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Machado Reyes D, Chao H, Hahn J, Shen L, Yan P, For The Alzheimer's Disease Neuroimaging Initiative. Identifying Progression-Specific Alzheimer's Subtypes Using Multimodal Transformer. J Pers Med 2024; 14:421. [PMID: 38673048 DOI: 10.3390/jpm14040421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/01/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the disease. Therefore, it is essential to identify disease subtypes at a very early stage. Current data-driven approaches can be used to classify subtypes during later stages of AD or related disorders, but making predictions in the asymptomatic or prodromal stage is challenging. Furthermore, the classifications of most existing models lack explainability, and these models rely solely on a single modality for assessment, limiting the scope of their analysis. Thus, we propose a multimodal framework that utilizes early-stage indicators, including imaging, genetics, and clinical assessments, to classify AD patients into progression-specific subtypes at an early stage. In our framework, we introduce a tri-modal co-attention mechanism (Tri-COAT) to explicitly capture cross-modal feature associations. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (slow progressing = 177, intermediate = 302, and fast = 15) were used to train and evaluate Tri-COAT using a 10-fold stratified cross-testing approach. Our proposed model outperforms baseline models and sheds light on essential associations across multimodal features supported by known biological mechanisms. The multimodal design behind Tri-COAT allows it to achieve the highest classification area under the receiver operating characteristic curve while simultaneously providing interpretability to the model predictions through the co-attention mechanism.
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Affiliation(s)
- Diego Machado Reyes
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Hanqing Chao
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Juergen Hahn
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Pingkun Yan
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
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Shi L, Xu J, Green R, Wretlind A, Homann J, Buckley NJ, Tijms BM, Vos SJB, Lill CM, Kate MT, Engelborghs S, Sleegers K, Frisoni GB, Wallin A, Lleó A, Popp J, Martinez-Lage P, Streffer J, Barkhof F, Zetterberg H, Visser PJ, Lovestone S, Bertram L, Nevado-Holgado AJ, Proitsi P, Legido-Quigley C. Multiomics profiling of human plasma and cerebrospinal fluid reveals ATN-derived networks and highlights causal links in Alzheimer's disease. Alzheimers Dement 2023; 19:3350-3364. [PMID: 36790009 DOI: 10.1002/alz.12961] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 12/07/2022] [Accepted: 12/20/2022] [Indexed: 02/16/2023]
Abstract
INTRODUCTION This study employed an integrative system and causal inference approach to explore molecular signatures in blood and CSF, the amyloid/tau/neurodegeneration [AT(N)] framework, mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD), and genetic risk for AD. METHODS Using the European Medical Information Framework (EMIF)-AD cohort, we measured 696 proteins in cerebrospinal fluid (n = 371), 4001 proteins in plasma (n = 972), 611 metabolites in plasma (n = 696), and genotyped whole-blood (7,778,465 autosomal single nucleotide epolymorphisms, n = 936). We investigated associations: molecular modules to AT(N), module hubs with AD Polygenic Risk scores and APOE4 genotypes, molecular hubs to MCI conversion and probed for causality with AD using Mendelian randomization (MR). RESULTS AT(N) framework associated with protein and lipid hubs. In plasma, Proprotein Convertase Subtilisin/Kexin Type 7 showed evidence for causal associations with AD. AD was causally associated with Reticulocalbin 2 and sphingomyelins, an association driven by the APOE isoform. DISCUSSION This study reveals multi-omics networks associated with AT(N) and causal AD molecular candidates.
