- Email: [email protected]
MCI STATUS, AMYLOID AND TAU BIOMARKERS, AND COMPOSITE COGNITIVE IMPAIRMENT SCORES ARE ASSOCIATED WITH COGSTATE PERFORMANCE IN THE WISCONSIN REGISTRY FOR ALZHEIMER'S PREVENTION
Poster Presentations: Saturday, July 23, 2016
P41
Veterans Hospital, Madison, WI, USA; 6Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden; 7University College London, Institute of Neurology, London, United Kingdom; 8Portland State University, Portland, OR, USA; 9 National Institute on Aging, Baltimore, MD, USA; 10Johns Hopkins University, Baltimore, MD, USA; 11Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI, USA. Contact e-mail: [email protected] Figure 2. Recruitment pipeline in strategy 3 (E4 carrier and positive for blood test) resulting in recruiting 508 subjects for a total cost of AU$321,371. See the Figure 1 legend for further details.
specificity of 96.8%, yielding a recruitment of 508 subjects for a total cost of AU$321,000 (w$225,000), Figure 2. Conclusions: The cost of recruiting 100 individuals older than 60 with confirmed Ab by PET scan could be reduced by 49% by testing subjects for carrying at least one E4 allele. Adding extra blood exams could reduce that cost by a further 8%.
IC-P-050
MCI STATUS, AMYLOID AND TAU BIOMARKERS, AND COMPOSITE COGNITIVE IMPAIRMENT SCORES ARE ASSOCIATED WITH COGSTATE PERFORMANCE IN THE WISCONSIN REGISTRY FOR ALZHEIMER’S PREVENTION
Annie M. Racine1,2,3, Rebecca L. Koscik4, Lindsay R. Clark1, Kimberly D. Mueller4, Sara Elizabeth Berman1, Christopher R. Nicholas1,5, Sanjay Asthana1,5, Kaj Blennow6, Henrik Zetterberg6,7, Bruno Jedynak8, Murat Bilgel9,10, Bradley T. Christian11, Cynthia M. Carlsson1,4,5, Sterling C. Johnson1,4,5,11, 1Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; 2Institute on Aging, University of Wisconsin, Madison, WI, USA; 3Neuroscience and Public Policy Program, University of Wisconsin, Madison, WI, USA; 4Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; 5 Geriatric Research Education and Clinical Center, Wm. S. Middleton
Table 1 Comparison of CogState variables between cognitively normal (CN) participants and those with MCI as determined by consensus. Values are mean (SD) unless otherwise indicated. NC¼normal control. Consensus MCI ¼ psychometric of clinical diagnosis of mild cognitive impairment as determined by consensus. CPAL¼continuous paired associates learning task; GMCT¼Groton maze chase test; GML¼Groton maze learning; GMR¼Groton maze recall; OCL¼one card learning test; ONB¼one-back; TWOB¼two-back. Acc¼arcsine proportion-corrected accuracy, higher score is better; MPS¼moves per second, higher score is better; err¼errors, high score is worse. ANCOVA¼controlling for all demographic variables. n2p ¼partial eta-squared, effect sizes are interpreted as small, medium, and large if they are equal to or greater than .01, .06, and .14, respectively. Significant p-values (p<.05) are italicized. CogState variable
NC (N¼243)
Consensus MCI (N¼36)
ANCOVA
CPAL (acc) GMCT (mps) GML (err) GMR (err) OCL (acc) ONB (acc) TWOB (acc)
0.76 (0.20) 1.14 (0.23) 52.89 (17.94) 8.50 (4.72) 1.02 (0.09) 1.38 (0.13) 1.27 (0.13)
0.64 (0.14) 0.94 (0.29) 62.11 (17.49) 11.85 (5.02) 0.97 (0.09) 1.35 (0.13) 1.18 (.13)
p¼.010, n2p p<.000, n2p p¼.030, n2p p<.001, n2p p¼.034, n2p p¼.199, n2p p¼.002, n2p
¼.025 ¼.046 ¼.017 ¼.047 ¼.017 ¼.006 ¼.035
