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Ioannou K, Bucci M, Tzortzakakis A, Savitcheva I, Nordberg A, Chiotis K. Tau PET positivity predicts clinically relevant cognitive decline driven by Alzheimer's disease compared to comorbid cases; proof of concept in the ADNI study. Mol Psychiatry 2024:10.1038/s41380-024-02672-9. [PMID: 39179903 DOI: 10.1038/s41380-024-02672-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/26/2024] [Accepted: 07/09/2024] [Indexed: 08/26/2024]
Abstract
β-amyloid (Aβ) pathology is not always coupled with Alzheimer's disease (AD) relevant cognitive decline. We assessed the accuracy of tau PET to identify Aβ(+) individuals who show prospective disease progression. 396 cognitively unimpaired and impaired individuals with baseline Aβ and tau PET and a follow-up of ≥ 2 years were selected from the Alzheimer's Disease Neuroimaging Initiative dataset. The participants were dichotomously grouped based on either clinical conversion (i.e., change of diagnosis) or cognitive deterioration (fast (FDs) vs. slow decliners (SDs)) using data-driven clustering of the individual annual rates of cognitive decline. To assess cognitive decline in individuals with isolated Aβ(+) or absence of both Aβ and tau (T) pathologies, we investigated the prevalence of non-AD comorbidities and FDG PET hypometabolism patterns suggestive of AD. Baseline tau PET uptake was higher in Aβ(+)FDs than in Aβ(-)FD/SDs and Aβ(+)SDs, independently of baseline cognitive status. Baseline tau PET uptake identified MCI Aβ(+) Converters and Aβ(+)FDs with an area under the curve of 0.85 and 0.87 (composite temporal region of interest) respectively, and was linearly related to the annual rate of cognitive decline in Aβ(+) individuals. The T(+) individuals constituted largely a subgroup of those being Aβ(+) and those clustered as FDs. The most common biomarker profiles in FDs (n = 70) were Aβ(+)T(+) (n = 34, 49%) and Aβ(+)T(-) (n = 19, 27%). Baseline Aβ load was higher in Aβ(+)T(+)FDs (M = 83.03 ± 31.42CL) than in Aβ(+)T(-)FDs (M = 63.67 ± 26.75CL) (p-value = 0.038). Depression diagnosis was more prevalent in Aβ(+)T(-)FDs compared to Aβ(+)T(+)FDs (47% vs. 15%, p-value = 0.021), as were FDG PET hypometabolism pattern not suggestive of AD (86% vs. 50%, p-value = 0.039). Our findings suggest that high tau PET uptake is coupled with both Aβ pathology and accelerated cognitive decline. In cases of isolated Aβ(+), cognitive decline may be associated with changes within the AD spectrum in a multi-morbidity context, i.e., mixed AD.
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Affiliation(s)
- Konstantinos Ioannou
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Marco Bucci
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Antonios Tzortzakakis
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Agneta Nordberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Konstantinos Chiotis
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
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Wang X, Ye T, Jiang D, Zhou W, Zhang J. Characterizing the clinical heterogeneity of early symptomatic Alzheimer's disease: a data-driven machine learning approach. Front Aging Neurosci 2024; 16:1410544. [PMID: 39193492 PMCID: PMC11348433 DOI: 10.3389/fnagi.2024.1410544] [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: 04/01/2024] [Accepted: 07/29/2024] [Indexed: 08/29/2024] Open
Abstract
Introduction Alzheimer's disease (AD) is highly heterogeneous, with substantial individual variabilities in clinical progression and neurobiology. Amyloid deposition has been thought to drive cognitive decline and thus a major contributor to the variations in cognitive deterioration in AD. However, the clinical heterogeneity of patients with early symptomatic AD (mild cognitive impairment or mild dementia due to AD) already with evidence of amyloid abnormality in the brain is still unknown. Methods Participants with a baseline diagnosis of mild cognitive impairment or mild dementia, a positive amyloid-PET scan, and more than one follow-up Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) administration within a period of 5-year follow-up were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 421; age = 73±7; years of education = 16 ± 3; percentage of female gender = 43%; distribution of APOE4 carriers = 68%). A non-parametric k-means longitudinal clustering analysis in the context of the ADAS-Cog-13 data was performed to identify cognitive subtypes. Results We found a highly variable profile of cognitive decline among patients with early AD and identified 4 clusters characterized by distinct rates of cognitive progression. Among the groups there were significant differences in the magnitude of rates of changes in other cognitive and functional outcomes, clinical progression from mild cognitive impairment to dementia, and changes in markers presumed to reflect neurodegeneration and neuronal injury. A nomogram based on a simplified logistic regression model predicted steep cognitive trajectory with an AUC of 0.912 (95% CI: 0.88 - 0.94). Simulation of clinical trials suggested that the incorporation of the nomogram into enrichment strategies would reduce the required sample sizes from 926.8 (95% CI: 822.6 - 1057.5) to 400.9 (95% CI: 306.9 - 516.8). Discussion Our findings show usefulness in the stratification of patients in early AD and may thus increase the chances of finding a treatment for future AD clinical trials.
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Affiliation(s)
- Xiwu Wang
- Department of Psychiatry, Wenzhou Seventh People’s Hospital, Wenzhou, China
| | - Teng Ye
- Department of Ultrasound, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Deguo Jiang
- Department of Psychiatry, Wenzhou Seventh People’s Hospital, Wenzhou, China
| | - Wenjun Zhou
- Research and Development, Hangzhou Shansier Medical Technologies Co., Ltd., Hangzhou, China
| | - Jie Zhang
- Department of Data Science, Hangzhou Shansier Medical Technologies Co., Ltd., Hangzhou, China
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Menegon F, De Marchi F, Aprile D, Zanelli I, Decaroli G, Comi C, Tondo G. From Mild Cognitive Impairment to Dementia: The Impact of Comorbid Conditions on Disease Conversion. Biomedicines 2024; 12:1675. [PMID: 39200140 PMCID: PMC11351954 DOI: 10.3390/biomedicines12081675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 07/21/2024] [Accepted: 07/23/2024] [Indexed: 09/01/2024] Open
Abstract
The conversion from mild cognitive impairment (MCI) to dementia is influenced by several factors, including comorbid conditions such as metabolic and vascular diseases. Understanding the impact of these comorbidities can help in the disease management of patients with a higher risk of progressing to dementia, improving outcomes. In the current study, we aimed to analyze data from a large cohort of MCI (n = 188) by principal component analysis (PCA) and cluster analysis (CA) to classify patients into distinct groups based on their comorbidity profile and to predict the risk of conversion to dementia. From our analysis, four clusters emerged. CA showed a significantly higher rate of disease progression for Cluster 1, which was predominantly characterized by extremely high obesity and diabetes compared to other clusters. In contrast, Cluster 3, which was defined by a lower prevalence of all comorbidities, had a lower conversion rate. Cluster 2, mainly including subjects with traumatic brain injuries, showed the lowest rate of conversion. Lastly, Cluster 4, including a high load of hearing loss and depression, showed an intermediate risk of conversion. This study underscores the significant impact of specific comorbidity profiles on the progression from MCI to dementia, highlighting the need for targeted interventions and management strategies for individuals with these comorbidity profiles to potentially delay or prevent the onset of dementia.
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Affiliation(s)
- Federico Menegon
- Neurology Unit, Department of Translational Medicine, Maggiore della Carità Hospital, University of Piemonte Orientale, 28100 Novara, Italy; (F.M.); (F.D.M.); (D.A.); (I.Z.)
| | - Fabiola De Marchi
- Neurology Unit, Department of Translational Medicine, Maggiore della Carità Hospital, University of Piemonte Orientale, 28100 Novara, Italy; (F.M.); (F.D.M.); (D.A.); (I.Z.)
| | - Davide Aprile
- Neurology Unit, Department of Translational Medicine, Maggiore della Carità Hospital, University of Piemonte Orientale, 28100 Novara, Italy; (F.M.); (F.D.M.); (D.A.); (I.Z.)
| | - Iacopo Zanelli
- Neurology Unit, Department of Translational Medicine, Maggiore della Carità Hospital, University of Piemonte Orientale, 28100 Novara, Italy; (F.M.); (F.D.M.); (D.A.); (I.Z.)
| | - Greta Decaroli
- Neurology Unit, Department of Translational Medicine, Sant’Andrea Hospital, University of Piemonte Orientale, Corso Abbiate 21, 13100 Vercelli, Italy; (G.D.); (C.C.)
| | - Cristoforo Comi
- Neurology Unit, Department of Translational Medicine, Sant’Andrea Hospital, University of Piemonte Orientale, Corso Abbiate 21, 13100 Vercelli, Italy; (G.D.); (C.C.)
- Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Piemonte Orientale, 28100 Novara, Italy
| | - Giacomo Tondo
- Neurology Unit, Department of Translational Medicine, Maggiore della Carità Hospital, University of Piemonte Orientale, 28100 Novara, Italy; (F.M.); (F.D.M.); (D.A.); (I.Z.)
- Neurology Unit, Department of Translational Medicine, Sant’Andrea Hospital, University of Piemonte Orientale, Corso Abbiate 21, 13100 Vercelli, Italy; (G.D.); (C.C.)
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Rudroff T, Rainio O, Klén R. AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers - A narrative review of a growing field. Neurol Sci 2024:10.1007/s10072-024-07649-8. [PMID: 38866971 DOI: 10.1007/s10072-024-07649-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 06/10/2024] [Indexed: 06/14/2024]
Abstract
OBJECTIVES The objectives of this narrative review are to summarize the current state of AI applications in neuroimaging for early Alzheimer's disease (AD) prediction and to highlight the potential of AI techniques in improving early AD diagnosis, prognosis, and management. METHODS We conducted a narrative review of studies using AI techniques applied to neuroimaging data for early AD prediction. We examined single-modality studies using structural MRI and PET imaging, as well as multi-modality studies integrating multiple neuroimaging techniques and biomarkers. Furthermore, they reviewed longitudinal studies that model AD progression and identify individuals at risk of rapid decline. RESULTS Single-modality studies using structural MRI and PET imaging have demonstrated high accuracy in classifying AD and predicting progression from mild cognitive impairment (MCI) to AD. Multi-modality studies, integrating multiple neuroimaging techniques and biomarkers, have shown improved performance and robustness compared to single-modality approaches. Longitudinal studies have highlighted the value of AI in modeling AD progression and identifying individuals at risk of rapid decline. However, challenges remain in data standardization, model interpretability, generalizability, clinical integration, and ethical considerations. CONCLUSION AI techniques applied to neuroimaging data have the potential to improve early AD diagnosis, prognosis, and management. Addressing challenges related to data standardization, model interpretability, generalizability, clinical integration, and ethical considerations is crucial for realizing the full potential of AI in AD research and clinical practice. Collaborative efforts among researchers, clinicians, and regulatory agencies are needed to develop reliable, robust, and ethical AI tools that can benefit AD patients and society.
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Affiliation(s)
- Thorsten Rudroff
- Department of Health and Human Physiology, University of Iowa, Iowa City, IA, 52242, USA.
- Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.
| | - Oona Rainio
- Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland
| | - Riku Klén
- Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland
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Hernández-Lorenzo L, García-Gutiérrez F, Solbas-Casajús A, Corrochano S, Matías-Guiu JA, Ayala JL. Genetic-based patient stratification in Alzheimer's disease. Sci Rep 2024; 14:9970. [PMID: 38693203 PMCID: PMC11063050 DOI: 10.1038/s41598-024-60707-1] [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: 12/19/2023] [Accepted: 04/26/2024] [Indexed: 05/03/2024] Open
Abstract
Alzheimer's disease (AD) shows a high pathological and symptomatological heterogeneity. To study this heterogeneity, we have developed a patient stratification technique based on one of the most significant risk factors for the development of AD: genetics. We addressed this challenge by including network biology concepts, mapping genetic variants data into a brain-specific protein-protein interaction (PPI) network, and obtaining individualized PPI scores that we then used as input for a clustering technique. We then phenotyped each obtained cluster regarding genetics, sociodemographics, biomarkers, fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging, and neurocognitive assessments. We found three clusters defined mainly by genetic variants found in MAPT, APP, and APOE, considering known variants associated with AD and other neurodegenerative disease genetic architectures. Profiling of these clusters revealed minimal variation in AD symptoms and pathology, suggesting different biological mechanisms may activate the neurodegeneration and pathobiological patterns behind AD and result in similar clinical and pathological presentations, even a shared disease diagnosis. Lastly, our research highlighted MAPT, APP, and APOE as key genes where these genetic distinctions manifest, suggesting them as potential targets for personalized drug development strategies to address each AD subgroup individually.
