1
|
Qin H, Shi X, Zhu Y, Ma J, Deng X, Wang L. Alzheimer's disease early screening and staged detection with plasma proteome using machine learning and convolutional neural network. Eur J Neurosci 2024. [PMID: 38764192 DOI: 10.1111/ejn.16392] [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: 01/18/2024] [Revised: 04/19/2024] [Accepted: 04/27/2024] [Indexed: 05/21/2024]
Abstract
Alzheimer's disease (AD) stands as the prevalent progressive neurodegenerative disease, precipitating cognitive impairment and even memory loss. Amyloid biomarkers have been extensively used in the diagnosis of AD. However, amyloid proteins offer limited information about the disease process and accurate diagnosis depends on the presence of a substantial accumulation of amyloid deposition which significantly impedes the early screening of AD. In this study, we have combined plasma proteomics with an ensemble learning model (CatBoost) to develop a cost-effective and non-invasive diagnostic method for AD. A longitudinal panel has been identified that can serve as reliable biomarkers across the entire progression of AD. Simultaneously, we have developed a neural network algorithm that utilizes plasma proteins to detect stages of Alzheimer's disease. Based on the developed longitudinal panel, the CatBoost model achieved an area under the operating curve of at least 0.90 in distinguishing mild cognitive impairment from cognitively normal. The neural network model was utilized for the detection of three stages of AD, and the results demonstrated that the neural network model exhibited an accuracy as high as 0.83, surpassing that of the traditional machine learning model.
Collapse
Affiliation(s)
- Hengyu Qin
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China
| | - Xiumin Shi
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China
| | - Yibo Zhu
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China
| | - Jiacheng Ma
- The Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Lu Wang
- Department of Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China
| |
Collapse
|
2
|
Bhattacharyya U, John J, Lam M, Fisher J, Sun B, Baird D, Chen CY, Lencz T. Large-Scale Mendelian Randomization Study Reveals Circulating Blood-based Proteomic Biomarkers for Psychopathology and Cognitive Task Performance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.18.24301455. [PMID: 38293198 PMCID: PMC10827252 DOI: 10.1101/2024.01.18.24301455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Background Research on peripheral (e.g., blood-based) biomarkers for psychiatric illness has typically been low-throughput in terms of both the number of subjects and the range of assays performed. Moreover, traditional case-control studies examining blood-based biomarkers are subject to potential confounds of treatment and other exposures common to patients with psychiatric illnesses. Our research addresses these challenges by leveraging large-scale, high-throughput proteomics data and Mendelian Randomization (MR) to examine the causal impact of circulating proteins on psychiatric phenotypes and cognitive task performance. Methods We utilized plasma proteomics data from the UK Biobank (3,072 proteins assayed in 34,557 European-ancestry individuals) and deCODE Genetics (4,719 proteins measured across 35,559 Icelandic individuals). Significant proteomic quantitative trait loci (both cis-pQTLs and trans-pQTLs) served as MR instruments, with the most recent GWAS for schizophrenia, bipolar disorder, major depressive disorder, and cognitive task performance (all excluding overlapping UK Biobank participants) as phenotypic outcomes. Results MR revealed 109 Bonferroni-corrected causal associations (44 novel) involving 88 proteins across the four phenotypes. Several immune-related proteins, including interleukins and complement factors, stood out as pleiotropic across multiple outcome phenotypes. Drug target enrichment analysis identified several novel potential pharmacologic repurposing opportunities, including anti-inflammatory agents for schizophrenia and bipolar disorder and duloxetine for cognitive performance. Conclusions Identification of causal effects for these circulating proteins suggests potential biomarkers for these conditions and offers insights for developing innovative therapeutic strategies. The findings also indicate substantial evidence for the pleiotropic effects of many proteins across different phenotypes, shedding light on the shared etiology among psychiatric conditions and cognitive ability.
