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Shen Y, Timsina J, Heo G, Beric A, Ali M, Wang C, Yang C, Wang Y, Western D, Liu M, Gorijala P, Budde J, Do A, Liu H, Gordon B, Llibre-Guerra JJ, Joseph-Mathurin N, Perrin RJ, Maschi D, Wyss-Coray T, Pastor P, Renton AE, Surace EI, Johnson ECB, Levey AI, Alvarez I, Levin J, Ringman JM, Allegri RF, Seyfried N, Day GS, Wu Q, Fernández MV, Tarawneh R, McDade E, Morris JC, Bateman RJ, Goate A, Ibanez L, Sung YJ, Cruchaga C. CSF proteomics identifies early changes in autosomal dominant Alzheimer's disease. Cell 2024:S0092-8674(24)00978-4. [PMID: 39332414 DOI: 10.1016/j.cell.2024.08.049] [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: 01/31/2024] [Revised: 07/02/2024] [Accepted: 08/23/2024] [Indexed: 09/29/2024]
Abstract
In this high-throughput proteomic study of autosomal dominant Alzheimer's disease (ADAD), we sought to identify early biomarkers in cerebrospinal fluid (CSF) for disease monitoring and treatment strategies. We examined CSF proteins in 286 mutation carriers (MCs) and 177 non-carriers (NCs). The developed multi-layer regression model distinguished proteins with different pseudo-trajectories between these groups. We validated our findings with independent ADAD as well as sporadic AD datasets and employed machine learning to develop and validate predictive models. Our study identified 137 proteins with distinct trajectories between MCs and NCs, including eight that changed before traditional AD biomarkers. These proteins are grouped into three stages: early stage (stress response, glutamate metabolism, neuron mitochondrial damage), middle stage (neuronal death, apoptosis), and late presymptomatic stage (microglial changes, cell communication). The predictive model revealed a six-protein subset that more effectively differentiated MCs from NCs, compared with conventional biomarkers.
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Affiliation(s)
- Yuanyuan Shen
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA
| | - Gyujin Heo
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA
| | - Aleksandra Beric
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA
| | - Muhammad Ali
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA
| | - Ciyang Wang
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA
| | - Chengran Yang
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA
| | - Yueyao Wang
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA
| | - Daniel Western
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA
| | - Menghan Liu
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA
| | - Priyanka Gorijala
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA
| | - John Budde
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA
| | - Anh Do
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA
| | - Haiyan Liu
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Brian Gordon
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jorge J Llibre-Guerra
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Nelly Joseph-Mathurin
- Mallinckrodt Institute of Radiology, Washington University St Louis, St Louis, MO 63110, USA
| | - Richard J Perrin
- Department of Pathology and Immunology, Washington University St. Louis, St. Louis, MO 63110, USA
| | - Dario Maschi
- Department of Cell Biology and Physiology, Washington University St. Louis, St. Louis, MO 63110, USA
| | - Tony Wyss-Coray
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA 94305, USA
| | - Pau Pastor
- Unit of Neurodegenerative Diseases, Department of Neurology, University Hospital Germans Trias i Pujol and The Germans Trias i Pujol Research Institute (IGTP), Badalona, Barcelona 08916, Spain
| | - Alan E Renton
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ezequiel I Surace
- Laboratory of Neurodegenerative Diseases, Institute of Neurosciences (INEU-Fleni-CONICET), Buenos Aires, Argentina
| | - Erik C B Johnson
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA 30307, USA; Department of Neurology, Emory University School of Medicine, Atlanta, GA 30307, USA
| | - Allan I Levey
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30307, USA
| | - Ignacio Alvarez
- Department of Neurology, University Hospital Mútua de Terrassa and Fundació Docència i Recerca Mútua de Terrassa, Terrassa 08221, Barcelona, Spain
| | - Johannes Levin
- Department of Neurology, LMU University Hospital, LMU Munich, Munich 80336, Germany; German Center for Neurodegenerative Diseases, site Munich, Munich 80336, Germany
| | - John M Ringman
- Alzheimer's Disease Research Center, Department of Neurology, Keck School of Medicine at USC, Los Angeles, CA 90033, USA
| | - Ricardo Francisco Allegri
- Department of Cognitive Neurology, Neuropsychology and Neuropsychiatry, FLENI, Buenos Aires, Argentina
| | - Nicholas Seyfried
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30307, USA
| | - Gregg S Day
- Department of Neurology, Mayo Clinic in Florida, Jacksonville, FL 32224, USA
| | - Qisi Wu
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | | | - Rawan Tarawneh
- The University of New Mexico, Albuquerque, NM 87131, USA
| | - Eric McDade
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Alison Goate
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Laura Ibanez
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Yun Ju Sung
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA; NeuroGenomics and Informatics, Washington University, St. Louis, MO 63110, USA; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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Weijie K, Nonaka T, Satoh K. Evaluation and Limitations of the Novel Chemiluminescent Enzyme Immunoassay Technique for Measuring Total Tau Protein in the Cerebrospinal Fluid of Patients with Human Prion Disease: A 10-Year Prospective Study (2011-2020). Diagnostics (Basel) 2024; 14:1520. [PMID: 39061657 PMCID: PMC11275853 DOI: 10.3390/diagnostics14141520] [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: 06/21/2024] [Revised: 07/05/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND Recently, the investigation of cerebrospinal fluid (CSF) biomarkers for diagnosing human prion diseases (HPD) has garnered significant attention. Reproducibility and accuracy are paramount in biomarker research, particularly in the measurement of total tau (T-tau) protein, which is a crucial diagnostic marker. Given the global impact of the coronavirus disease pandemic, the frequency of measuring this protein using one of the world's fully automated assays, chemiluminescent enzyme immunoassay (CLEA), has increased. At present, the diagnosis and monitoring of neurological diseases mainly rely on traditional methods, but their accuracy and responsiveness are limited. There is limited knowledge of the accuracy of CLEA in tau measurements. We aimed to measure T-tau protein using CLEA and to elucidate its merits and limitations. METHODS We randomly selected 60 patients with rapidly progressive dementia, using ELISA and CLEA analysis of cerebrospinal fluid specimens. Additionally, we used Western blotting to detect the presence of 14-3-3 protein and employed real-time quaking-induced conversion (RT-QuIC) assays to analyze the same set of samples. Furthermore, we examined the correlation coefficient between ELISA and CLEA results in a subset of 60 samples. Moreover, using CLEA, we evaluated the diurnal reproducibility, storage stability, dilutability, and freeze-thaw effects in three selected samples. RESULTS In 172 patients, 172 samples were extracted, with each patient providing only one sample, and a total of 88 (35 men and 53 women) tested positive for HPD in the RT-QuIC assay. In contrast, all CSF samples from the remaining 84 patients without HPD (50 men and 34 women) tested negative in the RT-QuIC assay. Both ELISA and CLEA showed perfect sensitivity and specificity (100%) in measuring T-tau protein levels. In addition, ELISA and CLEA are similar in terms of measurement sensitivity and marginal effect of detection extrema. CLEA analysis exhibited instability for certain samples with T-tau protein levels exceeding 2000 pg/mL, leading to low reproducibility during dilution analysis. CONCLUSIONS Our findings indicate that CLEA outperforms ELISA in terms of diurnal reproducibility, storage stability, and freeze-thaw effects. However, ELISA demonstrated superior performance in the dilution assay. Therefore, it is imperative to develop innovative approaches for the dilution of biomarker samples for CLEA measurements during clinical trials.
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Affiliation(s)
- Kong Weijie
- Division of Cellular and Molecular Biology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki City 852-8501, Japan; (K.W.); (T.N.)
| | - Toshiaki Nonaka
- Division of Cellular and Molecular Biology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki City 852-8501, Japan; (K.W.); (T.N.)
| | - Katsuya Satoh
- Division of Cellular and Molecular Biology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki City 852-8501, Japan; (K.W.); (T.N.)
