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The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence. Biomedicines 2022; 10:biomedicines10020315. [PMID: 35203524 PMCID: PMC8869403 DOI: 10.3390/biomedicines10020315] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 02/05/2023] Open
Abstract
Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.
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Leuzy A, Mattsson‐Carlgren N, Palmqvist S, Janelidze S, Dage JL, Hansson O. Blood-based biomarkers for Alzheimer's disease. EMBO Mol Med 2022; 14:e14408. [PMID: 34859598 PMCID: PMC8749476 DOI: 10.15252/emmm.202114408] [Citation(s) in RCA: 137] [Impact Index Per Article: 68.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 11/02/2021] [Accepted: 11/05/2021] [Indexed: 12/01/2022] Open
Abstract
Neurodegenerative disorders such as Alzheimer's disease (AD) represent a mounting public health challenge. As these diseases are difficult to diagnose clinically, biomarkers of underlying pathophysiology are playing an ever-increasing role in research, clinical trials, and in the clinical work-up of patients. Though cerebrospinal fluid (CSF) and positron emission tomography (PET)-based measures are available, their use is not widespread due to limitations, including high costs and perceived invasiveness. As a result of rapid advances in the development of ultra-sensitive assays, the levels of pathological brain- and AD-related proteins can now be measured in blood, with recent work showing promising results. Plasma P-tau appears to be the best candidate marker during symptomatic AD (i.e., prodromal AD and AD dementia) and preclinical AD when combined with Aβ42/Aβ40. Though not AD-specific, blood NfL appears promising for the detection of neurodegeneration and could potentially be used to detect the effects of disease-modifying therapies. This review provides an overview of the progress achieved thus far using AD blood-based biomarkers, highlighting key areas of application and unmet challenges.
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Affiliation(s)
- Antoine Leuzy
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
| | - Niklas Mattsson‐Carlgren
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
- Department of NeurologySkåne University HospitalLundSweden
- Wallenberg Centre for Molecular MedicineLund UniversityLundSweden
| | - Sebastian Palmqvist
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
- Memory ClinicSkåne University HospitalLundSweden
| | - Shorena Janelidze
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
| | - Jeffrey L Dage
- Stark Neuroscience Research InstituteIndiana University School of MedicineIndianapolisINUSA
| | - Oskar Hansson
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityMalmöSweden
- Memory ClinicSkåne University HospitalLundSweden
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Ingala S, van Maurik IS, Altomare D, Wurm R, Dicks E, van Schijndel RA, Zwan M, Bouwman F, Schoonenboom N, Boelaarts L, Roks G, van Marum R, van Harten B, van Uden I, Claus J, Wottschel V, Vrenken H, Wattjes MP, van der Flier WM, Barkhof F. Clinical applicability of quantitative atrophy measures on MRI in patients suspected of Alzheimer's disease. Eur Radiol 2022; 32:7789-7799. [PMID: 35639148 PMCID: PMC9668763 DOI: 10.1007/s00330-021-08503-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 11/03/2021] [Accepted: 12/01/2021] [Indexed: 01/03/2023]
Abstract
OBJECTIVES Neurodegeneration in suspected Alzheimer's disease can be determined using visual rating or quantitative volumetric assessments. We examined the feasibility of volumetric measurements of gray matter (GMV) and hippocampal volume (HCV) and compared their diagnostic performance with visual rating scales in academic and non-academic memory clinics. MATERIALS AND METHODS We included 231 patients attending local memory clinics (LMC) in the Netherlands and 501 of the academic Amsterdam Dementia Cohort (ADC). MRI scans were acquired using local protocols, including a T1-weighted sequence. Quantification of GMV and HCV was performed using FSL and FreeSurfer. Medial temporal atrophy and global atrophy were assessed with visual rating scales. ROC curves were derived to determine which measure discriminated best between cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's dementia (AD). RESULTS Patients attending LMC (age 70.9 ± 8.9 years; 47% females; 19% CN; 34% MCI; 47% AD) were older, had more cerebrovascular pathology, and had lower GMV and HCV compared to those of the ADC (age 64.9 ± 8.2 years; 42% females; 35% CN, 43% MCI, 22% AD). While visual ratings were feasible in > 95% of scans in both cohorts, quantification was achieved in 94-98% of ADC, but only 68-85% of LMC scans, depending on the software. Visual ratings and volumetric outcomes performed similarly in discriminating CN vs AD in both cohorts. CONCLUSION In clinical settings, quantification of GM and hippocampal atrophy currently fails in up to one-third of scans, probably due to lack of standardized acquisition protocols. Diagnostic accuracy is similar for volumetric measures and visual rating scales, making the latter suited for clinical practice. In a real-life clinical setting, volumetric assessment of MRI scans in dementia patients may require acquisition protocol optimization and does not outperform visual rating scales. KEY POINTS • In a real-life clinical setting, the diagnostic performance of visual rating scales is similar to that of automatic volumetric quantification and may be sufficient to distinguish Alzheimer's disease groups. • Volumetric assessment of gray matter and hippocampal volumes from MRI scans of patients attending non-academic memory clinics fails in up to 32% of cases. • Clinical MR acquisition protocols should be optimized to improve the output of quantitative software for segmentation of Alzheimer's disease-specific outcomes.
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Affiliation(s)
- Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands ,Department of Radiology and Nuclear Medicine, Noordwest Hospital Group, Alkmaar, The Netherlands
| | - Ingrid S. van Maurik
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands ,Department of Epidemiology and Data Science, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Daniele Altomare
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands ,Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland ,Memory Clinic, University Hospitals of Geneva, Geneva, Switzerland
| | - Raphael Wurm
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands ,Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Ellen Dicks
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Ronald A. van Schijndel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands
| | - Marissa Zwan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Femke Bouwman
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Niki Schoonenboom
- Geriatric Department, Noordwest Ziekenhuis Groep, Alkmaar, The Netherlands
| | - Leo Boelaarts
- Geriatric Department, Noordwest Ziekenhuis Groep, Alkmaar, The Netherlands
| | - Gerwin Roks
- Department of Neurology, Elisabeth-TweeSteden Ziekenhuis, Tilburg, The Netherlands
| | - Rob van Marum
- Department of Geriatrics, Jeroen Bosch Hospital, ‘S-Hertogenbosch, The Netherlands ,Department of Family Medicine and Elderly Care Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Barbera van Harten
- Department of Neurology, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands
| | - Inge van Uden
- Department of Neurology, Catharina Hospital, Eindhoven, The Netherlands
| | - Jules Claus
- Department of Neurology, Tergooi Hospital, Blaricum, The Netherlands
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands
| | - Mike P. Wattjes
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands ,Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands ,Department of Epidemiology and Data Science, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Location VUmc, PO Box 7057, 1007 MB Amsterdam, The Netherlands ,Institutes of Neurology and Healthcare Engineering, UCL, London, UK
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Study on Adjuvant Medication for Patients with Mild Cognitive Impairment Based on VR Technology and Health Education. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:1187704. [PMID: 34949967 PMCID: PMC8670913 DOI: 10.1155/2021/1187704] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 11/18/2021] [Accepted: 11/24/2021] [Indexed: 12/02/2022]
Abstract
In order to improve the efficiency of auxiliary medication for patients with mild cognitive impairment, this paper proposes a method based on VR technology and health education. Sixty elderly patients with COPD and MCI admitted to a hospital from January 2019 to February 2020 were randomly divided into a control group and study group, with 50 cases in each group. On the basis of conventional drug therapy, health education, and respiratory muscle training, patients in the control group received routine lung rehabilitation training, while patients in the study group received lung rehabilitation training using the BioMaster virtual scene interactive rehabilitation training system. Both groups continued training for 12 weeks. Lung function indexes, 6-minute walking distance, COPD assessment test (CAT) score, and Montreal Cognitive Function Assessment Scale (MoCA) score were compared between the 2 groups before training and 4, 8, and 12 weeks after training. The experimental results show that, in the study group, the percentage of FEV1 in the predicted value at 8 weeks after training, the percentage of FEV1 in the predicted value at 12 weeks after training, and FEV1/FVC were higher than those in the control group (P < 0.05). There was no significant difference in 6-minute walking distance, CAT score, and MoCA score between the two groups before training (P > 0.05). Twelve weeks after training, patients in the study group had a longer 6-minute walking distance, a lower CAT score, and a higher MoCA score than those in the control group (P < 0.05). It is proved that the application of virtual reality technology in lung rehabilitation training of elderly COPD patients with MCI is effective, which can effectively improve the lung function, cognitive function, and exercise tolerance of the patients and reduce the symptoms of dyspnea and the efficiency of medication.
