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He B, Wu R, Sangani N, Pugalenthi PV, Patania A, Risacher SL, Nho K, Apostolova LG, Shen L, Saykin AJ, Yan J. Integrating amyloid imaging and genetics for early risk stratification of Alzheimer's disease. Alzheimers Dement 2024. [PMID: 39285750 DOI: 10.1002/alz.14244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 07/24/2024] [Accepted: 08/15/2024] [Indexed: 09/21/2024]
Abstract
INTRODUCTION Alzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment. METHODS Given the genetic susceptibility of AD, a multi-factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk-stratified groups, focusing on patients with mild cognitive impairment (MCI). RESULTS Our risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI, APOE ε4- MCI, and amyloid+ MCI. DISCUSSION Our risk score holds great potential to improve the precision of early risk assessment. HIGHLIGHTS Accurate early risk assessment is critical for the success of clinical trials. A new risk score was built from integrating amyloid imaging and genetic data. Our risk score demonstrated improved capability in early risk stratification.
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Affiliation(s)
- Bing He
- Department of Biomedical Engineering and Informatics, Indiana University Luddy School of Informatics, Computing and Engineering, Indianapolis, Indiana, USA
| | - Ruiming Wu
- Department of Biomedical Engineering and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Neel Sangani
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Pradeep Varathan Pugalenthi
- Department of Biomedical Engineering and Informatics, Indiana University Luddy School of Informatics, Computing and Engineering, Indianapolis, Indiana, USA
| | - Alice Patania
- Department of Mathematics Statistics, University of Vermont, Burlington, Vermont, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Liana G Apostolova
- Department of Biomedical Engineering and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Li Shen
- Department of Biomedical Engineering and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Jingwen Yan
- Department of Biomedical Engineering and Informatics, Indiana University Luddy School of Informatics, Computing and Engineering, Indianapolis, Indiana, USA
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Yang HJ, Song JM, Lee S, Lee HK, Kim BS, Kim KW, Park JH. The Different Associations of White Matter Hyperintensities With Severity of Dementia and Cognitive Impairment According to the Distance From the Lateral Ventricular Surface in Patients With Alzheimer's Disease. Psychiatry Investig 2024; 21:850-859. [PMID: 39111744 PMCID: PMC11321875 DOI: 10.30773/pi.2024.0082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/18/2024] [Accepted: 05/24/2024] [Indexed: 08/15/2024] Open
Abstract
OBJECTIVE White matter hyperintensities (WMH) are common among the elderly. Although WMH play a key role in lowering the threshold for the clinical expression of dementia in Alzheimer's disease (AD)-related pathology, the clinical significance of their location is not fully understood. This study aimed to investigate the association between WMH and cognitive function according to the location of WMH in AD. METHODS Subjects underwent clinical evaluations including volumetric brain magnetic resonance imaging study and neuropsychological tests using the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet. WMH were calculated using automated quantification method. According to the distance from the lateral ventricular surface, WMH within 3 mm, WMH within 3-13 mm, and WMH over 13 mm were classified as juxtaventricular WMH (JVWMH), periventricular WMH (PVWMH), and deep WMH (DWMH), respectively. RESULTS Total WMH volume was associated with poor performance in categorical verbal fluency test (β=-0.197, p=0.035). JVWMH volume was associated with poor performances on categorical verbal fluency test (β=-0.201, p=0.032) and forward digit span test (β= -0.250, p=0.012). PVWMH volume was associated with poor performances on categorical verbal fluency test (β=-0.185, p=0.042) and word list memory test (β=-0.165, p=0.042), whereas DWMH volume showed no association with cognitive tests. PVWMH volume were also related to Clinical Dementia Rating Scale Sum of Boxes score (β=0.180, p=0.026). CONCLUSION WMH appear to exhibit different associations with the severity of dementia and cognitive impairment according to the distance from ventricle surface in AD.