- Department of Health Sciences, Unit of Medical and Dental Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki City 852-8523, Japan
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Shen Y, Ali M, Timsina J, Wang C, Do A, Western D, Liu M, Gorijala P, Budde J, Liu H, Gordon B, McDade E, Morris JC, Llibre-Guerra JJ, Bateman RJ, Joseph-Mathurin N, Perrin RJ, Maschi D, Wyss-Coray T, Pastor P, Goate A, Renton AE, Surace EI, Johnson ECB, Levey AI, Alvarez I, Levin J, Ringman JM, Allegri RF, Seyfried N, Day GS, Wu Q, Fernández MV, Ibanez L, Sung YJ, Cruchaga C. Systematic proteomics in Autosomal dominant Alzheimer's disease reveals decades-early changes of CSF proteins in neuronal death, and immune pathways. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.12.24301242. [PMID: 38260583 PMCID: PMC10802763 DOI: 10.1101/2024.01.12.24301242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Background To date, there is no high throughput proteomic study in the context of Autosomal Dominant Alzheimer's disease (ADAD). Here, we aimed to characterize early CSF proteome changes in ADAD and leverage them as potential biomarkers for disease monitoring and therapeutic strategies. Methods We utilized Somascan® 7K assay to quantify protein levels in the CSF from 291 mutation carriers (MCs) and 185 non-carriers (NCs). We employed a multi-layer regression model to identify proteins with different pseudo-trajectories between MCs and NCs. We replicated the results using publicly available ADAD datasets as well as proteomic data from sporadic Alzheimer's disease (sAD). To biologically contextualize the results, we performed network and pathway enrichment analyses. Machine learning was applied to create and validate predictive models. Findings We identified 125 proteins with significantly different pseudo-trajectories between MCs and NCs. Twelve proteins showed changes even before the traditional AD biomarkers (Aβ42, tau, ptau). These 125 proteins belong to three different modules that are associated with age at onset: 1) early stage module associated with stress response, glutamate metabolism, and mitochondria damage; 2) the middle stage module, enriched in neuronal death and apoptosis; and 3) the presymptomatic stage module was characterized by changes in microglia, and cell-to-cell communication processes, indicating an attempt of rebuilding and establishing new connections to maintain functionality. Machine learning identified a subset of nine proteins that can differentiate MCs from NCs better than traditional AD biomarkers (AUC>0.89). Interpretation Our findings comprehensively described early proteomic changes associated with ADAD and captured specific biological processes that happen in the early phases of the disease, fifteen to five years before clinical onset. We identified a small subset of proteins with the potentials to become therapy-monitoring biomarkers of ADAD MCs. Funding Proteomic data generation was supported by NIH: RF1AG044546.
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Kurihara M, Kondo S, Ohse K, Nojima H, Kikkawa-Saito E, Iwata A. Relationship Between Cerebrospinal Fluid Alzheimer's Disease Biomarker Values Measured via Lumipulse Assays and Conventional ELISA: Single-Center Experience and Systematic Review. J Alzheimers Dis 2024; 99:1077-1092. [PMID: 38759016 PMCID: PMC11191528 DOI: 10.3233/jad-240185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/01/2024] [Indexed: 05/19/2024]
Abstract
Background Although Lumipulse assays and conventional ELISA are strongly correlated, the precise relationship between their measured values remains undetermined. Objective To determine the relationship between Lumipulse and ELISA measurement values. Methods Patients who underwent cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarker measurements and consented to biobanking between December 2021 and June 2023 were included. The relationship between values measured via Lumipulse assays and conventional ELISA were evaluated by Passing-Bablok analyses for amyloid-β 1-42 (Aβ42), total tau (t-tau), and phospho-tau 181 (p-tau 181). Studies using both assays were systematically searched for in PubMed and summarized after quality assessment. Results Regression line slopes and intercepts were 1.41 (1.23 to 1.60) and -77.8 (-198.4 to 44.5) for Aβ42, 0.94 (0.88 to 1.01) and 98.2 (76.9 to 114.4) for t-tau, and 1.60 (1.43 to 1.75) and -21.1 (-26.9 to -15.6) for p-tau181. Spearman's correlation coefficients were 0.90, 0.95, and 0.95 for Aβ42, t-tau, and p-tau181, respectively. We identified 13 other studies that included 2,117 patients in total. Aβ42 slope varied among studies, suggesting inter-lab difference of ELISA. The slope and intercept of t-tau were approximately 1 and 0, respectively, suggesting small proportional and systematic differences. Conversely, the p-tau181 slope was significantly higher than 1, distributed between 1.5-2 in most studies, with intercepts significantly lower than 0, suggesting proportional and systematic differences. Conclusions We characterized different relationship between measurement values for each biomarker, which may be useful for understanding the differences in CSF biomarker measurement values on different platforms and for future global harmonization.
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Affiliation(s)
- Masanori Kurihara
- Department of Neurology, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
- Integrated Research Initiative for Living Well with Dementia, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | - Soichiro Kondo
- Department of Neurology, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | - Kensuke Ohse
- Integrated Research Initiative for Living Well with Dementia, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | | | | | - Atsushi Iwata
- Department of Neurology, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
- Integrated Research Initiative for Living Well with Dementia, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
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Salvadó G, Larsson V, Cody KA, Cullen NC, Jonaitis EM, Stomrud E, Kollmorgen G, Wild N, Palmqvist S, Janelidze S, Mattsson-Carlgren N, Zetterberg H, Blennow K, Johnson SC, Ossenkoppele R, Hansson O. Optimal combinations of CSF biomarkers for predicting cognitive decline and clinical conversion in cognitively unimpaired participants and mild cognitive impairment patients: A multi-cohort study. Alzheimers Dement 2023; 19:2943-2955. [PMID: 36648169 PMCID: PMC10350470 DOI: 10.1002/alz.12907] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 10/30/2022] [Accepted: 11/15/2022] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Our objective was determining the optimal combinations of cerebrospinal fluid (CSF) biomarkers for predicting disease progression in Alzheimer's disease (AD) and other neurodegenerative diseases. METHODS We included 1,983 participants from three different cohorts with longitudinal cognitive and clinical data, and baseline CSF levels of Aβ42, Aβ40, phosphorylated tau at threonine-181 (p-tau), neurofilament light (NfL), neurogranin, α-synuclein, soluble triggering receptor expressed on myeloid cells 2 (sTREM2), glial fibrillary acidic protein (GFAP), YKL-40, S100b, and interleukin 6 (IL-6) (Elecsys NeuroToolKit). RESULTS Change of modified Preclinical Alzheimer's Cognitive Composite (mPACC) in cognitively unimpaired (CU) was best predicted by p-tau/Aβ42 alone (R2 ≥ 0.31) or together with NfL (R2 = 0.25), while p-tau/Aβ42 (R2 ≥ 0.19) was sufficient to accurately predict change of the Mini-Mental State Examination (MMSE) in mild cognitive impairment (MCI) patients. P-tau/Aβ42 (AUC ≥ 0.87) and p-tau/Aβ42 together with NfL (AUC ≥ 0.75) were the best predictors of conversion to AD and all-cause dementia, respectively. DISCUSSION P-tau/Aβ42 is sufficient for predicting progression in AD, with very high accuracy. Adding NfL improves the prediction of all-cause dementia conversion and cognitive decline.
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Affiliation(s)
- Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Victoria Larsson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Karly A Cody
- Wisconsin Alzheimer’s Disease Research Center University of Wisconsin School of Medicine and Public Health Madison Wisconsin, Madison, Wisconsin, USA
| | - Nicholas C Cullen
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Erin M Jonaitis
- Wisconsin Alzheimer’s Disease Research Center University of Wisconsin School of Medicine and Public Health Madison Wisconsin, Madison, Wisconsin, USA
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | | | | | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, United Kingdom
- UK Dementia Research Institute at UCL, London, United Kingdom
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Sterling C Johnson
- Wisconsin Alzheimer’s Disease Research Center University of Wisconsin School of Medicine and Public Health Madison Wisconsin, Madison, Wisconsin, USA
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Geriatric Research, Education and Clinical Center at the William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, USA
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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A Novel Automated Chemiluminescence Method for Detecting Cerebrospinal Fluid Amyloid-Beta 1-42 and 1-40, Total Tau and Phosphorylated-Tau: Implications for Improving Diagnostic Performance in Alzheimer's Disease. Biomedicines 2022; 10:biomedicines10102667. [PMID: 36289929 PMCID: PMC9599653 DOI: 10.3390/biomedicines10102667] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/07/2022] [Accepted: 10/18/2022] [Indexed: 11/16/2022] Open
Abstract
Recently, a fully automated instrument for the detection of the Cerebrospinal Fluid (CSF) biomarker for Alzheimer’s disease (AD) (low concentration of Amyloid-beta 42 (Aβ42), high concentration of total tau (T-tau) and Phosphorylated-tau (P-tau181)), has been implemented, namely CLEIA. We conducted a comparative analysis between ELISA and CLEIA methods in order to evaluate the analytical precision and the diagnostic performance of the novel CLEIA system on 111 CSF samples. Results confirmed a robust correlation between ELISA and CLEIA methods, with an improvement of the accuracy with the new CLEIA methodology in the detection of the single biomarkers and in their ratio values. For Aβ42 regression analysis with Passing−Bablok showed a Pearson correlation coefficient r = 0.867 (0.8120; 0.907% 95% CI p < 0.0001), T-tau analysis: r = 0.968 (0.954; 0.978% 95% CI p < 0.0001) and P-tau181: r = 0.946 (0.922; 0.962 5% 95% CI p < 0.0001). The overall ROC AUC comparison between ROC in ELISA and ROC in CLEIA confirmed a more accurate ROC AUC with the new automatic method: T-tau AUC ELISA = 0.94 (95% CI 0.89; 0.99 p < 0.0001) vs. AUC CLEIA = 0.95 (95% CI 0.89; 1.00 p < 0.0001), and P-tau181 AUC ELISA = 0.91 (95% CI 0.85; 0.98 p < 0.0001) vs. AUC CLEIA = 0.98 (95% CI 0.95; 1.00 p < 0.0001). The performance of the new CLEIA method in automation is comparable and, for tau and P-tau181, even better, as compared with standard ELISA. Hopefully, in the future, automation could be useful in clinical diagnosis and also in the context of clinical studies.