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Affiliation(s)
- Laura Hernández-Lorenzo
- Department of Computer Architecture and Automation, Computer Science Faculty, Complutense University of Madrid, 28040, Madrid, Spain.
| | - Fernando García-Gutiérrez
- Department of Computer Architecture and Automation, Computer Science Faculty, Complutense University of Madrid, 28040, Madrid, Spain
| | - Ana Solbas-Casajús
- Department of Computer Architecture and Automation, Computer Science Faculty, Complutense University of Madrid, 28040, Madrid, Spain
| | - Silvia Corrochano
- Department of Neurology, San Carlos Research Institute (IdSSC), Hospital Clínico San Carlos, 28040, Madrid, Spain
| | - Jordi A Matías-Guiu
- Department of Neurology, San Carlos Research Institute (IdSSC), Hospital Clínico San Carlos, 28040, Madrid, Spain
| | - Jose L Ayala
- Department of Computer Architecture and Automation, Computer Science Faculty, Complutense University of Madrid, 28040, Madrid, Spain
- Instituto de Tecnología del Conocimiento, Universidad Complutense de Madrid, 28040, Madrid, Spain
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Zhao Y, Caffo BS, Luo X. Longitudinal regression of covariance matrix outcomes. Biostatistics 2024; 25:385-401. [PMID: 36451549 DOI: 10.1093/biostatistics/kxac045] [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: 03/24/2022] [Revised: 10/25/2022] [Accepted: 11/02/2022] [Indexed: 02/17/2024] Open
Abstract
In this study, a longitudinal regression model for covariance matrix outcomes is introduced. The proposal considers a multilevel generalized linear model for regressing covariance matrices on (time-varying) predictors. This model simultaneously identifies covariate-associated components from covariance matrices, estimates regression coefficients, and captures the within-subject variation in the covariance matrices. Optimal estimators are proposed for both low-dimensional and high-dimensional cases by maximizing the (approximated) hierarchical-likelihood function. These estimators are proved to be asymptotically consistent, where the proposed covariance matrix estimator is the most efficient under the low-dimensional case and achieves the uniformly minimum quadratic loss among all linear combinations of the identity matrix and the sample covariance matrix under the high-dimensional case. Through extensive simulation studies, the proposed approach achieves good performance in identifying the covariate-related components and estimating the model parameters. Applying to a longitudinal resting-state functional magnetic resonance imaging data set from the Alzheimer's Disease (AD) Neuroimaging Initiative, the proposed approach identifies brain networks that demonstrate the difference between males and females at different disease stages. The findings are in line with existing knowledge of AD and the method improves the statistical power over the analysis of cross-sectional data.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, 410 W 10th Street, Indianapolis, IN 46202, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, 1200 Pressler Street, Houston, TX 77030, USA
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Ma X, Shyer M, Harris K, Wang D, Hsu YC, Farrell C, Goodwin N, Anjum S, Bukhbinder AS, Dean S, Khan T, Hunter D, Schulz PE, Jiang X, Kim Y. Deep learning to predict rapid progression of Alzheimer's disease from pooled clinical trials: A retrospective study. PLOS DIGITAL HEALTH 2024; 3:e0000479. [PMID: 38598464 PMCID: PMC11006164 DOI: 10.1371/journal.pdig.0000479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 02/26/2024] [Indexed: 04/12/2024]
Abstract
The rate of progression of Alzheimer's disease (AD) differs dramatically between patients. Identifying the most is critical because when their numbers differ between treated and control groups, it distorts the outcome, making it impossible to tell whether the treatment was beneficial. Much recent effort, then, has gone into identifying RPs. We pooled de-identified placebo-arm data of three randomized controlled trials (RCTs), EXPEDITION, EXPEDITION 2, and EXPEDITION 3, provided by Eli Lilly and Company. After processing, the data included 1603 mild-to-moderate AD patients with 80 weeks of longitudinal observations on neurocognitive health, brain volumes, and amyloid-beta (Aβ) levels. RPs were defined by changes in four neurocognitive/functional health measures. We built deep learning models using recurrent neural networks with attention mechanisms to predict RPs by week 80 based on varying observation periods from baseline (e.g., 12, 28 weeks). Feature importance scores for RP prediction were computed and temporal feature trajectories were compared between RPs and non-RPs. Our evaluation and analysis focused on models trained with 28 weeks of observation. The models achieved robust internal validation area under the receiver operating characteristic (AUROCs) ranging from 0.80 (95% CI 0.79-0.82) to 0.82 (0.81-0.83), and the area under the precision-recall curve (AUPRCs) from 0.34 (0.32-0.36) to 0.46 (0.44-0.49). External validation AUROCs ranged from 0.75 (0.70-0.81) to 0.83 (0.82-0.84) and AUPRCs from 0.27 (0.25-0.29) to 0.45 (0.43-0.48). Aβ plasma levels, regional brain volumetry, and neurocognitive health emerged as important factors for the model prediction. In addition, the trajectories were stratified between predicted RPs and non-RPs based on factors such as ventricular volumes and neurocognitive domains. Our findings will greatly aid clinical trialists in designing tests for new medications, representing a key step toward identifying effective new AD therapies.
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Affiliation(s)
- Xiaotian Ma
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Madison Shyer
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Kristofer Harris
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Dulin Wang
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Yu-Chun Hsu
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Christine Farrell
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Nathan Goodwin
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Sahar Anjum
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Avram S. Bukhbinder
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- Division of Pediatric Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Sarah Dean
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Tanveer Khan
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - David Hunter
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Paul E. Schulz
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Xiaoqian Jiang
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Yejin Kim
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
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Wang K, Hua W, Wang M, Xu Y. A Bayesian semi-parametric model for learning biomarker trajectories and changepoints in the preclinical phase of Alzheimer's disease. Biometrics 2024; 80:ujae048. [PMID: 38775703 PMCID: PMC11110494 DOI: 10.1093/biomtc/ujae048] [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: 06/14/2023] [Revised: 04/26/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024]
Abstract
It has become consensus that mild cognitive impairment (MCI), one of the early symptoms onset of Alzheimer's disease (AD), may appear 10 or more years after the emergence of neuropathological abnormalities. Therefore, understanding the progression of AD biomarkers and uncovering when brain alterations begin in the preclinical stage, while patients are still cognitively normal, are crucial for effective early detection and therapeutic development. In this paper, we develop a Bayesian semiparametric framework that jointly models the longitudinal trajectory of the AD biomarker with a changepoint relative to the occurrence of symptoms onset, which is subject to left truncation and right censoring, in a heterogeneous population. Furthermore, unlike most existing methods assuming that everyone in the considered population will eventually develop the disease, our approach accounts for the possibility that some individuals may never experience MCI or AD, even after a long follow-up time. We evaluate the proposed model through simulation studies and demonstrate its clinical utility by examining an important AD biomarker, ptau181, using a dataset from the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) study.
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Affiliation(s)
- Kunbo Wang
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, United States
| | - William Hua
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, United States
| | - MeiCheng Wang
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, United States
| | - Yanxun Xu
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, United States
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9
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Li Y, Zhu W, Zhou S, Li H, Gao Z, Huang Z, Li X, Yu Y, Li X. Sex differences in functional connectivity and the predictive role of the connectome-based predictive model in Alzheimer's disease. J Neurosci Res 2024; 102:e25307. [PMID: 38444265 DOI: 10.1002/jnr.25307] [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: 04/05/2023] [Revised: 01/23/2024] [Accepted: 01/31/2024] [Indexed: 03/07/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease characterized by cognitive decline. Sex differences in the progression of AD exist, but the neural mechanisms are not well understood. The purpose of the current study was to explore sex differences in brain functional connectivity (FC) at different stages of AD and their predictive ability on Montreal Cognitive Assessment (MoCA) scores using connectome-based predictive modeling (CPM). Resting-state functional magnetic resonance imaging was collected from 81 AD patients (44 females), 78 amnestic mild cognitive impairment patients (44 females), and 92 healthy controls (50 females). The FC analysis was conducted and the interaction effect between sex and group was investigated using two-factor variance analysis. The CPM was used to predict MoCA scores. There were sex-by-group interaction effects on FC between the left dorsolateral superior frontal gyrus and left middle temporal gyrus, left precuneus and right calcarine fissure surrounding cortex, left precuneus and left middle occipital gyrus, left middle temporal gyrus and left precentral gyrus, and between the left middle temporal gyrus and right cuneus. In the CPM, the positive network predictive model significantly predicted MoCA scores in both males and females. There were significant sex-by-group interaction effects on FC between the left precuneus and left middle occipital gyrus, and between the left middle temporal gyrus and right cuneus could predict MoCA scores in female patients. Our results suggest that there are sex differences in FC at different stages of AD. The sex-specific FC can further predict MoCA scores at individual level.
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Affiliation(s)
- Yuqing Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wanqiu Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shanshan Zhou
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Hui Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Ziwen Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Ziang Huang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaohu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaoshu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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Panahi S, Mayo J, Kennedy E, Christensen L, Kamineni S, Sagiraju HKR, Cooper T, Tate DF, Rupper R, Pugh MJ. Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing. Front Neurol 2024; 15:1270688. [PMID: 38426171 PMCID: PMC10902457 DOI: 10.3389/fneur.2024.1270688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 01/09/2024] [Indexed: 03/02/2024] Open
Abstract
Introduction Frontotemporal dementia (FTD) encompasses a clinically and pathologically diverse group of neurodegenerative disorders, yet little work has quantified the unique phenotypic clinical presentations of FTD among post-9/11 era veterans. To identify phenotypes of FTD using natural language processing (NLP) aided medical chart reviews of post-9/11 era U.S. military Veterans diagnosed with FTD in Veterans Health Administration care. Methods A medical record chart review of clinician/provider notes was conducted using a Natural Language Processing (NLP) tool, which extracted features related to cognitive dysfunction. NLP features were further organized into seven Research Domain Criteria Initiative (RDoC) domains, which were clustered to identify distinct phenotypes. Results Veterans with FTD were more likely to have notes that reflected the RDoC domains, with cognitive and positive valence domains showing the greatest difference across groups. Clustering of domains identified three symptom phenotypes agnostic to time of an individual having FTD, categorized as Low (16.4%), Moderate (69.2%), and High (14.5%) distress. Comparison across distress groups showed significant differences in physical and psychological characteristics, particularly prior history of head injury, insomnia, cardiac issues, anxiety, and alcohol misuse. The clustering result within the FTD group demonstrated a phenotype variant that exhibited a combination of language and behavioral symptoms. This phenotype presented with manifestations indicative of both language-related impairments and behavioral changes, showcasing the coexistence of features from both domains within the same individual. Discussion This study suggests FTD also presents across a continuum of severity and symptom distress, both within and across variants. The intensity of distress evident in clinical notes tends to cluster with more co-occurring conditions. This examination of phenotypic heterogeneity in clinical notes indicates that sensitivity to FTD diagnosis may be correlated to overall symptom distress, and future work incorporating NLP and phenotyping may help promote strategies for early detection of FTD.
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Affiliation(s)
- Samin Panahi
- VA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United States
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Jamie Mayo
- VA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United States
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Eamonn Kennedy
- VA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United States
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Lee Christensen
- VA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United States
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Sreekanth Kamineni
- VA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United States
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United States
| | | | - Tyler Cooper
- VA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United States
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - David F. Tate
- VA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United States
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Randall Rupper
- VA Salt Lake City Health Care System, Geriatric Research, Education and Clinical Center, Salt Lake City, UT, United States
| | - Mary Jo Pugh
- VA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United States
- Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United States
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11
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Ekanayake A, Peiris S, Ahmed B, Kanekar S, Grove C, Kalra D, Eslinger P, Yang Q, Karunanayaka P. A Review of the Role of Estrogens in Olfaction, Sleep and Glymphatic Functionality in Relation to Sex Disparity in Alzheimer's Disease. Am J Alzheimers Dis Other Demen 2024; 39:15333175241272025. [PMID: 39116421 PMCID: PMC11311174 DOI: 10.1177/15333175241272025] [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] [Indexed: 08/10/2024]
Abstract
Several risk factors contribute to the development of Alzheimer's disease (AD), including genetics, metabolic health, cardiovascular history, and diet. It has been observed that women appear to face a higher risk of developing AD. Among the various hypotheses surrounding the gender disparity in AD, one pertains to the potential neuroprotective properties of estrogen. Compared to men, women are believed to be more susceptible to neuropathology due to the significant decline in circulating estrogen levels following menopause. Studies have shown, however, that estrogen replacement therapies in post-menopausal women do not consistently reduce the risk of AD. While menopause and estrogen levels are potential factors in the elevated incidence rates of AD among women, this review highlights the possible roles estrogen has in other pathways that may also contribute to the sex disparity observed in AD such as olfaction, sleep, and glymphatic functionality.