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van Gils AM, Visser LN, Hendriksen HM, Georges J, Muller M, Bouwman FH, van der Flier WM, Rhodius-Meester HF. Assessing the Views of Professionals, Patients, and Care Partners Concerning the Use of Computer Tools in Memory Clinics: International Survey Study. JMIR Form Res 2021; 5:e31053. [PMID: 34870612 PMCID: PMC8686488 DOI: 10.2196/31053] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/07/2021] [Accepted: 09/18/2021] [Indexed: 12/20/2022] Open
Abstract
Background Computer tools based on artificial intelligence could aid clinicians in memory clinics in several ways, such as by supporting diagnostic decision-making, web-based cognitive testing, and the communication of diagnosis and prognosis. Objective This study aims to identify the preferences as well as the main barriers and facilitators related to using computer tools in memory clinics for all end users, that is, clinicians, patients, and care partners. Methods Between July and October 2020, we sent out invitations to a web-based survey to clinicians using the European Alzheimer’s Disease Centers network and the Dutch Memory Clinic network, and 109 clinicians participated (mean age 45 years, SD 10; 53/109, 48.6% female). A second survey was created for patients and care partners. They were invited via Alzheimer Europe, Alzheimer’s Society United Kingdom, Amsterdam Dementia Cohort, and Amsterdam Aging Cohort. A total of 50 patients with subjective cognitive decline, mild cognitive impairment, or dementia (mean age 73 years, SD 8; 17/34, 34% female) and 46 care partners (mean age 65 years, SD 12; 25/54, 54% female) participated in this survey. Results Most clinicians reported a willingness to use diagnostic (88/109, 80.7%) and prognostic (83/109, 76.1%) computer tools. User-friendliness (71/109, 65.1%); Likert scale mean 4.5, SD 0.7), and increasing diagnostic accuracy (76/109, 69.7%; mean 4.3, SD 0.7) were reported as the main factors stimulating the adoption of a tool. Tools should also save time and provide clear information on reliability and validity. Inadequate integration with electronic patient records (46/109, 42.2%; mean 3.8, SD 1.0) and fear of losing important clinical information (48/109, 44%; mean 3.7, SD 1.2) were most frequently indicated as barriers. Patients and care partners were equally positive about the use of computer tools by clinicians, both for diagnosis (69/96, 72%) and prognosis (73/96, 76%). In addition, most of them thought favorably regarding the possibility of using the tools themselves. Conclusions This study showed that computer tools in memory clinics are positively valued by most end users. For further development and implementation, it is essential to overcome the technical and practical barriers of a tool while paying utmost attention to its reliability and validity.
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Affiliation(s)
- Aniek M van Gils
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Leonie Nc Visser
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands.,Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
| | - Heleen Ma Hendriksen
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | | | - Majon Muller
- Department of Internal Medicine, Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Femke H Bouwman
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Wiesje M van der Flier
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands.,Department of Epidemiology and Data Science, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Hanneke Fm Rhodius-Meester
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands.,Department of Internal Medicine, Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
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Teunissen CE, Verberk IMW, Thijssen EH, Vermunt L, Hansson O, Zetterberg H, van der Flier WM, Mielke MM, Del Campo M. Blood-based biomarkers for Alzheimer's disease: towards clinical implementation. Lancet Neurol 2021; 21:66-77. [PMID: 34838239 DOI: 10.1016/s1474-4422(21)00361-6] [Citation(s) in RCA: 392] [Impact Index Per Article: 130.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 09/06/2021] [Accepted: 09/07/2021] [Indexed: 12/13/2022]
Abstract
For many years, blood-based biomarkers for Alzheimer's disease seemed unattainable, but recent results have shown that they could become a reality. Convincing data generated with new high-sensitivity assays have emerged with remarkable consistency across different cohorts, but also independent of the precise analytical method used. Concentrations in blood of amyloid and phosphorylated tau proteins associate with the corresponding concentrations in CSF and with amyloid-PET or tau-PET scans. Moreover, other blood-based biomarkers of neurodegeneration, such as neurofilament light chain and glial fibrillary acidic protein, appear to provide information on disease progression and potential for monitoring treatment effects. Now the question emerges of when and how we can bring these biomarkers to clinical practice. This step would pave the way for blood-based biomarkers to support the diagnosis of, and development of treatments for, Alzheimer's disease and other dementias.
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Affiliation(s)
- Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands.
| | - Inge M W Verberk
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Elisabeth H Thijssen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Lisa Vermunt
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Sölvegatan, Sweden; Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; UK Dementia Research Institute at UCL, London, UK; Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK; Hong Kong Center for Neurodegenerative Diseases, Hong Kong Special Administrative Region, China
| | - Wiesje M van der Flier
- Alzheimer Center, Department of Neurology, and Department of Epidemiology and Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Michelle M Mielke
- Department of Quantitative Health Sciences, and Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Marta Del Campo
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands; Departamento de Ciencias Farmacéuticas y de la Salud, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
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57
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Milne R, Altomare D, Ribaldi F, Molinuevo JL, Frisoni GB, Brayne C. Societal and equity challenges for Brain Health Services. A user manual for Brain Health Services-part 6 of 6. Alzheimers Res Ther 2021; 13:173. [PMID: 34635173 PMCID: PMC8507368 DOI: 10.1186/s13195-021-00885-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 08/04/2021] [Indexed: 11/10/2022]
Abstract
Brain Health Services are a novel approach to the personalized prevention of dementia. In this paper, we consider how such services can best reflect their social, cultural, and economic context and, in doing so, deliver fair and equitable access to risk reduction. We present specific areas of challenge associated with the social context for dementia prevention. The first concentrates on how Brain Health Services engage with the "at-risk" individual, recognizing the range of factors that shape an individual's risk of dementia and the efficacy of risk reduction measures. The second emphasizes the social context of Brain Health Services themselves and their ability to provide equitable access to risk reduction. We then elaborate proposals for meeting or mitigating these challenges. We suggest that considering these challenges will enable Brain Health Services to address two fundamental questions: the balance between an individualized "high-risk" and population focus for public health prevention and the ability of services to meet ethical standards of justice and health equity.
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Affiliation(s)
- Richard Milne
- Society and Ethics Research Group, Wellcome Connecting Science, Hinxton, UK.
- Cambridge Public Health, University of Cambridge, Cambridge, UK.
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), Saint John of God Clinical Research Centre, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - Carol Brayne
- Cambridge Public Health, University of Cambridge, Cambridge, UK
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58
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Visser LNC, Minguillon C, Sánchez-Benavides G, Abramowicz M, Altomare D, Fauria K, Frisoni GB, Georges J, Ribaldi F, Scheltens P, van der Schaar J, Zwan M, van der Flier WM, Molinuevo JL. Dementia risk communication. A user manual for Brain Health Services-part 3 of 6. Alzheimers Res Ther 2021; 13:170. [PMID: 34635169 PMCID: PMC8507171 DOI: 10.1186/s13195-021-00840-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 05/03/2021] [Indexed: 11/17/2022]
Abstract
Growing evidence suggests dementia incidence can be reduced through prevention programs targeting risk factors. To accelerate the implementation of such prevention programs, a new generation of brain health services (BHS) is envisioned, involving risk profiling, risk communication, risk reduction, and cognitive enhancement. The purpose of risk communication is to enable individuals at risk to make informed decisions and take action to protect themselves and is thus a crucial step in tailored prevention strategies of the dementia incidence. However, communicating about dementia risk is complex and challenging.In this paper, we provide an overview of (i) perspectives on communicating dementia risk from an ethical, clinical, and societal viewpoint; (ii) insights gained from memory clinical practice; (iii) available evidence on the impact of disclosing APOE and Alzheimer's disease biomarker test results gathered from clinical trials and observational studies; (iv) the value of established registries in light of BHS; and (v) practical recommendations regarding effective strategies for communicating about dementia risk.In addition, we identify challenges, i.e., the current lack of evidence on what to tell on an individual level-the actual risk-and on how to optimally communicate about dementia risk, especially concerning worried yet cognitively unimpaired individuals. Ideally, dementia risk communication strategies should maximize the desired impact of risk information on individuals' understanding of their health/disease status and risk perception and minimize potential harms. More research is thus warranted on the impact of dementia risk communication, to (1) evaluate the merits of different approaches to risk communication on outcomes in the cognitive, affective and behavioral domains, (2) develop an evidence-based, harmonized dementia risk communication protocol, and (3) develop e-tools to support and promote adherence to this protocol in BHSs.Based on the research reviewed, we recommend that dementia risk communication should be precise; include the use of absolute risks, visual displays, and time frames; based on a process of shared decision-making; and address the inherent uncertainty that comes with any probability.