Collapse
Affiliation(s)
- Upasana Bhattacharyya
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
| | - Jibin John
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
| | - Max Lam
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
| | - Jonah Fisher
- Biogen Inc., Cambridge, MA
- Harvard T.H. Chan School of Public Health, Cambridge, MA
| | | | | | | | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY
- Departments of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY
| |
Collapse
|
3
|
Yu Z, Shi Z, Dan T, Dere M, Kim M, Li Q, Wu G. Uncovering Diverse Mechanistic Spreading Pathways in Disease Progression of Alzheimer's Disease. J Alzheimers Dis Rep 2023; 7:855-872. [PMID: 37662609 PMCID: PMC10473126 DOI: 10.3233/adr-230081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 07/24/2023] [Indexed: 09/05/2023] Open
Abstract
Background The AT[N] research framework focuses on three major biomarkers in Alzheimer's disease (AD): amyloid-β deposition (A), pathologic tau (T), and neurodegeneration [N]. Objective We hypothesize that the diverse mechanisms such as A⟶T and A⟶[N] pathways from one brain region to others, may underlie the wide variation in clinical symptoms. We aim to uncover the causal-like effect of regional AT[N] biomarkers on cognitive decline as well as the interaction with non-modifiable risk factors such as age and APOE4. Methods We apply multi-variate statistical inference to uncover all possible mechanistic spreading pathways through which the aggregation of an upstream biomarker (e.g., increased amyloid level) in a particular brain region indirectly impacts cognitive decline, via the cascade build-up of a downstream biomarker (e.g., reduced metabolism level) in another brain region. Furthermore, we investigate the survival time for each identified region-to-region pathological pathway toward the AD onset. Results We have identified a collection of critical brain regions on which the amyloid burdens exert an indirect effect on the decline in memory and executive function (EF) domain, being mediated by the reduction of metabolism level at other brain regions. APOE4 status has been found not only involved in many A⟶N mechanistic pathways but also significantly contributes to the risk of developing AD. Conclusion Our major findings include 1) the region-to-region A⟶N⟶MEM and A⟶N⟶MEM pathways exhibit distinct spatial patterns; 2) APOE4 is significantly associated with both direct and indirect effects on the cognitive decline while sex difference has not been identified in the mediation analysis.
Collapse
Affiliation(s)
- Zhentao Yu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Zhuoyu Shi
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Tingting Dan
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Mustafa Dere
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Minjeong Kim
- Department of Computer Science, University of North Carolina, Greensboro, NC, USA
| | - Quefeng Li
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA
- Carolina Institute of Developmental Disabilities, University of North Carolina, Chapel Hill, NC, USA
| | | |
Collapse
|
4
|
Ehtewish H, Mesleh A, Ponirakis G, De la Fuente A, Parray A, Bensmail I, Abdesselem H, Ramadan M, Khan S, Chandran M, Ayadathil R, Elsotouhy A, Own A, Al Hamad H, Abdelalim EM, Decock J, Alajez NM, Albagha O, Thornalley PJ, Arredouani A, Malik RA, El-Agnaf OMA. Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia. Int J Mol Sci 2023; 24:ijms24098117. [PMID: 37175824 PMCID: PMC10179172 DOI: 10.3390/ijms24098117] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/03/2023] [Accepted: 04/05/2023] [Indexed: 05/15/2023] Open
Abstract
Dementia is a progressive and debilitating neurological disease that affects millions of people worldwide. Identifying the minimally invasive biomarkers associated with dementia that could provide insights into the disease pathogenesis, improve early diagnosis, and facilitate the development of effective treatments is pressing. Proteomic studies have emerged as a promising approach for identifying the protein biomarkers associated with dementia. This pilot study aimed to investigate the plasma proteome profile and identify a panel of various protein biomarkers for dementia. We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). Limma-based differential expression analysis reported the dysregulation of 61 proteins in the plasma of those with dementia compared with controls, and machine learning algorithms identified 17 stable diagnostic biomarkers that differentiated individuals with AUC = 0.98 ± 0.02. There was also the dysregulation of 153 plasma proteins in individuals with dementia compared with those with MCI, and machine learning algorithms identified 8 biomarkers that classified dementia from MCI with an AUC of 0.87 ± 0.07. Moreover, multiple proteins selected in both diagnostic panels such as NEFL, IL17D, WNT9A, and PGF were negatively correlated with cognitive performance, with a correlation coefficient (r2) ≤ -0.47. Gene Ontology (GO) and pathway analysis of dementia-associated proteins implicated immune response, vascular injury, and extracellular matrix organization pathways in dementia pathogenesis. In conclusion, the combination of high-throughput proteomics and machine learning enabled us to identify a blood-based protein signature capable of potentially differentiating dementia from MCI and cognitively normal controls. Further research is required to validate these biomarkers and investigate the potential underlying mechanisms for the development of dementia.