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Affiliation(s)
- Anupa Ekanayake
- Department of Radiology, Penn State University College of Medicine, Hershey, PA, USA
- Grodno State Medical University, Grodno, Belarus
| | - Senal Peiris
- Department of Radiology, Penn State University College of Medicine, Hershey, PA, USA
| | - Biyar Ahmed
- Department of Radiology, Penn State University College of Medicine, Hershey, PA, USA
| | - Sangam Kanekar
- Department of Radiology, Penn State University College of Medicine, Hershey, PA, USA
| | - Cooper Grove
- Department of Radiology, Penn State University College of Medicine, Hershey, PA, USA
| | - Deepak Kalra
- Department of Neurology, Penn State University College of Medicine, Hershey, PA, USA
| | - Paul Eslinger
- Department of Radiology, Penn State University College of Medicine, Hershey, PA, USA
- Department of Neurology, Penn State University College of Medicine, Hershey, PA, USA
| | - Qing Yang
- Department of Radiology, Penn State University College of Medicine, Hershey, PA, USA
- Department of Neurosurgery, Penn State University College of Medicine, Hershey, PA, USA
| | - Prasanna Karunanayaka
- Department of Radiology, Penn State University College of Medicine, Hershey, PA, USA
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12
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Petrella JR, Jiang J, Sreeram K, Dalziel S, Doraiswamy PM, Hao W. Personalized Computational Causal Modeling of the Alzheimer Disease Biomarker Cascade. J Prev Alzheimers Dis 2024; 11:435-444. [PMID: 38374750 PMCID: PMC11082854 DOI: 10.14283/jpad.2023.134] [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] [Indexed: 02/21/2024]
Abstract
BACKGROUND Mathematical models of complex diseases, such as Alzheimer's disease, have the potential to play a significant role in personalized medicine. Specifically, models can be personalized by fitting parameters with individual data for the purpose of discovering primary underlying disease drivers, predicting natural history, and assessing the effects of theoretical interventions. Previous work in causal/mechanistic modeling of Alzheimer's Disease progression has modeled the disease at the cellular level and on a short time scale, such as minutes to hours. No previous studies have addressed mechanistic modeling on a personalized level using clinically validated biomarkers in individual subjects. OBJECTIVES This study aimed to investigate the feasibility of personalizing a causal model of Alzheimer's Disease progression using longitudinal biomarker data. DESIGN/SETTING/PARTICIPANTS/MEASUREMENTS We chose the Alzheimer Disease Biomarker Cascade model, a widely-referenced hypothetical model of Alzheimer's Disease based on the amyloid cascade hypothesis, which we had previously implemented mathematically as a mechanistic model. We used available longitudinal demographic and serial biomarker data in over 800 subjects across the cognitive spectrum from the Alzheimer's Disease Neuroimaging Initiative. The data included participants that were cognitively normal, had mild cognitive impairment, or were diagnosed with dementia (probable Alzheimer's Disease). The model consisted of a sparse system of differential equations involving four measurable biomarkers based on cerebrospinal fluid proteins, imaging, and cognitive testing data. RESULTS Personalization of the Alzheimer Disease Biomarker Cascade model with individual serial biomarker data yielded fourteen personalized parameters in each subject reflecting physiologically meaningful characteristics. These included growth rates, latency values, and carrying capacities of the various biomarkers, most of which demonstrated significant differences across clinical diagnostic groups. The model fits to training data across the entire cohort had a root mean squared error (RMSE) of 0.09 (SD 0.081) on a variable scale between zero and one, and were robust, with over 90% of subjects showing an RMSE of < 0.2. Similarly, in a subset of subjects with data on all four biomarkers in at least one test set, performance was high on the test sets, with a mean RMSE of 0.15 (SD 0.117), with 80% of subjects demonstrating an RMSE < 0.2 in the estimation of future biomarker points. Cluster analysis of parameters revealed two distinct endophenotypic groups, with distinct biomarker profiles and disease trajectories. CONCLUSION Results support the feasibility of personalizing mechanistic models based on individual biomarker trajectories and suggest that this approach may be useful for reclassifying subjects on the Alzheimer's clinical spectrum. This computational modeling approach is not limited to the Alzheimer Disease Biomarker Cascade hypothesis, and can be applied to any mechanistic hypothesis of disease progression in the Alzheimer's field that can be monitored with biomarkers. Thus, it offers a computational platform to compare and validate various disease hypotheses, personalize individual biomarker trajectories and predict individual response to theoretical prevention and therapeutic intervention strategies.
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Affiliation(s)
- J R Petrella
- Jeffrey R. Petrella, Department of Radiology, Duke University School of Medicine, DUMC - Box 3808 , 27710-3808, NC, USA
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13
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Reed EG, Keller-Norrell PR. Minding the Gap: Exploring Neuroinflammatory and Microglial Sex Differences in Alzheimer's Disease. Int J Mol Sci 2023; 24:17377. [PMID: 38139206 PMCID: PMC10743742 DOI: 10.3390/ijms242417377] [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: 11/20/2023] [Revised: 12/04/2023] [Accepted: 12/09/2023] [Indexed: 12/24/2023] Open
Abstract
Research into Alzheimer's Disease (AD) describes a link between AD and the resident immune cells of the brain, the microglia. Further, this suspected link is thought to have underlying sex effects, although the mechanisms of these effects are only just beginning to be understood. Many of these insights are the result of policies put in place by funding agencies such as the National Institutes of Health (NIH) to consider sex as a biological variable (SABV) and the move towards precision medicine due to continued lackluster therapeutic options. The purpose of this review is to provide an updated assessment of the current research that summarizes sex differences and the research pertaining to microglia and their varied responses in AD.
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Affiliation(s)
- Erin G. Reed
- Department of Pharmaceutical Sciences, Northeast Ohio Medical University, Rootstown, OH 44242, USA
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14
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Han SL, Liu DC, Tan CC, Tan L, Xu W. Male- and female-specific reproductive risk factors across the lifespan for dementia or cognitive decline: a systematic review and meta-analysis. BMC Med 2023; 21:457. [PMID: 37996855 PMCID: PMC10666320 DOI: 10.1186/s12916-023-03159-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 11/06/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Sex difference exists in the prevalence of dementia and cognitive decline. The impacts of sex-specific reproductive risk factors across the lifespan on the risk of dementia or cognitive decline are still unclear. Herein, we conducted this systemic review and meta-analysis to finely depict the longitudinal associations between sex-specific reproductive factors and dementia or cognitive decline. METHODS PubMed, EMBASE, and Cochrane Library were searched up to January 2023. Studies focused on the associations of female- and male-specific reproductive factors with dementia or cognitive decline were included. Multivariable-adjusted effects were pooled via the random effect models. Evidence credibility was scored by the GRADE system. The study protocol was pre-registered in PROSPERO and the registration number is CRD42021278732. RESULTS A total of 94 studies were identified for evidence synthesis, comprising 9,839,964 females and 3,436,520 males. Among the identified studies, 63 of them were included in the meta-analysis. According to the results, seven female-specific reproductive factors including late menarche (risk increase by 15%), nulliparous (11%), grand parity (32%), bilateral oophorectomy (8%), short reproductive period (14%), early menopause (22%), increased estradiol level (46%), and two male-specific reproductive factors, androgen deprivation therapy (18%), and serum sex hormone-binding globulin (22%) were associated with an elevated risk of dementia or cognitive decline. CONCLUSIONS These findings potentially reflect sex hormone-driven discrepancy in the occurrence of dementia and could help build sex-based precise strategies for preventing dementia.
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Affiliation(s)
- Shuang-Ling Han
- Department of Neurology, Qingdao Municipal Hospital Group, Qingdao University, Donghai Middle Road, No.5, Qingdao, 266000, China
- Medical College, Qingdao University, Qingdao, 266000, China
| | - De-Chun Liu
- Department of Obstetrics, Qingdao Municipal Hospital Group, Qingdao, 266000, China
| | - Chen-Chen Tan
- Department of Neurology, Qingdao Municipal Hospital Group, Qingdao University, Donghai Middle Road, No.5, Qingdao, 266000, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital Group, Qingdao University, Donghai Middle Road, No.5, Qingdao, 266000, China
| | - Wei Xu
- Department of Neurology, Qingdao Municipal Hospital Group, Qingdao University, Donghai Middle Road, No.5, Qingdao, 266000, China.
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15
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Barisch-Fritz B, Bezold J, Scharpf A, Trautwein S, Krell-Roesch J, Woll A. A New Approach to Individualize Physical Activity Interventions for Individuals With Dementia: Cluster Analysis Based on Physical and Cognitive Performance. J Geriatr Phys Ther 2023:00139143-990000000-00038. [PMID: 37820354 DOI: 10.1519/jpt.0000000000000396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
BACKGROUND AND PURPOSE Physical activity (PA) can have a beneficial effect on cognitive and physical performance in individuals with dementia (IWD), including those residing in nursing homes. However, PA interventions in nursing homes are usually delivered using a group setting, which may limit the effectiveness of the intervention due to the heterogenous nature of IWD. Therefore, the purpose of this study was to identify clusters based on cognitive and physical performance values, which could be used to improve individualization of PA interventions. METHODS Based on the cognitive and physical performance variables of 230 IWD, a cluster analysis was conducted. Global cognition (Mini-Mental State Examination), mobility (6-Meter Walking Test), balance (Frailty and Injuries: Cooperative Studies of Intervention Techniques-subtest-4), and strength and function of lower extremities (30-Second Chair-Stand Test) were assessed, and values were used to perform a hierarchical cluster analysis with Ward's method. Differences in physical and cognitive performance as well as other secondary outcomes (age, sex, body mass index, use of walking aids, diagnosis and etiology of dementia, number of medications, and Cumulative Illness Rating Scale) were tested using 1-factorial analyses of variance. RESULTS AND DISCUSSION Out of 230 data sets, 3-cluster solutions were identified with similar cluster sizes of 73 to 79. The silhouette coefficients for all calculated clusters ranged between 0.15 and 0.34. The cluster solutions were discussed in the context of cognitive and physical functions as well as training modalities and opportunities. The 4-cluster solution appears to be best suited for providing or developing an individualized PA intervention. CONCLUSIONS The identified clusters of the 4-cluster solution may be used in future research to improve individualization of dementia-specific PA interventions. By assigning IWD to these clusters, more homogenous groups with regard to cognitive and physical performance can be formed. This allows for more individualized PA interventions and may result in a higher effectiveness, particularly in nursing homes. Our findings are relevant for therapists and nursing staff who design or deliver PA interventions in nursing homes or similar settings.
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Affiliation(s)
- Bettina Barisch-Fritz
- Karlsruhe Institute of Technology, Institute of Sports and Sports Science, Karlsruhe, Germany
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16
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Felsky D, Cannitelli A, Pipitone J. Whole Person Modeling: a transdisciplinary approach to mental health research. DISCOVER MENTAL HEALTH 2023; 3:16. [PMID: 37638348 PMCID: PMC10449734 DOI: 10.1007/s44192-023-00041-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 08/10/2023] [Indexed: 08/29/2023]
Abstract
The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.
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Affiliation(s)
- Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON Canada
- Rotman Research Institute, Baycrest Hospital, Toronto, ON Canada
- Faculty of Medicine, McMaster University, Hamilton, ON Canada
| | - Alyssa Cannitelli
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
- Faculty of Medicine, McMaster University, Hamilton, ON Canada
| | - Jon Pipitone
- Department of Psychiatry, Queen’s University, Kingston, ON Canada
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17
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Lutshumba J, Wilcock DM, Monson NL, Stowe AM. Sex-based differences in effector cells of the adaptive immune system during Alzheimer's disease and related dementias. Neurobiol Dis 2023; 184:106202. [PMID: 37330146 PMCID: PMC10481581 DOI: 10.1016/j.nbd.2023.106202] [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: 01/19/2023] [Revised: 06/12/2023] [Accepted: 06/13/2023] [Indexed: 06/19/2023] Open
Abstract
Neurological conditions such as Alzheimer's disease (AD) and related dementias (ADRD) present with many challenges due to the heterogeneity of the related disease(s), making it difficult to develop effective treatments. Additionally, the progression of ADRD-related pathologies presents differently between men and women. With two-thirds of the population affected with ADRD being women, ADRD has presented itself with a bias toward the female population. However, studies of ADRD generally do not incorporate sex-based differences in investigating the development and progression of the disease, which is detrimental to understanding and treating dementia. Additionally, recent implications for the adaptive immune system in the development of ADRD bring in new factors to be considered as part of the disease, including sex-based differences in immune response(s) during ADRD development. Here, we review the sex-based differences of pathological hallmarks of ADRD presentation and progression, sex-based differences in the adaptive immune system and how it changes with ADRD, and the importance of precision medicine in the development of a more targeted and personalized treatment for this devastating and prevalent neurodegenerative condition.
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Affiliation(s)
- Jenny Lutshumba
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, United States of America
| | - Donna M Wilcock
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States of America; Department of Physiology, College of Medicine, University of Kentucky, Lexington, KY, United States of America
| | - Nancy L Monson
- Department of Neurology and Immunology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Ann M Stowe
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, United States of America; Center for Advanced Translational Stroke Science, University of Kentucky, Lexington, KY, United States of America.
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18
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Nicoletti A, Baschi R, Cicero CE, Iacono S, Re VL, Luca A, Schirò G, Monastero R. Sex and gender differences in Alzheimer's disease, Parkinson's disease, and Amyotrophic Lateral Sclerosis: a narrative review. Mech Ageing Dev 2023; 212:111821. [PMID: 37127082 DOI: 10.1016/j.mad.2023.111821] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/24/2023] [Accepted: 04/27/2023] [Indexed: 05/03/2023]
Abstract
Neurodegenerative diseases (NDs), including Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), exhibit high phenotypic variability and they are very common in the general population. These diseases are associated with poor prognosis and a significant burden on patients and their caregivers. Although increasing evidence suggests that biological sex is an important factor for the development and phenotypical expression of some NDs, the role of sex and gender in the diagnosis and prognosis of NDs has been poorly explored. Current knowledge relating to sex- and gender-related differences in the epidemiology, clinical features, biomarkers, and treatment of AD, PD, and ALS will be summarized in this narrative review. The cumulative evidence hitherto collected suggests that sex and gender are factors to be considered in explaining the heterogeneity of these NDs. Clarifying the role of sex and gender in AD, PD, and ALS is a key topic in precision medicine, which will facilitate sex-specific prevention and treatment strategies to be implemented in the near future.