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Greenberg BD, Pettigrew C, Soldan A, Wang J, Wang MC, Darrow JA, Albert MS, Moghekar A. CSF Alzheimer Disease Biomarkers: Time-Varying Relationships With MCI Symptom Onset and Associations With Age, Sex, and ApoE4. Neurology 2022; 99:e1640-e1650. [PMID: 36216518 PMCID: PMC9559947 DOI: 10.1212/wnl.0000000000200953] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 05/24/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND AND OBJECTIVES This study aimed to examine whether baseline CSF measures of Alzheimer disease (AD)-related pathology are associated with the time to onset of mild cognitive impairment (MCI) and whether these associations differ by age, sex, Apolipoprotein E (ApoE4) status, and proximal (≤7 years) vs distal (>7 years) time to symptom onset. METHODS Measures of amyloid (Aβ1-42 and Aβ1-40), phospho-tau (ptau181), and total tau (t-tau) were determined from CSF samples obtained at baseline from participants in an ongoing longitudinal project, known as the Biomarkers for Older Controls at Risk for Alzheimer Disease study (BIOCARD) study. The fully automated, Lumipulse G immunoassay was used to analyze the specimens. Cox regression models were used to examine the relationship of baseline biomarker levels with time to symptom onset of MCI and interactions with age, sex, and ApoE allelic status in subjects who progressed from normal cognition to MCI. RESULTS Analyses included 273 participants from the BIOCARD cohort, who were cognitively normal and predominantly middle-aged at baseline, and have been followed for an average of 16 years (max = 23.6). During follow-up, 94 progressed to MCI (median time to symptom onset = 6.9 years). In Cox regression models, elevated ptau181 and t-tau levels were associated with time to MCI symptom onset if it occurred within 7 years of baseline (HR 1.386 and 1.329; p = 0.009 and 0.017, respectively), while a lower Aβ42/Aβ40 ratio was associated with symptom onset if it occurred >7 years from baseline (HR 0.596, p = 0.003). There were also significant 3-way CSF × age × sex interactions for ptau181 and Aβ42/Aβ40, with follow-up analyses indicating that associations between these biomarkers and progression to MCI were stronger among men than among women, but this difference between sexes diminished with increasing age. DISCUSSION The lengthy follow-up of BIOCARD participants permitted an examination of time-varying associations between CSF AD biomarkers with MCI symptom onset and the influence of sex, baseline age, and ApoE4 genotype on these associations. These factors may inform clinical trial enrollment strategies, or trial duration and outcomes, which may use these measures as surrogate markers of treatment response.
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Affiliation(s)
- Barry D Greenberg
- From the Department of Neurology (B.D.G., C.P., A.S., J.A.D., M.S.A., A.M.), Johns Hopkins University School of Medicine; and Department of Biostatistics (J.W., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
| | - Corinne Pettigrew
- From the Department of Neurology (B.D.G., C.P., A.S., J.A.D., M.S.A., A.M.), Johns Hopkins University School of Medicine; and Department of Biostatistics (J.W., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Anja Soldan
- From the Department of Neurology (B.D.G., C.P., A.S., J.A.D., M.S.A., A.M.), Johns Hopkins University School of Medicine; and Department of Biostatistics (J.W., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Jiangxia Wang
- From the Department of Neurology (B.D.G., C.P., A.S., J.A.D., M.S.A., A.M.), Johns Hopkins University School of Medicine; and Department of Biostatistics (J.W., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Mei-Cheng Wang
- From the Department of Neurology (B.D.G., C.P., A.S., J.A.D., M.S.A., A.M.), Johns Hopkins University School of Medicine; and Department of Biostatistics (J.W., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Jacqueline A Darrow
- From the Department of Neurology (B.D.G., C.P., A.S., J.A.D., M.S.A., A.M.), Johns Hopkins University School of Medicine; and Department of Biostatistics (J.W., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Marilyn S Albert
- From the Department of Neurology (B.D.G., C.P., A.S., J.A.D., M.S.A., A.M.), Johns Hopkins University School of Medicine; and Department of Biostatistics (J.W., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Abhay Moghekar
- From the Department of Neurology (B.D.G., C.P., A.S., J.A.D., M.S.A., A.M.), Johns Hopkins University School of Medicine; and Department of Biostatistics (J.W., M.-C.W.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
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Puig-Pijoan A, García-Escobar G, Fernández-Lebrero A, Manero Borràs R, Sánchez-Benavides G, Navalpotro-Gómez I, Cascales Lahoz D, Suárez-Calvet M, Grau-Rivera O, Boltes Alandí A, Pont-Sunyer M, Ortiz-Gil J, Carrillo-Molina S, López-Villegas D, Abellán-Vidal M, Martínez-Casamitjana M, Hernández-Sánchez J, Peña-Casanova J, Roquer J, Padrós Fluvià A, Puente-Périz V. Estudio CORCOBIA: determinación de puntos de corte de biomarcadores de enfermedad de Alzheimer en LCR en una cohorte clínica. Neurologia 2022. [DOI: 10.1016/j.nrl.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022] Open
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Puig-Pijoan A, García-Escobar G, Fernández-Lebrero A, Manero-Borràs RM, Sánchez-Benavides G, Navalpotro-Gómez I, Cascales Lahoz D, Suárez-Calvet M, Grau-Rivera O, Boltes Alandí A, Pont-Sunyer MC, Ortiz-Gil J, Carrillo-Molina S, López-Villegas D, Abellán-Vidal MT, Martínez-Casamitjana MI, Hernández-Sánchez JJ, Peña-Casanova J, Roquer J, Padrós Fluvià A, Puente-Périz V. The CORCOBIA study: Cut-off points of Alzheimer's disease CSF biomarkers in a clinical cohort. Neurologia 2022:S2173-5808(22)00084-0. [PMID: 35961506 DOI: 10.1016/j.nrleng.2022.05.002] [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: 02/24/2022] [Accepted: 05/24/2022] [Indexed: 10/15/2022] Open
Abstract
INTRODUCTION The analysis of the core biomarkers of Alzheimer's Disease (AD) in the cerebrospinal fluid (CSF) is recommended in the clinical units where it is available. Because of the absence of universal validated values, the determination of specific cut-off points for each center and its population is recommended. The main objective of the CORCOBIA study was to determine the cut-off points of core AD CSF biomarkers for several centers (Parc de Salut Mar, Barcelona and Hospital General de Granollers), which work with the same reference laboratory (Laboratori de Referència de Catalunya). METHODS Prospective study including cognitively unimpaired individuals (CU, n = 42), subjects with amnestic mild cognitive impairment (aMCI, n = 35) and patients with dementia due to Alzheimer's Disease (AD, n = 48), in whom clinical and neuropsychological assessment, neuroimaging, APOE genotyping and lumbar puncture to analyse amyloid beta peptides (Aβ42, Aβ40), total tau (tTau) and phosphorylated Tau (pTau181) using the Lumipulse G600II (Fujirebio) was performed. The values of sensitivity (SE), specificity (SP), predictive values and area under the curve (AUC) were calculated, determining the cut-off point according to the Youden index by comparing the CU and AD groups. RESULTS The resulting cut-offs and their AUC were the following: Aβ42 750 pg/mL (AUC 0.809); Aβ42/Aβ40 0.062 (AUC 0.78); pTau181 69.85 pg/mL (AUC 0.81); tTau 522.0 pg/mL (AUC 0.79); Aβ42/tTau 1.76 (AUC 0.86); Aβ42/pTau181 10.25 (AUC 0.86). CONCLUSIONS The determination of cut-off points of core AD CSF biomarkers for the participating centers allows a better diagnostic accuracy. The ratio CSF Aβ42/pTau181 shows the highest AUC and better balance between sensitivity and specificity.