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Affiliation(s)
- Leonie N C Visser
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
- Center for Alzheimer Research, Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden.
| | - Carolina Minguillon
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain.
| | - Gonzalo Sánchez-Benavides
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Marc Abramowicz
- Division of Genetic Medicine, Department of Diagnostics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - Karine Fauria
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | | | - Federica Ribaldi
- Division of Genetic Medicine, Department of Diagnostics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), Saint John of God Clinical Research Centre, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Jetske van der Schaar
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marissa Zwan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Epidemiology and Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
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Yoon B, Guo T, Provost K, Korman D, Ward TJ, Landau SM, Jagust WJ. Abnormal tau in amyloid PET negative individuals. Neurobiol Aging 2021; 109:125-134. [PMID: 34715443 DOI: 10.1016/j.neurobiolaging.2021.09.019] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 09/03/2021] [Accepted: 09/22/2021] [Indexed: 12/14/2022]
Abstract
We examined the characteristics of individuals with biomarker evidence of tauopathy but without β-amyloid (Aβ) (A-T+) in relation to individuals with (A+T+) and without (A-T-) evidence of Alzheimer's disease (AD). We included 561 participants with Aβ and tau PET from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We compared A-T- (n = 316), A-T+ (n = 63), and A+T+ (n = 182) individuals on demographics, amyloid, tau, hippocampal volumes, and cognition. A-T+ individuals were low on apolipoprotein E ɛ4 prevalence (17%) and had no evidence of subtly elevated brain Aβ within the negative range. The severity of tau deposition, hippocampal atrophy, and cognitive dysfunction in the A-T+ group was intermediate between A-T- and A+T+ (all p < 0.001). Tau uptake patterns in A-T+ individuals were heterogeneous, but approximately 29% showed tau deposition in the medial temporal lobe only, consistent with primary age-related tauopathy and an additional 32% showed a pattern consistent with AD. A-T+ individuals also share other features that are characteristic of AD such as cognitive impairment and neurodegeneration, but this group is heterogeneous and likely reflects more than one disorder.
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Affiliation(s)
- Bora Yoon
- Department of Neurology, Konyang University Hospital, Konyang University, College of Medicine, Daejeon, Korea.
| | - Tengfei Guo
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA; Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Karine Provost
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Deniz Korman
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Tyler J Ward
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Susan M Landau
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA; Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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Kühnel L, Bouteloup V, Lespinasse J, Chêne G, Dufouil C, Molinuevo JL, Raket LL. Personalized prediction of progression in pre-dementia patients based on individual biomarker profile: A development and validation study. Alzheimers Dement 2021; 17:1938-1949. [PMID: 34581496 DOI: 10.1002/alz.12363] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 03/24/2021] [Accepted: 04/01/2021] [Indexed: 11/10/2022]
Abstract
INTRODUCTION The prognosis of patients at the pre-dementia stage is difficult to define. The aim of this study is to develop and validate a biomarker-based continuous model for predicting the individual cognitive level at any future moment. In addition to personalized prognosis, such a model could reduce trial sample size requirements by allowing inclusion of a homogenous patient population. METHODS Disease-progression modeling of longitudinal cognitive scores of pre-dementia patients (baseline Clinical Dementia Rating ≤ 0.5) was used to derive a biomarker profile that was predictive of patient's cognitive progression along the dementia continuum. The biomarker profile model was developed and validated in the MEMENTO cohort and externally validated in the Alzheimer's Disease Neuroimaging Initiative. RESULTS Of nine candidate biomarkers in the development analysis, three cerebrospinal fluid and two magnetic resonance imaging measures were selected to form the final biomarker profile. The model-based prognosis of individual future cognitive deficit was shown to significantly improve when incorporating biomarker information on top of cognition and demographic data. In trial power calculations, adjusting the primary analysis for the baseline biomarker profile reduced sample size requirements by ≈10%. Compared to conventional cognitive cut-offs, inclusion criteria based on biomarker-profile cut-offs resulted in up to 28% reduced sample size requirements due to increased homogeneity in progression patterns. DISCUSSION The biomarker profile allows prediction of personalized trajectories of future cognitive progression. This enables accurate personalized prognosis in clinical care and better selection of patient populations for clinical trials. A web-based application for prediction of patients' future cognitive progression is available online.
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Affiliation(s)
- Line Kühnel
- H. Lundbeck A/S, Copenhagen, Denmark.,Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Vincent Bouteloup
- Inserm, Population Health Research Center, University of Bordeaux, Bordeaux, France.,CHU de Bordeaux, Pole Santé Publique, Talence, France
| | - Jérémie Lespinasse
- Inserm, Population Health Research Center, University of Bordeaux, Bordeaux, France.,CHU de Bordeaux, Pole Santé Publique, Talence, France
| | - Geneviève Chêne
- Inserm, Population Health Research Center, University of Bordeaux, Bordeaux, France.,CHU de Bordeaux, Pole Santé Publique, Talence, France
| | - Carole Dufouil
- Inserm, Population Health Research Center, University of Bordeaux, Bordeaux, France.,CHU de Bordeaux, Pole Santé Publique, Talence, France
| | | | - Lars Lau Raket
- H. Lundbeck A/S, Copenhagen, Denmark.,Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
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Mank A, van Maurik IS, Bakker ED, van de Glind EMM, Jönsson L, Kramberger MG, Novak P, Diaz A, Gove D, Scheltens P, van der Flier WM, Visser LNC. Identifying relevant outcomes in the progression of Alzheimer's disease; what do patients and care partners want to know about prognosis? ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2021; 7:e12189. [PMID: 34458555 PMCID: PMC8377775 DOI: 10.1002/trc2.12189] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 04/19/2021] [Accepted: 05/06/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND Prognostic studies in the context of Alzheimer's disease (AD) mainly predicted time to dementia. However, it is questionable whether onset of dementia is the most relevant outcome along the AD disease trajectory from the perspective of patients and their care partners. Therefore, we aimed to identify the most relevant outcomes from the viewpoint of patients and care partners. METHODS We used a two-step, mixed-methods approach. As a first step we conducted four focus groups in the Netherlands to elicit a comprehensive list of outcomes considered important by patients (n = 12) and care partners (n = 14) in the prognosis of AD. The focus groups resulted in a list of 59 items, divided into five categories. Next, in an online European survey, we asked participants (n = 232; 99 patients, 133 care partners) to rate the importance of all 59 items (5-point Likert scale). As participants were likely to rate a large number of outcomes as "important" (4) or "very important" (5), we subsequently asked them to select the three items they considered most important. RESULTS The top-10 lists of items most frequently mentioned as "most important" by patients and care partners were merged into one core outcome list, comprising 13 items. Both patients and care partners selected outcomes from the category "cognition" most often, followed by items in the categories "functioning and dependency" and "physical health." No items from the category "behavior and neuropsychiatry" and "social environment" ended up in our core list of relevant outcomes. CONCLUSION We identified a core list of outcomes relevant to patients and care partner, and found that prognostic information related to cognitive decline, dependency, and physical health are considered most relevant by both patients and their care partners.