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Affiliation(s)
- Alessandra Nicoletti
- Department of Medical, Surgical Sciences and Advanced Technologies "G.F. Ingrassia", University of Catania, Via Santa Sofia 78, 95123, Catania, Italy.
| | - Roberta Baschi
- Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University of Palermo, Via La Loggia 1, 90129 Palermo, Italy
| | - Calogero Edoardo Cicero
- Department of Medical, Surgical Sciences and Advanced Technologies "G.F. Ingrassia", University of Catania, Via Santa Sofia 78, 95123, Catania, Italy
| | - Salvatore Iacono
- Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University of Palermo, Via La Loggia 1, 90129 Palermo, Italy
| | - Vincenzina Lo Re
- Neurology Service, Mediterranean Institute for Transplantation and Advanced Specialized Therapies (IRCCS-ISMETT), Via Ernesto Tricomi 5, 90127 Palermo, Italy; Women's Brain Project, Guntershausen, Switzerland
| | - Antonina Luca
- Department of Medical, Surgical Sciences and Advanced Technologies "G.F. Ingrassia", University of Catania, Via Santa Sofia 78, 95123, Catania, Italy
| | - Giuseppe Schirò
- Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University of Palermo, Via La Loggia 1, 90129 Palermo, Italy
| | - Roberto Monastero
- Department of Biomedicine, Neurosciences and Advanced Diagnostics (BiND), University of Palermo, Via La Loggia 1, 90129 Palermo, Italy.
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Tian Y, Xie Y, Guo Z, Feng P, You Y, Yu Q. 17β-oestradiol inhibits ferroptosis in the hippocampus by upregulating DHODH and further improves memory decline after ovariectomy. Redox Biol 2023; 62:102708. [PMID: 37116254 PMCID: PMC10163677 DOI: 10.1016/j.redox.2023.102708] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/20/2023] [Accepted: 04/20/2023] [Indexed: 04/30/2023] Open
Abstract
Ovariectomy (OVX) conducted before the onset of natural menopause is considered to bringing forward and accelerate the process of ageing-associated neurodegeneration. However, the mechanisms underlying memory decline and other cognitive dysfunctions following OVX are unclear. Given that iron accumulates during ageing and after OVX, we hypothesized that excess iron accumulation in the hippocampus would cause ferroptosis-induced increased neuronal degeneration and death associated with memory decline. In the current study, female rats that underwent OVX showed decreased dihydroorotate dehydrogenase (DHODH) expression and reduced performance in the Morris water maze (MWM). We used primary cultured hippocampal cells to explore the ferroptosis resistance-inducing effect of 17β-oestradiol (E2). The data supported a vital role of DHODH in neuronal ferroptosis. Specifically, E2 alleviated ferroptosis induced by erastin and ferric ammonium citrate (FAC), which can be blocked by brequinar (BQR). Further in vitro studies showed that E2 reduced lipid peroxidation levels and improved the behavioural performance of OVX rats. Our research interprets OVX-related neurodegeneration with respect to ferroptosis, and both our in vivo and in vitro data show that E2 supplementation exerts beneficial antiferroptotic effects by upregulating DHODH. Our data demonstrate the utility of E2 supplementation after OVX and provide a potential target, DHODH, for which hormone therapy has not been available.
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Affiliation(s)
- Ying Tian
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital (Dongdan Campus), No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
| | - Yuan Xie
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital (Dongdan Campus), No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
| | - Zaixin Guo
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital (Dongdan Campus), No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
| | - Penghui Feng
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital (Dongdan Campus), No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
| | - Yang You
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital (Dongdan Campus), No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
| | - Qi Yu
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital (Dongdan Campus), No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
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Katabathula S, Davis PB, Xu R. Comorbidity-driven multi-modal subtype analysis in mild cognitive impairment of Alzheimer's disease. Alzheimers Dement 2023; 19:1428-1439. [PMID: 36166485 PMCID: PMC10040466 DOI: 10.1002/alz.12792] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
BACKGROUND Mild cognitive impairment (MCI) is a heterogeneous condition with high individual variabilities in clinical outcomes driven by patient demographics, genetics, brain structure features, blood biomarkers, and comorbidities. Multi-modality data-driven approaches have been used to discover MCI subtypes; however, disease comorbidities have not been included as a modality though multiple diseases including hypertension are well-known risk factors for Alzheimer's disease (AD). The aim of this study was to examine MCI heterogeneity in the context of AD-related comorbidities along with other AD-relevant features and biomarkers. METHODS A total of 325 MCI subjects with 32 AD-relevant comorbidities and features were considered. Mixed-data clustering is applied to discover and compare MCI subtypes with and without including AD-related comorbidities. Finally, the relevance of each comorbidity-driven subtype was determined by examining their MCI to AD disease prognosis, descriptive statistics, and conversion rates. RESULTS We identified four (five) MCI subtypes: poor-, average-, good-, and best-AD prognosis by including comorbidities (without including comorbidities). We demonstrated that comorbidity-driven MCI subtypes differed from those identified without comorbidity information. We further demonstrated the clinical relevance of comorbidity-driven MCI subtypes. Among the four comorbidity-driven MCI subtypes there were substantial differences in the proportions of participants who reverted to normal function, remained stable, or converted to AD. The groups showed different behaviors, having significantly different MCI to AD prognosis, significantly different means for cognitive test-related and plasma features, and by the proportion of comorbidities. CONCLUSIONS Our study indicates that AD comorbidities should be considered along with other diverse AD-relevant characteristics to better understand MCI heterogeneity.
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Affiliation(s)
- Sreevani Katabathula
- Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Pamela B Davis
- Center for Community Health Integration, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
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21
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Wang X, Ye T, Zhou W, Zhang J. Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach. Alzheimers Res Ther 2023; 15:57. [PMID: 36941651 PMCID: PMC10026406 DOI: 10.1186/s13195-023-01205-w] [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: 12/06/2022] [Accepted: 03/12/2023] [Indexed: 03/23/2023]
Abstract
BACKGROUND Given the complex and progressive nature of mild cognitive impairment (MCI), the ability to delineate and understand the heterogeneous cognitive trajectories is crucial for developing personalized medicine and informing trial design. The primary goals of this study were to examine whether different cognitive trajectories can be identified within subjects with MCI and, if present, to characterize each trajectory in relation to changes in all major Alzheimer's disease (AD) biomarkers over time. METHODS Individuals with a diagnosis of MCI at the first visit and ≥ 1 follow-up cognitive assessment were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 936; age 73 ± 8; 40% female; 16 ± 3 years of education; 50% APOE4 carriers). Based on the Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) total scores from baseline up to 5 years follow-up, a non-parametric k-means longitudinal clustering method was performed to obtain clusters of individuals with similar patterns of cognitive decline. We further conducted a series of linear mixed-effects models to study the associations of cluster membership with longitudinal changes in other cognitive measures, neurodegeneration, and in vivo AD pathologies. RESULTS Four distinct cognitive trajectories emerged. Cluster 1 consisted of 255 individuals (27%) with a nearly non-existent rate of change in the ADAS-Cog-13 over 5 years of follow-up and a healthy-looking biomarker profile. Individuals in the cluster 2 (n = 336, 35%) and 3 (n = 240, 26%) groups showed relatively mild and moderate cognitive decline trajectories, respectively. Cluster 4, comprising about 11% of our study sample (n = 105), exhibited an aggressive cognitive decline trajectory and was characterized by a pronouncedly abnormal biomarker profile. CONCLUSIONS Individuals with MCI show substantial heterogeneity in cognitive decline. Our findings may potentially contribute to improved trial design and patient stratification.
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Affiliation(s)
- Xiwu Wang
- Department of Psychiatry, Wenzhou Seventh People's Hospital, Wenzhou, China
| | - Teng Ye
- Department of Ultrasound, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Wenjun Zhou
- Research and Development, Hangzhou Shansier Medical Technologies Co., Ltd., Hangzhou, China.
| | - Jie Zhang
- Department of Data Science, Hangzhou Shansier Medical Technologies Co., Ltd., Hangzhou, China.
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22
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Novakova Martinkova J, Ferretti MT, Ferrari A, Lerch O, Matuskova V, Secnik J, Hort J. Longitudinal progression of choroid plexus enlargement is associated with female sex, cognitive decline and ApoE E4 homozygote status. Front Psychiatry 2023; 14:1039239. [PMID: 36970283 PMCID: PMC10031049 DOI: 10.3389/fpsyt.2023.1039239] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 01/27/2023] [Indexed: 03/29/2023] Open
Abstract
Introduction Choroid plexus (CP)-related mechanisms have been implicated in the pathogenesis of neurodegenerative diseases, including Alzheimer's disease. In this pilot study, we aimed to elucidate the association between longitudinal changes in CP volume, sex and cognitive impairment. Methods We assessed longitudinal changes in CP volume in a cohort of n = 613 subjects across n = 2,334 datapoints from ADNI 2 and ADNI-GO, belonging to cognitively unimpaired (CN), stable mild cognitive impairment (MCI), clinically diagnosed Alzheimer's disease dementia (AD) or convertor (to either AD or MCI) subgroups. CP volume was automatically segmented and used as a response variable in linear mixed effect models with random intercept clustered by patient identity. Temporal effects of select variables were assessed by interactions and subgroup analyses. Results We found an overall significant increase of CP volume in time (14.92 mm3 per year, 95% confidence interval, CI (11.05, 18.77), p < 0.001). Sex-disaggregated results showed an annual rate of increase 9.48 mm3 in males [95% CI (4.08, 14.87), p < 0.001], and 20.43 mm3 in females [95% CI (14.91, 25.93), p < 0.001], indicating more than double the rate of increase in females, which appeared independent of other temporal variables. The only diagnostic group with a significant CP increase as compared to CN was the convertors group, with an increase of 24.88 mm3/year [95% CI (14, 35.82), p < 0.001]. ApoE exhibited a significant temporal effect, with the E4 homozygote group's CP increasing at more than triple the rate of non-carrier or heterozygote groups [40.72, 95% CI (25.97, 55.46), p < 0.001 vs. 12.52, 95% CI (8.02, 17.02), p < 0.001 for ApoE E4 homozygotes and E4 non-carriers, respectively], and may have modified the diagnostic group relationship. Conclusion Our results contribute to potential mechanisms for sex differences in cognitive impairment with a novel finding of twice the annual choroid plexus enlargement in females and provide putative support for CP-related mechanisms of cognitive deterioration and its relationship to ApoE E4.
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Affiliation(s)
- Julie Novakova Martinkova
- Cognitive Center, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | | | | | - Ondrej Lerch
- Cognitive Center, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Veronika Matuskova
- Cognitive Center, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Juraj Secnik
- Cognitive Center, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
- Center for Alzheimer Research, Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden
| | - Jakub Hort
- Cognitive Center, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
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23
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Chu CS, Wang DY, Liang CK, Chou MY, Hsu YH, Wang YC, Liao MC, Chu WT, Lin YT. Automated Video Analysis of Audio-Visual Approaches to Predict and Detect Mild Cognitive Impairment and Dementia in Older Adults. J Alzheimers Dis 2023; 92:875-886. [PMID: 36847001 DOI: 10.3233/jad-220999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
BACKGROUND Early identification of different stages of cognitive impairment is important to provide available intervention and timely care for the elderly. OBJECTIVE This study aimed to examine the ability of the artificial intelligence (AI) technology to distinguish participants with mild cognitive impairment (MCI) from those with mild to moderate dementia based on automated video analysis. METHODS A total of 95 participants were recruited (MCI, 41; mild to moderate dementia, 54). The videos were captured during the Short Portable Mental Status Questionnaire process; the visual and aural features were extracted using these videos. Deep learning models were subsequently constructed for the binary differentiation of MCI and mild to moderate dementia. Correlation analysis of the predicted Mini-Mental State Examination, Cognitive Abilities Screening Instrument scores, and ground truth was also performed. RESULTS Deep learning models combining both the visual and aural features discriminated MCI from mild to moderate dementia with an area under the curve (AUC) of 77.0% and accuracy of 76.0% . The AUC and accuracy increased to 93.0% and 88.0%, respectively, when depression and anxiety were excluded. Significant moderate correlations were observed between the predicted cognitive function and ground truth, and the correlation was strong excluding depression and anxiety. Interestingly, female, but not male, exhibited a correlation. CONCLUSION The study showed that video-based deep learning models can differentiate participants with MCI from those with mild to moderate dementia and can predict cognitive function. This approach may offer a cost-effective and easily applicable method for early detection of cognitive impairment.