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Affiliation(s)
- A Puig-Pijoan
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Farmacologia Integrada i Neurociència de Sistemes, Programa de Neurociències, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain.
| | - G García-Escobar
- Farmacologia Integrada i Neurociència de Sistemes, Programa de Neurociències, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain
| | - A Fernández-Lebrero
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Farmacologia Integrada i Neurociència de Sistemes, Programa de Neurociències, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain; Barcelonaβeta Brain Research Center (BBRC), Fundació Pasqual Maragall, Barcelona, Spain
| | - R M Manero-Borràs
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Farmacologia Integrada i Neurociència de Sistemes, Programa de Neurociències, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain
| | - G Sánchez-Benavides
- Barcelonaβeta Brain Research Center (BBRC), Fundació Pasqual Maragall, Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Hospital del Mar, Barcelona, Spain
| | - I Navalpotro-Gómez
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Farmacologia Integrada i Neurociència de Sistemes, Programa de Neurociències, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain; Barcelonaβeta Brain Research Center (BBRC), Fundació Pasqual Maragall, Barcelona, Spain
| | - D Cascales Lahoz
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain
| | - M Suárez-Calvet
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Barcelonaβeta Brain Research Center (BBRC), Fundació Pasqual Maragall, Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Hospital del Mar, Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - O Grau-Rivera
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Barcelonaβeta Brain Research Center (BBRC), Fundació Pasqual Maragall, Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Hospital del Mar, Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - A Boltes Alandí
- Servei de Neurologia, Hospital General de Granollers, Granollers, Barcelona, Spain
| | - M C Pont-Sunyer
- Servei de Neurologia, Hospital General de Granollers, Granollers, Barcelona, Spain
| | - J Ortiz-Gil
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain; Unitat de Psicologia, Hospital General de Granollers, Granollers, Barcelona, Spain; Fundación para la Investigación y Docencia Maria Angustias Gimenez (FIDMAG), Sant Boi de Llobregat, Barcelona, Spain
| | - S Carrillo-Molina
- Servei de Neurologia, Hospital General de Granollers, Granollers, Barcelona, Spain; Unitat de Psicologia, Hospital General de Granollers, Granollers, Barcelona, Spain
| | - D López-Villegas
- Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Hospital del Mar, Barcelona, Spain; Centre Emili Mira, Institut de Neuropsiquiatria i Addiccions (INAD), Parc de Salut Mar, Santa Coloma de Gramenet, Barcelona, Spain
| | - M T Abellán-Vidal
- Centre Emili Mira, Institut de Neuropsiquiatria i Addiccions (INAD), Parc de Salut Mar, Santa Coloma de Gramenet, Barcelona, Spain
| | - M I Martínez-Casamitjana
- Centre Emili Mira, Institut de Neuropsiquiatria i Addiccions (INAD), Parc de Salut Mar, Santa Coloma de Gramenet, Barcelona, Spain
| | | | - J Peña-Casanova
- Farmacologia Integrada i Neurociència de Sistemes, Programa de Neurociències, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain
| | - J Roquer
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Hospital del Mar, Barcelona, Spain
| | - A Padrós Fluvià
- Laboratori de Referència de Catalunya, Sant Boi de Llobregat, Barcelona, Spain
| | - V Puente-Périz
- Unitat de Deteriorament Cognitiu i Transtorns del Moviment, Servei de Neurologia, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain; Farmacologia Integrada i Neurociència de Sistemes, Programa de Neurociències, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain.
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Orellana A, García-González P, Valero S, Montrreal L, de Rojas I, Hernández I, Rosende-Roca M, Vargas L, Tartari JP, Esteban-De Antonio E, Bojaryn U, Narvaiza L, Alarcón-Martín E, Alegret M, Alcolea D, Lleó A, Tárraga L, Pytel V, Cano A, Marquié M, Boada M, Ruiz A. Establishing In-House Cutoffs of CSF Alzheimer’s Disease Biomarkers for the AT(N) Stratification of the Alzheimer Center Barcelona Cohort. Int J Mol Sci 2022; 23:ijms23136891. [PMID: 35805894 PMCID: PMC9266894 DOI: 10.3390/ijms23136891] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/15/2022] [Accepted: 06/16/2022] [Indexed: 02/04/2023] Open
Abstract
Background: Clinical diagnosis of Alzheimer’s disease (AD) increasingly incorporates CSF biomarkers. However, due to the intrinsic variability of the immunodetection techniques used to measure these biomarkers, establishing in-house cutoffs defining the positivity/negativity of CSF biomarkers is recommended. However, the cutoffs currently published are usually reported by using cross-sectional datasets, not providing evidence about its intrinsic prognostic value when applied to real-world memory clinic cases. Methods: We quantified CSF Aβ1-42, Aβ1-40, t-Tau, and p181Tau with standard INNOTEST® ELISA and Lumipulse G® chemiluminescence enzyme immunoassay (CLEIA) performed on the automated Lumipulse G600II. Determination of cutoffs included patients clinically diagnosed with probable Alzheimer’s disease (AD, n = 37) and subjective cognitive decline subjects (SCD, n = 45), cognitively stable for 3 years and with no evidence of brain amyloidosis in 18F-Florbetaben-labeled positron emission tomography (FBB-PET). To compare both methods, a subset of samples for Aβ1-42 (n = 519), t-Tau (n = 399), p181Tau (n = 77), and Aβ1-40 (n = 44) was analyzed. Kappa agreement of single biomarkers and Aβ1-42/Aβ1-40 was evaluated in an independent group of mild cognitive impairment (MCI) and dementia patients (n = 68). Next, established cutoffs were applied to a large real-world cohort of MCI subjects with follow-up data available (n = 647). Results: Cutoff values of Aβ1-42 and t-Tau were higher for CLEIA than for ELISA and similar for p181Tau. Spearman coefficients ranged between 0.81 for Aβ1-40 and 0.96 for p181TAU. Passing–Bablok analysis showed a systematic and proportional difference for all biomarkers but only systematic for Aβ1-40. Bland–Altman analysis showed an average difference between methods in favor of CLEIA. Kappa agreement for single biomarkers was good but lower for the Aβ1-42/Aβ1-40 ratio. Using the calculated cutoffs, we were able to stratify MCI subjects into four AT(N) categories. Kaplan–Meier analyses of AT(N) categories demonstrated gradual and differential dementia conversion rates (p = 9.815−27). Multivariate Cox proportional hazard models corroborated these findings, demonstrating that the proposed AT(N) classifier has prognostic value. AT(N) categories are only modestly influenced by other known factors associated with disease progression. Conclusions: We established CLEIA and ELISA internal cutoffs to discriminate AD patients from amyloid-negative SCD individuals. The results obtained by both methods are not interchangeable but show good agreement. CLEIA is a good and faster alternative to manual ELISA for providing AT(N) classification of our patients. AT(N) categories have an impact on disease progression. AT(N) classifiers increase the certainty of the MCI prognosis, which can be instrumental in managing real-world MCI subjects.
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Affiliation(s)
- Adelina Orellana
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Pablo García-González
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Sergi Valero
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Laura Montrreal
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Itziar de Rojas
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Isabel Hernández
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Maitee Rosende-Roca
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Liliana Vargas
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Juan Pablo Tartari
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Ester Esteban-De Antonio
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Urszula Bojaryn
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Leire Narvaiza
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Emilio Alarcón-Martín
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Montserrat Alegret
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Daniel Alcolea
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, 08029 Barcelona, Spain
| | - Alberto Lleó
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, 08029 Barcelona, Spain
| | - Lluís Tárraga
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Vanesa Pytel
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
| | - Amanda Cano
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Marta Marquié
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Mercè Boada
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
| | - Agustín Ruiz
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain; (A.O.); (P.G.-G.); (S.V.); (L.M.); (I.d.R.); (I.H.); (M.R.-R.); (L.V.); (J.P.T.); (E.E.-D.A.); (U.B.); (L.N.); (E.A.-M.); (M.A.); (L.T.); (V.P.); (A.C.); (M.M.); (M.B.)
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain; (D.A.); (A.L.)
- Correspondence:
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Gobom J, Parnetti L, Rosa-Neto P, Vyhnalek M, Gauthier S, Cataldi S, Lerch O, Laczo J, Cechova K, Clarin M, Benet AI, Pascoal TA, Rahmouni N, Vandijck M, Huyck E, Le Bastard N, Stevenson J, Chamoun M, Alcolea D, Lleó A, Andreasson U, Verbeek MM, Bellomo G, Rinaldi R, Ashton N, Zetterberg H, Sheardova K, Hort J, Blennow K. Validation of the LUMIPULSE automated immunoassay for the measurement of core AD biomarkers in cerebrospinal fluid. Clin Chem Lab Med 2021; 60:207-219. [PMID: 34773730 DOI: 10.1515/cclm-2021-0651] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 11/02/2021] [Indexed: 01/12/2023]
Abstract
OBJECTIVES The core cerebrospinal fluid (CSF) biomarkers; total tau (tTau), phospho-tau (pTau), amyloid β 1-42 (Aβ 1-42), and the Aβ 1-42/Aβ 1-40 ratio have transformed Alzheimer's disease (AD) research and are today increasingly used in clinical routine laboratories as diagnostic tools. Fully automated immunoassay instruments with ready-to-use assay kits and calibrators has simplified their analysis and improved reproducibility of measurements. We evaluated the analytical performance of the fully automated immunoassay instrument LUMIPULSE G (Fujirebio) for measurement of the four core AD CSF biomarkers and determined cutpoints for AD diagnosis. METHODS Comparison of the LUMIPULSE G assays was performed with the established INNOTEST ELISAs (Fujirebio) for hTau Ag, pTau 181, β-amyloid 1-42, and with V-PLEX Plus Aβ Peptide Panel 1 (6E10) (Meso Scale Discovery) for Aβ 1-42/Aβ 1-40, as well as with a LC-MS reference method for Aβ 1-42. Intra- and inter-laboratory reproducibility was evaluated for all assays. Clinical cutpoints for Aβ 1-42, tTau, and pTau was determined by analysis of three cohorts of clinically diagnosed patients, comprising 651 CSF samples. For the Aβ 1-42/Aβ 1-40 ratio, the cutpoint was determined by mixture model analysis of 2,782 CSF samples. RESULTS The LUMIPULSE G assays showed strong correlation to all other immunoassays (r>0.93 for all assays). The repeatability (intra-laboratory) CVs ranged between 2.0 and 5.6%, with the highest variation observed for β-amyloid 1-40. The reproducibility (inter-laboratory) CVs ranged between 2.1 and 6.5%, with the highest variation observed for β-amyloid 1-42. The clinical cutpoints for AD were determined to be 409 ng/L for total tau, 50.2 ng/L for pTau 181, 526 ng/L for β-amyloid 1-42, and 0.072 for the Aβ 1-42/Aβ 1-40 ratio. CONCLUSIONS Our results suggest that the LUMIPULSE G assays for the CSF AD biomarkers are fit for purpose in clinical laboratory practice. Further, they corroborate earlier presented reference limits for the biomarkers.