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Affiliation(s)
- Hyun Ju Yang
- Department of Psychiatry, Jeju National University School of Medicine, Jeju National University Hospital, Jeju Special Self-Governing Province, Jeju, Republic of Korea
| | - Jae Min Song
- Department of Psychiatry, Jeju Medical Center, Jeju Special Self-Governing Province, Jeju, Republic of Korea
| | - Subin Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Ho Kyu Lee
- Department of Radiology, Jeju National University, Jeju Special Self-Governing Province, Jeju, Republic of Korea
| | - Bong Soo Kim
- Department of Radiology, Jeju National University, Jeju Special Self-Governing Province, Jeju, Republic of Korea
| | - Ki Woong Kim
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
| | - Joon Hyuk Park
- Department of Psychiatry, Jeju National University School of Medicine, Jeju National University Hospital, Jeju Special Self-Governing Province, Jeju, Republic of Korea
- Jeju Dementia Center, Jeju Special Self-Governing Province, Jeju, Republic of Korea
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Cody KA, Langhough RE, Zammit MD, Clark L, Chin N, Christian BT, Betthauser TJ, Johnson SC. Characterizing brain tau and cognitive decline along the amyloid timeline in Alzheimer's disease. Brain 2024; 147:2144-2157. [PMID: 38667631 PMCID: PMC11146417 DOI: 10.1093/brain/awae116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 02/23/2024] [Accepted: 03/24/2024] [Indexed: 06/04/2024] Open
Abstract
Recent longitudinal PET imaging studies have established methods to estimate the age at which amyloid becomes abnormal at the level of the individual. Here we recontextualized amyloid levels into the temporal domain to better understand the downstream Alzheimer's disease processes of tau neurofibrillary tangle (NFT) accumulation and cognitive decline. This cohort study included a total of 601 individuals from the Wisconsin Registry for Alzheimer's Prevention and Wisconsin Alzheimer's Disease Research Center that underwent amyloid and tau PET, longitudinal neuropsychological assessments and met clinical criteria for three clinical diagnosis groups: cognitively unimpaired (n = 537); mild cognitive impairment (n = 48); or dementia (n = 16). Cortical 11C-Pittsburgh compound B (PiB) distribution volume ratio (DVR) and sampled iterative local approximation were used to estimate amyloid positive (A+; global PiB DVR > 1.16 equivalent to 17.1 centiloids) onset age and years of A+ duration at tau PET (i.e. amyloid chronicity). Tau PET burden was quantified using 18F-MK-6240 standardized uptake value ratios (70-90 min, inferior cerebellar grey matter reference region). Whole-brain and region-specific approaches were used to examine tau PET binding along the amyloid timeline and across the Alzheimer's disease clinical continuum. Voxel-wise 18F-MK-6240 analyses revealed that with each decade of A+, the spatial extent of measurable tau spread (i.e. progressed) from regions associated with early to late NFT tau stages. Regional analyses indicated that tau burden in the entorhinal cortex was detectable, on average, within 10 years of A+ onset. Additionally, the entorhinal cortex was the region most sensitive to early amyloid pathology and clinical impairment in this predominantly preclinical sample. Among initially cognitively unimpaired (n = 472) individuals with longitudinal cognitive follow-up, mixed effects models showed significant linear and non-linear interactions of A+ duration and entorhinal tau on cognitive decline, suggesting a synergistic effect whereby greater A+ duration, together with a higher entorhinal tau burden, increases the likelihood of cognitive decline beyond their separable effects. Overall, the amyloid time framework enabled a spatiotemporal characterization of tau deposition patterns across the Alzheimer's disease continuum. This approach, which examined cross-sectional tau PET data along the amyloid timeline to make longitudinal disease course inferences, demonstrated that A+ duration explains a considerable amount of variability in the magnitude and topography of tau spread, which largely recapitulated NFT staging observed in human neuropathological studies. By anchoring disease progression to the onset of amyloid, this study provides a temporal disease context, which may help inform disease prognosis and timing windows for anti-amyloid therapies.