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Affiliation(s)
- Che-Sheng Chu
- Department of Psychiatry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Non-Invasive Neuromodulation Consortium for Mental Disorders, Society of Psychophysiology, Taipei, Taiwan.,Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Di-Yuan Wang
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Chih-Kuang Liang
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan.,Department of Geriatric Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan.,Department of Internal Medicine, Division of Neurology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, Kaohsiung City, Taiwan
| | - Ming-Yueh Chou
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan.,Department of Geriatric Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan
| | - Ying-Hsin Hsu
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Department of Internal Medicine, Division of Neurology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, Kaohsiung City, Taiwan.,Chia Nan University, Tainan, Taiwan, Tainan City, Taiwan
| | - Yu-Chun Wang
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Mei-Chen Liao
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Wei-Ta Chu
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Yu-Te Lin
- Center for Geriatrics and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, Taipei City, Taiwan.,Department of Pharmacy, Tajen University, Pingtung, Taiwan, Yanpu Township, Pingtung County, Taiwan
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24
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Valencia-Olvera AC, Maldonado Weng J, Christensen A, LaDu MJ, Pike CJ. Role of estrogen in women's Alzheimer's disease risk as modified by APOE. J Neuroendocrinol 2023; 35:e13209. [PMID: 36420620 PMCID: PMC10049970 DOI: 10.1111/jne.13209] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 09/29/2022] [Accepted: 10/13/2022] [Indexed: 12/15/2022]
Abstract
Alzheimer's disease (AD) is characterized by numerous sexual dimorphisms that impact the development, progression, and probably the strategies to prevent and treat the most common form of dementia. In this review, we consider this topic from a female perspective with a specific focus on how women's vulnerability to the disease is affected by the individual and interactive effects of estrogens and apolipoprotein E (APOE) genotype. Importantly, APOE appears to modulate systemic and neural outcomes of both menopause and estrogen-based hormone therapy. In the brain, dementia risk is greater in APOE4 carriers, and the impacts of hormone therapy on cognitive decline and dementia risk vary according to both outcome measure and APOE genotype. Beyond the CNS, estrogen and APOE genotype affect vulnerability to menopause-associated bone loss, dyslipidemia and cardiovascular disease risk. An emerging concept that may link these relationships is the possibility that the effects of APOE in women interact with estrogen status by mechanisms that may include modulation of estrogen responsiveness. This review highlights the need to consider the key AD risk factors of advancing age in a sex-specific manner to optimize development of therapeutic approaches for AD, a view aligned with the principle of personalized medicine.
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Affiliation(s)
- AC Valencia-Olvera
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL 60612 USA
| | - J Maldonado Weng
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL 60612 USA
| | - A Christensen
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089 USA
| | - MJ LaDu
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL 60612 USA
| | - CJ Pike
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089 USA
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25
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Katabathula S, Davis PB, Xu R. Sex-Specific Heterogeneity of Mild Cognitive Impairment Identified Based on Multi-Modal Data Analysis. J Alzheimers Dis 2023; 91:233-243. [PMID: 36404544 PMCID: PMC11391386 DOI: 10.3233/jad-220600] [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] [Indexed: 11/16/2022]
Abstract
BACKGROUND Mild cognitive impairment (MCI), a prodromal phase of Alzheimer's disease (AD), is heterogeneous with different rates and risks of progression to AD. There are significant gender disparities in the susceptibility, prognosis, and outcomes in patients with MCI, with female being disproportionately negatively impacted. OBJECTIVE The aim of this study was to identify sex-specific heterogeneity of MCI using multi-modality data and examine the differences in the respective MCI subtypes with different prognostic outcomes or different risks for MCI to AD conversion. METHODS A total of 325 MCI subjects (146 women, 179 men) and 30 relevant features were considered. Mixed-data clustering was applied to women and men separately to discover gender-specific MCI subtypes. Gender differences were compared in the respective subtypes of MCI by examining their MCI to AD disease prognosis, descriptive statistics, and conversion rates. RESULTS We identified three MCI subtypes: poor-, good-, and best-prognosis for women and for men, separately. The subtype-wise comparison (for example, poor-prognosis subtype in women versus poor-prognosis subtype in men) showed significantly different means for brain volumetric, cognitive test-related, also for the proportion of comorbidities. Also, there were substantial gender differences in the proportions of participants who reverted to normal function, remained stable, or converted to AD. CONCLUSION Analyzing sex-specific heterogeneity of MCI offers the opportunity to advance the understanding of the pathophysiology of both MCI and AD, allows stratification of risk in clinical trials of interventions, and suggests gender-based early intervention with targeted treatment for patients at risk of developing AD.
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Affiliation(s)
- Sreevani Katabathula
- Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Pamela B Davis
- Center for Community Health Integration, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
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26
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Egiazarian MA, Strømstad S, Sakshaug T, Nunez-Nescolarde AB, Bethge N, Bjørås M, Scheffler K. Age- and sex-dependent effects of DNA glycosylase Neil3 on amyloid pathology, adult neurogenesis, and memory in a mouse model of Alzheimer's disease. Free Radic Biol Med 2022; 193:685-693. [PMID: 36395955 DOI: 10.1016/j.freeradbiomed.2022.11.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 10/21/2022] [Accepted: 11/12/2022] [Indexed: 11/16/2022]
Abstract
Oxidative stress generating DNA damage has been shown to be a key characteristic in Alzheimer's disease (AD). However, how it affects the pathogenesis of AD is not yet fully understood. Neil3 is a DNA glycosylase initiating repair of oxidative DNA base lesions and with a distinct expression pattern in proliferating cells. In brain, its function has been linked to hippocampal-dependent memory and to induction of neurogenesis after stroke and in prion disease. Here, we generated a novel AD mouse model deficient for Neil3 to study the impact of impaired oxidative base lesion repair on the pathogenesis of AD. Our results demonstrate an age-dependent decrease in amyloid-β (Aβ) plaque deposition in female Neil3-deficient AD mice, whereas no significant difference was observed in male mice. Furthermore, male but not female Neil3-deficient AD mice show reduced neural stem cell proliferation in the adult hippocampus and impaired working memory compared to controls. These effects seem to be independent of DNA repair as both sexes show increased level of oxidative base lesions in the hippocampus upon loss of Neil3. Thus, our findings suggest an age- and sex-dependent role of Neil3 in the progression of AD by altering cerebral Aβ accumulation and promoting adult hippocampal neurogenesis to maintain cognitive function.
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Affiliation(s)
- Milena A Egiazarian
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Silje Strømstad
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Teri Sakshaug
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Ana B Nunez-Nescolarde
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Nicole Bethge
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Magnar Bjørås
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway; Department of Microbiology, Oslo University Hospital HF, Rikshospitalet and University of Oslo, Oslo, Norway
| | - Katja Scheffler
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway; Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway; Department of Neurology and Clinical Neurophysiology, University Hospital Trondheim, Trondheim, Norway.
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27
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Salwierz P, Davenport C, Sumra V, Iulita MF, Ferretti MT, Tartaglia MC. Sex and gender differences in dementia. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2022; 164:179-233. [PMID: 36038204 DOI: 10.1016/bs.irn.2022.07.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The dementia landscape has undergone a striking paradigm shift. The advances in understanding of neurodegeneration and proteinopathies has changed our approach to patients with cognitive impairment. Firstly, it has recently been shown that the various proteinopathies that are the cause of the dementia begin to build up long before the appearance of any obvious symptoms. This has cemented the idea that there is an urgency in diagnosis as it occurs very late in the pathophysiology of these diseases. Secondly, that accurate diagnosis is required to deliver targeted therapies, that is precision medicine. With this latter point, the realization that various factors of a person need to be considered as they may impact the presentation and progression of disease has risen to the forefront. Two of these factors aside from race and age are biological sex and gender (social construct), as both can have tremendous impact on manifestation of disease. This chapter will cover what is known and remains to be known on the interaction of sex and gender with some of the major causes of dementia.
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Affiliation(s)
- Patrick Salwierz
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Carly Davenport
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Vishaal Sumra
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - M Florencia Iulita
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain; Women's Brain Project, Guntershausen, Switzerland
| | | | - Maria Carmela Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada; Memory Clinic, Krembil Brain Institute, University Health Network, Toronto, ON, Canada.
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28
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Jett S, Malviya N, Schelbaum E, Jang G, Jahan E, Clancy K, Hristov H, Pahlajani S, Niotis K, Loeb-Zeitlin S, Havryliuk Y, Isaacson R, Brinton RD, Mosconi L. Endogenous and Exogenous Estrogen Exposures: How Women's Reproductive Health Can Drive Brain Aging and Inform Alzheimer's Prevention. Front Aging Neurosci 2022; 14:831807. [PMID: 35356299 PMCID: PMC8959926 DOI: 10.3389/fnagi.2022.831807] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 02/07/2022] [Indexed: 01/14/2023] Open
Abstract
After advanced age, female sex is the major risk factor for late-onset Alzheimer's disease (AD), the most common cause of dementia affecting over 24 million people worldwide. The prevalence of AD is higher in women than in men, with postmenopausal women accounting for over 60% of all those affected. While most research has focused on gender-combined risk, emerging data indicate sex and gender differences in AD pathophysiology, onset, and progression, which may help account for the higher prevalence in women. Notably, AD-related brain changes develop during a 10-20 year prodromal phase originating in midlife, thus proximate with the hormonal transitions of endocrine aging characteristic of the menopause transition in women. Preclinical evidence for neuroprotective effects of gonadal sex steroid hormones, especially 17β-estradiol, strongly argue for associations between female fertility, reproductive history, and AD risk. The level of gonadal hormones to which the female brain is exposed changes considerably across the lifespan, with relevance to AD risk. However, the neurobiological consequences of hormonal fluctuations, as well as that of hormone therapies, are yet to be fully understood. Epidemiological studies have yielded contrasting results of protective, deleterious and null effects of estrogen exposure on dementia risk. In contrast, brain imaging studies provide encouraging evidence for positive associations between greater cumulative lifetime estrogen exposure and lower AD risk in women, whereas estrogen deprivation is associated with negative consequences on brain structure, function, and biochemistry. Herein, we review the existing literature and evaluate the strength of observed associations between female-specific reproductive health factors and AD risk in women, with a focus on the role of endogenous and exogenous estrogen exposures as a key underlying mechanism. Chief among these variables are reproductive lifespan, menopause status, type of menopause (spontaneous vs. induced), number of pregnancies, and exposure to hormonal therapy, including hormonal contraceptives, hormonal therapy for menopause, and anti-estrogen treatment. As aging is the greatest risk factor for AD followed by female sex, understanding sex-specific biological pathways through which reproductive history modulates brain aging is crucial to inform preventative and therapeutic strategies for AD.
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Affiliation(s)
- Steven Jett
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
| | - Niharika Malviya
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
| | - Eva Schelbaum
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
| | - Grace Jang
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
| | - Eva Jahan
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
| | - Katherine Clancy
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
| | - Hollie Hristov
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
| | - Silky Pahlajani
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
- Department of Radiology, Weill Cornell Medical College, New York, NY, United States
| | - Kellyann Niotis
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
| | - Susan Loeb-Zeitlin
- Department of Obstetrics and Gynecology, Weill Cornell Medical College, New York, NY, United States
| | - Yelena Havryliuk
- Department of Obstetrics and Gynecology, Weill Cornell Medical College, New York, NY, United States
| | - Richard Isaacson
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
| | - Roberta Diaz Brinton
- Department of Pharmacology, University of Arizona, Tucson, AZ, United States
- Department of Neurology, University of Arizona, Tucson, AZ, United States
| | - Lisa Mosconi
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
- Department of Radiology, Weill Cornell Medical College, New York, NY, United States
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29
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Seo Y, Jang H, Lee H. Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease. Life (Basel) 2022; 12:life12020275. [PMID: 35207561 PMCID: PMC8879055 DOI: 10.3390/life12020275] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/05/2022] [Accepted: 02/09/2022] [Indexed: 01/18/2023] Open
Abstract
Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.
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Affiliation(s)
| | | | - Hyejoo Lee
- Correspondence: ; Tel.: +82-2-3410-1233; Fax: +82-2-3410-0052
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30
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Díaz-Álvarez J, Matias-Guiu JA, Cabrera-Martín MN, Pytel V, Segovia-Ríos I, García-Gutiérrez F, Hernández-Lorenzo L, Matias-Guiu J, Carreras JL, Ayala JL. Genetic Algorithms for Optimized Diagnosis of Alzheimer's Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging. Front Aging Neurosci 2022; 13:708932. [PMID: 35185510 PMCID: PMC8851241 DOI: 10.3389/fnagi.2021.708932] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 12/20/2021] [Indexed: 11/13/2022] Open
Abstract
Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer's disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism.
