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Theeke LA, Liu Y, Wang S, Luo X, Navia RO, Xiao D, Xu C, Wang K. Plasma Proteomic Biomarkers in Alzheimer's Disease and Cardiovascular Disease: A Longitudinal Study. Int J Mol Sci 2024; 25:10751. [PMID: 39409080 PMCID: PMC11477191 DOI: 10.3390/ijms251910751] [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: 08/27/2024] [Revised: 09/23/2024] [Accepted: 09/30/2024] [Indexed: 10/20/2024] Open
Abstract
The co-occurrence of Alzheimer's disease (AD) and cardiovascular diseases (CVDs) in older adults highlights the necessity for the exploration of potential shared risk factors. A total of 566 adults were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 111 individuals with AD, 383 with mild cognitive impairment (MCI), and 410 with CVD. The multivariable linear mixed model (LMM) was used to investigate the associations of AD and CVD with longitudinal changes in 146 plasma proteomic biomarkers (measured at baseline and the 12-month follow-up). The LMM showed that 48 biomarkers were linked to AD and 46 to CVD (p < 0.05). Both AD and CVD were associated with longitudinal changes in 14 biomarkers (α1Micro, ApoH, β2M, BNP, complement C3, cystatin C, KIM1, NGAL, PPP, TIM1, THP, TFF3, TM, and VEGF), and both MCI and CVD were associated with 12 biomarkers (ApoD, AXL, BNP, Calcitonin, CD40, C-peptide, pM, PPP, THP, TNFR2, TTR, and VEGF), suggesting intricate connections between cognitive decline and cardiovascular health. Among these, the Tamm Horsfall Protein (THP) was associated with AD, MCI, CVD, and APOE-ε4. This study provides valuable insights into shared and distinct biological markers and mechanisms underlying AD and CVD.
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Affiliation(s)
- Laurie A. Theeke
- Department of Community of Acute and Chronic Care, School of Nursing, The George Washington University, Ashburn, VA 20147, USA;
| | - Ying Liu
- Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, Johnson City, TN 37614, USA;
| | - Silas Wang
- Department of Statistics & Data Science, Dietrich College of Humanities and Social Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
| | - Xingguang Luo
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06516, USA;
| | - R. Osvaldo Navia
- Department of Medicine and Rockefeller Neuroscience Institute, West Virginia University, Morgantown, WV 26506, USA;
| | - Danqing Xiao
- Department of STEM, School of Arts and Sciences, Regis College, Weston, MA 02493, USA;
| | - Chun Xu
- Department of Health and Biomedical Sciences, College of Health Affairs, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA;
| | - Kesheng Wang
- Department of Biobehavioral Health & Nursing Science, College of Nursing, University of South Carolina, Columbia, 1601 Greene Street, Columbia, SC 29208, USA
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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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Na H, Shin KY, Lee D, Yoon C, Han SH, Park JC, Mook-Jung I, Jang J, Kwon S. The QPLEX™ Plus Assay Kit for the Early Clinical Diagnosis of Alzheimer's Disease. Int J Mol Sci 2023; 24:11119. [PMID: 37446296 DOI: 10.3390/ijms241311119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/02/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
We recently developed a multiplex diagnostic kit, QPLEX™ Alz plus assay kit, which captures amyloid-β1-40, galectin-3 binding protein, angiotensin-converting enzyme, and periostin simultaneously using microliters of peripheral blood and utilizes an optimized algorithm for screening Alzheimer's disease (AD) by correlating with cerebral amyloid deposition. Owing to the demand for early AD detection, we investigate the potential of our kit for the early clinical diagnosis of AD. A total of 1395 participants were recruited, and their blood samples were analyzed with the QPLEX™ kit. The average of QPLEX™ algorithm values in each group increased gradually in the order of the clinical progression continuum of AD: cognitively normal (0.382 ± 0.150), subjective cognitive decline (0.452 ± 0.130), mild cognitive impairment (0.484 ± 0.129), and AD (0.513 ± 0.136). The algorithm values between each group showed statistically significant differences among groups divided by Mini-Mental State Examination and Clinical Dementia Rating. The QPLEX™ algorithm values could be used to distinguish the clinical continuum of AD or cognitive function. Because blood-based diagnosis is more accessible, convenient, and cost- and time-effective than cerebral spinal fluid or positron emission tomography imaging-based diagnosis, the QPLEX™ kit can potentially be used for health checkups and the early clinical diagnosis of AD.