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Affiliation(s)
- Karly A Cody
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
| | - Rebecca E Langhough
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
| | - Matthew D Zammit
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI 53792, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Lindsay Clark
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
| | - Nathaniel Chin
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
| | - Bradley T Christian
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI 53792, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Tobey J Betthauser
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA
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Du L, Hermann BP, Jonaitis EM, Cody KA, Rivera-Rivera L, Rowley H, Field A, Eisenmenger L, Christian BT, Betthauser TJ, Larget B, Chappell R, Janelidze S, Hansson O, Johnson SC, Langhough R. Harnessing cognitive trajectory clusterings to examine subclinical decline risk factors. Brain Commun 2023; 5:fcad333. [PMID: 38107504 PMCID: PMC10724051 DOI: 10.1093/braincomms/fcad333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 10/23/2023] [Accepted: 11/30/2023] [Indexed: 12/19/2023] Open
Abstract
Cognitive decline in Alzheimer's disease and other dementias typically begins long before clinical impairment. Identifying people experiencing subclinical decline may facilitate earlier intervention. This study developed cognitive trajectory clusters using longitudinally based random slope and change point parameter estimates from a Preclinical Alzheimer's disease Cognitive Composite and examined how baseline and most recently available clinical/health-related characteristics, cognitive statuses and biomarkers for Alzheimer's disease and vascular disease varied across these cognitive clusters. Data were drawn from the Wisconsin Registry for Alzheimer's Prevention, a longitudinal cohort study of adults from late midlife, enriched for a parental history of Alzheimer's disease and without dementia at baseline. Participants who were cognitively unimpaired at the baseline visit with ≥3 cognitive visits were included in trajectory modelling (n = 1068). The following biomarker data were available for subsets: positron emission tomography amyloid (amyloid: n = 367; [11C]Pittsburgh compound B (PiB): global PiB distribution volume ratio); positron emission tomography tau (tau: n = 321; [18F]MK-6240: primary regions of interest meta-temporal composite); MRI neurodegeneration (neurodegeneration: n = 581; hippocampal volume and global brain atrophy); T2 fluid-attenuated inversion recovery MRI white matter ischaemic lesion volumes (vascular: white matter hyperintensities; n = 419); and plasma pTau217 (n = 165). Posterior median estimate person-level change points, slopes' pre- and post-change point and estimated outcome (intercepts) at change point for cognitive composite were extracted from Bayesian Bent-Line Regression modelling and used to characterize cognitive trajectory groups (K-means clustering). A common method was used to identify amyloid/tau/neurodegeneration/vascular biomarker thresholds. We compared demographics, last visit cognitive status, health-related factors and amyloid/tau/neurodegeneration/vascular biomarkers across the cognitive groups using ANOVA, Kruskal-Wallis, χ2, and Fisher's exact tests. Mean (standard deviation) baseline and last cognitive assessment ages were 58.4 (6.4) and 66.6 (6.6) years, respectively. Cluster analysis identified three cognitive trajectory groups representing steep, n = 77 (7.2%); intermediate, n = 446 (41.8%); and minimal, n = 545 (51.0%) cognitive decline. The steep decline group was older, had more females, APOE e4 carriers and mild cognitive impairment/dementia at last visit; it also showed worse self-reported general health-related and vascular risk factors and higher amyloid, tau, neurodegeneration and white matter hyperintensity positive proportions at last visit. Subtle cognitive decline was consistently evident in the steep decline group and was associated with generally worse health. In addition, cognitive trajectory groups differed on aetiology-informative biomarkers and risk factors, suggesting an intimate link between preclinical cognitive patterns and amyloid/tau/neurodegeneration/vascular biomarker differences in late middle-aged adults. The result explains some of the heterogeneity in cognitive performance within cognitively unimpaired late middle-aged adults.