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Affiliation(s)
- Johan Gobom
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Lucilla Parnetti
- Laboratory of Clinical Neurochemistry, Section of Neurology, University of Perugia, Perugia, Italy
| | - Pedro Rosa-Neto
- Department of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada.,Montreal Neurological Institute, Montreal, QC, Canada
| | - Martin Vyhnalek
- Department of Neurology, Second Medical Faculty, Charles University, Prague, Czech Republic.,Motol University Hospital, Prague, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Serge Gauthier
- Department of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada.,Montreal Neurological Institute, Montreal, QC, Canada
| | - Samuela Cataldi
- Laboratory of Clinical Neurochemistry, Section of Neurology, University of Perugia, Perugia, Italy
| | - Ondrej Lerch
- Department of Neurology, Second Medical Faculty, Charles University, Prague, Czech Republic.,Motol University Hospital, Prague, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Jan Laczo
- Department of Neurology, Second Medical Faculty, Charles University, Prague, Czech Republic.,Motol University Hospital, Prague, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Katerina Cechova
- Department of Neurology, Second Medical Faculty, Charles University, Prague, Czech Republic.,Motol University Hospital, Prague, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Marcus Clarin
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Andrea I Benet
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Translational Neuroimaging Laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Tharick A Pascoal
- Translational Neuroimaging Laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Neserine Rahmouni
- Translational Neuroimaging Laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | | | | | | | - Jenna Stevenson
- Translational Neuroimaging Laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Mira Chamoun
- Translational Neuroimaging Laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Daniel Alcolea
- Department of Neurology, Memory Unit, Hospital de la Santa Creu i Sant Pau- Biomedical Research Institute Sant Pau-Universitat Autònoma de Barcelona, Barcelona, Spain.,Centro de Investigación Biomédica en Red Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Alberto Lleó
- Department of Neurology, Memory Unit, Hospital de la Santa Creu i Sant Pau- Biomedical Research Institute Sant Pau-Universitat Autònoma de Barcelona, Barcelona, Spain.,Centro de Investigación Biomédica en Red Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Ulf Andreasson
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Marcel M Verbeek
- Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Neurology, Radboud Alzheimer Centre, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Giovanni Bellomo
- Laboratory of Clinical Neurochemistry, Section of Neurology, University of Perugia, Perugia, Italy
| | - Roberta Rinaldi
- Laboratory of Clinical Neurochemistry, Section of Neurology, University of Perugia, Perugia, Italy
| | - Nicholas Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.,King's College London, Institute of Psychiatry, Psychology & Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK.,NIHR Biomedical Research Centre for Mental Health & Biomedical Research Unit for Dementia at South London & Maudsley NHS Foundation, London, UK
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.,UK Dementia Research Institute at UCL, London, UK
| | - Katerina Sheardova
- Department of Neurology, Second Medical Faculty, Charles University, Prague, Czech Republic.,First Department of Neurology, Faculty of Medicine, Masaryk University and St. Anne's University Hospital, Brno, Czech Republic
| | - Jakub Hort
- Department of Neurology, Second Medical Faculty, Charles University, Prague, Czech Republic.,Motol University Hospital, Prague, Czech Republic.,First Department of Neurology, Faculty of Medicine, Masaryk University and St. Anne's University Hospital, Brno, Czech Republic
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
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12
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Willemse EAJ, Tijms BM, van Berckel BNM, Le Bastard N, van der Flier WM, Scheltens P, Teunissen CE. Comparing CSF amyloid-beta biomarker ratios for two automated immunoassays, Elecsys and Lumipulse, with amyloid PET status. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12182. [PMID: 33969174 PMCID: PMC8088096 DOI: 10.1002/dad2.12182] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 03/07/2021] [Accepted: 03/15/2021] [Indexed: 12/13/2022]
Abstract
INTRODUCTION We evaluated for two novel automated biomarker assays how cerebrospinal fluid (CSF) amyloid beta (Aβ)1- 42-ratios improved the concordance with amyloid positron emission tomography (PET) positivity compared to Aβ1- 42 alone. METHODS We selected 288 individuals from the Amsterdam Dementia Cohort across the Alzheimer's disease clinical spectrum when they had both CSF and amyloid PET visual read available, regardless of diagnosis. CSF Aβ1- 42, phosphorylated tau (p-tau), and total tau (t-tau) were measured with Elecsys and Lumipulse assays, and Aβ1-40 with Lumipulse. CSF cut-points were defined using receiver operating characteristic (ROC) for amyloid PET positivity. RESULTS For both Elecsys and Lumipulse the p-tau/Aβ1- 42, Aβ1- 42/Aβ1- 40, and t-tau/Aβ1- 42 ratios showed similarly good concordance with amyloid PET (Elecsys: 93,90,90%; Lumipulse: 94,92,90%) and were higher than Aβ1- 42 alone (Elecsys 85%; Lumipulse 84%). DISCUSSION Biomarker ratios p-tau/Aβ1- 42, Aβ1- 42/Aβ1- 40, t-tau/Aβ1- 42 on two automated platforms show similar optimal concordance with amyloid PET in a memory clinic cohort.
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Affiliation(s)
- Eline A. J. Willemse
- Department of Clinical ChemistryNeurochemistry LaboratoryAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Betty M. Tijms
- Department of NeurologyAlzheimer CenterAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Bart N. M. van Berckel
- Department of Radiology & Nuclear MedicineAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | | | - Wiesje M. van der Flier
- Department of NeurologyAlzheimer CenterAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
- Department of Epidemiology and BiostatisticsAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Philip Scheltens
- Department of NeurologyAlzheimer CenterAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Charlotte E. Teunissen
- Department of Clinical ChemistryNeurochemistry LaboratoryAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
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13
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Bellomo G, Indaco A, Chiasserini D, Maderna E, Paolini Paoletti F, Gaetani L, Paciotti S, Petricciuolo M, Tagliavini F, Giaccone G, Parnetti L, Di Fede G. Machine Learning Driven Profiling of Cerebrospinal Fluid Core Biomarkers in Alzheimer's Disease and Other Neurological Disorders. Front Neurosci 2021; 15:647783. [PMID: 33867925 PMCID: PMC8044304 DOI: 10.3389/fnins.2021.647783] [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: 12/30/2020] [Accepted: 03/08/2021] [Indexed: 12/19/2022] Open
Abstract
Amyloid-beta (Aβ) 42/40 ratio, tau phosphorylated at threonine-181 (p-tau), and total-tau (t-tau) are considered core biomarkers for the diagnosis of Alzheimer’s disease (AD). The use of fully automated biomarker assays has been shown to reduce the intra- and inter-laboratory variability, which is a critical factor when defining cut-off values. The calculation of cut-off values is often influenced by the composition of AD and control groups. Indeed, the clinically defined AD group may include patients affected by other forms of dementia, while the control group is often very heterogeneous due to the inclusion of subjects diagnosed with other neurological diseases (OND). In this context, unsupervised machine learning approaches may overcome these issues providing unbiased cut-off values and data-driven patient stratification according to the sole distribution of biomarkers. In this work, we took advantage of the reproducibility of automated determination of the CSF core AD biomarkers to compare two large cohorts of patients diagnosed with different neurological disorders and enrolled in two centers with established expertise in AD biomarkers. We applied an unsupervised Gaussian mixture model clustering algorithm and found that our large series of patients could be classified in six clusters according to their CSF biomarker profile, some presenting a typical AD-like profile and some a non-AD profile. By considering the frequencies of clinically defined OND and AD subjects in clusters, we subsequently computed cluster-based cut-off values for Aβ42/Aβ40, p-tau, and t-tau. This approach promises to be useful for large-scale biomarker studies aimed at providing efficient biochemical phenotyping of neurological diseases.