Table 2 Relationships between a composite cognitive impairment score estimated at age 50 and CogState variables at a mean age of 63. Ab¼amyloid-beta. T-tau¼total tau. PiB¼Pittsburgh compound B. CPAL¼continuous paired associates learning task; GMCT¼Groton maze chase test; GML¼Groton maze learning; GMR¼Groton maze recall; OCL¼one card learning test; ONB¼one-back; TWOB¼two-back. Acc¼arcsine proportion-corrected accuracy, higher score is better; MPS¼moves per second, higher score is better; err¼errors, high score is worse. P-value is for the coefficient of the composite cognitive impairment score in a multiple regression controlling for age, sex, literacy, and computer familiarity. Higher composite cognitive impairment scores indicate lower cognitive performance. CogState
Regression coefficient
T-statistic
p-value
CPAL (acc) GMCT (mps) GML (err) GMR (err) OCL (acc) ONB (acc) TWOB (acc)
-0.069 -0.043 5.083 1.999 -0.023 -0.020 -0.040
-6.083 -3.153 4.794 6.932 -4.120 -2.442 -5.080
<.001 .002 <.001 <.001 <.001 .015 <.001
Table 3 Relationships between biomarkers and CogState variables. Ab¼amyloidbeta. T- tau¼total tau. PiB¼Pittsburgh compound B. CPAL¼continuous paired associates learning task; GMCT¼Groton maze chase test; GML¼Groton maze learning; GMR¼Groton maze recall; OCL¼one card learning test; ONB¼one-back; TWOB¼two-back. Acc¼arcsine proportion- corrected accuracy, higher score is better; MPS¼moves per second, higher score is better; err¼errors, high score is worse. P-value is for the coefficient of the biomarker variable in a multiple regression controlling forage, sex, literacy, and computer familiarity. CSF
Cog State
Regression coefficient T-statistic p-value
Ab42/Ab40 (n¼36) CPAL (acc) 3.386 GMCT (mps) 0.794 GML (err) -119.4 GMR (err) -58.14 OCL (acc) 2.020 ONB (acc) 2.016 TWOB (acc) 0.656 t-tau/Ab42 (n¼36) CPAL (acc) -0.177 GMCT (mps) 0.034 GML (err) 3.961 GMR (err) 3.639 OCL (acc) -0.114 ONB (acc) -0.167 TWOB (acc) 0.027 Global PiB (N¼46) CPAL (acc) -0.264 GMCT (mps) -0.086 GML (err) 23.68 GMR (err) 4.204 OCL (acc) -0.246 ONB (acc) -.0233 TWOB (acc) -0.059
2.793 0.541 -0.814 -1.634 3.340 2.170 0.715 -2.364 0.383 0.447 1.715 -3.080 -3.181 0.482 -1.916 -0.605 1.530 1.078 -3.792 -2.529 -0.531
.009 .593 .422 .113 .002 .038 .480 .025 .704 .658 .097 .004 .003 .633 .063 .548 .134 .288 .001 .016 .598
P42
Poster Presentations: Saturday, July 23, 2016
Figure 1. Boxplots depicting comparison of CogState variables between cognitively normal (CN) participants and those with MCI as determined by consensus. CPAL¼continuous paired associates learning task; GMCT¼Groton maze chase test; GML¼Groton maze learning; GMR¼Groton maze recall; OCL¼one card learning test; ONB¼oneback; TWOB¼two-back. Acc¼arcsine proportion-corrected accuracy, higher score is better; MPS¼moves per second, higher score is better; err¼errors, high score is worse. Figure 3. Scatter plots of relationships between biomarkers and CogState variables. Ab¼amyloid-beta. T-tau¼total tau. PiB¼Pittsburgh compound B. CPAL¼continuous paired associates learning task; GMCT¼Groton maze chase test; GML¼Groton maze learning; GMR¼Groton maze recall; OCL¼one card learning test; ONB¼oneback; TWOB¼two-back. Acc¼arcsine proportion-corrected accuracy, higher score is better; MPS¼moves per second, higher score is better; err¼errors, high score is worse.