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Affiliation(s)
- Lianlian Du
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Bruce P Hermann
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Neurology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53705, USA
| | - Erin M Jonaitis
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Karly Alex Cody
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Leonardo Rivera-Rivera
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53705, USA
| | - Howard Rowley
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Aaron Field
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Laura Eisenmenger
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Bradley T Christian
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53705, USA
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Tobey J Betthauser
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Bret Larget
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Rick Chappell
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA
| | | | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund 205 02, Sweden
| | - Sterling C Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
| | - Rebecca Langhough
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792, USA
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Cai Y, Schrack JA, Agrawal Y, Armstrong NM, Wanigatunga AA, Kitner-Triolo M, Moghekar A, Ferrucci L, Simonsick EM, Resnick SM, Gross AL. Application and validation of an algorithmic classification of early impairment in cognitive performance. Aging Ment Health 2023; 27:2187-2192. [PMID: 37354067 PMCID: PMC10592406 DOI: 10.1080/13607863.2023.2227118] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 06/09/2023] [Indexed: 06/26/2023]
Abstract
OBJECTIVE Due to the long prodromal period for dementia pathology, approaches are needed to detect cases before clinically recognizable symptoms are apparent, by which time it is likely too late to intervene. This study contrasted two theoretically-based algorithms for classifying early cognitive impairment (ECI) in adults aged ≥50 enrolled in the Baltimore Longitudinal Study of Aging. METHOD Two ECI algorithms were defined as poor performance (1 standard deviation [SD] below age-, sex-, race-, and education-specific means) in: (1) Card Rotations or California Verbal Learning Test (CVLT) immediate recall and (2) ≥1 (out of 2) memory or ≥3 (out of 6) non-memory tests. We evaluated concurrent criterion validity against consensus diagnoses of mild cognitive impairment (MCI) or dementia and global cognitive scores using receiver operating characteristic (ROC) curve analysis. Predictive criterion validity was evaluated using Cox proportional hazards models to examine the associations between algorithmic status and future adjudicated MCI/dementia. RESULTS Among 1,851 participants (mean age = 65.2 ± 11.8 years, 50% women, 74% white), the two ECI algorithms yielded comparably moderate concurrent criterion validity with adjudicated MCI/dementia. For predictive criterion validity, the algorithm based on impairment in Card Rotations or CVLT immediate recall was the better predictor of MCI/dementia (HR = 3.53, 95%CI: 1.59-7.84) over 12.3 follow-up years. CONCLUSIONS Impairment in visuospatial ability or memory may be capable of detecting early cognitive changes in the preclinical phase among cognitively normal individuals.
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Affiliation(s)
- Yurun Cai
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Health and Community Systems, University of Pittsburgh School of Nursing, Pittsburgh, PA, USA
| | - Jennifer A. Schrack
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center on Aging and Health, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Yuri Agrawal
- Department of Otolaryngology - Head and Neck Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Nicole M. Armstrong
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University, Providence, RI, USA
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - Amal A. Wanigatunga
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center on Aging and Health, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Abhay Moghekar
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Luigi Ferrucci
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | | | - Susan M. Resnick
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - Alden L. Gross
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center on Aging and Health, Johns Hopkins School of Medicine, Baltimore, MD, USA
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Saint-Jalmes M, Fedyashov V, Beck D, Baldwin T, Faux NG, Bourgeat P, Fripp J, Masters CL, Goudey B. Disease progression modelling of Alzheimer's disease using probabilistic principal components analysis. Neuroimage 2023; 278:120279. [PMID: 37454702 DOI: 10.1016/j.neuroimage.2023.120279] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/27/2023] [Accepted: 07/12/2023] [Indexed: 07/18/2023] Open
Abstract
The recent biological redefinition of Alzheimer's Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the estimation of patient-realigning time-shifts. These time-shifts are indispensable for meaningful biomarker modelling, but may impact fitting time or vary with missing data in jointly estimated models. In this work, we estimate an individual's progression through Alzheimer's disease by combining multiple biomarkers into a single value using a probabilistic formulation of principal components analysis. Our results show that this variable, which summarises AD through observable biomarkers, is remarkably similar to jointly estimated time-shifts when we compute our scores for the baseline visit, on cross-sectional data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Reproducing the expected properties of clinical datasets, we confirm that estimated scores are robust to missing data or unavailable biomarkers. In addition to cross-sectional insights, we can model the latent variable as an individual progression score by repeating estimations at follow-up examinations and refining long-term estimates as more data is gathered, which would be ideal in a clinical setting. Finally, we verify that our score can be used as a pseudo-temporal scale instead of age to ignore some patient heterogeneity in cohort data and highlight the general trend in expected biomarker evolution in affected individuals.
