling for Cognitive Trajectory and Risk of Dementia in the Presence of Death , 2010, Biometrics.

[4]  Anders M. Dale,et al.  Sequence-independent segmentation of magnetic resonance images , 2004, NeuroImage.

[5]  W. Jagust,et al.  The Alzheimer's Disease Neuroimaging Initiative positron emission tomography core , 2010, Alzheimer's & Dementia.

[6]  Jean-François Dartigues,et al.  Random Changepoint Model for Joint Modeling of Cognitive Decline and Dementia , 2006, Biometrics.

[7]  S. Maxwell,et al.  Testing mediational models with longitudinal data: questions and tips in the use of structural equation modeling. , 2003, Journal of abnormal psychology.

[8]  F. David,et al.  Statistical Estimates and Transformed Beta-Variables. , 1960 .

[9]  Denise C. Park,et al.  Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging and the Alzheimer's Association workgroup , 2011 .

[10]  Vijaya L. Melnick,et al.  Alzheimer’s Dementia , 1985, Contemporary Issues in Biomedicine, Ethics, and Society.

[11]  J. Schneider,et al.  Individual differences in rates of change in cognitive abilities of older persons. , 2002, Psychology and aging.

[12]  Matthew S. Fritz,et al.  Mediation analysis. , 2019, Annual review of psychology.

[13]  D. Butterfield,et al.  Evidence that amyloid beta-peptide-induced lipid peroxidation and its sequelae in Alzheimer’s disease brain contribute to neuronal death , 2002, Neurobiology of Aging.

[14]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[15]  M. Bobinski,et al.  Prediction of cognitive decline in normal elderly subjects with 2-[18F]fluoro-2-deoxy-d-glucose/positron-emission tomography (FDG/PET) , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Carol Brayne,et al.  Age, neuropathology, and dementia. , 2009, The New England journal of medicine.

[17]  D. Bennett,et al.  Neuropathological Associates of Multiple Cognitive Functions in Two Community-Based Cohorts of Older Adults , 2010, Journal of the International Neuropsychological Society.

[18]  Yaakov Stern,et al.  Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease , 2008, NeuroImage.

[19]  H. Hampel,et al.  Advances in the development of biomarkers for Alzheimer’s disease: from CSF total tau and Aβ1–42 proteins to phosphorylated tau protein , 2003, Brain Research Bulletin.

[20]  Cognition and neuropathology in aging: multidimensional perspectives from the Rush Religious Orders Study and Rush Memory And Aging Project. , 2011, Current Alzheimer research.

[21]  Alzheimer’s Association,et al.  2009 Alzheimer's disease facts and figures , 2009, Alzheimer's & Dementia.

[22]  Jean-Claude Baron,et al.  Resting-state brain glucose utilization as measured by PET is directly related to regional synaptophysin levels: a study in baboons , 2003, NeuroImage.

[23]  J. Morris,et al.  The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging and the Alzheimer's Association workgroup , 2011 .

[24]  J. Morris,et al.  Longitudinal study of the transition from healthy aging to Alzheimer disease. , 2009, Archives of neurology.

[25]  K. Blennow,et al.  CSF Aβ 42 levels correlate with amyloid-neuropathology in a population-based autopsy study , 2003, Neurology.

[26]  E. Tangalos,et al.  The Auditory-Verbal Learning Test (AVLT): Norms for Ages 55 Years and Older , 1990 .

[27]  W. M. van der Flier,et al.  CSF biomarkers predict rate of cognitive decline in Alzheimer disease , 2009, Neurology.

[28]  Kewei Chen,et al.  Correlations between apolipoprotein E epsilon4 gene dose and brain-imaging measurements of regional hypometabolism. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[29]  S. Maxwell,et al.  Bias in cross-sectional analyses of longitudinal mediation. , 2007, Psychological methods.

[30]  D. Bennett,et al.  Alzheimer disease in the US population: prevalence estimates using the 2000 census. , 2003, Archives of neurology.

[31]  C. Jack,et al.  Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade , 2010, The Lancet Neurology.

[32]  F. LaFerla,et al.  Reduction of Soluble Aβ and Tau, but Not Soluble Aβ Alone, Ameliorates Cognitive Decline in Transgenic Mice with Plaques and Tangles* , 2006, Journal of Biological Chemistry.

[33]  G. Alexander,et al.  Correlations between apolipoprotein E ε4 gene dose and brain-imaging measurements of regional hypometabolism , 2005 .

[34]  Ana Ivelisse Avilés,et al.  Linear Mixed Models for Longitudinal Data , 2001, Technometrics.

[35]  P. Diggle Analysis of Longitudinal Data , 1995 .

[36]  H. Braak,et al.  Staging of Alzheimer-related cortical destruction. , 1997, International psychogeriatrics.

[37]  Jessica C. Payne-Murphy,et al.  Trajectory of mild cognitive impairment onset , 2008, Journal of the International Neuropsychological Society.

[38]  Felice Sun,et al.  Brain imaging evidence of preclinical Alzheimer's disease in normal aging , 2006, Annals of neurology.

[39]  H. Soininen,et al.  CSF phosphorylated tau protein correlates with neocortical neurofibrillary pathology in Alzheimer's disease. , 2006, Brain : a journal of neurology.

[40]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[41]  C. Jack,et al.  Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD , 2004, Neurology.

[42]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.

[43]  C. Jack,et al.  MRI and CSF biomarkers in normal, MCI, and AD subjects , 2009, Neurology.