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Affiliation(s)
- Liu Shi
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Jin Xu
- Institute of Pharmaceutical Science, King's College London, London, UK
| | - Rebecca Green
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- UK National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre, South London and Maudsley Trust, London, UK
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, London, UK
| | | | - Jan Homann
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany
| | - Noel J Buckley
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Betty M Tijms
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands
| | - Stephanie J B Vos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Christina M Lill
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany
- Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - Mara Ten Kate
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
- Department of Neurology, UZ Brussel and Center for Neurociences (C4N), Vrije Universiteit Brussel, Brussels, Belgium
| | - Kristel Sleegers
- Complex Genetics Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Institute Born-Bunge, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Giovanni B Frisoni
- University of Geneva, Geneva, Switzerland
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Anders Wallin
- Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Alberto Lleó
- Neurology Department, Centro de Investigación en Red en enfermedades neurodegenerativas (CIBERNED), Hospital Sant Pau, Barcelona, Spain
| | - Julius Popp
- University Hospital of Lausanne, Lausanne, Switzerland
- Department of Geriatric Psychiatry, University Hospital of Psychiatry and University of Zürich, Zürich, Switzerland
| | | | - Johannes Streffer
- AC Immune SA, formerly Janssen R&D, LLC. Beerse, Belgium at the time of study conduct, Lausanne, Switzerland
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherland
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Pieter Jelle Visser
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Simon Lovestone
- Department of Psychiatry, University of Oxford, Oxford, UK
- Janssen Medical (UK), High Wycombe, UK
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany
- Department of Psychology, University of Oslo, Oslo, Norway
| | | | - Petroula Proitsi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Cristina Legido-Quigley
- Institute of Pharmaceutical Science, King's College London, London, UK
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
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Kim JM, Kang HJ, Kim JW, Choi W, Lee JY, Kim SW, Shin IS, Kim MG, Chun BJ, Stewart R. Multiple serum biomarkers for predicting suicidal behaviours in depressive patients receiving pharmacotherapy. Psychol Med 2023; 53:4385-4394. [PMID: 35578580 PMCID: PMC10388309 DOI: 10.1017/s0033291722001180] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 04/05/2022] [Accepted: 04/06/2022] [Indexed: 01/21/2023]
Abstract
BACKGROUND Predictive values of multiple serum biomarkers for suicidal behaviours (SBs) have rarely been tested. This study sought to evaluate and develop a panel of multiple serum biomarkers for predicting SBs in outpatients receiving a 12-month pharmacotherapy programme for depressive disorders. METHODS At baseline, 14 serum biomarkers and socio-demographic/clinical characteristics including previous suicidal attempt and present suicidal severity were evaluated in 1094 patients with depressive disorders without a bipolar diagnosis. Of these, 884 were followed for increased suicidal severity and fatal/non-fatal suicide attempt outcomes over a 12-month treatment period. Individual and combined effects of serum biomarkers on these two prospective SBs were estimated using logistic regression analysis after adjustment for relevant covariates. RESULTS Increased suicidal severity and fatal/non-fatal suicide attempt during the 12-month pharmacotherapy were present in 155 (17.5%) and 38 (4.3%) participants, respectively. Combined cortisol, total cholesterol, and folate serum biomarkers predicted fatal/non-fatal suicide attempt, and these with interleukin-1 beta and homocysteine additionally predicted increased suicidal severity, with clear gradients robust to adjustment (p values < 0.001). CONCLUSIONS Application of multiple serum biomarkers could considerably improve the predictability of SBs during the outpatient treatment of depressive disorders, potentially highlighting the need for more frequent monitoring and risk appraisal.
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Affiliation(s)
- Jae-Min Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Hee-Ju Kang
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Ju-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Wonsuk Choi
- Department of Internal Medicine, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| | - Ju-Yeon Lee
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Il-Seon Shin
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Min-Gon Kim
- Department of Chemistry, School of Physics and Chemistry, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Byung Jo Chun
- Department of Emergency Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Robert Stewart
- King's College London (Institute of Psychiatry, Psychology and Neuroscience), London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
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Mao X, Cai Y, Long S, Perez-Losada J, Mao JH, Chang H. Pan-cancer evaluation of clinical value of mitotic network activity index (MNAI) and its predictive value for immunotherapy. Front Oncol 2023; 13:1178568. [PMID: 37456231 PMCID: PMC10349373 DOI: 10.3389/fonc.2023.1178568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Increased mitotic activity is associated with the genesis and aggressiveness of many cancers. To assess the clinical value of mitotic activity as prognostic biomarker, we performed a pan-cancer study on the mitotic network activity index (MNAI) constructed based on 54-gene mitotic apparatus network. Our pan-cancer assessment on TCGA (33 tumor types, 10,061 patients) and validation on other publicly available cohorts (23 tumor types, 9,209 patients) confirmed the significant association of MNAI with overall survival, progression-free survival, and other prognostic endpoints in multiple cancer types, including lower-grade gliomas (LGG), breast invasive carcinoma (BRCA), as well as many others. We also showed significant association between MNAI and genetic instability, which provides a biological explanation of its prognostic impact at pan-cancer landscape. Our association analysis revealed that patients with high MNAI benefitted more from anti-PD-1 and Anti-CTLA-4 treatment. In addition, we demonstrated that multimodal integration of MNAI and the AI-empowered Cellular Morphometric Subtypes (CMS) significantly improved the predictive power of prognosis compared to using MNAI and CMS alone. Our results suggest that MNAI can be used as a potential prognostic biomarker for different tumor types toward different clinical endpoints, and multimodal integration of MNAI and CMS exceeds individual biomarker for precision prognosis.