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Affiliation(s)
- Hunjong Na
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea
- QuantaMatrix Inc., Seoul 08506, Republic of Korea
| | - Ki Young Shin
- Bio-MAX Institute, Seoul National University, Seoul 08826, Republic of Korea
| | - Dokyung Lee
- QuantaMatrix Inc., Seoul 08506, Republic of Korea
| | | | - Sun-Ho Han
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
- Neuroscience Research Institute, Medical Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
- SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
| | - Jong-Chan Park
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
- Neuroscience Research Institute, Medical Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
- SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
| | - Inhee Mook-Jung
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
- Neuroscience Research Institute, Medical Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
- SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
| | - Jisung Jang
- QuantaMatrix Inc., Seoul 08506, Republic of Korea
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea
- QuantaMatrix Inc., Seoul 08506, Republic of Korea
- Bio-MAX Institute, Seoul National University, Seoul 08826, Republic of Korea
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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: 0.3] [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: 17] [Impact Index Per Article: 5.7] [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: 21] [Impact Index Per Article: 5.3] [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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Hasanzadeh Z, Nourazarian A, Nikanfar M, Laghousi D, Vatankhah AM, Sadrirad S. Evaluation of the Serum Dkk-1, Tenascin-C, Oxidative Stress Markers Levels and Wnt Signaling Pathway Genes Expression in Patients with Alzheimer's Disease. J Mol Neurosci 2020; 71:879-887. [PMID: 32935274 DOI: 10.1007/s12031-020-01710-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 09/09/2020] [Indexed: 11/28/2022]
Abstract
Early diagnosis of Alzheimer's disease (AD) using potential biomarkers may help with implementing early therapeutic interventions, monitoring, and ultimately disease treatment. The current study aimed to evaluate serum levels of DKK-1, TNC, and oxidative stress markers, as well as analyzing the expression of LRP6, GSK3A, and GSK3B genes in patients with AD. Serum levels of DKK-1, TNC, TOS, TAC, and MDA were measured in 40 AD patients and 40 healthy individuals. Additionally, the relative expressions of LRP6, GSK3A, and GSK3B genes in whole blood were evaluated. Receiver operating characteristic (ROC) analysis was used to investigate the incremental diagnostic value of each factor in the study groups. Mean serum levels of DKK-1, TNC, TOS, TAC, and MDA were significantly higher in the AD group compared to the healthy group (p < 0.001). Moreover, a significant difference was observed in the expression of LRP6 and GSK3A genes (p < 0.001) between patients and healthy groups. However, the expression of GSK3B did not significantly differ between the two groups (p > 0.05). With considerable sensitivity and specificity, ROC analysis demonstrated the diagnostic efficacy of DKK-1 and TNC serum levels in AD within an area under the ROC curve of ≥ 0.98 (p ˂ 0.001). The results showed that evaluating serum levels of DKK-1 and TNC, as well as assessing the expression of LRP6, could be utilized for diagnosis and monitoring of AD patients.