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Affiliation(s)
- Josefa Díaz-Álvarez
- Department of Computer Architecture and Communications, Centro Universitario de Mérida, Universidad de Extremadura, Badajoz, Spain
| | - Jordi A. Matias-Guiu
- Department of Neurology, Hospital Clinico San Carlos, San Carlos Research Health Institute (IdISSC), Universidad Complutense, Madrid, Spain
| | - María Nieves Cabrera-Martín
- Department of Nuclear Medicine, Hospital Clinico San Carlos, San Carlos Research Health Institute (IdISSC), Universidad Complutense, Madrid, Spain
| | - Vanesa Pytel
- Department of Neurology, Hospital Clinico San Carlos, San Carlos Research Health Institute (IdISSC), Universidad Complutense, Madrid, Spain
| | - Ignacio Segovia-Ríos
- Department of Computer Architecture and Communications, Centro Universitario de Mérida, Universidad de Extremadura, Badajoz, Spain
| | - Fernando García-Gutiérrez
- Department of Neurology, Hospital Clinico San Carlos, San Carlos Research Health Institute (IdISSC), Universidad Complutense, Madrid, Spain
- Department of Computer Architecture and Automation, Universidad Complutense, Madrid, Spain
| | - Laura Hernández-Lorenzo
- Department of Neurology, Hospital Clinico San Carlos, San Carlos Research Health Institute (IdISSC), Universidad Complutense, Madrid, Spain
- Department of Computer Architecture and Automation, Universidad Complutense, Madrid, Spain
| | - Jorge Matias-Guiu
- Department of Neurology, Hospital Clinico San Carlos, San Carlos Research Health Institute (IdISSC), Universidad Complutense, Madrid, Spain
| | - José Luis Carreras
- Department of Nuclear Medicine, Hospital Clinico San Carlos, San Carlos Research Health Institute (IdISSC), Universidad Complutense, Madrid, Spain
| | - José L. Ayala
- Department of Computer Architecture and Automation, Universidad Complutense, Madrid, Spain
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SimulAD: A dynamical model for personalized simulation and disease staging in Alzheimer’s disease. Neurobiol Aging 2022; 113:73-83. [DOI: 10.1016/j.neurobiolaging.2021.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 12/13/2021] [Accepted: 12/30/2021] [Indexed: 11/17/2022]
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Odusami M, Maskeliūnas R, Damaševičius R. An Intelligent System for Early Recognition of Alzheimer's Disease Using Neuroimaging. SENSORS (BASEL, SWITZERLAND) 2022; 22:740. [PMID: 35161486 PMCID: PMC8839926 DOI: 10.3390/s22030740] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/14/2022] [Accepted: 01/17/2022] [Indexed: 05/08/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient brain cells from further damage and direct additional medical treatment to prevent its progression. Lately, the use of deep learning for the early identification of AD has generated a lot of interest. However, one of the limitations of such algorithms is their inability to identify changes in the functional connectivity in the functional brain network of patients with MCI. In this paper, we attempt to elucidate this issue with randomized concatenated deep features obtained from two pre-trained models, which simultaneously learn deep features from brain functional networks from magnetic resonance imaging (MRI) images. We experimented with ResNet18 and DenseNet201 to perform the task of AD multiclass classification. A gradient class activation map was used to mark the discriminating region of the image for the proposed model prediction. Accuracy, precision, and recall were used to assess the performance of the proposed system. The experimental analysis showed that the proposed model was able to achieve 98.86% accuracy, 98.94% precision, and 98.89% recall in multiclass classification. The findings indicate that advanced deep learning with MRI images can be used to classify and predict neurodegenerative brain diseases such as AD.
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Affiliation(s)
- Modupe Odusami
- Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania; (M.O.); (R.M.)
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania; (M.O.); (R.M.)
| | - Robertas Damaševičius
- Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania
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Lopez-Lee C, Kodama L, Gan L. Sex Differences in Neurodegeneration: The Role of the Immune System in Humans. Biol Psychiatry 2022; 91:72-80. [PMID: 33715827 PMCID: PMC8263798 DOI: 10.1016/j.biopsych.2021.01.002] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 12/29/2020] [Accepted: 01/04/2021] [Indexed: 01/03/2023]
Abstract
Growing evidence supports significant involvement of immune dysfunction in the etiology of neurodegenerative diseases, several of which also display prominent sex differences across prevalence, pathology, and symptomology. In this review, we summarize evidence from human studies of established and recent findings of sex differences in multiple sclerosis, Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis and discuss how sex-specific central nervous system innate immune activity could contribute to downstream sex differences in these diseases. We examine human genomic and transcriptomics studies in each neurodegenerative disease through the lens of sex differences in the neuroimmune system and highlight the importance of stratifying sex in clinical and translational research studies. Finally, we discuss the limitations of the existing studies and outline recommendations for further advancing sex-based analyses to uncover novel disease mechanisms that could ultimately help treat both sexes.
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Affiliation(s)
- Chloe Lopez-Lee
- Neuroscience Graduate Program, Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York; Helen and Robert Appel Alzheimer's Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York
| | - Lay Kodama
- Helen and Robert Appel Alzheimer's Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York; Medical Scientist Training Program and Neuroscience Graduate Program, University of California San Francisco, San Francisco, California.
| | - Li Gan
- Neuroscience Graduate Program, Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York; Helen and Robert Appel Alzheimer's Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York.
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Massetti N, Russo M, Franciotti R, Nardini D, Mandolini G, Granzotto A, Bomba M, Delli Pizzi S, Mosca A, Scherer R, Onofrj M, Sensi SL. A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum. J Alzheimers Dis 2021; 85:1639-1655. [PMID: 34958014 DOI: 10.3233/jad-210573] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD. OBJECTIVE To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion. METHODS We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables. RESULTS The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects. CONCLUSION Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.
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Affiliation(s)
- Noemi Massetti
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Mirella Russo
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Raffaella Franciotti
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | | | | | - Alberto Granzotto
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy.,Sue and Bill Gross Stem Cell Research Center, University of California - Irvine, Irvine, CA, USA
| | - Manuela Bomba
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Stefano Delli Pizzi
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Alessandra Mosca
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Reinhold Scherer
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
| | - Marco Onofrj
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy
| | - Stefano L Sensi
- Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.,Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy.,Institute for Mind Impairments and Neurological Disorders - iMIND, University of California - Irvine, Irvine, CA, USA
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Sayed FA, Kodama L, Fan L, Carling GK, Udeochu JC, Le D, Li Q, Zhou L, Wong MY, Horowitz R, Ye P, Mathys H, Wang M, Niu X, Mazutis L, Jiang X, Wang X, Gao F, Brendel M, Telpoukhovskaia M, Tracy TE, Frost G, Zhou Y, Li Y, Qiu Y, Cheng Z, Yu G, Hardy J, Coppola G, Wang F, DeTure MA, Zhang B, Xie L, Trajnowski JQ, Lee VM, Gong S, Sinha SC, Dickson DW, Luo W, Gan L. AD-linked R47H- TREM2 mutation induces disease-enhancing microglial states via AKT hyperactivation. Sci Transl Med 2021; 13:eabe3947. [PMID: 34851693 PMCID: PMC9345574 DOI: 10.1126/scitranslmed.abe3947] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The hemizygous R47H variant of triggering receptor expressed on myeloid cells 2 (TREM2), a microglia-specific gene in the brain, increases risk for late-onset Alzheimer’s disease (AD). Using transcriptomic analysis of single nuclei from brain tissues of patients with AD carrying the R47H mutation or the common variant (CV)–TREM2, we found that R47H-associated microglial subpopulations had enhanced inflammatory signatures reminiscent of previously identified disease-associated microglia (DAM) and hyperactivation of AKT, one of the signaling pathways downstream of TREM2. We established a tauopathy mouse model with heterozygous knock-in of the human TREM2 with the R47H mutation or CV and found that R47H induced and exacerbated TAU-mediated spatial memory deficits in female mice. Single-cell transcriptomic analysis of microglia from these mice also revealed transcriptomic changes induced by R47H that had substantial overlaps with R47H microglia in human AD brains, including robust increases in proinflammatory cytokines, activation of AKT signaling, and elevation of a subset of DAM signatures. Pharmacological AKT inhibition with MK-2206 largely reversed the enhanced inflammatory signatures in primary R47H microglia treated with TAU fibrils. In R47H heterozygous tauopathy mice, MK-2206 treatment abolished a tauopathy-dependent microglial subcluster and rescued tauopathy-induced synapse loss. By uncovering disease-enhancing mechanisms of the R47H mutation conserved in human and mouse, our study supports inhibitors of AKT signaling as a microglial modulating strategy to treat AD.
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Affiliation(s)
- Faten A. Sayed
- Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institute of Neurological Disease, San Francisco, CA 94107, USA
| | - Lay Kodama
- Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institute of Neurological Disease, San Francisco, CA 94107, USA
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
- Medical Scientist Training Program and Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Li Fan
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Gillian K. Carling
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Joe C. Udeochu
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - David Le
- Gladstone Institute of Neurological Disease, San Francisco, CA 94107, USA
| | - Qingyun Li
- Department of Neuroscience and Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Lu Zhou
- Department of Neuroscience and Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Man Ying Wong
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Rose Horowitz
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Pearly Ye
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Hansruedi Mathys
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Minghui Wang
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences, NY 10029, USA
| | - Xiang Niu
- Tri-Institutional Computational Biology & Medicine Program, Weill Cornell Medical College, NY, USA
| | - Linas Mazutis
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Xueqiao Jiang
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Xueting Wang
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Fuying Gao
- Departments of Psychiatry and Neurology, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Matthew Brendel
- Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA
| | | | - Tara E. Tracy
- Gladstone Institute of Neurological Disease, San Francisco, CA 94107, USA
| | - Georgia Frost
- Chemical Biology Program, Weill Graduate School of Medical Sciences of Cornell University, New York, NY 10065, USA
| | - Yungui Zhou
- Gladstone Institute of Neurological Disease, San Francisco, CA 94107, USA
| | - Yaqiao Li
- Gladstone Institute of Neurological Disease, San Francisco, CA 94107, USA
| | - Yue Qiu
- Department of Computer Science, Hunter College, & The Graduate Center, The City University of New York, New York, NY 10065, USA
| | - Zuolin Cheng
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 24061, USA
| | - Guoqiang Yu
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 24061, USA
| | - John Hardy
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1E 6BT, UK
| | - Giovanni Coppola
- Departments of Psychiatry and Neurology, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10065, USA
| | | | - Bin Zhang
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences, NY 10029, USA
| | - Lei Xie
- Chemical Biology Program, Weill Graduate School of Medical Sciences of Cornell University, New York, NY 10065, USA
| | - John Q. Trajnowski
- Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Virginia M.Y. Lee
- Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Shiaoching Gong
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Subhash C. Sinha
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | | | - Wenjie Luo
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Li Gan
- Gladstone Institute of Neurological Disease, San Francisco, CA 94107, USA
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
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Nezhadmoghadam F, Martinez-Torteya A, Treviño V, Martínez E, Santos A, Tamez-Peña J, Alzheimer's Disease Neuroimaging Initiative. Robust Discovery of Mild Cognitive Impairment Subtypes and Their Risk of Alzheimer's Disease Conversion Using Unsupervised Machine Learning and Gaussian Mixture Modeling. Curr Alzheimer Res 2021; 18:595-606. [PMID: 34488612 DOI: 10.2174/1567205018666210831145825] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 05/30/2021] [Accepted: 06/30/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Alzheimer's Disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills. The ability to correctly predict the diagnosis of Alzheimer's disease in its earliest stages can help physicians make more informed clinical decisions on therapy plans. OBJECTIVE This study aimed to determine whether the unsupervised discovering of latent classes of subjects with Mild Cognitive Impairment (MCI) may be useful in finding different prodromal AD stages and/or subjects with a low MCI to AD conversion risk. METHODS Total 18 features relevant to the MCI to AD conversion process led to the identification of 681 subjects with early MCI. Subjects were divided into training (70%) and validation (30%) sets. Subjects from the training set were analyzed using consensus clustering, and Gaussian Mixture Models (GMM) were used to describe the latent classes. The discovered GMM predicted the latent class of the validation set. Finally, descriptive statistics, rates of conversion, and Odds Ratios (OR) were computed for each discovered class. RESULTS Through consensus clustering, we discovered three different clusters among MCI subjects. The three clusters were associated with low-risk (OR = 0.12, 95%CI = 0.04 to 0.3|), medium-risk (OR = 1.33, 95%CI = 0.75 to 2.37), and high-risk (OR = 3.02, 95%CI = 1.64 to 5.57) of converting from MCI to AD, with the high-risk and low-risk groups highly contrasting. Hence, prodromal AD subjects were present in only two clusters. CONCLUSION We successfully discovered three different latent classes among MCI subjects with varied risks of MCI-to-AD conversion through consensus clustering. Two of the discovered classes may represent two different prodromal presentations of Alzheimer´s disease.
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Affiliation(s)
- Fahimeh Nezhadmoghadam
- Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., 64849, Mexico
| | - Antonio Martinez-Torteya
- Universidad de Monterrey, School of Engineering and Technologies, Av. Ignacio Morones Prieto 4500, San Pedro Garza García 66238, Mexico
| | - Victor Treviño
- Tecnologico de Monterrey, School of Medicine and Health Sciences, Ave. Ignacio Morones Prieto 3000, Sertoma, Monterrey, N.L, 64710, Mexico
| | - Emmanuel Martínez
- Tecnologico de Monterrey, School of Medicine and Health Sciences, Ave. Ignacio Morones Prieto 3000, Sertoma, Monterrey, N.L, 64710, Mexico
| | - Alejandro Santos
- Tecnologico de Monterrey, School of Medicine and Health Sciences, Ave. Ignacio Morones Prieto 3000, Sertoma, Monterrey, N.L, 64710, Mexico
| | - Jose Tamez-Peña
- Tecnologico de Monterrey, School of Medicine and Health Sciences, Ave. Ignacio Morones Prieto 3000, Sertoma, Monterrey, N.L, 64710, Mexico
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Fabrizio C, Termine A, Caltagirone C, Sancesario G. Artificial Intelligence for Alzheimer's Disease: Promise or Challenge? Diagnostics (Basel) 2021; 11:1473. [PMID: 34441407 PMCID: PMC8391160 DOI: 10.3390/diagnostics11081473] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/12/2021] [Accepted: 08/12/2021] [Indexed: 01/23/2023] Open
Abstract
Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer's disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.