Collapse
Affiliation(s)
- Hanan Ehtewish
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
- Neurological Disorders Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
| | - Areej Mesleh
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
- Neurological Disorders Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
| | - Georgios Ponirakis
- Department of Medicine, Weill Cornell Medicine-Qatar, Qatar Foundation (QF), Doha P.O. Box 24144, Qatar
| | - Alberto De la Fuente
- Diabetes Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
| | - Aijaz Parray
- The Neuroscience Institute, Academic Health System, Hamad Medical Corporation (HMC), Doha P.O. Box 3050, Qatar
| | - Ilham Bensmail
- Proteomics Core Facility, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
| | - Houari Abdesselem
- Proteomics Core Facility, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
| | - Marwan Ramadan
- Geriatric and Memory Clinic, Rumailah Hospital, Hamad Medical Corporation (HMC), Doha P.O. Box 3050, Qatar
| | - Shafi Khan
- Geriatric and Memory Clinic, Rumailah Hospital, Hamad Medical Corporation (HMC), Doha P.O. Box 3050, Qatar
| | - Mani Chandran
- Geriatric and Memory Clinic, Rumailah Hospital, Hamad Medical Corporation (HMC), Doha P.O. Box 3050, Qatar
| | - Raheem Ayadathil
- The Neuroscience Institute, Academic Health System, Hamad Medical Corporation (HMC), Doha P.O. Box 3050, Qatar
| | - Ahmed Elsotouhy
- The Neuroscience Institute, Academic Health System, Hamad Medical Corporation (HMC), Doha P.O. Box 3050, Qatar
- Department of Clinical Radiology, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha P.O. Box 24144, Qatar
| | - Ahmed Own
- The Neuroscience Institute, Academic Health System, Hamad Medical Corporation (HMC), Doha P.O. Box 3050, Qatar
- Neuroradiology Department, Hamad General Hospital, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar
| | - Hanadi Al Hamad
- Geriatric and Memory Clinic, Rumailah Hospital, Hamad Medical Corporation (HMC), Doha P.O. Box 3050, Qatar
| | - Essam M Abdelalim
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
- Diabetes Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
| | - Julie Decock
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
- Translational Cancer and Immunity Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
| | - Nehad M Alajez
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
- Translational Cancer and Immunity Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
| | - Omar Albagha
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
| | - Paul J Thornalley
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
- Diabetes Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
| | - Abdelilah Arredouani
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
- Diabetes Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
| | - Rayaz A Malik
- Department of Medicine, Weill Cornell Medicine-Qatar, Qatar Foundation (QF), Doha P.O. Box 24144, Qatar
| | - Omar M A El-Agnaf
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
- Neurological Disorders Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Doha P.O. Box 34110, Qatar
| |
Collapse
|
5
|
Gomes KB, Pereira RG, Braga AA, Guimarães HC, Resende EDPF, Teixeira AL, Barbosa MT, Junior WM, Carvalho MDG, Caramelli P. Machine Learning-Based Routine Laboratory Tests Predict One-Year Cognitive and Functional Decline in a Population Aged 75+ Years. Brain Sci 2023; 13:brainsci13040690. [PMID: 37190655 DOI: 10.3390/brainsci13040690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 03/30/2023] [Accepted: 04/15/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Cognitive and functional decline are common problems in older adults, especially in those 75+ years old. Currently, there is no specific plasma biomarker able to predict this decline in healthy old-age people. Machine learning (ML) is a subarea of artificial intelligence (AI), which can be used to predict outcomes Aim: This study aimed to evaluate routine laboratory variables able to predict cognitive and functional impairment, using ML algorithms, in a cohort aged 75+ years, in a one-year follow-up study. METHOD One hundred and thirty-two older adults aged 75+ years were selected