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Affiliation(s)
- Giovanni Bellomo
- Laboratory of Clinical Neurochemistry, Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Antonio Indaco
- Neurology 5/Neuropathology Unit, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy
| | - Davide Chiasserini
- Section of Biochemistry, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Emanuela Maderna
- Neurology 5/Neuropathology Unit, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy
| | | | - Lorenzo Gaetani
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Silvia Paciotti
- Laboratory of Clinical Neurochemistry, Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Maya Petricciuolo
- Laboratory of Clinical Neurochemistry, Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Fabrizio Tagliavini
- Neurology 5/Neuropathology Unit, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy
| | - Giorgio Giaccone
- Neurology 5/Neuropathology Unit, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy
| | - Lucilla Parnetti
- Laboratory of Clinical Neurochemistry, Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy.,Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Giuseppe Di Fede
- Neurology 5/Neuropathology Unit, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy
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14
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Keshavan A, Wellington H, Chen Z, Khatun A, Chapman M, Hart M, Cash DM, Coath W, Parker TD, Buchanan SM, Keuss SE, Harris MJ, Murray‐Smith H, Heslegrave A, Fox NC, Zetterberg H, Schott JM. Concordance of CSF measures of Alzheimer's pathology with amyloid PET status in a preclinical cohort: A comparison of Lumipulse and established immunoassays. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12131. [PMID: 33598527 PMCID: PMC7867115 DOI: 10.1002/dad2.12131] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 01/19/2023]
Abstract
INTRODUCTION We assessed the concordance of cerebrospinal fluid (CSF) amyloid beta (Aβ) and tau measured on the fully automated Lumipulse platform with pre-symptomatic Alzheimer's disease (AD) pathology on amyloid positron emission tomography (PET). METHODS In 72 individuals from the Insight 46 study, CSF Aβ40, Aβ42, total tau (t-tau), and phosphorylated tau at site 181 (p-tau181) were measured using Lumipulse, INNOTEST, and Meso Scale Discovery (MSD) assays and inter-platform Pearson correlations derived. Lumipulse Aβ42 measures were adjusted to incorporate standardization to certified reference materials. Logistic regressions and receiver operating characteristics analysis generated CSF cut-points optimizing concordance with 18F-florbetapir amyloid PET status (n = 63). RESULTS Measurements of CSF Aβ, p-tau181, and their ratios correlated well across platforms (r 0.84 to 0.94, P < .0001); those of t-tau and t-tau/Aβ42 correlated moderately (r 0.57 to 0.79, P < .0001). The best concordance with amyloid PET (100% sensitivity and 94% specificity) was afforded by cut-points of 0.075 for Lumipulse Aβ42/Aβ40, 0.087 for MSD Aβ42/Aβ40 and 17.3 for Lumipulse Aβ42/p-tau181. DISCUSSION The Lumipulse platform provides comparable sensitivity and specificity to established CSF immunoassays in identifying pre-symptomatic AD pathology.
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Affiliation(s)
- Ashvini Keshavan
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Henrietta Wellington
- UK Dementia Research Institute Fluid Biomarkers LaboratoryUK DRI at University College LondonLondonUK
| | - Zhongbo Chen
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Ayesha Khatun
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Miles Chapman
- Neuroimmunology and CSF LaboratoryNational Hospital for Neurology and NeurosurgeryLondonUK
| | - Melanie Hart
- Neuroimmunology and CSF LaboratoryNational Hospital for Neurology and NeurosurgeryLondonUK
- Department of NeuroinflammationUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - David M. Cash
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - William Coath
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Thomas D. Parker
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Sarah M. Buchanan
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Sarah E. Keuss
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Matthew J. Harris
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Heidi Murray‐Smith
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Amanda Heslegrave
- UK Dementia Research Institute Fluid Biomarkers LaboratoryUK DRI at University College LondonLondonUK
| | - Nick C. Fox
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Henrik Zetterberg
- UK Dementia Research Institute Fluid Biomarkers LaboratoryUK DRI at University College LondonLondonUK
- Clinical Neurochemistry Laboratory, Department of Psychiatry and NeurochemistryInstitute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University HospitalMölndalSweden
| | - Jonathan M Schott
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
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15
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Trelle AN, Carr VA, Wilson EN, Swarovski MS, Hunt MP, Toueg TN, Tran TT, Channappa D, Corso NK, Thieu MK, Jayakumar M, Nadiadwala A, Guo W, Tanner NJ, Bernstein JD, Litovsky CP, Guerin SA, Khazenzon AM, Harrison MB, Rutt BK, Deutsch GK, Chin FT, Davidzon GA, Hall JN, Sha SJ, Fredericks CA, Andreasson KI, Kerchner GA, Wagner AD, Mormino EC. Association of CSF Biomarkers With Hippocampal-Dependent Memory in Preclinical Alzheimer Disease. Neurology 2021; 96:e1470-e1481. [PMID: 33408146 DOI: 10.1212/wnl.0000000000011477] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 12/02/2020] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE To determine whether memory tasks with demonstrated sensitivity to hippocampal function can detect variance related to preclinical Alzheimer disease (AD) biomarkers, we examined associations between performance in 3 memory tasks and CSF β-amyloid (Aβ)42/Aβ40 and phosopho-tau181 (p-tau181) in cognitively unimpaired older adults (CU). METHODS CU enrolled in the Stanford Aging and Memory Study (n = 153; age 68.78 ± 5.81 years; 94 female) completed a lumbar puncture and memory assessments. CSF Aβ42, Aβ40, and p-tau181 were measured with the automated Lumipulse G system in a single-batch analysis. Episodic memory was assayed using a standardized delayed recall composite, paired associate (word-picture) cued recall, and a mnemonic discrimination task that involves discrimination between studied "target" objects, novel "foil" objects, and perceptually similar "lure" objects. Analyses examined cross-sectional relationships among memory performance, age, and CSF measures, controlling for sex and education. RESULTS Age and lower Aβ42/Aβ40 were independently associated with elevated p-tau181. Age, Aβ42/Aβ40, and p-tau181 were each associated with (1) poorer associative memory and (2) diminished improvement in mnemonic discrimination performance across levels of decreased task difficulty (i.e., target-lure similarity). P-tau mediated the effect of Aβ42/Aβ40 on memory. Relationships between CSF proteins and delayed recall were similar but nonsignificant. CSF Aβ42 was not significantly associated with p-tau181 or memory. CONCLUSIONS Tests designed to tax hippocampal function are sensitive to subtle individual differences in memory among CU and correlate with early AD-associated biomarker changes in CSF. These tests may offer utility for identifying CU with preclinical AD pathology.
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Affiliation(s)
- Alexandra N Trelle
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA.
| | - Valerie A Carr
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Edward N Wilson
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Michelle S Swarovski
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Madison P Hunt
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Tyler N Toueg
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Tammy T Tran
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Divya Channappa
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Nicole K Corso
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Monica K Thieu
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Manasi Jayakumar
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Ayesha Nadiadwala
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Wanjia Guo
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Natalie J Tanner
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Jeffrey D Bernstein
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Celia P Litovsky
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Scott A Guerin
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Anna M Khazenzon
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Marc B Harrison
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Brian K Rutt
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Gayle K Deutsch
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Frederick T Chin
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Guido A Davidzon
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Jacob N Hall
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Sharon J Sha
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Carolyn A Fredericks
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Katrin I Andreasson
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Geoffrey A Kerchner
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Anthony D Wagner
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
| | - Elizabeth C Mormino
- From the Department of Psychology (A.N.T., V.A.C., M.P.H., T.T.T., M.K.T., M.J., W.G., N.J.T., J.D.B., C.P.L., S.A.G., A.M.K., M.B.H., A.D.W.), Stanford University; and Department of Neurology and Neurological Sciences (E.N.W., M.S.S., T.N.T., D.C., N.K.C., A.N., G.K.D., J.N.H., S.J.S., C.A.F., K.I.A., G.A.K., E.C.M.) and Division of Nuclear Medicine & Molecular Imaging Division, Department of Radiology (B.K.R., F.T.C., G.A.D.), Stanford Medical School, CA
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Gaetani L, Paolini Paoletti F, Bellomo G, Mancini A, Simoni S, Di Filippo M, Parnetti L. CSF and Blood Biomarkers in Neuroinflammatory and Neurodegenerative Diseases: Implications for Treatment. Trends Pharmacol Sci 2020; 41:1023-1037. [PMID: 33127098 DOI: 10.1016/j.tips.2020.09.011] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 09/25/2020] [Accepted: 09/30/2020] [Indexed: 12/11/2022]
Abstract
Neuroinflammatory and neurodegenerative diseases are characterized by the interplay of a number of molecular pathways that can be assessed through biofluids, especially cerebrospinal fluid and blood. Accordingly, the definition and classification of these disorders will move from clinical and pathological to biological criteria. The consequences of this biomarker-based diagnostic and prognostic approach are highly relevant to the field of drug development. Indeed, in view of the availability of disease-modifying drugs, fluid biomarkers offer a unique opportunity for improving the quality and applicability of results from clinical trials. Herein, we discuss the benefits of using fluid biomarkers for patient stratification, target engagement, and outcome assessment, as well as the most recent developments in neuroinflammatory and neurodegenerative diseases.
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Affiliation(s)
- Lorenzo Gaetani
- Section of Neurology, Department of Medicine, University of Perugia, Perugia, Italy
| | | | - Giovanni Bellomo
- Section of Neurology, Department of Medicine, University of Perugia, Perugia, Italy
| | - Andrea Mancini
- Section of Neurology, Department of Medicine, University of Perugia, Perugia, Italy
| | - Simone Simoni
- Section of Neurology, Department of Medicine, University of Perugia, Perugia, Italy
| | | | - Lucilla Parnetti
- Section of Neurology, Department of Medicine, University of Perugia, Perugia, Italy.