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Affiliation(s)
- Carlo Fabrizio
- Laboratory of Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; (C.F.); (A.T.)
| | - Andrea Termine
- Laboratory of Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; (C.F.); (A.T.)
| | - Carlo Caltagirone
- Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy;
| | - Giulia Sancesario
- Biobank, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
- European Center for Brain Research, Experimental Neuroscience, 00143 Rome, Italy
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Royse SK, Cohen AD, Snitz BE, Rosano C. Differences in Alzheimer's Disease and Related Dementias Pathology Among African American and Hispanic Women: A Qualitative Literature Review of Biomarker Studies. Front Syst Neurosci 2021; 15:685957. [PMID: 34366799 PMCID: PMC8334184 DOI: 10.3389/fnsys.2021.685957] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 06/28/2021] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION The population of older adults with Alzheimer's disease and Related Dementias (ADRD) is growing larger and more diverse. Prevalence of ADRD is higher in African American (AA) and Hispanic populations relative to non-Hispanic whites (nHW), with larger differences for women compared to men of the same race. Given the public health importance of this issue, we sought to determine if AA and Hispanic women exhibit worse ADRD pathology compared to men of the same race and nHW women. We hypothesized that such differences may explain the discrepancy in ADRD prevalence. METHODS We evaluated 932 articles that measured at least one of the following biomarkers of ADRD pathology in vivo and/or post-mortem: beta-amyloid (Aß), tau, neurodegeneration, and cerebral small vessel disease (cSVD). Criteria for inclusion were: (1) mean age of participants >65 years; (2) inclusion of nHW participants and either AA or Hispanics or both; (3) direct comparison of ADRD pathology between racial groups. RESULTS We included 26 articles (Aß = 9, tau = 6, neurodegeneration = 16, cSVD = 18), with seven including sex-by-race comparisons. Studies differed by sampling source (e.g., clinic or population), multivariable analytical approach (e.g., adjusted for risk factors for AD), and cognitive status of participants. Aß burden did not differ by race or sex. Tau differed by race (AA < nHW), and by sex (women > men). Both severity of neurodegeneration and cSVD differed by race (AA > nHW; Hispanics < nHW) and sex (women < men). Among the studies that tested sex-by-race interactions, results were not significant. CONCLUSION Few studies have examined the burden of ADRD pathology by both race and sex. The higher prevalence of ADRD in women compared to men of the same race may be due to both higher tau load and more vulnerability to cognitive decline in the presence of similar Aß and cSVD burden. AA women may also exhibit more neurodegeneration and cSVD relative to nHW populations. Studies suggest that between-group differences in ADRD pathology are complex, but they are too sparse to completely explain why minority women have the highest ADRD prevalence. Future work should recruit diverse cohorts, compare ADRD biomarkers by both race and sex, and collect relevant risk factor and cognitive data.
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Affiliation(s)
- Sarah K. Royse
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Ann D. Cohen
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Beth E. Snitz
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Caterina Rosano
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
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Amini M, Pedram MM, Moradi A, Jamshidi M, Ouchani M. Single and Combined Neuroimaging Techniques for Alzheimer's Disease Detection. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:9523039. [PMID: 34335726 PMCID: PMC8292054 DOI: 10.1155/2021/9523039] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 06/04/2021] [Accepted: 06/30/2021] [Indexed: 02/07/2023]
Abstract
Alzheimer's disease (AD) consists of the gradual process of decreasing volume and quality of neuron connection in the brain, which consists of gradual synaptic integrity and loss of cognitive functions. In recent years, there has been significant attention in AD classification and early detection with machine learning algorithms. There are different neuroimaging techniques for capturing data and using it for the classification task. Input data as images will help machine learning models to detect different biomarkers for AD classification. This marker has a more critical role for AD detection than other diseases because beta-amyloid can extract complex structures with some metal ions. Most researchers have focused on using 3D and 4D convolutional neural networks for AD classification due to reasonable amounts of data. Also, combination neuroimaging techniques like functional magnetic resonance imaging and positron emission tomography for AD detection have recently gathered much attention. However, gathering a combination of data can be expensive, complex, and tedious. For time consumption reasons, most patients prefer to throw one of the neuroimaging techniques. So, in this review article, we have surveyed different research studies with various neuroimaging techniques and ML methods to see the effect of using combined data as input. The result has shown that the use of the combination method would increase the accuracy of AD detection. Also, according to the sensitivity metrics from different machine learning methods, MRI and fMRI showed promising results.
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Affiliation(s)
- Morteza Amini
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Shahid Beheshti University, Tehran, Iran
| | - Mir Mohsen Pedram
- Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Tehran, Iran
| | - Alireza Moradi
- Department of Clinical Psychology, Faculty of Psychology and Educational Science, Kharazmi University, Tehran, Iran
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - Mahdieh Jamshidi
- Department of Mathematical Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
| | - Mahshad Ouchani
- Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran
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Genotype Load Modulates Amyloid Burden and Anxiety-Like Patterns in Male 3xTg-AD Survivors despite Similar Neuro-Immunoendocrine, Synaptic and Cognitive Impairments. Biomedicines 2021; 9:biomedicines9070715. [PMID: 34201608 PMCID: PMC8301351 DOI: 10.3390/biomedicines9070715] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/20/2021] [Accepted: 06/20/2021] [Indexed: 01/08/2023] Open
Abstract
The wide heterogeneity and complexity of Alzheimer’s disease (AD) patients’ clinical profiles and increased mortality highlight the relevance of personalized-based interventions and the need for end-of-life/survival predictors. At the translational level, studying genetic and age interactions in a context of different levels of expression of AD-genetic-load can help to understand this heterogeneity better. In the present report, a singular cohort of long-lived (19-month-old survivors) heterozygous and homozygous male 3xTg-AD mice were studied to determine whether their AD-genotype load can modulate the brain and peripheral pathological burden, behavioral phenotypes, and neuro-immunoendocrine status, compared to age-matched non-transgenic controls. The results indicated increased amyloid precursor protein (APP) levels in a genetic-load-dependent manner but convergent synaptophysin and choline acetyltransferase brain levels. Cognitive impairment and HPA-axis hyperactivation were salient traits in both 3xTg-AD survivor groups. In contrast, genetic load elicited different anxiety-like profiles, with hypoactive homozygous, while heterozygous resembled controls in some traits and risk assessment. Complex neuro-immunoendocrine crosstalk was also observed. Bodyweight loss and splenic, renal, and hepatic histopathological injury scores provided evidence of the systemic features of AD, despite similar peripheral organs’ oxidative stress. The present study provides an interesting translational scenario to study further genetic-load and age-dependent vulnerability/compensatory mechanisms in Alzheimer’s disease.
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Wang H, Lo MT, Rosenthal SB, Makowski C, Andreassen OA, Salem RM, McEvoy LK, Fiecas M, Chen CH. Similar Genetic Architecture of Alzheimer's Disease and Differential APOE Effect Between Sexes. Front Aging Neurosci 2021; 13:674318. [PMID: 34122051 PMCID: PMC8194397 DOI: 10.3389/fnagi.2021.674318] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 04/27/2021] [Indexed: 01/09/2023] Open
Abstract
Sex differences have been observed in the clinical manifestations of Alzheimer’s disease (AD) and elucidating their genetic basis is an active research topic. Based on autosomal genotype data of 7,216 men and 10,680 women, including 8,136 AD cases and 9,760 controls, we explored sex-related genetic heterogeneity in AD by investigating SNP heritability, genetic correlation, as well as SNP- and gene-based genome-wide analyses. We found similar SNP heritability (men: 19.5%; women: 21.5%) and high genetic correlation (Rg = 0.96) between the sexes. The heritability of APOE ε4-related risks for AD, after accounting for effects of all SNPs excluding chromosome 19, was nominally, but not significantly, higher in women (10.6%) than men (9.7%). In age-stratified analyses, ε3/ε4 was associated with a higher risk of AD among women than men aged 65–75 years, but not in the full sample. Apart from APOE, no new significant locus was identified in sex-stratified gene-based analyses. Our result of the high genetic correlation indicates overall similar genetic architecture of AD in both sexes at the genome-wide averaged level. Our study suggests that clinically observed sex differences may arise from sex-specific variants with small effects or more complicated mechanisms involving epigenetic alterations, sex chromosomes, or gene-environment interactions.
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Affiliation(s)
- Hao Wang
- Department of Radiology, Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, United States
| | - Min-Tzu Lo
- Department of Radiology, Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, United States
| | - Sara Brin Rosenthal
- Center for Computational Biology and Bioinformatics, University of California, San Diego, San Diego, CA, United States
| | - Carolina Makowski
- Department of Radiology, Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, United States
| | - Ole A Andreassen
- Division of Mental Health and Addiction, NORMENT Centre, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Rany M Salem
- Department of Family Medicine and Public Health, Division of Epidemiology, University of California, San Diego, San Diego, CA, United States
| | - Linda K McEvoy
- Department of Radiology, Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, United States.,Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, San Diego, CA, United States
| | - Mark Fiecas
- School of Public Health, Division of Biostatistics, University of Minnesota, Minneapolis, MN, United States
| | - Chi-Hua Chen
- Department of Radiology, Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, United States
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Abi Nader C, Ayache N, Frisoni GB, Robert P, Lorenzi M. Simulating the outcome of amyloid treatments in Alzheimer's disease from imaging and clinical data. Brain Commun 2021; 3:fcab091. [PMID: 34085040 PMCID: PMC8168944 DOI: 10.1093/braincomms/fcab091] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 01/25/2021] [Accepted: 02/23/2021] [Indexed: 11/14/2022] Open
Abstract
In this study, we investigate SimulAD, a novel quantitative instrument for the development of intervention strategies for disease-modifying drugs in Alzheimer's disease. SimulAD is based on the modeling of the spatio-temporal dynamics governing the joint evolution of imaging and clinical biomarkers along the history of the disease, and allows the simulation of the effect of intervention time and drug dosage on the biomarkers' progression. When applied to multi-modal imaging and clinical data from the Alzheimer's Disease Neuroimaging Initiative the method enables to generate hypothetical scenarios of amyloid lowering interventions. The results quantify the crucial role of intervention time, and provide a theoretical justification for testing amyloid modifying drugs in the pre-clinical stage. Our experimental simulations are compatible with the outcomes observed in past clinical trials, and suggest that anti-amyloid treatments should be administered at least 7 years earlier than what is currently being done in order to obtain statistically powered improvement of clinical endpoints.
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Affiliation(s)
- Clément Abi Nader
- Université Côte d'Azur, INRIA Sophia Antipolis, EPIONE Research Project, 06902, Sophia-Antipolis, France
| | - Nicholas Ayache
- Université Côte d'Azur, INRIA Sophia Antipolis, EPIONE Research Project, 06902, Sophia-Antipolis, France
| | - Giovanni B Frisoni
- Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, Hospitals and University of Geneva, 1205, Geneva, Switzerland
| | - Philippe Robert
- Université Côte d'Azur, CoBTeK Lab, MNC3 Program, 06103, Nice, France
| | - Marco Lorenzi
- Université Côte d'Azur, INRIA Sophia Antipolis, EPIONE Research Project, 06902, Sophia-Antipolis, France
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Karoglu-Eravsar ET, Tuz-Sasik MU, Adams MM. Environmental enrichment applied with sensory components prevents age-related decline in synaptic dynamics: Evidence from the zebrafish model organism. Exp Gerontol 2021; 149:111346. [PMID: 33838219 DOI: 10.1016/j.exger.2021.111346] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 03/31/2021] [Accepted: 04/03/2021] [Indexed: 10/21/2022]
Abstract
Progression of cognitive decline with or without neurodegeneration varies among elderly subjects. The main aim of the current study was to illuminate the molecular mechanisms that promote and retain successful aging in the context of factors such as environment and gender, both of which alter the resilience of the aging brain. Environmental enrichment (EE) is one intervention that may lead to the maintenance of cognitive processing at older ages in both humans and animal subjects. EE is easily applied to different model organisms, including zebrafish, which show similar age-related molecular and behavioral changes as humans. Global changes in cellular and synaptic markers with respect to age, gender and 4-weeks of EE applied with sensory stimulation were investigated using the zebrafish model organism. Results indicated that EE increases brain weight in an age-dependent manner without affecting general body parameters like body mass index (BMI). Age-related declines in the presynaptic protein synaptophysin, AMPA-type glutamate receptor subunits and a post-mitotic neuronal marker were observed and short-term EE prevents these changes in aged animals, as well as elevates levels of the inhibitory scaffolding protein, gephyrin. Gender-driven alterations were observed in the levels of the glutamate receptor subunits. Oxidative stress markers were significantly increased in the old animals, while exposure to EE did not alter this pattern. These data suggest that EE with sensory stimulation exerts its effects mainly on age-related changes in synaptic dynamics, which likely increase brain resilience through specific cellular mechanisms.
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Affiliation(s)
- Elif Tugce Karoglu-Eravsar
- Interdisciplinary Program in Neuroscience, Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey; National Nanotechnology Research Center (UNAM), Bilkent University, Ankara, Turkey; Department of Molecular Biology and Genetics, Zebrafish Facility, Bilkent University, Ankara, Turkey; Department of Psychology, Selcuk University, Konya, Turkey
| | - Melek Umay Tuz-Sasik
- Interdisciplinary Program in Neuroscience, Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey; National Nanotechnology Research Center (UNAM), Bilkent University, Ankara, Turkey; Department of Molecular Biology and Genetics, Zebrafish Facility, Bilkent University, Ankara, Turkey
| | - Michelle M Adams
- Interdisciplinary Program in Neuroscience, Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey; National Nanotechnology Research Center (UNAM), Bilkent University, Ankara, Turkey; Department of Molecular Biology and Genetics, Zebrafish Facility, Bilkent University, Ankara, Turkey; Department of Psychology, Bilkent University, Ankara, Turkey.