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Affiliation(s)
- Arenda Mank
- Alzheimer Center AmsterdamDepartment of NeurologyVU University Medical CenterAmsterdam UMCAmsterdamthe Netherlands
| | - Ingrid S. van Maurik
- Alzheimer Center AmsterdamDepartment of NeurologyVU University Medical CenterAmsterdam UMCAmsterdamthe Netherlands
- Department of Epidemiology and Data ScienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Els D. Bakker
- Alzheimer Center AmsterdamDepartment of NeurologyVU University Medical CenterAmsterdam UMCAmsterdamthe Netherlands
| | | | | | - Milica G. Kramberger
- Center for Cognitive ImpairmentsUniversity Medical Centre LjubljanaLjubljanaSlovenia
| | - Petr Novak
- Institute of NeuroimmunologySlovak Academy of SciencesBratislavaSlovakia
| | - Ana Diaz
- Alzheimer Europe (AE)Luxembourg CityLuxembourg
| | - Dianne Gove
- Alzheimer Europe (AE)Luxembourg CityLuxembourg
| | - Philip Scheltens
- Alzheimer Center AmsterdamDepartment of NeurologyVU University Medical CenterAmsterdam UMCAmsterdamthe Netherlands
| | - Wiesje M. van der Flier
- Alzheimer Center AmsterdamDepartment of NeurologyVU University Medical CenterAmsterdam UMCAmsterdamthe Netherlands
| | - Leonie N. C. Visser
- Alzheimer Center AmsterdamDepartment of NeurologyVU University Medical CenterAmsterdam UMCAmsterdamthe Netherlands
- Department of Medical PsychologyAmsterdam Public Health Research InstituteAmsterdam UMCAmsterdamthe Netherlands
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Jaleel J, Tripathi M, Baghel V, Arunraj ST, Kumar P, Khan D, Tripathi M, Dey AB, Bal C. F-18 ML-104 tau PET imaging in mild cognitive impairment. Nucl Med Commun 2021; 42:914-921. [PMID: 33852534 DOI: 10.1097/mnm.0000000000001415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE This study was undertaken to evaluate the tau distribution patterns in patients with amnestic mild cognitive impairment (aMCI) using PET radiotracer F-18 ML-104. MATERIALS AND METHODS Thirty patients, clinically diagnosed as aMCI [mini mental state evaluation ≥24] in the neurology or geriatric memory clinics, were included in the study. Each aMCI patient underwent F-18 fluorodeoxyglucose and F-18 ML-104 tau PET. Standardized uptake value ratios for cortical gray matter regions were evaluated for F-18 ML-104 tau PET and compared with normal controls and with early Alzheimer's disease (AD) patients (used from a previous study). RESULTS aMCI revealed significantly higher standardized uptake value ratios in both medial temporal cortices, precuneus and posterior cingulate cortices in comparison to normal controls and a significantly lesser binding in bilateral medial and lateral temporal, precuneus and posterior cingulate cortices in comparison to early AD. A negative correlation was noted between F-18 fluorodeoxyglucose uptake and F-18 ML-104 retention in the precuneus and posterior cingulate cortices in aMCI, while F-18 ML-104 retention and mini mental state evaluation scores revealed a moderate negative correlation in the posterior cingulate cortices. CONCLUSION We could demonstrate a significant increase in cortical tau deposition in aMCI patients in comparison to normal controls, thus providing in vivo evidence of the underlying pathological process in this subgroup of patients with high probability of conversion to AD.
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Frederiksen KS, Nielsen TR, Winblad B, Schmidt R, Kramberger MG, Jones RW, Hort J, Grimmer T, Georges J, Frölich L, Engelborghs S, Dubois B, Waldemar G. European Academy of Neurology/European Alzheimer's Disease Consortium position statement on diagnostic disclosure, biomarker counseling, and management of patients with mild cognitive impairment. Eur J Neurol 2021; 28:2147-2155. [PMID: 33368924 PMCID: PMC8246881 DOI: 10.1111/ene.14668] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/30/2020] [Accepted: 12/01/2020] [Indexed: 01/12/2023]
Abstract
BACKGROUND AND PURPOSE Careful counseling through the diagnostic process and adequate postdiagnostic support in patients with mild cognitive impairment (MCI) is important. Previous studies have indicated heterogeneity in practice and the need for guidance for clinicians. METHODS A joint European Academy of Neurology/European Alzheimer's Disease Consortium panel of dementia specialists was appointed. Through online meetings and emails, positions were developed regarding disclosing a syndrome diagnosis of MCI, pre- and postbiomarker sampling counseling, and postdiagnostic support. RESULTS Prior to diagnostic evaluation, motives and wishes of the patient should be sought. Diagnostic disclosure should be carried out by a dementia specialist taking the ethical principles of "the right to know" versus "the wish not to know" into account. Disclosure should be accompanied by written information and a follow-up plan. It should be made clear that MCI is not dementia. Prebiomarker counseling should always be carried out if biomarker sampling is considered and postbiomarker counseling if sampling is carried out. A dementia specialist knowledgeable about biomarkers should inform about pros and cons, including alternatives, to enable an autonomous and informed decision. Postbiomarker counseling will depend in part on the results of biomarkers. Follow-up should be considered for all patients with MCI and include brain-healthy advice and possibly treatment for specific underlying causes. Advice on advance directives may be relevant. CONCLUSIONS Guidance to clinicians on various aspects of the diagnostic process in patients with MCI is presented here as position statements. Further studies are needed to enable more evidence-based and standardized recommendations in the future.
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Affiliation(s)
| | - T. Rune Nielsen
- Department of NeurologyDanish Dementia Research CentreRigshospitaletCopenhagenDenmark
| | - Bengt Winblad
- Division of NeurogeriatricsDepartment of Neurobiology, Care Sciences and SocietyCenter for Alzheimer ResearchKarolinska InstituteSolnaSweden
- Theme AgingKarolinska University HospitalStockholmSweden
| | | | - Milica G. Kramberger
- Department of NeurologyCenter for Cognitive ImpairmentsUniversity Medical CentreLjubljanaSlovenia
| | - Roy W. Jones
- RICE (The Research Institute for the Care of Older People)Royal United HospitalBath and University of BristolBristolUK
| | - Jakub Hort
- Department of NeurologyCognitive CenterSecond Faculty of Medicine and Motol University HospitalCharles UniversityPragueCzech Republic
| | - Timo Grimmer
- Department of Psychiatry and PsychotherapySchool of MedicineRechts der Isar HospitalTechnical University of MunichMunichGermany
| | | | - Lutz Frölich
- Department of Geriatric PsychiatryUniversity of HeidelbergMannheimGermany
| | - Sebastiaan Engelborghs
- Department of Neurology and Center for NeurosciencesUZ Brussel and Free University of Brussels (VUBBrusselsBelgium
- Reference Center for Biological Markers of Dementia (BIODEM)Institute Born‐BungeUniversity of AntwerpAntwerpBelgium
| | - Bruno Dubois
- Department of NeurologyDementia Research CenterSalpêtrière HospitalSorbonne UniversityParisFrance
| | - Gunhild Waldemar
- Department of NeurologyDanish Dementia Research CentreRigshospitaletCopenhagenDenmark
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Cullen NC, Leuzy A, Janelidze S, Palmqvist S, Svenningsson AL, Stomrud E, Dage JL, Mattsson-Carlgren N, Hansson O. Plasma biomarkers of Alzheimer's disease improve prediction of cognitive decline in cognitively unimpaired elderly populations. Nat Commun 2021; 12:3555. [PMID: 34117234 PMCID: PMC8196018 DOI: 10.1038/s41467-021-23746-0] [Citation(s) in RCA: 127] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/10/2021] [Indexed: 12/12/2022] Open
Abstract
Plasma biomarkers of amyloid, tau, and neurodegeneration (ATN) need to be characterized in cognitively unimpaired (CU) elderly individuals. We therefore tested if plasma measurements of amyloid-β (Aβ)42/40, phospho-tau217 (P-tau217), and neurofilament light (NfL) together predict clinical deterioration in 435 CU individuals followed for an average of 4.8 ± 1.7 years in the BioFINDER study. A combination of all three plasma biomarkers and basic demographics best predicted change in cognition (Pre-Alzheimer's Clinical Composite; R2 = 0.14, 95% CI [0.12-0.17]; P < 0.0001) and subsequent AD dementia (AUC = 0.82, 95% CI [0.77-0.91], P < 0.0001). In a simulated clinical trial, a screening algorithm combining all three plasma biomarkers would reduce the required sample size by 70% (95% CI [54-81]; P < 0.001) with cognition as trial endpoint, and by 63% (95% CI [53-70], P < 0.001) with subsequent AD dementia as trial endpoint. Plasma ATN biomarkers show usefulness in cognitively unimpaired populations and could make large clinical trials more feasible and cost-effective.
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Affiliation(s)
| | - Antoine Leuzy
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | | | - Sebastian Palmqvist
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Lund, Sweden
| | - Anna L Svenningsson
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Lund, Sweden
| | | | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Lund University, Lund, Sweden.
- Department of Neurology, Skåne University Hospital, Lund, Sweden.
- Wallenberg Centre for Molecular Medicine, Lund University, Lund, Sweden.
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Lund, Sweden.