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Affiliation(s)
- Zahra Hasanzadeh
- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,Department of Biochemistry and Clinical Laboratories, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Alireza Nourazarian
- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. .,Department of Biochemistry and Clinical Laboratories, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Masoud Nikanfar
- Department of Neurology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Delara Laghousi
- Social Determinants of Health Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - Somayeh Sadrirad
- Department of Biochemistry and Clinical Laboratories, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
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Zetterberg H, Burnham SC. Blood-based molecular biomarkers for Alzheimer's disease. Mol Brain 2019; 12:26. [PMID: 30922367 PMCID: PMC6437931 DOI: 10.1186/s13041-019-0448-1] [Citation(s) in RCA: 168] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Accepted: 03/15/2019] [Indexed: 12/18/2022] Open
Abstract
A major barrier to the effective conduct of clinical trials of new drug candidates against Alzheimer’s disease (AD) and to identifying patients for receiving future disease-modifying treatments is the limited capacity of the current health system to find and diagnose patients with early AD pathology. This may be related in part to the limited capacity of the current health systems to select those people likely to have AD pathology in order to confirm the diagnosis with available cerebrospinal fluid and imaging biomarkers at memory clinics. In the current narrative review, we summarize the literature on candidate blood tests for AD that could be implemented in primary care settings and used for the effective identification of individuals at increased risk of AD pathology, who could be referred for potential inclusion in clinical trials or future approved treatments following additional testing. We give an updated account of blood-based candidate biomarkers and biomarker panels for AD-related brain changes. Our analysis centres on biomarker candidates that have been replicated in more than one study and discusses the need of further studies to achieve the goal of a primary care-based screening algorithm for AD.
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Affiliation(s)
- Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, he Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden. .,Clinical Neurochemistry Laboratory, Sahlgrenska, University Hospital, Mölndal, Sweden. .,Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London, UK. .,UK Dementia Research Institute at UCL, London, UK.
| | - Samantha C Burnham
- CSIRO Health and Biosecurity, Parkville, Victoria, 3052, Australia. .,Centre of Excellence for Alzheimer's Disease Research and Care, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, 6027, Australia.
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Peña-Bautista C, Baquero M, Vento M, Cháfer-Pericás C. Omics-based Biomarkers for the Early Alzheimer Disease Diagnosis and Reliable Therapeutic Targets Development. Curr Neuropharmacol 2019; 17:630-647. [PMID: 30255758 PMCID: PMC6712290 DOI: 10.2174/1570159x16666180926123722] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 08/31/2018] [Accepted: 09/19/2018] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD), the most common cause of dementia in adulthood, has great medical, social, and economic impact worldwide. Available treatments result in symptomatic relief, and most of them are indicated from the early stages of the disease. Therefore, there is an increasing body of research developing accurate and early diagnoses, as well as diseasemodifying therapies. OBJECTIVE Advancing the knowledge of AD physiopathological mechanisms, improving early diagnosis and developing effective treatments from omics-based biomarkers. METHODS Studies using omics technologies to detect early AD, were reviewed with a particular focus on the metabolites/lipids, micro-RNAs and proteins, which are identified as potential biomarkers in non-invasive samples. RESULTS This review summarizes recent research on metabolomics/lipidomics, epigenomics and proteomics, applied to early AD detection. Main research lines are the study of metabolites from pathways, such as lipid, amino acid and neurotransmitter metabolisms, cholesterol biosynthesis, and Krebs and urea cycles. In addition, some microRNAs and proteins (microglobulins, interleukins), related to a common network with amyloid precursor protein and tau, have been also identified as potential biomarkers. Nevertheless, the reproducibility of results among studies is not good enough and a standard methodological approach is needed in order to obtain accurate information. CONCLUSION The assessment of metabolomic/lipidomic, epigenomic and proteomic changes associated with AD to identify early biomarkers in non-invasive samples from well-defined participants groups will potentially allow the advancement in the early diagnosis and improvement of therapeutic interventions.
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Affiliation(s)
| | | | | | - Consuelo Cháfer-Pericás
- Address correspondence to this author at the Health Research Institute La Fe, Avda de Fernando Abril Martorell, 106; 46026 Valencia, Spain;Tel: +34 96 124 66 61; Fax: + 34 96 124 57 46; E-mail:
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