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Krix S, Wilczynski E, Falgàs N, Sánchez-Valle R, Yoles E, Nevo U, Baruch K, Fröhlich H. Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches. Front Immunol 2024; 15:1343900. [PMID: 38720902 PMCID: PMC11078023 DOI: 10.3389/fimmu.2024.1343900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 04/08/2024] [Indexed: 05/12/2024] Open
Abstract
Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.
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Affiliation(s)
- Sophia Krix
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Bonn, Germany
| | - Ella Wilczynski
- Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Neus Falgàs
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (FCRB-IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Raquel Sánchez-Valle
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (FCRB-IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Eti Yoles
- ImmunoBrain Checkpoint Ltd., Rechovot, Israel
| | - Uri Nevo
- Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Kuti Baruch
- ImmunoBrain Checkpoint Ltd., Rechovot, Israel
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Bonn, Germany
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Lin H, Himali JJ, Satizabal CL, Beiser AS, Levy D, Benjamin EJ, Gonzales MM, Ghosh S, Vasan RS, Seshadri S, McGrath ER. Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study. Cells 2022; 11:1506. [PMID: 35563811 PMCID: PMC9100323 DOI: 10.3390/cells11091506] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 04/20/2022] [Accepted: 04/27/2022] [Indexed: 11/25/2022] Open
Abstract
Blood biomarkers for dementia have the potential to identify preclinical disease and improve participant selection for clinical trials. Machine learning is an efficient analytical strategy to simultaneously identify multiple candidate biomarkers for dementia. We aimed to identify important candidate blood biomarkers for dementia using three machine learning models. We included 1642 (mean 69 ± 6 yr, 53% women) dementia-free Framingham Offspring Cohort participants attending examination, 7 who had available blood biomarker data. We developed three machine learning models, support vector machine (SVM), eXtreme gradient boosting of decision trees (XGB), and artificial neural network (ANN), to identify candidate biomarkers for incident dementia. Over a mean 12 ± 5 yr follow-up, 243 (14.8%) participants developed dementia. In multivariable models including all 38 available biomarkers, the XGB model demonstrated the strongest predictive accuracy for incident dementia (AUC 0.74 ± 0.01), followed by ANN (AUC 0.72 ± 0.01), and SVM (AUC 0.69 ± 0.01). Stepwise feature elimination by random sampling identified a subset of the nine most highly informative biomarkers. Machine learning models confined to these nine biomarkers showed improved model predictive accuracy for dementia (XGB, AUC 0.76 ± 0.01; ANN, AUC 0.75 ± 0.004; SVM, AUC 0.73 ± 0.01). A parsimonious panel of nine candidate biomarkers were identified which showed moderately good predictive accuracy for incident dementia, although our results require external validation.
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Affiliation(s)
- Honghuang Lin
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Jayandra J. Himali
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- School of Public Health, Boston University, Boston, MA 02118, USA
- School of Medicine, Boston University, Boston, MA 02118, USA
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 77072, USA
| | - Claudia L. Satizabal
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 77072, USA
| | - Alexa S. Beiser
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- School of Public Health, Boston University, Boston, MA 02118, USA
- School of Medicine, Boston University, Boston, MA 02118, USA
| | - Daniel Levy
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- Population Sciences Branch, National Heart, Lung and Blood Institutes of Health, Bethesda, MD 20824, USA
| | - Emelia J. Benjamin
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- School of Public Health, Boston University, Boston, MA 02118, USA
- School of Medicine, Boston University, Boston, MA 02118, USA
| | - Mitzi M. Gonzales
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 77072, USA
| | - Saptaparni Ghosh
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- School of Medicine, Boston University, Boston, MA 02118, USA
| | - Ramachandran S. Vasan
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- School of Medicine, Boston University, Boston, MA 02118, USA
| | - Sudha Seshadri
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- School of Medicine, Boston University, Boston, MA 02118, USA
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 77072, USA
| | - Emer R. McGrath
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- HRB Clinical Research Facility, National University of Ireland Galway, University Road, H91TK33 Galway, Ireland
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Hardy-Sosa A, León-Arcia K, Llibre-Guerra JJ, Berlanga-Acosta J, Baez SDLC, Guillen-Nieto G, Valdes-Sosa PA. Diagnostic Accuracy of Blood-Based Biomarker Panels: A Systematic Review. Front Aging Neurosci 2022; 14:683689. [PMID: 35360215 PMCID: PMC8963375 DOI: 10.3389/fnagi.2022.683689] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 01/24/2022] [Indexed: 01/10/2023] Open
Abstract
Background Because of high prevalence of Alzheimer's disease (AD) in low- and middle-income countries (LMICs), there is an urgent need for inexpensive and minimally invasive diagnostic tests to detect biomarkers in the earliest and asymptomatic stages of the disease. Blood-based biomarkers are predicted to have the most impact for use as a screening tool and predict the onset of AD, especially in LMICs. Furthermore, it has been suggested that panels of markers may perform better than single protein candidates. Methods Medline/Pubmed was searched to identify current relevant studies published from January 2016 to December 2020. We included all full-text articles examining blood-based biomarkers as a set of protein markers or panels to aid in AD's early diagnosis, prognosis, and characterization. Results Seventy-six articles met the inclusion criteria for systematic review. Majority of the studies reported plasma and serum as the main source for biomarker determination in blood. Protein-based biomarker panels were reported to aid in AD diagnosis and prognosis with better accuracy than individual biomarkers. Conventional (amyloid-beta and tau) and neuroinflammatory biomarkers, such as amyloid beta-42, amyloid beta-40, total tau, phosphorylated tau-181, and other tau isoforms, were the most represented. We found the combination of amyloid beta-42/amyloid beta-40 ratio and APOEε4 status to be most represented with high accuracy for predicting amyloid beta-positron emission tomography status. Conclusion Assessment of Alzheimer's disease biomarkers in blood as a non-invasive and cost-effective alternative will potentially contribute to early diagnosis and improvement of therapeutic interventions. Given the heterogeneous nature of AD, combination of markers seems to perform better in the diagnosis and prognosis of the disease than individual biomarkers.
