TY - JOUR
T1 - Neurofibrillary tangle stage and the rate of progression of Alzheimer symptoms
T2 - Modeling using an autopsy cohort and application to clinical trial design
AU - Qian, Jing
AU - Hyman, Bradley T.
AU - Betensky, Rebecca A.
N1 - Funding Information:
Supported by the Massachusetts Alzheimer Disease Research Center P50 AG005134 (Principal Investigator, Bradley Hyman, MD, PhD, Massachusetts General Hospital, Charlestown). Dr Betensky was supported by grant AG005134 and Drs Qian and Betensky were partially funded by grants R21AG053695 and R01NS094610 from the Harvard NeuroDiscovery Center. The National Alzheimer Coordinating Center database is funded by National Institutes of Aging/National Institutes of Health grant U01 AG016976. National Alzheimer Coordinating Center data are contributed by the National Institutes of Aging-funded Alzheimer Disease Centers: P30 AG019610 (PI, Eric Reiman, MD, Banner Alzheimer Institute, Phoenix, Arizona), P30 AG013846 (PI, Neil Kowall, MD, Boston University, Boston, Massachusetts), P50 AG008702 (PI, Scott Small, MD, Columbia University, New York, New York), P50 AG025688 (PI, Allan Levey, MD, PhD, Emory University, Atlanta, Georgia), P50 AG047266 (PI, Todd Golde, MD, PhD, University of Florida, Gainesville), P30 AG010133 (PI, Andrew Saykin, PsyD, Indiana University, Bloomington), P50 AG005146 (PI, Marilyn Albert, PhD, Johns Hopkins University, Baltimore, Maryland), P50 AG005134 (PI, Bradley Hyman, MD, PhD, Massachusetts General Hospital, Charlestown, Massachusetts), P50 AG016574 (PI, Ronald Petersen, MD, PhD, Mayo Clinic, Rochester, Minnesota), P50 AG005138 (PI, Mary Sano, PhD, Mt Sinai School of Medicine, New York, New York), P30 AG008051 (PI, Steven Ferris, PhD, New York University, New York, New York), P30 AG013854 (PI, M. Marsel Mesulam, MD, Northwestern University, Chicago, Illinois), P30 AG008017 (PI, Jeffrey Kaye, MD, Oregon Health & Science University, Portland), P30 AG010161 (PI, David Bennett, MD, Rush University, Chicago, Illinois), P50 AG047366 (PI, Victor Henderson, MD, MS, Stanford University, Stanford, California), P30 AG010129 (PI, Charles DeCarli, MD, University California, Davis), P50 AG016573 (PI, Frank LaFerla, PhD, University of California, Irvine), P50 AG016570 (PI, Marie-Francoise Chesselet, MD, PhD, University of California, Los Angeles), P50 AG005131 (PI, Douglas Galasko, MD, University of California, San Diego), P50 AG023501 (PI, Bruce Miller, MD, University of California, San Francisco), P30 AG035982 (PI, Russell Swerdlow, MD, University of Kansas, Lawrence), P30 AG028383 (PI, Linda Van Eldik, PhD, University of Kentucky, Lexington), P30 AG010124 (PI, John Trojanowski, MD, PhD, University of Pennsylvania, Philadelphia, ), P50 AG005133 (PI, Oscar Lopez, MD, University of Pittsburgh, Pittsburgh, Pennsylvania), P50 AG005142 (PI, Helena Chui, MD, University of Southern California, Los Angeles), P30 AG012300 (PI, Roger Rosenberg, MD, University of Texas Southwestern, Dallas), P50 AG005136 (PI, Thomas Montine, MD, PhD, University ofWashington, Seattle), P50 AG033514 (PI Sanjay Asthana, MD, FRCP, University of Wisconsin, Madison), P50 AG005681 (PI, John Morris, MD, Washington University, St. Louis, Missouri), and P50 AG047270 (PI, Stephen Strittmatter, MD, PhD, Yale University, New Haven, Connecticut).
Publisher Copyright:
© 2017 American Medical Association. All rights reserved.