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Affiliation(s)
- Martin Saint-Jalmes
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia.
| | - Victor Fedyashov
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Daniel Beck
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; School of Computing and Information Systems, The University of Melbourne, Australia
| | - Timothy Baldwin
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; School of Computing and Information Systems, The University of Melbourne, Australia; Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Noel G Faux
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia; Melbourne Data Analytics Platform, The University of Melbourne, Australia
| | | | - Jurgen Fripp
- CSIRO Health and Biosecurity, Brisbane, Australia
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Benjamin Goudey
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia
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Cai Y, Schrack JA, Agrawal Y, Armstrong NM, Wanigatunga A, Kitner-Triolo M, Moghekar A, Ferrucci L, Simonsick EM, Resnick SM, Gross AL. Application and validation of an algorithmic classification of early impairment in cognitive performance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.04.23285477. [PMID: 36798178 PMCID: PMC9934722 DOI: 10.1101/2023.02.04.23285477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Objective Due to the long prodromal period for dementia pathology, approaches are needed to detect cases before clinically recognizable symptoms are apparent, by which time it is likely too late to intervene. This study contrasted two theoretically-based algorithms for classifying early cognitive impairment (ECI) in adults aged ≥50 enrolled in the Baltimore Longitudinal Study of Aging. Method Two ECI algorithms were defined as poor performance (1 standard deviation [SD] below age-, sex-, race-, and education-specific means) in: (1) Card Rotations or California Verbal Learning Test (CVLT) immediate recall and (2) ≥1 (out of 2) memory or ≥3 (out of 6) non- memory tests. We evaluated concurrent criterion validity against consensus diagnoses of mild cognitive impairment (MCI) or dementia and global cognitive scores using receiver operating characteristic (ROC) curve analysis. Predictive criterion validity was evaluated using Cox proportional hazards models to examine the associations between algorithmic status and future adjudicated MCI/dementia. Results Among 1,851 participants (mean age=65.2±11.8 years, 50% women, 74% white), the two ECI algorithms yielded comparably moderate concurrent criterion validity with adjudicated MCI/dementia. For predictive criterion validity, the algorithm based on impairment in Card Rotations or CVLT immediate recall was the better predictor of MCI/dementia (HR=3.53, 95%CI: 1.59-7.84) over 12.3 follow-up years. Conclusions Impairment in visuospatial ability or memory may be capable of detecting early cognitive changes in the preclinical phase among cognitively normal individuals.
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James SN, Nicholas JM, Lu K, Keshavan A, Lane CA, Parker T, Buchanan SM, Keuss SE, Murray-Smith H, Wong A, Cash DM, Malone IB, Barnes J, Sudre CH, Coath W, Modat M, Ourselin S, Crutch SJ, Kuh D, Fox NC, Schott JM, Richards M. Adulthood cognitive trajectories over 26 years and brain health at 70 years of age: findings from the 1946 British Birth Cohort. Neurobiol Aging 2023; 122:22-32. [PMID: 36470133 PMCID: PMC10564626 DOI: 10.1016/j.neurobiolaging.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 10/06/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022]
Abstract
Few studies can address how adulthood cognitive trajectories relate to brain health in 70-year-olds. Participants (n = 468, 49% female) from the 1946 British birth cohort underwent 18F-Florbetapir PET/MRI. Cognitive function was measured in childhood (age 8 years) and across adulthood (ages 43, 53, 60-64 and 69 years) and was examined in relation to brain health markers of β-amyloid (Aβ) status, whole brain and hippocampal volume, and white matter hyperintensity volume (WMHV). Taking into account key contributors of adult cognitive decline including childhood cognition, those with greater Aβ and WMHV at age 70 years had greater decline in word-list learning memory in the preceding 26 years, particularly after age 60. In contrast, those with smaller whole brain and hippocampal volume at age 70 years had greater decline in processing search speed, subtly manifest from age 50 years. Subtle changes in memory and processing speed spanning 26 years of adulthood were associated with markers of brain health at 70 years of age, consistent with detectable prodromal cognitive effects in early older age.
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Affiliation(s)
- Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK.