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Bellomo G, Cataldi S, Paciotti S, Paolini Paoletti F, Chiasserini D, Parnetti L. Measurement of CSF core Alzheimer disease biomarkers for routine clinical diagnosis: do fresh vs frozen samples differ? ALZHEIMERS RESEARCH & THERAPY 2020; 12:121. [PMID: 32993776 PMCID: PMC7526419 DOI: 10.1186/s13195-020-00689-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 09/15/2020] [Indexed: 12/13/2022]
Abstract
Background Cerebrospinal fluid (CSF) amyloid-beta (Aβ) 42/40 ratio, threonine-181-phosphorylated-tau (p-tau), and total-tau (t-tau) represent core biomarkers of Alzheimer disease (AD). The recent availability of automated platforms has represented a significant achievement for reducing the pre-analytical variability of these determinations in clinical setting. With respect to classical manual ELISAs, these platforms give us also the possibility to measure any single sample and to get the result within approximately 30 min. So far, reference values have been calculated from measurements obtained in frozen samples. In this work, we wanted to check if the values obtained in fresh CSF samples differ from those obtained in frozen samples, since this issue is mandatory in routine diagnostic work. Methods Fifty-eight consecutive CSF samples have been analyzed immediately after lumbar puncture and after 1-month deep freezing (− 80 °C). As an automated platform, we used Lumipulse G600-II (Fujirebio Inc.). Both the fresh and the frozen aliquots were analyzed in their storage tubes. Results In fresh samples, a mean increase of Aβ40 (6%), Aβ42 (2%), p-tau (2%), and t-tau (4%) was observed as compared to frozen samples, whereas a slight decrease was observed for Aβ42/Aβ40 ratio (4%), due to the higher deviation of Aβ40 in fresh samples compared to Aβ42. These differences are significant for Aβ40, Aβ42/Aβ40 ratio, p-tau, and t-tau. Nevertheless, the Aβ42/Aβ40 ratio showed a lower variability (smaller standard deviation of relative differences) with respect to Aβ42. With respect to the AD profile according to the A/T/(N) criteria for AD diagnosis, no significant changes in classification were observed when comparing results obtained in fresh vs frozen samples. Conclusions Small but significant differences have been found for Aβ40, Aβ42/Aβ40 ratio, p-tau, and t-tau in fresh vs frozen samples. Importantly, these differences did not imply a modification in the A/T/(N) classification system. In order to know if different cutoffs for fresh and frozen samples are required, larger, multi-center investigations are needed.
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Affiliation(s)
- Giovanni Bellomo
- Laboratory of Clinical Neurochemistry, Section of Neurology, University of Perugia, Piazzale Lucio Severi 1/8, 06132, Perugia, PG, Italy
| | - Samuela Cataldi
- Laboratory of Clinical Neurochemistry, Section of Neurology, University of Perugia, Piazzale Lucio Severi 1/8, 06132, Perugia, PG, Italy
| | - Silvia Paciotti
- Laboratory of Clinical Neurochemistry, Section of Neurology, University of Perugia, Piazzale Lucio Severi 1/8, 06132, Perugia, PG, Italy.,Department of Experimental Medicine, Section of Physiology and Biochemistry, University of Perugia, Piazza Lucio Severi 1/8, 06132, Perugia, PG, Italy
| | | | - Davide Chiasserini
- Department of Experimental Medicine, Section of Physiology and Biochemistry, University of Perugia, Piazza Lucio Severi 1/8, 06132, Perugia, PG, Italy
| | - Lucilla Parnetti
- Laboratory of Clinical Neurochemistry, Section of Neurology, University of Perugia, Piazzale Lucio Severi 1/8, 06132, Perugia, PG, Italy. .,Section of Neurology, University of Perugia, Piazzale Lucio Severi 1/8, 06132, Perugia, PG, Italy.
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Keshavan A, Wellington H, Chen Z, Khatun A, Chapman M, Hart M, Cash DM, Coath W, Parker TD, Buchanan SM, Keuss SE, Harris MJ, Murray‐Smith H, Heslegrave A, Fox NC, Zetterberg H, Schott JM. Concordance of CSF measures of Alzheimer's pathology with amyloid PET status in a preclinical cohort: A comparison of Lumipulse and established immunoassays. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12097. [PMID: 32999915 PMCID: PMC7503103 DOI: 10.1002/dad2.12097] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 07/27/2020] [Accepted: 07/30/2020] [Indexed: 12/23/2022]
Abstract
INTRODUCTION We assessed the concordance of cerebrospinal fluid (CSF) amyloid beta (Aβ) and tau measured on the fully automated Lumipulse platform with pre-symptomatic Alzheimer's disease (AD) pathology on amyloid positron emission tomography (PET). METHODS In 72 individuals from the Insight 46 study, CSF Aβ40, Aβ42, total tau (t-tau), and phosphorylated tau at site 181 (p-tau181) were measured using Lumipulse, INNOTEST, and Meso Scale Discovery (MSD) assays, and inter-platform Pearson correlations were derived. Logistic regressions and receiver-operating characteristic analysis generated CSF cut-points optimizing concordance with 18F-florbetapir amyloid PET status (n = 63). RESULTS Measurements of CSF Aβ, p-tau181, and their ratios correlated well across platforms (r 0.84-.94, P < .0001); those of t-tau and t-tau/Aβ42 correlated moderately (r 0.57-0.79, P < .0001). The best concordance with amyloid PET (100% sensitivity and 94% specificity) was afforded by cut-points of 0.110 for Lumipulse Aβ42/Aβ40, 0.087 for MSD Aβ42/Aβ40, and 25.3 for Lumipulse Aβ42/p-tau181. DISCUSSION The Lumipulse platform provides comparable sensitivity and specificity to established CSF immunoassays in identifying pre-symptomatic AD pathology.
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Affiliation(s)
- Ashvini Keshavan
- Dementia Research CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Henrietta Wellington
- UK Dementia Research Institute Fluid Biomarkers LaboratoryUK DRI at University College LondonLondonUK
| | - Zhongbo Chen
- Dementia Research CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Ayesha Khatun
- Dementia Research CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Miles Chapman
- Neuroimmunology and CSF LaboratoryNational Hospital for Neurology and NeurosurgeryLondonUK
| | - Melanie Hart
- Neuroimmunology and CSF LaboratoryNational Hospital for Neurology and NeurosurgeryLondonUK
- Department of NeuroinflammationUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - David M. Cash
- Dementia Research CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - William Coath
- Dementia Research CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Thomas D. Parker
- Dementia Research CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Sarah M. Buchanan
- Dementia Research CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Sarah E. Keuss
- Dementia Research CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Matthew J. Harris
- Dementia Research CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Heidi Murray‐Smith
- Dementia Research CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Amanda Heslegrave
- UK Dementia Research Institute Fluid Biomarkers LaboratoryUK DRI at University College LondonLondonUK
| | - Nick C. Fox
- Dementia Research CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Henrik Zetterberg
- UK Dementia Research Institute Fluid Biomarkers LaboratoryUK DRI at University College LondonLondonUK
- Clinical Neurochemistry LaboratoryDepartment of Psychiatry and NeurochemistryInstitute of Neuroscience and PhysiologyThe Sahlgrenska Academy at University of GothenburgSahlgrenska University HospitalMölndalSweden
| | - Jonathan M. Schott
- Dementia Research CentreUCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
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Agnello L, Piccoli T, Vidali M, Cuffaro L, Lo Sasso B, Iacolino G, Giglio VR, Lupo F, Alongi P, Bivona G, Ciaccio M. Diagnostic accuracy of cerebrospinal fluid biomarkers measured by chemiluminescent enzyme immunoassay for Alzheimer disease diagnosis. Scandinavian Journal of Clinical and Laboratory Investigation 2020; 80:313-317. [PMID: 32255379 DOI: 10.1080/00365513.2020.1740939] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
In the last decades, an important role of cerebrospinal fluid (CSF) biomarkers for Alzheimer disease (AD) diagnosis has emerged. The evaluation of the triad consisting of 42 aminoacid-long amyloid-beta peptide (Aβ42), total Tau (tTau) and Tau phosphorylated at threonine 181 (pTau) have been recently integrated into the research diagnostic criteria of AD. For a long time, the enzyme-linked immunosorbent assay (ELISA) has represented the most commonly used method for the measurement of CSF biomarkers levels. This study aimed to assess the diagnostic accuracy of CSF biomarkers, namely Aβ42, tTau and pTau and their ratio, measured by fully automated CLEIA assay (Lumipulse). We included 96 patients clinically diagnosed as AD (48) and non-AD (48). All CSF biomarkers levels were measured on Lumipulse G1200 fully automated platform (Fujirebio Inc. Europe, Gent, Belgium). Aβ42 levels, 42/40 ratio, 42/tTau ratio, 42/PTau ratio were significantly reduced, and tTau and PTau levels were significantly increased in AD patients in comparison with non-AD patients. The receiving operator curve (ROC) analysis showed good diagnostic accuracy of all CSF biomarkers and their ratios for discriminating AD patients from non-AD patients, with 42/40 ratio having the best AUC (0.724, 95%CI 0.619-0.828; p < 0.001). Our findings support the use of CSF biomarkers measured by CLEIA method on a fully automated platform for AD diagnosis.