through a community-health public program or from long-term-care institutions. Their functional and cognitive performances were evaluated at baseline and one year later using a functional activities questionnaire, Mini-Mental State Examination, and the Brief Cognitive Screening Battery. Routine laboratory tests were performed at baseline. ML algorithms-random forest, support vector machine (SVM), and XGBoost-were applied in order to describe the best model able to predict cognitive and functional decline using routine tests as features. RESULTS The random forest model showed better accuracy than other algorithms and included triglycerides, glucose, hematocrit, red cell distribution width (RDW), albumin, hemoglobin, globulin, high-density lipoprotein cholesterol (HDL-c), thyroid-stimulating hormone (TSH), creatinine, lymphocyte, erythrocyte, platelet/leucocyte (PLR), and neutrophil/leucocyte (NLR) ratios, and alanine transaminase (ALT), leukocyte, low-density lipoprotein cholesterol (LDL-c), cortisol, gamma-glutamyl transferase (GGT), and eosinophil as features to predict cognitive decline (accuracy = 0.79). For functional decline, the most important features were platelet, PLR and NLR, hemoglobin, globulin, cortisol, RDW, glucose, basophil, B12 vitamin, creatinine, GGT, ALT, aspartate transferase (AST), eosinophil, hematocrit, erythrocyte, triglycerides, HDL-c, and monocyte (accuracy = 0.92). CONCLUSIONS Routine laboratory variables could be applied to predict cognitive and functional decline in oldest-old populations using ML algorithms.
Collapse
Affiliation(s)
- Karina Braga Gomes
- Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
| | - Ramon Gonçalves Pereira
- Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
| | - Alexandre Alberto Braga
- Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
| | | | | | | | - Maira Tonidandel Barbosa
- Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
| | - Wagner Meira Junior
- Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
| | | | - Paulo Caramelli
- Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
| |
Collapse
|
6
|
Ochneva AG, Soloveva KP, Savenkova VI, Ikonnikova AY, Gryadunov DA, Andryuschenko AV. Modern Approaches to the Diagnosis of Cognitive Impairment and Alzheimer's Disease: A Narrative Literature Review. CONSORTIUM PSYCHIATRICUM 2023; 4:53-62. [PMID: 38239570 PMCID: PMC10790729 DOI: 10.17816/cp716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 03/13/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND The aging of the worlds population leads to an increase in the prevalence of age-related diseases, including cognitive impairment. At the stage of dementia, therapeutic interventions become usually ineffective. Therefore, researchers and clinical practitioners today are looking for methods that allow for early diagnosis of cognitive impairment, including techniques that are based on the use of biological markers. AIM The aim of this literature review is to delve into scientific papers that are centered on modern laboratory tests for Alzheimers disease, including tests for biological markers at the early stages of cognitive impairment. METHODS The authors have carried out a descriptive review of scientific papers published from 2015 to 2023. Studies that are included in the PubMed and Web of Science electronic databases were analyzed. A descriptive analysis was used to summarized the gleaned information. RESULTS Blood and cerebrospinal fluid (CSF) biomarkers, as well as the advantages and disadvantages of their use, are reviewed. The most promising neurotrophic, neuroinflammatory, and genetic markers, including polygenic risk models, are also discussed. CONCLUSION The use of biomarkers in clinical practice will contribute to the early diagnosis of cognitive impairment associated with Alzheimers disease. Genetic screening tests can improve the detection threshold of preclinical abnormalities in the absence of obvious symptoms of cognitive decline. The active use of biomarkers in clinical practice, in combination with genetic screening for the early diagnosis of cognitive impairment in Alzheimers disease, can improve the timeliness and effectiveness of medical interventions.
Collapse
|