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK; Department of Medical Statistics, London School of Hygiene and Tropical Medicine, University of London, London, UK
| | - Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ashvini Keshavan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Thomas Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK; UK Dementia Research Institute Centre for Care Research and Technology, Imperial College London, UK; Department of Medicine, Division of Brain Sciences, Imperial College London
| | - Sarah M Buchanan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah E Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK; UK Dementia Research Institute at UCL, University College London, London, UK
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Josephine Barnes
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Marc Modat
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sebastien Ourselin
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sebastian J Crutch
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK; UK Dementia Research Institute at UCL, University College London, London, UK
| | - Jonathan M Schott
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK; Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK; UK Dementia Research Institute at UCL, University College London, London, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
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Singham T, Saunders R, Brooker H, Creese B, Aarsland D, Hampshire A, Ballard C, Corbett A, Desai R, Stott J. Are subtypes of affective symptoms differentially associated with change in cognition over time: A latent class analysis. J Affect Disord 2022; 309:437-445. [PMID: 35490883 DOI: 10.1016/j.jad.2022.04.139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/14/2022] [Accepted: 04/24/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND In the absence of disease-modifying treatments, identifying potential psychosocial risk factors for dementia is paramount. Depression and anxiety have been identified as potential risk factors. Studies however have yielded mixed findings, lending possibility to the fact that potential constellations of co-occurring depression and anxiety symptoms may better explain the link between affective symptoms and cognitive decline. METHODS Data from participants (aged 50 and above) of the PROTECT study was used. Latent Class Analysis (LCA) was conducted on 21,684 participants with baseline anxiety and depression measures. Multiple linear regressions models, using a subset of these participants (N = 6136) who had complete cognition data at baseline and at 2-year follow-up, were conducted to assess for associations between class membership and longitudinal changes in cognition. RESULTS The LCA identified a 5-class solution: "No Symptoms", "Sleep", "Sleep and Worry", "Sleep and Anhedonia", and "Co-morbid Depression and Anxiety". Class membership was significantly associated with longitudinal change in cognition. Furthermore, this association differed across different cognitive measures. LIMITATIONS Limitations included significant attrition and a generally healthy sample which may impact generalisability. CONCLUSIONS Substantial heterogeneity in affective symptoms could explain previous inconsistent findings concerning the association between affective symptoms and cognition. Clinicians should not focus solely on total symptom scores on a single affective domain, but instead on the presence and patterns of symptoms (even if sub-clinical) on measures across multiple affective domains. Identifying particular subgroups that are at greater risk of poor cognitive outcomes may support targeted prevention work.
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Affiliation(s)
- Timothy Singham
- Adapt Lab, Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Rob Saunders
- Adapt Lab, Research Department of Clinical, Educational and Health Psychology, University College London, London, UK; Centre for Outcomes Research and Effectiveness, Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Helen Brooker
- College of Medicine and Health, University of Exeter, UK
| | - Byron Creese
- College of Medicine and Health, University of Exeter, UK
| | - Dag Aarsland
- Department of Old age Psychiatry, IoPPN, Kings College London, UK; Centre for Age-related research, Stavanger University Hospital, Stavanger, Norway
| | - Adam Hampshire
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, UK
| | - Clive Ballard
- College of Medicine and Health, University of Exeter, UK
| | - Anne Corbett
- College of Medicine and Health, University of Exeter, UK
| | - Roopal Desai
- Adapt Lab, Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Joshua Stott
- Adapt Lab, Research Department of Clinical, Educational and Health Psychology, University College London, London, UK.
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10
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Zimmerman SC, Brenowitz WD, Calmasini C, Ackley SF, Graff RE, Asiimwe SB, Staffaroni AM, Hoffmann TJ, Glymour MM. Association of Genetic Variants Linked to Late-Onset Alzheimer Disease With Cognitive Test Performance by Midlife. JAMA Netw Open 2022; 5:e225491. [PMID: 35377426 PMCID: PMC8980909 DOI: 10.1001/jamanetworkopen.2022.5491] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