Figure 2. Scatter plots of relationships between a composite cognitive impairment score estimated at age 50 and CogState variables at a mean age of 63. Ab¼amyloid-beta. T- tau¼total tau. PiB¼Pittsburgh compound B. CPAL¼continuous paired associates learning task; GMCT¼Groton maze chase test; GML¼Groton maze learning; GMR¼Groton maze recall; OCL¼one card learning test; ONB¼one-back; TWOB¼two-back. Acc¼arcsine proportion-corrected accuracy, higher score is better; MPS¼moves per second, higher score is better; err¼errors, high score is worse. Higher composite cognitive impairment scores indicate lower cognitive performance. 95% confidence intervals are displayed. Background: CogState is a computerized cognitive battery spanning domains of memory, executive function, and speed and processing. CogState, designed to be robust to education level and efficient for repeated administration with minimal practice effects, holds potential for detecting early cognitive deficits that may prove to be due to preclinical Alzheimer’s disease (AD). This project aimed to provide convergent and construct validity for CogState in detecting preclinical AD during late-middle-age. Methods: 279 late-middleaged participants from the Wisconsin Registry for Alzheimer’s Prevention (mean age 6467 years; 69% female; 37% APOE4+) completed a traditional paper-based neuropsychological battery and a CogState battery consisting of seven tests approximately six years post-baseline. A composite cognitive impairment score (CCI) was calculated using eight neuropsychological tests acquired
longitudinally and estimated at age 50 to remove confounding age effects; higher CCI indicates lower cognitive performance. Mild Cognitive Impairment (MCI) status (n¼36) was determined by consensus using clinical and/or pyschometric criteria. A subset underwent cerebrospinal fluid (CSF) collection (n¼36) and PET-PiB imaging (n¼46). To determine clinical relevance of CogState, MCI and normal controls were compared by ANCOVA on select CogState variables controlling for age, literacy, gender, APOE4, family history of AD, self-rated computer familiarity, and depression. To determine whether biomarkers (CSF Ab42/Ab40, CSF total-tau/ Ab42, global PiB burden) or CCI predict CogState performance, we ran multiple regression models controlling for age, sex, literacy, and computer familiarity. Results: MCI participants performed significantly worse (p<.05) compared to normal controls on all CogState tests except the one back test (ONB; Table 1, Fig. 1). Greater CCI predicted worse performance on all CogState tests approximately a decade later (p<.05, Table 2, Fig. 2). Lower Ab42/Ab40 and higher total-tau/Ab42 were associated with worse performance on continuous paired associates learning task, one card learning test (OCL), and ONB (p<.05, Table 3, Fig. 3). Greater global PiB burden was associated with worse performance on OCL and ONB (p<.05, Table 3, Fig. 3). Conclusions: MCI status, biomarkers for amyloid and tau, and CCI all predict performance on the CogState variables assessed in this study of late-middle-aged adults. CogState performance at a single time point may be an important indicator of preclinical AD processes.
P41
Veterans Hospital, Madison, WI, USA; 6Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden; 7University College London, Institute of Neurology, London, United Kingdom; 8Portland State University, Portland, OR, USA; 9 National Institute on Aging, Baltimore, MD, USA; 10Johns Hopkins University, Baltimore, MD, USA; 11Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI, USA. Contact e-mail: [email protected] Figure 2. Recruitment pipeline in strategy 3 (E4 carrier and positive for blood test) resulting in recruiting 508 subjects for a total cost of AU$321,371. See the Figure 1 legend for further details.
specificity of 96.8%, yielding a recruitment of 508 subjects for a total cost of AU$321,000 (w$225,000), Figure 2. Conclusions: The cost of recruiting 100 individuals older than 60 with confirmed Ab by PET scan could be reduced by 49% by testing subjects for carrying at least one E4 allele. Adding extra blood exams could reduce that cost by a further 8%.