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Kärkkäinen M, Prakash M, Zare M, Tohka J, for the Alzheimer's Disease Neuroimaging Initiative. Structural Brain Imaging Phenotypes of Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) Found by Hierarchical Clustering. Int J Alzheimers Dis 2020; 2020:2142854. [PMID: 33299603 PMCID: PMC7708019 DOI: 10.1155/2020/2142854] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 05/27/2020] [Accepted: 06/29/2020] [Indexed: 11/27/2022] Open
Abstract
A hierarchical clustering algorithm was applied to magnetic resonance images (MRI) of a cohort of 751 subjects having a mild cognitive impairment (MCI), 282 subjects having received Alzheimer's disease (AD) diagnosis, and 428 normal controls (NC). MRIs were preprocessed to gray matter density maps and registered to a stereotactic space. By first rendering the gray matter density maps comparable by regressing out age, gender, and years of education, and then performing the hierarchical clustering, we found clusters displaying structural features of typical AD, cortically-driven atypical AD, limbic-predominant AD, and early-onset AD (EOAD). Among these clusters, EOAD subjects displayed marked cortical gray matter atrophy and atrophy of the precuneus. Furthermore, EOAD subjects had the highest progression rates as measured with ADAS slopes during the longitudinal follow-up of 36 months. Striking heterogeneities in brain atrophy patterns were observed with MCI subjects. We found clusters of stable MCI, clusters of diffuse brain atrophy with fast progression, and MCI subjects displaying similar atrophy patterns as the typical or atypical AD subjects. Bidirectional differences in structural phenotypes were found with MCI subjects involving the anterior cerebellum and the frontal cortex. The diversity of the MCI subjects suggests that the structural phenotypes of MCI subjects would deserve a more detailed investigation with a significantly larger cohort. Our results demonstrate that the hierarchical agglomerative clustering method is an efficient tool in dividing a cohort of subjects with gray matter atrophy into coherent clusters manifesting different structural phenotypes.
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Affiliation(s)
- Mikko Kärkkäinen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Mithilesh Prakash
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Marzieh Zare
- Signal Processing, Tampere University, Tampere, Finland
| | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
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Elmaleh DR, Farlow MR, Conti PS, Tompkins RG, Kundakovic L, Tanzi RE. Developing Effective Alzheimer's Disease Therapies: Clinical Experience and Future Directions. J Alzheimers Dis 2020; 71:715-732. [PMID: 31476157 PMCID: PMC6839593 DOI: 10.3233/jad-190507] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Alzheimer's disease (AD) clinical trials, focused on disease modifying drugs and conducted in patients with mild to moderate AD, as well as prodromal (early) AD, have failed to reach efficacy endpoints in improving cognitive function in most cases to date or have been terminated due to adverse events. Drugs that have reached clinical stage were reviewed using web resources (such as clinicaltrials.gov, alzforum.org, company press releases, and peer reviewed literature) to identify late stage (Phase II and Phase III) efficacy clinical trials and summarize reasons for their failure. For each drug, only the latest clinical trials and ongoing trials that aimed at improving cognitive function were included in the analysis. Here we highlight the potential reasons that have hindered clinical success, including clinical trial design and choice of outcome measures, heterogeneity of patient populations, difficulties in diagnosing and staging the disease, drug design, mechanism of action, and toxicity related to the long-term use. We review and suggest approaches for AD clinical trial design aimed at improving our ability to identify novel therapies for this devastating disease.
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Affiliation(s)
- David R Elmaleh
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.,AZTherapies Inc., Boston, MA, USA
| | - Martin R Farlow
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Peter S Conti
- Molecular Imaging Center, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ronald G Tompkins
- Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Rudolph E Tanzi
- Genetics and Aging Research Unit, MassGeneral Institute for Neurodegenerative Diseases (MIND), Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
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Habes M, Grothe MJ, Tunc B, McMillan C, Wolk DA, Davatzikos C. Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods. Biol Psychiatry 2020; 88:70-82. [PMID: 32201044 PMCID: PMC7305953 DOI: 10.1016/j.biopsych.2020.01.016] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Revised: 11/30/2019] [Accepted: 01/21/2020] [Indexed: 12/14/2022]
Abstract
Brain aging is a complex process that includes atrophy, vascular injury, and a variety of age-associated neurodegenerative pathologies, together determining an individual's course of cognitive decline. While Alzheimer's disease and related dementias contribute to the heterogeneity of brain aging, these conditions themselves are also heterogeneous in their clinical presentation, progression, and pattern of neural injury. We reviewed studies that leveraged data-driven approaches to examining heterogeneity in Alzheimer's disease and related dementias, with a principal focus on neuroimaging studies exploring subtypes of regional neurodegeneration patterns. Over the past decade, the steadily increasing wealth of clinical, neuroimaging, and molecular biomarker information collected within large-scale observational cohort studies has allowed for a richer understanding of the variability of disease expression within the aging and Alzheimer's disease and related dementias continuum. Moreover, the availability of these large-scale datasets has supported the development and increasing application of clustering techniques for studying disease heterogeneity in a data-driven manner. In particular, data-driven studies have led to new discoveries of previously unappreciated disease subtypes characterized by distinct neuroimaging patterns of regional neurodegeneration, which are paralleled by heterogeneous profiles of pathological, clinical, and molecular biomarker characteristics. Incorporating these findings into novel frameworks for more differentiated disease stratification holds great promise for improving individualized diagnosis and prognosis of expected clinical progression, and provides opportunities for development of precision medicine approaches for therapeutic intervention. We conclude with an account of the principal challenges associated with data-driven heterogeneity analyses and outline avenues for future developments in the field.
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Affiliation(s)
- Mohamad Habes
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Memory Center, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, Texas.
| | - Michel J. Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany,Wallenberg Center for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Birkan Tunc
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Corey McMillan
- Department of Neurology and Penn FTD Center, University of Pennsylvania, Philadelphia, USA
| | - David A. Wolk
- Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics and Department of Radiology, University of Pennsylvania, Philadelphia, USA
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Duffy IR, Boyle AJ, Vasdev N. Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology. Mol Imaging 2020; 18:1536012119869070. [PMID: 31429375 PMCID: PMC6702769 DOI: 10.1177/1536012119869070] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Machine learning (ML) algorithms have found increasing utility in the medical imaging field and numerous applications in the analysis of digital biomarkers within positron emission tomography (PET) imaging have emerged. Interest in the use of artificial intelligence in PET imaging for the study of neurodegenerative diseases and oncology stems from the potential for such techniques to streamline decision support for physicians providing early and accurate diagnosis and allowing personalized treatment regimens. In this review, the use of ML to improve PET image acquisition and reconstruction is presented, along with an overview of its applications in the analysis of PET images for the study of Alzheimer's disease and oncology.
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Affiliation(s)
- Ian R Duffy
- 1 Azrieli Centre for Neuro-Radiochemistry, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Amanda J Boyle
- 1 Azrieli Centre for Neuro-Radiochemistry, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Neil Vasdev
- 1 Azrieli Centre for Neuro-Radiochemistry, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,2 Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
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Martí-Juan G, Sanroma-Guell G, Piella G. A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 189:105348. [PMID: 31995745 DOI: 10.1016/j.cmpb.2020.105348] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 01/10/2020] [Accepted: 01/18/2020] [Indexed: 05/02/2023]
Abstract
BACKGROUND AND OBJECTIVES Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods are needed to successfully leverage and distill meaningful information on the progression of the disease from the deluge of available data. Machine learning has been used successfully for many different tasks, including neuroimaging related problems. In this paper, we review recent statistical and machine learning applications in Alzheimer's disease using longitudinal neuroimaging. METHODS We search for papers using longitudinal imaging data, focused on Alzheimer's Disease and published between 2007 and 2019 on four different search engines. RESULTS After the search, we obtain 104 relevant papers. We analyze their approach to typical challenges in longitudinal data analysis, such as missing data and variability in the number and extent of acquisitions. CONCLUSIONS Reviewed works show that machine learning methods using longitudinal data have potential for disease progression modelling and computer-aided diagnosis. We compare results and models, and propose future research directions in the field.
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Affiliation(s)
- Gerard Martí-Juan
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | | | - Gemma Piella
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
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Amyloid-β Positivity Predicts Cognitive Decline but Cognition Predicts Progression to Amyloid-β Positivity. Biol Psychiatry 2020; 87:819-828. [PMID: 32067693 PMCID: PMC7166153 DOI: 10.1016/j.biopsych.2019.12.021] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 12/19/2019] [Accepted: 12/19/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Stage 1 of the National Institute on Aging-Alzheimer's Association's proposed Alzheimer's disease continuum is defined as amyloid-β (Aβ) positive but cognitively normal. Identifying at-risk individuals before Aβ reaches pathological levels could have great benefits for early intervention. Although Aβ levels become abnormal long before severe cognitive impairments appear, increasing evidence suggests that subtle cognitive changes may begin early, potentially before Aβ surpasses the threshold for abnormality. We examined whether baseline cognitive performance would predict progression from normal to abnormal levels of Aβ. METHODS We examined the association of baseline cognitive composites (Preclinical Alzheimer Cognitive Composite, Alzheimer's Disease Neuroimaging Initiative (ADNI) memory factor composite) with progression to Aβ positivity in 292 nondemented, Aβ-negative ADNI participants. Additional analyses included continuous cerebrospinal fluid biomarker levels to examine the effects of subthreshold pathology. RESULTS Forty participants progressed to Aβ positivity during follow-up. Poorer baseline performance on both cognitive measures was significantly associated with increased odds of progression. More abnormal levels of baseline cerebrospinal fluid phosphorylated tau and subthreshold Aβ were associated with increased odds of progression to Aβ positivity. Nevertheless, baseline ADNI memory factor composite performance predicted progression even after controlling for baseline biomarker levels and APOE genotype (Preclinical Alzheimer Cognitive Composite was trend level). Survival analyses were largely consistent: controlling for baseline biomarker levels, baseline Preclinical Alzheimer Cognitive Composite still significantly predicted progression time to Aβ positivity (ADNI memory factor composite was trend level). CONCLUSIONS The possibility of intervening before Aβ reaches pathological levels is of obvious benefit. Low-cost, noninvasive cognitive measures can be informative for determining who is likely to progress to Aβ positivity, even after accounting for baseline subthreshold biomarker levels.
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Hampel H, Williams C, Etcheto A, Goodsaid F, Parmentier F, Sallantin J, Kaufmann WE, Missling CU, Afshar M. A precision medicine framework using artificial intelligence for the identification and confirmation of genomic biomarkers of response to an Alzheimer's disease therapy: Analysis of the blarcamesine (ANAVEX2-73) Phase 2a clinical study. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2020; 6:e12013. [PMID: 32318621 PMCID: PMC7167374 DOI: 10.1002/trc2.12013] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 02/17/2020] [Indexed: 01/02/2023]
Abstract
INTRODUCTION The search for drugs to treat Alzheimer's disease (AD) has failed to yield effective therapies. Here we report the first genome-wide search for biomarkers associated with therapeutic response in AD. Blarcamesine (ANAVEX2-73), a selective sigma-1 receptor (SIGMAR1) agonist, was studied in a 57-week Phase 2a trial (NCT02244541). The study was extended for a further 208 weeks (NCT02756858) after meeting its primary safety endpoint. METHODS Safety, clinical features, pharmacokinetic, and efficacy, measured by changes in the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Cooperative Study-Activities of Daily Living scale (ADCS-ADL), were recorded. Whole exome and transcriptome sequences were obtained for 21 patients. The relationship between all available patient data and efficacy outcome measures was analyzed with unsupervised formal concept analysis (FCA), integrated in the Knowledge Extraction and Management (KEM) environment. RESULTS Biomarkers with a significant impact on clinical outcomes were identified at week 57: mean plasma concentration of blarcamesine (slope MMSE:P < .041), genomic variants SIGMAR1 p.Gln2Pro (ΔMMSE:P < .039; ΔADCS-ADL:P < .063) and COMT p.Leu146fs (ΔMMSE:P < .039; ΔADCS-ADL:P < .063), and baseline MMSE score (slope MMSE:P < .015). Their combined impact on drug response was confirmed at week 148 with linear mixed effect models. DISCUSSION Confirmatory Phase 2b/3 clinical studies of these patient selection markers are ongoing. This FCA/KEM analysis is a template for the identification of patient selection markers in early therapeutic development for neurologic disorders.
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Affiliation(s)
- Harald Hampel
- Sorbonne UniversityGRC n° 21, Alzheimer Precision Medicine (APM)AP‐HP, Pitié‐Salpêtrière HospitalBoulevard de l'hôpitalParisFrance
| | | | | | | | | | - Jean Sallantin
- Laboratoire d'Intelligence ArtificielleLIRMM, CNRSMontpellierFrance
| | - Walter E. Kaufmann
- Anavex Life Sciences Corp.New YorkNew YorkUSA
- Department of Human GeneticsEmory University School of MedicineAtlantaGeorgiaUSA
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