IMPORTANCE Identifying the youngest age when Alzheimer disease (AD) influences cognition and the earliest affected cognitive domains will improve understanding of the natural history of AD and approaches to early diagnosis. OBJECTIVE To evaluate the age at which cognitive differences between individuals with higher compared with lower genetic risk of AD are first apparent and which cognitive assessments show the earliest difference. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study used data from UK Biobank participants of European genetic ancestry, aged 40 years or older, who contributed genotypic and cognitive test data from January 1, 2006, to December 31, 2015. Data analysis was performed from March 10, 2020, to January 4, 2022. EXPOSURE The AD genetic risk score (GRS), which is a weighted sum of 23 single-nucleotide variations. MAIN OUTCOMES AND MEASURES Seven cognitive tests were administered via touchscreen at in-person visits or online. Cognitive domains assessed included fluid intelligence, episodic memory, processing speed, executive functioning, and prospective memory. Multiple cognitive measures were derived from some tests, yielding 32 separate measures. Interactions between age and AD-GRS for each of the 32 cognitive measures were tested with linear regression using a Bonferroni-corrected P value threshold. For cognitive measures with significant evidence of age by AD-GRS interaction, the youngest age of interaction was assessed with new regression models, with nonlinear specification of age terms. Models with youngest age of interaction from 40 to 70 years, in 1-year increments, were compared, and the best-fitting model for each cognitive measure was chosen. Results across cognitive measures were compared to determine which cognitive indicators showed earliest AD-related change. RESULTS A total of 405 050 participants (mean [SD] age, 57.1 [7.9] years; 54.1% female) were included. Sample sizes differed across cognitive tests (from 12 455 to 404 682 participants). The AD-GRS significantly modified the association with age on 13 measures derived from the pairs matching (range in difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 2.5%-11.5%), symbol digit substitution (range in difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 2.0%-5.8%), and numeric memory tests (difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 8.8%) (P = 1.56 × 10-3). Best-fitting models suggested that cognitive scores of individuals with a high vs low AD-GRS began to diverge by 56 years of age for all 13 measures and by 47 years of age for 9 measures. CONCLUSIONS AND RELEVANCE In this cross-sectional study, by early midlife, subtle differences in memory and attention were detectable among individuals with higher genetic risk of AD.
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Affiliation(s)
- Scott C. Zimmerman
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Willa D. Brenowitz
- Department of Epidemiology and Biostatistics, University of California, San Francisco
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
| | - Camilla Calmasini
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Sarah F. Ackley
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Rebecca E. Graff
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Stephen B. Asiimwe
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Adam M. Staffaroni
- Weill Institute for Neurosciences, Department of Neurology, Memory and Aging Center, University of California, San Francisco
| | - Thomas J. Hoffmann
- Department of Epidemiology and Biostatistics, University of California, San Francisco
- Institute for Human Genetics, University of California, San Francisco
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco
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11
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Giorgio J, Landau SM, Jagust WJ, Tino P, Kourtzi Z. Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease. Neuroimage Clin 2020; 26:102199. [PMID: 32106025 PMCID: PMC7044529 DOI: 10.1016/j.nicl.2020.102199] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 01/24/2020] [Accepted: 01/25/2020] [Indexed: 01/13/2023]
Abstract
Alzheimer's disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities that may lead to MCI diagnosis without progression to AD. Despite the importance of early diagnosis of AD for prognosis and personalised interventions, we still lack robust tools for predicting individual progression to dementia. Here, we propose a novel trajectory modelling approach based on metric learning (Generalised Metric Learning Vector Quantization) that mines multimodal data from MCI patients in the Alzheimer's disease Neuroimaging Initiative (ADNI) cohort to derive individualised prognostic scores of cognitive decline due to AD. We develop an integrated biomarker generation- using partial least squares regression- and classification methodology that extends beyond binary patient classification into discrete subgroups (i.e. stable vs. progressive MCI), determines individual profiles from baseline (i.e. cognitive or biological) data and predicts individual cognitive trajectories (i.e. change in memory scores from baseline). We demonstrate that a metric learning model trained on baseline cognitive data (memory, executive function, affective measurements) discriminates stable vs. progressive MCI individuals with high accuracy (81.4%), revealing an interaction between cognitive (memory, executive functions) and affective scores that may relate to MCI comorbidity (e.g. affective disturbance). Training the model to perform the same binary classification on biological data (mean cortical β-amyloid burden, grey matter density, APOE 4) results in similar prediction accuracy (81.9%). Extending beyond binary classifications, we develop and implement a trajectory modelling approach that shows significantly better performance in predicting individualised rate of future cognitive decline (i.e. change in memory scores from baseline), when the metric learning model is trained with biological (r = -0.68) compared to cognitive (r = -0.4) data. Our trajectory modelling approach reveals interpretable and interoperable markers of progression to AD and has strong potential to guide effective stratification of individuals based on prognostic disease trajectories, reducing MCI patient misclassification, that is critical for clinical practice and discovery of personalised interventions.
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Affiliation(s)
- Joseph Giorgio
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Susan M Landau
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA USA
| | - Peter Tino
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Zoe Kourtzi
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom.