IC-P-050
MCI STATUS, AMYLOID AND TAU BIOMARKERS, AND COMPOSITE COGNITIVE IMPAIRMENT SCORES ARE ASSOCIATED WITH COGSTATE PERFORMANCE IN THE WISCONSIN REGISTRY FOR ALZHEIMER’S PREVENTION
Annie M. Racine1,2,3, Rebecca L. Koscik4, Lindsay R. Clark1, Kimberly D. Mueller4, Sara Elizabeth Berman1, Christopher R. Nicholas1,5, Sanjay Asthana1,5, Kaj Blennow6, Henrik Zetterberg6,7, Bruno Jedynak8, Murat Bilgel9,10, Bradley T. Christian11, Cynthia M. Carlsson1,4,5, Sterling C. Johnson1,4,5,11, 1Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; 2Institute on Aging, University of Wisconsin, Madison, WI, USA; 3Neuroscience and Public Policy Program, University of Wisconsin, Madison, WI, USA; 4Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; 5 Geriatric Research Education and Clinical Center, Wm. S. Middleton
Table 1 Comparison of CogState variables between cognitively normal (CN) participants and those with MCI as determined by consensus. Values are mean (SD) unless otherwise indicated. NC¼normal control. Consensus MCI ¼ psychometric of clinical diagnosis of mild cognitive impairment as determined by consensus. CPAL¼continuous paired associates learning task; GMCT¼Groton maze chase test; GML¼Groton maze learning; GMR¼Groton maze recall; OCL¼one card learning test; ONB¼one-back; TWOB¼two-back. Acc¼arcsine proportion-corrected accuracy, higher score is better; MPS¼moves per second, higher score is better; err¼errors, high score is worse. ANCOVA¼controlling for all demographic variables. n2p ¼partial eta-squared, effect sizes are interpreted as small, medium, and large if they are equal to or greater than .01, .06, and .14, respectively. Significant p-values (p<.05) are italicized. CogState variable
NC (N¼243)
Consensus MCI (N¼36)
ANCOVA
CPAL (acc) GMCT (mps) GML (err) GMR (err) OCL (acc) ONB (acc) TWOB (acc)
0.76 (0.20) 1.14 (0.23) 52.89 (17.94) 8.50 (4.72) 1.02 (0.09) 1.38 (0.13) 1.27 (0.13)
0.64 (0.14) 0.94 (0.29) 62.11 (17.49) 11.85 (5.02) 0.97 (0.09) 1.35 (0.13) 1.18 (.13)
p¼.010, n2p p<.000, n2p p¼.030, n2p p<.001, n2p p¼.034, n2p p¼.199, n2p p¼.002, n2p
¼.025 ¼.046 ¼.017 ¼.047 ¼.017 ¼.006 ¼.035
Table 2 Relationships between a composite cognitive impairment score estimated at age 50 and CogState variables at a mean age of 63. Ab¼amyloid-beta. T-tau¼total tau. PiB¼Pittsburgh compound B. CPAL¼continuous paired associates learning task; GMCT¼Groton maze chase test; GML¼Groton maze learning; GMR¼Groton maze recall; OCL¼one card learning test; ONB¼one-back; TWOB¼two-back. Acc¼arcsine proportion-corrected accuracy, higher score is better; MPS¼moves per second, higher score is better; err¼errors, high score is worse. P-value is for the coefficient of the composite cognitive impairment score in a multiple regression controlling for age, sex, literacy, and computer familiarity. Higher composite cognitive impairment scores indicate lower cognitive performance. CogState
Regression coefficient
T-statistic
p-value
CPAL (acc) GMCT (mps) GML (err) GMR (err) OCL (acc) ONB (acc) TWOB (acc)
-0.069 -0.043 5.083 1.999 -0.023 -0.020 -0.040
-6.083 -3.153 4.794 6.932 -4.120 -2.442 -5.080
<.001 .002 <.001 <.001 <.001 .015 <.001
Table 3 Relationships between biomarkers and CogState variables. Ab¼amyloidbeta. T- tau¼total tau. PiB¼Pittsburgh compound B. CPAL¼continuous paired associates learning task; GMCT¼Groton maze chase test; GML¼Groton maze learning; GMR¼Groton maze recall; OCL¼one card learning test; ONB¼one-back; TWOB¼two-back. Acc¼arcsine proportion- corrected accuracy, higher score is better; MPS¼moves per second, higher score is better; err¼errors, high score is worse. P-value is for the coefficient of the biomarker variable in a multiple regression controlling forage, sex, literacy, and computer familiarity. CSF
Cog State
Regression coefficient T-statistic p-value
Ab42/Ab40 (n¼36) CPAL (acc) 3.386 GMCT (mps) 0.794 GML (err) -119.4 GMR (err) -58.14 OCL (acc) 2.020 ONB (acc) 2.016 TWOB (acc) 0.656 t-tau/Ab42 (n¼36) CPAL (acc) -0.177 GMCT (mps) 0.034 GML (err) 3.961 GMR (err) 3.639 OCL (acc) -0.114 ONB (acc) -0.167 TWOB (acc) 0.027 Global PiB (N¼46) CPAL (acc) -0.264 GMCT (mps) -0.086 GML (err) 23.68 GMR (err) 4.204 OCL (acc) -0.246 ONB (acc) -.0233 TWOB (acc) -0.059
2.793 0.541 -0.814 -1.634 3.340 2.170 0.715 -2.364 0.383 0.447 1.715 -3.080 -3.181 0.482 -1.916 -0.605 1.530 1.078 -3.792 -2.529 -0.531