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Danso SO, Zeng Z, Muniz-Terrera G, Ritchie CW. Developing an Explainable Machine Learning-Based Personalised Dementia Risk Prediction Model: A Transfer Learning Approach With Ensemble Learning Algorithms. Front Big Data 2021; 4:613047. [PMID: 34124650 PMCID: PMC8187875 DOI: 10.3389/fdata.2021.613047] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 04/13/2021] [Indexed: 02/02/2023] Open
Abstract
Alzheimer's disease (AD) has its onset many decades before dementia develops, and work is ongoing to characterise individuals at risk of decline on the basis of early detection through biomarker and cognitive testing as well as the presence/absence of identified risk factors. Risk prediction models for AD based on various computational approaches, including machine learning, are being developed with promising results. However, these approaches have been criticised as they are unable to generalise due to over-reliance on one data source, poor internal and external validations, and lack of understanding of prediction models, thereby limiting the clinical utility of these prediction models. We propose a framework that employs a transfer-learning paradigm with ensemble learning algorithms to develop explainable personalised risk prediction models for dementia. Our prediction models, known as source models, are initially trained and tested using a publicly available dataset (n = 84,856, mean age = 69 years) with 14 years of follow-up samples to predict the individual risk of developing dementia. The decision boundaries of the best source model are further updated by using an alternative dataset from a different and much younger population (n = 473, mean age = 52 years) to obtain an additional prediction model known as the target model. We further apply the SHapely Additive exPlanation (SHAP) algorithm to visualise the risk factors responsible for the prediction at both population and individual levels. The best source model achieves a geometric accuracy of 87%, specificity of 99%, and sensitivity of 76%. In comparison to a baseline model, our target model achieves better performance across several performance metrics, within an increase in geometric accuracy of 16.9%, specificity of 2.7%, and sensitivity of 19.1%, an area under the receiver operating curve (AUROC) of 11% and a transfer learning efficacy rate of 20.6%. The strength of our approach is the large sample size used in training the source model, transferring and applying the "knowledge" to another dataset from a different and undiagnosed population for the early detection and prediction of dementia risk, and the ability to visualise the interaction of the risk factors that drive the prediction. This approach has direct clinical utility.
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Affiliation(s)
- Samuel O Danso
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh Medical School, Edinburgh, United Kingdom
| | - Zhanhang Zeng
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh Medical School, Edinburgh, United Kingdom
| | - Craig W Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh Medical School, Edinburgh, United Kingdom
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Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chételat G, Teunissen CE, Cummings J, van der Flier WM. Alzheimer's disease. Lancet 2021; 397:1577-1590. [PMID: 33667416 PMCID: PMC8354300 DOI: 10.1016/s0140-6736(20)32205-4] [Citation(s) in RCA: 1765] [Impact Index Per Article: 588.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 08/21/2020] [Accepted: 10/15/2020] [Indexed: 12/16/2022]
Abstract
In this Seminar, we highlight the main developments in the field of Alzheimer's disease. The most recent data indicate that, by 2050, the prevalence of dementia will double in Europe and triple worldwide, and that estimate is 3 times higher when based on a biological (rather than clinical) definition of Alzheimer's disease. The earliest phase of Alzheimer's disease (cellular phase) happens in parallel with accumulating amyloid β, inducing the spread of tau pathology. The risk of Alzheimer's disease is 60-80% dependent on heritable factors, with more than 40 Alzheimer's disease-associated genetic risk loci already identified, of which the APOE alleles have the strongest association with the disease. Novel biomarkers include PET scans and plasma assays for amyloid β and phosphorylated tau, which show great promise for clinical and research use. Multidomain lifestyle-based prevention trials suggest cognitive benefits in participants with increased risk of dementia. Lifestyle factors do not directly affect Alzheimer's disease pathology, but can still contribute to a positive outcome in individuals with Alzheimer's disease. Promising pharmacological treatments are poised at advanced stages of clinical trials and include anti-amyloid β, anti-tau, and anti-inflammatory strategies.
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Affiliation(s)
- Philip Scheltens
- Alzheimer Centre Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands; Department of Neurology, Amsterdam University Medical Centers, Amsterdam, Netherlands; Life Science Partners, Amsterdam, Netherlands.
| | - Bart De Strooper
- VIB Center for Brain and Disease Research, Leuven, Belgium; KU Leuven Department for Neurology, Leuven, Belgium; Dementia Research Institute, University College London, London, UK
| | - Miia Kivipelto
- Division of Clinical Geriatrics and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska University Hospital, Stockholm, Sweden; Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland; Ageing and Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
| | - Henne Holstege
- Alzheimer Centre Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands; Department of Clinical Genetics, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Gael Chételat
- Normandie Université, Université de Caen, Institut National de la Santé et de la Recherche Médicale, Groupement d'Intérêt Public Cyceron, Caen, France
| | - Charlotte E Teunissen
- Department of Clinical Chemistry, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, University of Nevada, Las Vegas, NV, USA; Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | - Wiesje M van der Flier
- Alzheimer Centre Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands; Department of Epidemiology and Datascience, Amsterdam University Medical Centers, Amsterdam, Netherlands
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Buckley RF. Recent Advances in Imaging of Preclinical, Sporadic, and Autosomal Dominant Alzheimer's Disease. Neurotherapeutics 2021; 18:709-727. [PMID: 33782864 PMCID: PMC8423933 DOI: 10.1007/s13311-021-01026-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2021] [Indexed: 12/25/2022] Open
Abstract
Observing Alzheimer's disease (AD) pathological changes in vivo with neuroimaging provides invaluable opportunities to understand and predict the course of disease. Neuroimaging AD biomarkers also allow for real-time tracking of disease-modifying treatment in clinical trials. With recent neuroimaging advances, along with the burgeoning availability of longitudinal neuroimaging data and big-data harmonization approaches, a more comprehensive evaluation of the disease has shed light on the topographical staging and temporal sequencing of the disease. Multimodal imaging approaches have also promoted the development of data-driven models of AD-associated pathological propagation of tau proteinopathies. Studies of autosomal dominant, early sporadic, and late sporadic courses of the disease have shed unique insights into the AD pathological cascade, particularly with regard to genetic vulnerabilities and the identification of potential drug targets. Further, neuroimaging markers of b-amyloid, tau, and neurodegeneration have provided a powerful tool for validation of novel fluid cerebrospinal and plasma markers. This review highlights some of the latest advances in the field of human neuroimaging in AD across these topics, particularly with respect to positron emission tomography and structural and functional magnetic resonance imaging.
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Affiliation(s)
- Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital & Brigham and Women's, Harvard Medical School, Boston, MA, USA.
- Melbourne School of Psychological Sciences and Florey Institutes, University of Melbourne, Melbourne, VIC, Australia.
- Department of Neurology, Massachusetts General Hospital, 149 13th St, Charlestown, MA, 02129, USA.
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Eckerström C, Svensson J, Kettunen P, Jonsson M, Eckerström M. Evaluation of the ATN model in a longitudinal memory clinic sample with different underlying disorders. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12031. [PMID: 33816750 PMCID: PMC8015813 DOI: 10.1002/dad2.12031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 03/06/2020] [Accepted: 03/06/2020] [Indexed: 11/10/2022]
Abstract
INTRODUCTION To evaluate the usefulness of the 2018 NIA-AA (National Institute on Aging and Alzheimer's Association) research framework in a longitudinal memory clinic study with different clinical outcomes and underlying disorders. METHODS We included 420 patients with mild cognitive impairment or subjective cognitive impairment. During the follow up, 27% of the patients converted to dementia, with the majority converting to Alzheimer's disease (AD) or mixed dementia. Based on the baseline values of the cerebrospinal fluid biomarkers, the patients were classified into one of the eight possible ATN groups (amyloid beta [Aβ] aggregation [A], tau aggregation reflecting neurofibrillary tangles [T], and neurodegeneration [N]). RESULTS The majority of the patients converting to AD and mixed dementia were in ATN groups positive for A (71%). The A+T+N+ group was highly overrepresented among converters to AD and mixed dementia. Patients converting to dementias other than AD or mixed dementia were evenly distributed across the ATN groups. DISCUSSION Our findings provide support for the usefulness of the ATN system to detect incipient AD or mixed dementia.