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12
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Bilgel M, Jedynak BM. Predicting time to dementia using a quantitative template of disease progression. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2019; 11:205-215. [PMID: 30859120 PMCID: PMC6396328 DOI: 10.1016/j.dadm.2019.01.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
INTRODUCTION Characterization of longitudinal trajectories of biomarkers implicated in sporadic Alzheimer's disease (AD) in decades before clinical diagnosis is important for disease prevention and monitoring. METHODS We used a multivariate Bayesian model to temporally align 1369 Alzheimer's disease Neuroimaging Initiative participants based on the similarity of their longitudinal biomarker measures and estimated a quantitative template of the temporal evolution of cerebrospinal fluid Aβ 1 - 42 , p- ta u 181 p , and t-tau and hippocampal volume, brain glucose metabolism, and cognitive measurements. We computed biomarker trajectories as a function of time to AD dementia and predicted AD dementia onset age in a disjoint sample. RESULTS Quantitative template showed early changes in verbal memory, cerebrospinal fluid Aβ1-42 and p-tau181p, and hippocampal volume. Mean error in predicted AD dementia onset age was < 1.5 years. DISCUSSION Our method provides a quantitative approach for characterizing the natural history of AD starting at preclinical stages despite the lack of individual-level longitudinal data spanning the entire disease timeline.
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Affiliation(s)
- Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bruno M. Jedynak
- Dept. of Mathematics and Statistics, Portland State University, Portland, OR, USA
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13
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Johnson SC, Koscik RL, Jonaitis EM, Clark LR, Mueller KD, Berman SE, Bendlin BB, Engelman CD, Okonkwo OC, Hogan KJ, Asthana S, Carlsson CM, Hermann BP, Sager MA. The Wisconsin Registry for Alzheimer's Prevention: A review of findings and current directions. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2017; 10:130-142. [PMID: 29322089 PMCID: PMC5755749 DOI: 10.1016/j.dadm.2017.11.007] [Citation(s) in RCA: 156] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The Wisconsin Registry for Alzheimer's Prevention is a longitudinal observational cohort study enriched with persons with a parental history (PH) of probable Alzheimer's disease (AD) dementia. Since late 2001, Wisconsin Registry for Alzheimer's Prevention has enrolled 1561 people at a mean baseline age of 54 years. Participants return for a second visit 4 years after baseline, and subsequent visits occur every 2 years. Eighty-one percent (1270) of participants remain active in the study at a current mean age of 64 and 9 years of follow-up. Serially assessed cognition, self-reported medical and lifestyle histories (e.g., diet, physical and cognitive activity, sleep, and mood), laboratory tests, genetics, and linked studies comprising molecular imaging, structural imaging, and cerebrospinal fluid data have yielded many important findings. In this cohort, PH of probable AD is associated with 46% apolipoprotein E (APOE) ε4 positivity, more than twice the rate of 22% among persons without PH. Subclinical or worse cognitive decline relative to internal normative data has been observed in 17.6% of the cohort. Twenty-eight percent exhibit amyloid and/or tau positivity. Biomarker elevations, but not APOE or PH status, are associated with cognitive decline. Salutary health and lifestyle factors are associated with better cognition and brain structure and lower AD pathophysiologic burden. Of paramount importance is establishing the amyloid and tau AD endophenotypes to which cognitive outcomes can be linked. Such data will provide new knowledge on the early temporal course of AD pathophysiology and inform the design of secondary prevention clinical trials.
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Affiliation(s)
- Sterling C. Johnson
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, Wm. S. Middleton Veterans Hospital, Madison WI, USA
| | - Rebecca L. Koscik
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Erin M. Jonaitis
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Lindsay R. Clark
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, Wm. S. Middleton Veterans Hospital, Madison WI, USA
| | - Kimberly D. Mueller
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Sara E. Berman
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Barbara B. Bendlin
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Corinne D. Engelman
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Ozioma C. Okonkwo
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Kirk J. Hogan
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Sanjay Asthana
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, Wm. S. Middleton Veterans Hospital, Madison WI, USA
| | - Cynthia M. Carlsson
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, Wm. S. Middleton Veterans Hospital, Madison WI, USA
| | - Bruce P. Hermann
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Mark A. Sager
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
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