PY - 2017/5
Y1 - 2017/5
N2 - IMPORTANCE The heterogeneity of rate of clinical progression among patients with Alzheimer disease leads to difficulty in providing clinical counseling and diminishes the power of clinical trials using disease-modifying agents. OBJECTIVE To gain a better understanding of the factors that affect the natural history of progression in Alzheimer disease for the purpose of improving both clinical care and clinical trial design. DESIGN, SETTING, AND PARTICIPANTS A longitudinal cohort study of aging from 2005 to 2014 in the National Alzheimer Coordinating Center. Clinical evaluation of the participants was conducted in 31 National Institute on Aging's Alzheimer Disease Centers. Nine hundred eighty-four participants in the National Alzheimer Coordinating Center cohort study who died and underwent autopsy and met inclusion and exclusion criteria. MAIN OUTCOMES AND MEASURES We sought to model the possibility that knowledge of neurofibrillary tangle burden in the presence of moderate or frequent plaques would add to the ability to predict clinical rate of progression during the ensuing 2 to 3 years.We examined the National Alzheimer Coordinating Center autopsy data to evaluate the effect of different neurofibrillary tangle stages on the rates of progression on several standard clinical instruments: the Clinical Dementia Rating Scale sum of boxes, a verbal memory test (logical memory), and a controlled oral word association task (vegetable naming), implementing a reverse-time longitudinal modeling approach in conjunction with latent class estimation to adjust for unmeasured sources of heterogeneity. RESULTS Several correlations between clinical variables and neurocognitive performance suggest a basis for heterogeneity: Higher education level was associated with lower Clinical Dementia Rating Scale sum of boxes (β = -0.19; P < .001), and frequent vs moderate neuritic plaques were associated with higher Clinical Dementia Rating Scale sum of boxes (β = 1.64; P < .001) and lower logical memory score (β = -1.07; P = .005). The rate of change of the clinical and cognitive scores varied depending on Braak stage, when adjusting for plaques, age of death, sex, education, and APOE genotype. For example, comparing high vs low Braak stage with other variables fixed, the logical memory score decreased a substantial 0.38 additional units per year (95%CI, -0.70 to -0.06; P = .02). Using these data, we estimate that a 300-participant clinical trial with end point of a 20% improvement in slope in rate of change of Clinical Dementia Rating Scale sum of boxes has 89%power when all participants in the trial are from the high Braak stage, compared with 29% power if Braak stage had not used for eligibility. CONCLUSIONS AND RELEVANCE We found that knowledge of neurofibrillary tangle stage, modeled as the sort of information that could be available from tau positron-emission tomography scans and its use to determine eligibility to a trial, could dramatically improve the power of clinical trials and equivalently reduce the required sample sizes of clinical trials.
AB - IMPORTANCE The heterogeneity of rate of clinical progression among patients with Alzheimer disease leads to difficulty in providing clinical counseling and diminishes the power of clinical trials using disease-modifying agents. OBJECTIVE To gain a better understanding of the factors that affect the natural history of progression in Alzheimer disease for the purpose of improving both clinical care and clinical trial design. DESIGN, SETTING, AND PARTICIPANTS A longitudinal cohort study of aging from 2005 to 2014 in the National Alzheimer Coordinating Center. Clinical evaluation of the participants was conducted in 31 National Institute on Aging's Alzheimer Disease Centers. Nine hundred eighty-four participants in the National Alzheimer Coordinating Center cohort study who died and underwent autopsy and met inclusion and exclusion criteria. MAIN OUTCOMES AND MEASURES We sought to model the possibility that knowledge of neurofibrillary tangle burden in the presence of moderate or frequent plaques would add to the ability to predict clinical rate of progression during the ensuing 2 to 3 years.We examined the National Alzheimer Coordinating Center autopsy data to evaluate the effect of different neurofibrillary tangle stages on the rates of progression on several standard clinical instruments: the Clinical Dementia Rating Scale sum of boxes, a verbal memory test (logical memory), and a controlled oral word association task (vegetable naming), implementing a reverse-time longitudinal modeling approach in conjunction with latent class estimation to adjust for unmeasured sources of heterogeneity. RESULTS Several correlations between clinical variables and neurocognitive performance suggest a basis for heterogeneity: Higher education level was associated with lower Clinical Dementia Rating Scale sum of boxes (β = -0.19; P < .001), and frequent vs moderate neuritic plaques were associated with higher Clinical Dementia Rating Scale sum of boxes (β = 1.64; P < .001) and lower logical memory score (β = -1.07; P = .005). The rate of change of the clinical and cognitive scores varied depending on Braak stage, when adjusting for plaques, age of death, sex, education, and APOE genotype. For example, comparing high vs low Braak stage with other variables fixed, the logical memory score decreased a substantial 0.38 additional units per year (95%CI, -0.70 to -0.06; P = .02). Using these data, we estimate that a 300-participant clinical trial with end point of a 20% improvement in slope in rate of change of Clinical Dementia Rating Scale sum of boxes has 89%power when all participants in the trial are from the high Braak stage, compared with 29% power if Braak stage had not used for eligibility. CONCLUSIONS AND RELEVANCE We found that knowledge of neurofibrillary tangle stage, modeled as the sort of information that could be available from tau positron-emission tomography scans and its use to determine eligibility to a trial, could dramatically improve the power of clinical trials and equivalently reduce the required sample sizes of clinical trials.
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U2 - 10.1001/jamaneurol.2016.5953
DO - 10.1001/jamaneurol.2016.5953
M3 - Article
C2 - 28288263
AN - SCOPUS:85018909019
SN - 2168-6149
VL - 74
SP - 540
EP - 548
JO - JAMA Neurology
JF - JAMA Neurology
IS - 5
ER -