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Affiliation(s)
- Xuanyu Mao
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Yimeng Cai
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, United States
| | - Sarah Long
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Jesus Perez-Losada
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, Spain
| | - Jian-Hua Mao
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Hang Chang
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
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Liao Z, Dang C, Li M, Bu Y, Han R, Jiang W. Microstructural damage of normal-appearing white matter in subcortical ischemic vascular dementia is associated with Montreal Cognitive Assessment scores. J Int Med Res 2019; 47:5723-5731. [PMID: 31342825 PMCID: PMC6862888 DOI: 10.1177/0300060519863520] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Objectives This study was performed to determine whether multimodal biomarkers are more strongly associated with the Montreal Cognitive Assessment (MoCA) scores compared with the Mini-Mental State Examination (MMSE) scores, and whether they are correlated with the Clinical Dementia Rating (CDR) in patients with subcortical ischemic vascular dementia (SIVD). Methods Patients diagnosed with SIVD were enrolled. Peripheral blood hypersensitive C-reactive protein, white matter lesion volume (WMLV), fractional anisotropy (FA)/mean diffusivity (MD) of whole brain white matter (WBWM), and normal-appearing white matter (NAWM) were measured and correlated with MMSE, MoCA, and CDR scores. Results Bivariate analyses of data from 48 included patients revealed that both MoCA and MMSE were significantly associated with age, education, and FA of NAWM. Only MD of NAWM was correlated with MoCA scores. In partial correlation analysis adjusted for demographic and neuroimaging variables, MD/FA of NAWM and the MoCA scores were significantly correlated. FA/MD of NAWM had a modest trend toward a correlation with the CDR, but it was not significant. Conclusions In the patients with SIVD, FA/MD of NAWM were more strongly related to MoCA scores compared with MMSE scores.
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Affiliation(s)
- Zhangyuan Liao
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Neurological Institute, Key Laboratory of Post-neurotrauma Neuro-repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Tianjin, China
| | - Chun Dang
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Neurological Institute, Key Laboratory of Post-neurotrauma Neuro-repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Tianjin, China
| | - Meijie Li
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Neurological Institute, Key Laboratory of Post-neurotrauma Neuro-repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Tianjin, China
| | - Yali Bu
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Neurological Institute, Key Laboratory of Post-neurotrauma Neuro-repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Tianjin, China
| | - Ranran Han
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Neurological Institute, Key Laboratory of Post-neurotrauma Neuro-repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Tianjin, China
| | - Wei Jiang
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China.,Tianjin Neurological Institute, Key Laboratory of Post-neurotrauma Neuro-repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Tianjin, China
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Martinez-Torteya A, Rodriguez-Rojas J, Celaya-Padilla JM, Galván-Tejada JI, Treviño V, Tamez-Peña J. Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer's disease progression. J Med Imaging (Bellingham) 2014; 1:031005. [PMID: 26158047 DOI: 10.1117/1.jmi.1.3.031005] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 07/27/2014] [Accepted: 08/22/2014] [Indexed: 01/31/2023] Open
Abstract
Early diagnoses of Alzheimer's disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different ([Formula: see text]). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones.
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Affiliation(s)
- Antonio Martinez-Torteya
- Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Ingeniería, Departamento de Ciencias Computacionales, Monterrey 64849, Mexico
| | - Juan Rodriguez-Rojas
- Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Ingeniería, Departamento de Ciencias Computacionales, Monterrey 64849, Mexico
| | - José M Celaya-Padilla
- Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Ingeniería, Departamento de Ciencias Computacionales, Monterrey 64849, Mexico
| | - Jorge I Galván-Tejada
- Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Ingeniería, Departamento de Ciencias Computacionales, Monterrey 64849, Mexico
| | - Victor Treviño
- Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Ingeniería, Departamento de Ciencias Computacionales, Monterrey 64849, Mexico ; Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Medicina, Departamento de Investigación e Innovación, Monterrey 64710, Mexico
| | - Jose Tamez-Peña
- Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Ingeniería, Departamento de Ciencias Computacionales, Monterrey 64849, Mexico ; Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Medicina, Departamento de Investigación e Innovación, Monterrey 64710, Mexico
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