.009 .593 .422 .113 .002 .038 .480 .025 .704 .658 .097 .004 .003 .633 .063 .548 .134 .288 .001 .016 .598
P42
Poster Presentations: Saturday, July 23, 2016
Figure 1. Boxplots depicting comparison of CogState variables between cognitively normal (CN) participants and those with MCI as determined by consensus. CPAL¼continuous paired associates learning task; GMCT¼Groton maze chase test; GML¼Groton maze learning; GMR¼Groton maze recall; OCL¼one card learning test; ONB¼oneback; TWOB¼two-back. Acc¼arcsine proportion-corrected accuracy, higher score is better; MPS¼moves per second, higher score is better; err¼errors, high score is worse. Figure 3. Scatter plots of relationships between biomarkers and CogState variables. Ab¼amyloid-beta. T-tau¼total tau. PiB¼Pittsburgh compound B. CPAL¼continuous paired associates learning task; GMCT¼Groton maze chase test; GML¼Groton maze learning; GMR¼Groton maze recall; OCL¼one card learning test; ONB¼oneback; TWOB¼two-back. Acc¼arcsine proportion-corrected accuracy, higher score is better; MPS¼moves per second, higher score is better; err¼errors, high score is worse.
Figure 2. Scatter plots of relationships between a composite cognitive impairment score estimated at age 50 and CogState variables at a mean age of 63. Ab¼amyloid-beta. T- tau¼total tau. PiB¼Pittsburgh compound B. CPAL¼continuous paired associates learning task; GMCT¼Groton maze chase test; GML¼Groton maze learning; GMR¼Groton maze recall; OCL¼one card learning test; ONB¼one-back; TWOB¼two-back. Acc¼arcsine proportion-corrected accuracy, higher score is better; MPS¼moves per second, higher score is better; err¼errors, high score is worse. Higher composite cognitive impairment scores indicate lower cognitive performance. 95% confidence intervals are displayed. Background: CogState is a computerized cognitive battery spanning domains of memory, executive function, and speed and processing. CogState, designed to be robust to education level and efficient for repeated administration with minimal practice effects, holds potential for detecting early cognitive deficits that may prove to be due to preclinical Alzheimer’s disease (AD). This project aimed to provide convergent and construct validity for CogState in detecting preclinical AD during late-middle-age. Methods: 279 late-middleaged participants from the Wisconsin Registry for Alzheimer’s Prevention (mean age 6467 years; 69% female; 37% APOE4+) completed a traditional paper-based neuropsychological battery and a CogState battery consisting of seven tests approximately six years post-baseline. A composite cognitive impairment score (CCI) was calculated using eight neuropsychological tests acquired
longitudinally and estimated at age 50 to remove confounding age effects; higher CCI indicates lower cognitive performance. Mild Cognitive Impairment (MCI) status (n¼36) was determined by consensus using clinical and/or pyschometric criteria. A subset underwent cerebrospinal fluid (CSF) collection (n¼36) and PET-PiB imaging (n¼46). To determine clinical relevance of CogState, MCI and normal controls were compared by ANCOVA on select CogState variables controlling for age, literacy, gender, APOE4, family history of AD, self-rated computer familiarity, and depression. To determine whether biomarkers (CSF Ab42/Ab40, CSF total-tau/ Ab42, global PiB burden) or CCI predict CogState performance, we ran multiple regression models controlling for age, sex, literacy, and computer familiarity. Results: MCI participants performed significantly worse (p<.05) compared to normal controls on all CogState tests except the one back test (ONB; Table 1, Fig. 1). Greater CCI predicted worse performance on all CogState tests approximately a decade later (p<.05, Table 2, Fig. 2). Lower Ab42/Ab40 and higher total-tau/Ab42 were associated with worse performance on continuous paired associates learning task, one card learning test (OCL), and ONB (p<.05, Table 3, Fig. 3). Greater global PiB burden was associated with worse performance on OCL and ONB (p<.05, Table 3, Fig. 3). Conclusions: MCI status, biomarkers for amyloid and tau, and CCI all predict performance on the CogState variables assessed in this study of late-middle-aged adults. CogState performance at a single time point may be an important indicator of preclinical AD processes.