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Affiliation(s)
- C. Eckerström
- Department of Psychiatry and NeurochemistrySahlgrenska AcademyInstitute of Neuroscience and PhysiologyUniversity of GothenburgSweden
- Department of Immunology and Transfusion MedicineRegion Västra GötalandSahlgrenska University HospitalSweden
| | - J. Svensson
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska AcademyUniversity of GothenburgSweden
| | - P. Kettunen
- Department of Psychiatry and NeurochemistrySahlgrenska AcademyInstitute of Neuroscience and PhysiologyUniversity of GothenburgSweden
| | - M. Jonsson
- Department of Psychiatry and NeurochemistrySahlgrenska AcademyInstitute of Neuroscience and PhysiologyUniversity of GothenburgSweden
| | - M. Eckerström
- Department of Psychiatry and NeurochemistrySahlgrenska AcademyInstitute of Neuroscience and PhysiologyUniversity of GothenburgSweden
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2020 update on the clinical validity of cerebrospinal fluid amyloid, tau, and phospho-tau as biomarkers for Alzheimer's disease in the context of a structured 5-phase development framework. Eur J Nucl Med Mol Imaging 2021; 48:2121-2139. [PMID: 33674895 PMCID: PMC8175301 DOI: 10.1007/s00259-021-05258-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 02/11/2021] [Indexed: 12/15/2022]
Abstract
Purpose In the last decade, the research community has focused on defining reliable biomarkers for the early detection of Alzheimer’s disease (AD) pathology. In 2017, the Geneva AD Biomarker Roadmap Initiative adapted a framework for the systematic validation of oncological biomarkers to cerebrospinal fluid (CSF) AD biomarkers—encompassing the 42 amino-acid isoform of amyloid-β (Aβ42), phosphorylated-tau (P-tau), and Total-tau (T-tau)—with the aim to accelerate their development and clinical implementation. The aim of this work is to update the current validation status of CSF AD biomarkers based on the Biomarker Roadmap methodology. Methods A panel of experts in AD biomarkers convened in November 2019 at a 2-day workshop in Geneva. The level of maturity (fully achieved, partly achieved, preliminary evidence, not achieved, unsuccessful) of CSF AD biomarkers was assessed based on the Biomarker Roadmap methodology before the meeting and presented and discussed during the workshop. Results By comparison to the previous 2017 Geneva Roadmap meeting, the primary advances in CSF AD biomarkers have been in the area of a unified protocol for CSF sampling, handling and storage, the introduction of certified reference methods and materials for Aβ42, and the introduction of fully automated assays. Additional advances have occurred in the form of defining thresholds for biomarker positivity and assessing the impact of covariates on their discriminatory ability. Conclusions Though much has been achieved for phases one through three, much work remains in phases four (real world performance) and five (assessment of impact/cost). To a large degree, this will depend on the availability of disease-modifying treatments for AD, given these will make accurate and generally available diagnostic tools key to initiate therapy. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05258-7.
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Belloy ME, Napolioni V, Han SS, Le Guen Y, Greicius MD. Association of Klotho-VS Heterozygosity With Risk of Alzheimer Disease in Individuals Who Carry APOE4. JAMA Neurol 2021; 77:849-862. [PMID: 32282020 DOI: 10.1001/jamaneurol.2020.0414] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Importance Identification of genetic factors that interact with the apolipoprotein e4 (APOE4) allele to reduce risk for Alzheimer disease (AD) would accelerate the search for new AD drug targets. Klotho-VS heterozygosity (KL-VSHET+ status) protects against aging-associated phenotypes and cognitive decline, but whether it protects individuals who carry APOE4 from AD remains unclear. Objectives To determine if KL-VSHET+ status is associated with reduced AD risk and β-amyloid (Aβ) pathology in individuals who carry APOE4. Design, Setting, and Participants This study combined 25 independent case-control, family-based, and longitudinal AD cohorts that recruited referred and volunteer participants and made data available through public repositories. Analyses were stratified by APOE4 status. Three cohorts were used to evaluate conversion risk, 1 provided longitudinal measures of Aβ CSF and PET, and 3 provided cross-sectional measures of Aβ CSF. Genetic data were available from high-density single-nucleotide variant microarrays. All data were collected between September 2015 and September 2019 and analyzed between April 2019 and December 2019. Main Outcomes and Measures The risk of AD was evaluated through logistic regression analyses under a case-control design. The risk of conversion to mild cognitive impairment (MCI) or AD was evaluated through competing risks regression. Associations with Aβ, measured from cerebrospinal fluid (CSF) or brain positron emission tomography (PET), were evaluated using linear regression and mixed-effects modeling. Results Of 36 530 eligible participants, 13 782 were excluded for analysis exclusion criteria or refusal to participate. Participants were men and women aged 60 years and older who were non-Hispanic and of Northwestern European ancestry and had been diagnosed as being cognitively normal or having MCI or AD. The sample included 20 928 participants in case-control studies, 3008 in conversion studies, 556 in Aβ CSF regression analyses, and 251 in PET regression analyses. The genotype KL-VSHET+ was associated with reduced risk for AD in individuals carrying APOE4 who were 60 years or older (odds ratio, 0.75 [95% CI, 0.67-0.84]; P = 7.4 × 10-7), and this was more prominent at ages 60 to 80 years (odds ratio, 0.69 [95% CI, 0.61-0.79]; P = 3.6 × 10-8). Additionally, control participants carrying APOE4 with KL-VS heterozygosity were at reduced risk of converting to MCI or AD (hazard ratio, 0.64 [95% CI, 0.44-0.94]; P = .02). Finally, in control participants who carried APOE4 and were aged 60 to 80 years, KL-VS heterozygosity was associated with higher Aβ in CSF (β, 0.06 [95% CI, 0.01-0.10]; P = .03) and lower Aβ on PET scans (β, -0.04 [95% CI, -0.07 to -0.00]; P = .04). Conclusions and Relevance The genotype KL-VSHET+ is associated with reduced AD risk and Aβ burden in individuals who are aged 60 to 80 years, cognitively normal, and carrying APOE4. Molecular pathways associated with KL merit exploration for novel AD drug targets. The KL-VS genotype should be considered in conjunction with the APOE genotype to refine AD prediction models used in clinical trial enrichment and personalized genetic counseling.
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Affiliation(s)
- Michael E Belloy
- Department of Neurology and Neurological Sciences, Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Stanford University, Stanford, California
| | - Valerio Napolioni
- Department of Neurology and Neurological Sciences, Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Stanford University, Stanford, California
| | - Summer S Han
- Department of Neurosurgery, Stanford University, Stanford, California.,Quantitative Sciences Unit, Stanford Medicine, Stanford, California
| | - Yann Le Guen
- Department of Neurology and Neurological Sciences, Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Stanford University, Stanford, California
| | - Michael D Greicius
- Department of Neurology and Neurological Sciences, Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Stanford University, Stanford, California
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van Maurik IS, Rhodius-Meester HFM, Teunissen CE, Scheltens P, Barkhof F, Palmqvist S, Hansson O, van der Flier WM, Berkhof J. Biomarker testing in MCI patients-deciding who to test. ALZHEIMERS RESEARCH & THERAPY 2021; 13:14. [PMID: 33413634 PMCID: PMC7792312 DOI: 10.1186/s13195-020-00763-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 12/23/2020] [Indexed: 11/10/2022]
Abstract
BACKGROUND We aimed to derive an algorithm to define the optimal proportion of patients with mild cognitive impairment (MCI) in whom cerebrospinal fluid (CSF) testing is of added prognostic value. METHODS MCI patients were selected from the Amsterdam Dementia Cohort (n = 402). Three-year progression probabilities to dementia were predicted using previously published models with and without CSF data (amyloid-beta1-42 (Abeta), phosphorylated tau (p-tau)). We incrementally augmented the proportion of patients undergoing CSF, starting with the 10% patients with prognostic probabilities based on clinical data around the median (percentile 45-55), until all patients received CSF. The optimal proportion was defined as the proportion where the stepwise algorithm showed similar prognostic discrimination (Harrell's C) and accuracy (three-year Brier scores) compared to CSF testing of all patients. We used the BioFINDER study (n = 221) for validation. RESULTS The optimal proportion of MCI patients to receive CSF testing selected by the stepwise approach was 50%. CSF testing in only this proportion improved the performance of the model with clinical data only from Harrell's C = 0.60, Brier = 0.198 (Harrell's C = 0.61, Brier = 0.197 if the information on magnetic resonance imaging was available) to Harrell's C = 0.67 and Brier = 0.190, and performed similarly to a model in which all patients received CSF testing. Applying the stepwise approach in the BioFINDER study would again select half of the MCI patients and yielded robust results with respect to prognostic performance. INTERPRETATION CSF biomarker testing adds prognostic value in half of the MCI patients. As such, we achieve a CSF saving recommendation while simultaneously retaining optimal prognostic accuracy.