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Affiliation(s)
- Anette Hardy-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- Centro de Ingeniería Genética y Biotecnología, La Habana, Cuba
| | | | | | | | - Saiyet de la C. Baez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- Centro de Ingeniería Genética y Biotecnología, La Habana, Cuba
| | | | - Pedro A. Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- Centro de Neurociencias de Cuba, La Habana, Cuba
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Eke CS, Jammeh E, Li X, Carroll C, Pearson S, Ifeachor E. Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines. IEEE J Biomed Health Inform 2021; 25:218-226. [PMID: 32340968 DOI: 10.1109/jbhi.2020.2984355] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The successful development of amyloid-based biomarkers and tests for Alzheimer's disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities, we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) > 80%, specificity (SP) > 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease, suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using non-amyloid based biomarkers.
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Aman Y, Frank J, Lautrup SH, Matysek A, Niu Z, Yang G, Shi L, Bergersen LH, Storm-Mathisen J, Rasmussen LJ, Bohr VA, Nilsen H, Fang EF. The NAD +-mitophagy axis in healthy longevity and in artificial intelligence-based clinical applications. Mech Ageing Dev 2020; 185:111194. [PMID: 31812486 PMCID: PMC7545219 DOI: 10.1016/j.mad.2019.111194] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 11/24/2019] [Accepted: 12/03/2019] [Indexed: 12/11/2022]
Abstract
Nicotinamide adenine dinucleotide (NAD+) is an important natural molecule involved in fundamental biological processes, including the TCA cycle, OXPHOS, β-oxidation, and is a co-factor for proteins promoting healthy longevity. NAD+ depletion is associated with the hallmarks of ageing and may contribute to a wide range of age-related diseases including metabolic disorders, cancer, and neurodegenerative diseases. One of the central pathways by which NAD+ promotes healthy ageing is through regulation of mitochondrial homeostasis via mitochondrial biogenesis and the clearance of damaged mitochondria via mitophagy. Here, we highlight the contribution of the NAD+-mitophagy axis to ageing and age-related diseases, and evaluate how boosting NAD+ levels may emerge as a promising therapeutic strategy to counter ageing as well as neurodegenerative diseases including Alzheimer's disease. The potential use of artificial intelligence to understand the roles and molecular mechanisms of the NAD+-mitophagy axis in ageing is discussed, including possible applications in drug target identification and validation, compound screening and lead compound discovery, biomarker development, as well as efficacy and safety assessment. Advances in our understanding of the molecular and cellular roles of NAD+ in mitophagy will lead to novel approaches for facilitating healthy mitochondrial homoeostasis that may serve as a promising therapeutic strategy to counter ageing-associated pathologies and/or accelerated ageing.
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Affiliation(s)
- Yahyah Aman
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478, Lørenskog, Norway
| | - Johannes Frank
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478, Lørenskog, Norway
| | - Sofie Hindkjær Lautrup
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478, Lørenskog, Norway
| | - Adrian Matysek
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478, Lørenskog, Norway; School of Pharmacy and Division of Laboratory Medicine in Sosnowiec, Medical University of Silesia in Katowice, 40-055, Katowice, Poland
| | - Zhangming Niu
- Aladdin Healthcare Technologies Ltd., 24-26 Baltic Street West, London, EC1Y OUR, UK
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Liu Shi
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Linda H Bergersen
- The Brain and Muscle Energy Group, Electron Microscopy Laboratory, Department of Oral Biology, University of Oslo, NO-0316, Oslo, Norway; Amino Acid Transporters, Division of Anatomy, Department of Molecular Medicine, Institute of Basic Medical Sciences (IMB) and Healthy Brain Ageing Centre (SERTA), University of Oslo, NO-0317, Oslo, Norway; Center for Healthy Aging, Department of Neuroscience and Pharmacology, Faculty of Health Sciences, University of Copenhagen, DK-2200, Copenhagen N, Denmark; The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway
| | - Jon Storm-Mathisen
- Amino Acid Transporters, Division of Anatomy, Department of Molecular Medicine, Institute of Basic Medical Sciences (IMB) and Healthy Brain Ageing Centre (SERTA), University of Oslo, NO-0317, Oslo, Norway; The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway
| | - Lene J Rasmussen
- The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway; Center for Healthy Aging, Department of Cellular and Molecular Medicine, Faculty of Health Sciences, University of Copenhagen, DK-2200, Copenhagen N, Denmark
| | - Vilhelm A Bohr
- Laboratory of Molecular Gerontology, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, United States; The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway; Center for Healthy Aging, Department of Cellular and Molecular Medicine, Faculty of Health Sciences, University of Copenhagen, DK-2200, Copenhagen N, Denmark
| | - Hilde Nilsen
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478, Lørenskog, Norway; The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway
| | - Evandro F Fang
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478, Lørenskog, Norway; The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway.
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