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Affiliation(s)
- Luisa Agnello
- Department of Biomedicine, Neurosciences and Advanced Diagnostics, Institute of Clinical Biochemistry, Clinical Molecular Medicine and Laboratory Medicine, University of Palermo, Palermo, Italy
| | - Tommaso Piccoli
- Unit of Neurology, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Matteo Vidali
- Unit of Clinical Chemistry, Maggiore della Carità Hospital, Novara, Italy
| | - Luca Cuffaro
- Unit of Neurology, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Bruna Lo Sasso
- Department of Biomedicine, Neurosciences and Advanced Diagnostics, Institute of Clinical Biochemistry, Clinical Molecular Medicine and Laboratory Medicine, University of Palermo, Palermo, Italy
| | - Giorgia Iacolino
- Department of Biomedicine, Neurosciences and Advanced Diagnostics, Institute of Clinical Biochemistry, Clinical Molecular Medicine and Laboratory Medicine, University of Palermo, Palermo, Italy
| | - Vincenza Rosaria Giglio
- Department of Biomedicine, Neurosciences and Advanced Diagnostics, Institute of Clinical Biochemistry, Clinical Molecular Medicine and Laboratory Medicine, University of Palermo, Palermo, Italy
| | - Federica Lupo
- Unit of Neurology, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Pierpaolo Alongi
- Unit of Neurology, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Giulia Bivona
- Department of Biomedicine, Neurosciences and Advanced Diagnostics, Institute of Clinical Biochemistry, Clinical Molecular Medicine and Laboratory Medicine, University of Palermo, Palermo, Italy
| | - Marcello Ciaccio
- Department of Biomedicine, Neurosciences and Advanced Diagnostics, Institute of Clinical Biochemistry, Clinical Molecular Medicine and Laboratory Medicine, University of Palermo, Palermo, Italy.,Department of Laboratory Medicine, AOUP "P. Giaccone", Palermo, Italy
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20
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Wilson EN, Swarovski MS, Linortner P, Shahid M, Zuckerman AJ, Wang Q, Channappa D, Minhas PS, Mhatre SD, Plowey ED, Quinn JF, Zabetian CP, Tian L, Longo FM, Cholerton B, Montine TJ, Poston KL, Andreasson KI. Soluble TREM2 is elevated in Parkinson's disease subgroups with increased CSF tau. Brain 2020; 143:932-943. [PMID: 32065223 PMCID: PMC7089668 DOI: 10.1093/brain/awaa021] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 10/26/2019] [Accepted: 12/11/2019] [Indexed: 12/16/2022] Open
Abstract
Parkinson's disease is the second most common neurodegenerative disease after Alzheimer's disease and affects 1% of the population above 60 years old. Although Parkinson's disease commonly manifests with motor symptoms, a majority of patients with Parkinson's disease subsequently develop cognitive impairment, which often progresses to dementia, a major cause of morbidity and disability. Parkinson's disease is characterized by α-synuclein accumulation that frequently associates with amyloid-β and tau fibrils, the hallmarks of Alzheimer's disease neuropathological changes; this co-occurrence suggests that onset of cognitive decline in Parkinson's disease may be associated with appearance of pathological amyloid-β and/or tau. Recent studies have highlighted the appearance of the soluble form of the triggering receptor expressed on myeloid cells 2 (sTREM2) receptor in CSF during development of Alzheimer's disease. Given the known association of microglial activation with advancing Parkinson's disease, we investigated whether CSF and/or plasma sTREM2 differed between CSF biomarker-defined Parkinson's disease participant subgroups. In this cross-sectional study, we examined 165 participants consisting of 17 cognitively normal elderly subjects, 45 patients with Parkinson's disease with no cognitive impairment, 86 with mild cognitive impairment, and 17 with dementia. Stratification of subjects by CSF amyloid-β and tau levels revealed that CSF sTREM2 concentrations were elevated in Parkinson's disease subgroups with a positive tau CSF biomarker signature, but not in Parkinson's disease subgroups with a positive CSF amyloid-β biomarker signature. These findings indicate that CSF sTREM2 could serve as a surrogate immune biomarker of neuronal injury in Parkinson's disease.
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Affiliation(s)
- Edward N Wilson
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Michelle S Swarovski
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Patricia Linortner
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Marian Shahid
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Abigail J Zuckerman
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Qian Wang
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Divya Channappa
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
- Pathology, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Paras S Minhas
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Siddhita D Mhatre
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Edward D Plowey
- Pathology, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Joseph F Quinn
- Neurology, Oregon Health and Sciences University, Portland, OR, USA
- Neurology, Portland VA Medical Center, Portland, OR, USA
| | - Cyrus P Zabetian
- VA Puget Sound Health Care System, Seattle, WA, USA
- Neurology, University of Washington, Seattle, WA, USA
| | - Lu Tian
- Biomedical Data Science and Statistics, Stanford University, Stanford, CA, USA
| | - Frank M Longo
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Brenna Cholerton
- Pathology, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Thomas J Montine
- Pathology, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Kathleen L Poston
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
- Neurosurgery, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Katrin I Andreasson
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
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21
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Leitão MJ, Silva-Spínola A, Santana I, Olmedo V, Nadal A, Le Bastard N, Baldeiras I. Clinical validation of the Lumipulse G cerebrospinal fluid assays for routine diagnosis of Alzheimer's disease. Alzheimers Res Ther 2019; 11:91. [PMID: 31759396 PMCID: PMC6875031 DOI: 10.1186/s13195-019-0550-8] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 10/28/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND Ongoing efforts within the Alzheimer's disease (AD) field have focused on improving the intra- and inter-laboratory variability for cerebrospinal fluid (CSF) biomarkers. Fully automated assays offer the possibility to eliminate sample manipulation steps and are expected to contribute to this improvement. Recently, fully automated chemiluminescence enzyme immunoassays for the quantification of all four AD biomarkers in CSF became available. The aims of this study were to (i) evaluate the analytical performance of the Lumipulse G β-Amyloid 1-42 (restandardized to Certified Reference Materials), β-Amyloid 1-40, total Tau, and pTau 181 assays on the fully automated LUMIPULSE G600II; (ii) compare CSF biomarker results of the Lumipulse G assays with the established manual ELISA assays (INNOTEST®) from the same company (Fujirebio); and (iii) establish cut-off values and the clinical performance of the Lumipulse G assays for AD diagnosis. METHODS Intra- and inter-assay variation was assessed in CSF samples with low, medium, and high concentrations of each parameter. Method comparison and clinical evaluation were performed on 40 neurological controls (NC) and 80 patients with a diagnosis of probable AD supported by a follow-up ≥ 3 years and/or positive amyloid PET imaging. A small validation cohort of 10 NC and 20 AD patients was also included to validate the cut-off values obtained on the training cohort. RESULTS The maximal observed intra-assay and inter-assay coefficients of variation (CVs) were 3.25% and 5.50%, respectively. Method comparisons revealed correlation coefficients ranging from 0.89 (for Aβ40) to 0.98 (for t-Tau), with those for Aβ42 (0.93) and p-Tau (0.94) in-between. ROC curve analysis showed area under the curve values consistently above 0.85 for individual biomarkers other than Aβ40, and with the Aβ42/40, Aβ42/t-Tau, and Aβ42/p-Tau ratios outperforming Aβ42. Validation of the cut-off values in the independent cohort showed a sensitivity ranging from 75 to 95% and a specificity of 100%. The overall percentage of agreement between Lumipulse and INNOTEST was very high (> 87.5%). CONCLUSIONS The Lumipulse G assays show a very good analytical performance that makes them well-suited for CSF clinical routine measurements. The good clinical concordance between the Lumipulse G and INNOTEST assays facilitates the implementation of the new method in routine practice.
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Affiliation(s)
- Maria João Leitão
- Laboratory of Neurochemistry, Neurology Department, Centro Hospitalar e Universitário de Coimbra, 3000-075 Coimbra, Portugal
- Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal
| | - Anuschka Silva-Spínola
- Laboratory of Neurochemistry, Neurology Department, Centro Hospitalar e Universitário de Coimbra, 3000-075 Coimbra, Portugal
- Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal
| | - Isabel Santana
- Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal
- Dementia Clinic, Neurology Department, Centro Hospitalar e Universitário de Coimbra, 3000-075 Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, 3000-548 Coimbra, Portugal
| | | | | | | | - Inês Baldeiras
- Laboratory of Neurochemistry, Neurology Department, Centro Hospitalar e Universitário de Coimbra, 3000-075 Coimbra, Portugal
- Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, 3000-548 Coimbra, Portugal
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