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Affiliation(s)
- Ingrid S van Maurik
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands. .,Department of Epidemiology and Data Sciences, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands.,Department of Internal Medicine, Geriatric Medicine Section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Institutes of Neurology and Healthcare Engineering, University College London, London, England
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1118, 1081 HZ, Amsterdam, The Netherlands.,Department of Epidemiology and Data Sciences, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Johannes Berkhof
- Department of Epidemiology and Data Sciences, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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Blazhenets G, Frings L, Ma Y, Sörensen A, Eidelberg D, Wiltfang J, Meyer PT. Validation of the Alzheimer Disease Dementia Conversion-Related Pattern as an ATN Biomarker of Neurodegeneration. Neurology 2021; 96:e1358-e1368. [PMID: 33408150 DOI: 10.1212/wnl.0000000000011521] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 11/09/2020] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To determine whether the Alzheimer disease (AD) dementia conversion-related pattern (ADCRP) on [18F]FDG PET can serve as a valid predictor for the development of AD dementia, the individual expression of the ADCRP (subject score) and its prognostic value were examined in patients with mild cognitive impairment (MCI) and biologically defined AD. METHODS A total of 269 patients with available [18F]FDG PET, [18F]AV-45 PET, phosphorylated and total tau in CSF, and neurofilament light chain in plasma were included. Following the AT(N) classification scheme, where AD is defined biologically by in vivo biomarkers of β-amyloid (Aβ) deposition ("A") and pathologic tau ("T"), patients were categorized to the A-T-, A+T-, A+T+ (AD), and A-T+ groups. RESULTS The mean subject score of the ADCRP was significantly higher in the A+T+ group compared to each of the other group (all p < 0.05) but was similar among the latter (all p > 0.1). Within the A+T+ group, the subject score of ADCRP was a significant predictor of conversion to dementia (hazard ratio, 2.02 per z score increase; p < 0.001), with higher predictive value than of alternative biomarkers of neurodegeneration (total tau and neurofilament light chain). Stratification of A+T+ patients by the subject score of ADCRP yielded well-separated groups of high, medium, and low conversion risks. CONCLUSIONS The ADCRP is a valuable biomarker of neurodegeneration in patients with MCI and biologically defined AD. It shows great potential for stratifying the risk and estimating the time to conversion to dementia in patients with MCI and underlying AD (A+T+). CLASSIFICATION OF EVIDENCE This study provides Class I evidence that [18F]FDG PET predicts the development of AD dementia in individuals with MCI and underlying AD as defined by the AT(N) framework.
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Affiliation(s)
- Ganna Blazhenets
- From the Department of Nuclear Medicine (G.B., L.F., A.S., P.T.M.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg; Center for Neurosciences (Y.M., D.E.), Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY; and Department of Psychiatry and Psychotherapy (J.W.), University Medical Center, Georg-August-University, Göttingen, Germany.
| | - Lars Frings
- From the Department of Nuclear Medicine (G.B., L.F., A.S., P.T.M.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg; Center for Neurosciences (Y.M., D.E.), Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY; and Department of Psychiatry and Psychotherapy (J.W.), University Medical Center, Georg-August-University, Göttingen, Germany
| | - Yilong Ma
- From the Department of Nuclear Medicine (G.B., L.F., A.S., P.T.M.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg; Center for Neurosciences (Y.M., D.E.), Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY; and Department of Psychiatry and Psychotherapy (J.W.), University Medical Center, Georg-August-University, Göttingen, Germany
| | - Arnd Sörensen
- From the Department of Nuclear Medicine (G.B., L.F., A.S., P.T.M.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg; Center for Neurosciences (Y.M., D.E.), Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY; and Department of Psychiatry and Psychotherapy (J.W.), University Medical Center, Georg-August-University, Göttingen, Germany
| | - David Eidelberg
- From the Department of Nuclear Medicine (G.B., L.F., A.S., P.T.M.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg; Center for Neurosciences (Y.M., D.E.), Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY; and Department of Psychiatry and Psychotherapy (J.W.), University Medical Center, Georg-August-University, Göttingen, Germany
| | - Jens Wiltfang
- From the Department of Nuclear Medicine (G.B., L.F., A.S., P.T.M.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg; Center for Neurosciences (Y.M., D.E.), Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY; and Department of Psychiatry and Psychotherapy (J.W.), University Medical Center, Georg-August-University, Göttingen, Germany
| | - Philipp T Meyer
- From the Department of Nuclear Medicine (G.B., L.F., A.S., P.T.M.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg; Center for Neurosciences (Y.M., D.E.), Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY; and Department of Psychiatry and Psychotherapy (J.W.), University Medical Center, Georg-August-University, Göttingen, Germany
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Individualized prognosis of cognitive decline and dementia in mild cognitive impairment based on plasma biomarker combinations. ACTA ACUST UNITED AC 2020; 1:114-123. [PMID: 37117993 DOI: 10.1038/s43587-020-00003-5] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 10/30/2020] [Indexed: 12/22/2022]
Abstract
We developed models for individualized risk prediction of cognitive decline in mild cognitive impairment (MCI) using plasma biomarkers of β-amyloid (Aβ), tau and neurodegeneration. A total of 573 patients with MCI from the Swedish BioFINDER study and the Alzheimer's Disease Neuroimaging Initiative (ADNI) were included in the study. The primary outcomes were longitudinal cognition and conversion to Alzheimer's disease (AD) dementia. A model combining tau phosphorylated at threonine 181 (P-tau181) and neurofilament light (NfL), but not Aβ42/Aβ40, had the best prognosis performance of all models (area under the curve = 0.88 for 4-year conversion to AD in BioFINDER, validated in ADNI), was stronger than a basic model of age, sex, education and baseline cognition, and performed similarly to cerebrospinal fluid biomarkers. A publicly available online tool for individualized prognosis in MCI based on our combined plasma biomarker models is introduced. Combination of plasma biomarkers may be of high value to identify individuals with MCI who will progress to AD dementia in clinical trials and in clinical practice.
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75
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Rostamzadeh A, Jessen F. [Early detection of Alzheimer's disease and dementia prediction in patients with mild cognitive impairment : Summary of current recommendations]. DER NERVENARZT 2020; 91:832-842. [PMID: 32300816 DOI: 10.1007/s00115-020-00907-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Mild cognitive impairment (MCI) is characterized by cognitive deficits but essentially preserved competence in activities of daily living. It is a risk factor for the development of dementia and can reflect a prodromal predementia state of Alzheimer's disease (AD). The pathology of AD is defined by cerebral deposition of amyloid-beta-1-42 protein and aggregation of phosphorylated tau protein, which can be identified in vivo by biomarkers for these alterations. As a result of advances in the field of biomarker-based early detection of AD, it is possible to differentiate between MCI patients with and without a pathological AD condition and therefore, between patients with a low and those with a high risk for the development of dementia. At present there are no specific guideline recommendations in Germany for the diagnostic use of biomarkers in predementia detection of AD and for dementia risk assessment in patients with MCI. This article summarizes the current recommendations of a European expert consensus publication and a multidisciplinary working group of the Alzheimer's Association on the clinical application of cerebrospinal fluid (CSF) biomarkers for the diagnostics of AD in patients with MCI. If the clinical diagnostic criteria for MCI are fulfilled according to the medical history and neuropsychological testing, it is recommended to carry out further diagnostics (blood test, brain imaging) in order to more precisely define the differential diagnostic classification. Counseling on the potential benefits, limits and risks of biomarker testing for early AD detection and dementia risk prediction should always precede assessment of CSF biomarkers. Information about the individual risk of developing dementia has potential consequences for the psychological well-being and life planning; therefore, clinical follow-up visits are recommended.
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Affiliation(s)
- Ayda Rostamzadeh
- Klinik für Psychiatrie und Psychotherapie, Uniklinik Köln, Medizinische Fakultät, Köln, Deutschland.
| | - Frank Jessen
- Klinik für Psychiatrie und Psychotherapie, Uniklinik Köln, Medizinische Fakultät, Köln, Deutschland.,Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Deutschland
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76
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Bartels C, Kögel A, Schweda M, Wiltfang J, Pentzek M, Schicktanz S, Schneider A. Use of Cerebrospinal Fluid Biomarkers of Alzheimer's Disease Risk in Mild Cognitive Impairment and Subjective Cognitive Decline in Routine Clinical Care in Germany. J Alzheimers Dis 2020; 78:1137-1148. [PMID: 33104034 DOI: 10.3233/jad-200794] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The National Institute of Aging and Alzheimer's Association's diagnostic recommendations for preclinical Alzheimer's disease (AD) and mild cognitive impairment (MCI) define AD by pathological processes which can be detected by biomarkers. These criteria were established as part of a research framework intended for research purposes but progressively enter the clinical practice. OBJECTIVE We investigated the availability, frequency of use, interpretation, and therapeutic implications of biomarkers for the etiologic diagnosis and prognosis in MCI and subjective cognitive decline (SCD) in routine clinical care. METHODS We conducted a cross-sectional questionnaire survey among 215 expert dementia centers (hospitals and memory clinics) in Germany. RESULTS From the 98 centers (45.6% of contacted centers) included, two-thirds reported use of the cerebrospinal fluid (CSF) biomarkers Aβ42, tau, and phospho-tau in the diagnostic workup of MCI and one third in SCD. CSF biomarker analysis was more often employed by neurological (MCI 84%; SCD 42%) compared to psychiatric institutions (MCI 61%; SCD 33%; p≤0.001). Although dementia experts disagreed on the risk of progression associated with different CSF biomarker constellations, CSF biomarker results guided therapeutic decisions: ∼40% of responders reported to initiate cholinesterase inhibitor therapy in MCI and 18% in SCD (p = 0.006), given that all CSF biomarkers were in the pathological range. CONCLUSION Considering the vast heterogeneity among dementia expert centers in use of CSF biomarker analysis, interpretation of results, and therapeutic consequences, a standardization of biomarker-based diagnosis practice in pre-dementia stages is needed.
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Affiliation(s)
- Claudia Bartels
- Department for Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen, Germany
| | - Anna Kögel
- Department of Medical Ethics and History of Medicine, University Medical Center Goettingen, Goettingen, Germany
| | - Mark Schweda
- Department of Medical Ethics and History of Medicine, University Medical Center Goettingen, Goettingen, Germany.,Department of Health Services Research, School for Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, Germany
| | - Jens Wiltfang
- Department for Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen, Germany.,German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany.,iBiMED, Medical Sciences Department, University of Aveiro, Aveiro, Portugal
| | - Michael Pentzek
- Institute of General Practice, Centre for Health and Society, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Silke Schicktanz
- Department of Medical Ethics and History of Medicine, University Medical Center Goettingen, Goettingen, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, 7 Bonn, Germany
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77
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[Memory clinics in Germany-structural requirements and areas of responsibility]. DER NERVENARZT 2020; 92:708-715. [PMID: 33025072 PMCID: PMC8257515 DOI: 10.1007/s00115-020-01007-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 09/04/2020] [Indexed: 01/06/2023]
Abstract
Hintergrund Gedächtnisambulanzen (GA) sind auf (Differenzial‑)Diagnostik, Therapie, Aufklärung, Management und Beratung von kognitiven Störungen im Alter und deren Risikostadien spezialisierte Einrichtungen. In der Praxis haben sie sehr unterschiedliche Organisationsformen. Aufgrund der wachsenden diagnostischen Möglichkeiten bei neurodegenerativen Erkrankungen, dem steigenden Bedarf an Früherkennung und Prädiktion sowie absehbaren neuen diagnostischen Verfahren und krankheitsmodifizierenden Therapien ist eine Vereinheitlichung der strukturellen Voraussetzungen und Aufgabenbereiche für GA sinnvoll. Ziel der Arbeit Der Artikel macht Vorschläge für strukturelle und organisatorische Voraussetzungen, Aufgaben sowie einheitliche Arbeitsweisen von GA in Deutschland. Methoden Expertenkonsens von Psychiatern, Neurologen und Geriatern aus universitären und außeruniversitären Einrichtungen. Ergebnisse Gedächtnisambulanzen sollen den Facharztstandard für Psychiatrie und/oder Neurologie vorhalten und sich in ihrer Arbeitsweise bez. (Differenzial‑)Diagnostik und Therapie von Demenzen eng an der S3-Leitlinie (S3LL-)Demenz orientieren. In Bezug auf Früherkennung und Prädiktion neurodegenerativer Erkrankungen gehen sie über die S3LL-Demenz hinaus. So werden leichte kognitive Störungen (MCI) als Risiko- oder auch Prodromalstadien neurodegenerativer Demenzen verstanden und Biomarker regelhaft für eine ätiologische (Früh- und Differenzial‑)Diagnostik eingesetzt. Es soll eine enge Vernetzung mit den diagnostischen Fachdisziplinen bestehen. Ferner sollen sie Beratung zu sozialen und rechtlichen Fragen sowie Angehörigenberatung anbieten. Aktuelle Erkenntnisse aus der Forschung sollen durch sie frühzeitig in die Versorgung integriert werden. GA sind damit regionale Expertenzentren. Diskussion Gedächtnisambulanzen implementieren den evidenzbasierten Standard in Diagnostik und Therapie in die klinische Versorgung von Patienten mit kognitiven Störungen im Alter. Zusätzlich führen sie diagnostische und therapeutische Innovationen in die Versorgung dieser Patienten ein. Ihre Rolle in der Regelversorgung muss gestärkt werden, wobei auch Finanzierungsfragen geklärt werden müssen, da die derzeitigen Finanzierungsmodelle in der Regel nicht kostendeckend sind.
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Ball HA, McWhirter L, Ballard C, Bhome R, Blackburn DJ, Edwards MJ, Fleming SM, Fox NC, Howard R, Huntley J, Isaacs JD, Larner AJ, Nicholson TR, Pennington CM, Poole N, Price G, Price JP, Reuber M, Ritchie C, Rossor MN, Schott JM, Teodoro T, Venneri A, Stone J, Carson AJ. Functional cognitive disorder: dementia's blind spot. Brain 2020; 143:2895-2903. [PMID: 32791521 PMCID: PMC7586080 DOI: 10.1093/brain/awaa224] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 05/06/2020] [Accepted: 05/21/2020] [Indexed: 12/25/2022] Open
Abstract
An increasing proportion of cognitive difficulties are recognized to have a functional cause, the chief clinical indicator of which is internal inconsistency. When these symptoms are impairing or distressing, and not better explained by other disorders, this can be conceptualized as a cognitive variant of functional neurological disorder, termed functional cognitive disorder (FCD). FCD is likely very common in clinical practice but may be under-diagnosed. Clinicians in many settings make liberal use of the descriptive term mild cognitive impairment (MCI) for those with cognitive difficulties not impairing enough to qualify as dementia. However, MCI is an aetiology-neutral description, which therefore includes patients with a wide range of underlying causes. Consequently, a proportion of MCI cases are due to non-neurodegenerative processes, including FCD. Indeed, significant numbers of patients diagnosed with MCI do not 'convert' to dementia. The lack of diagnostic specificity for MCI 'non-progressors' is a weakness inherent in framing MCI primarily within a deterministic neurodegenerative pathway. It is recognized that depression, anxiety and behavioural changes can represent a prodrome to neurodegeneration; empirical data are required to explore whether the same might hold for subsets of individuals with FCD. Clinicians and researchers can improve study efficacy and patient outcomes by viewing MCI as a descriptive term with a wide differential diagnosis, including potentially reversible components such as FCD. We present a preliminary definition of functional neurological disorder-cognitive subtype, explain its position in relation to other cognitive diagnoses and emerging biomarkers, highlight clinical features that can lead to positive diagnosis (as opposed to a diagnosis of exclusion), and red flags that should prompt consideration of alternative diagnoses. In the research setting, positive identifiers of FCD will enhance our recognition of individuals who are not in a neurodegenerative prodrome, while greater use of this diagnosis in clinical practice will facilitate personalized interventions.
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Affiliation(s)
- Harriet A Ball
- Population Health Sciences, University of Bristol, BS8 1QU, UK
| | - Laura McWhirter
- Centre for Clinical Brain Sciences, The University of Edinburgh, EH16 4SB, UK
| | - Clive Ballard
- College of Medicine and Health, University of Exeter, EX1 2LU, UK
| | - Rohan Bhome
- Division of Psychiatry, University College London, W1T 7NF, UK
| | - Daniel J Blackburn
- Department of Neuroscience, Medical School, The University of Sheffield, S10 2TN, UK
| | - Mark J Edwards
- Neuroscience Research Centre, St George's, University of London, SW17 0RE, UK
| | - Stephen M Fleming
- Wellcome Centre for Human Neuroimaging, University College London, WC1N 3AR, UK
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegenerative Diseases, UCL Queen Square Institute of Neurology, WC1E 6BT, UK
| | - Robert Howard
- Division of Psychiatry, University College London, W1T 7NF, UK
| | | | - Jeremy D Isaacs
- Neuroscience Research Centre, St George's, University of London, SW17 0RE, UK.,Department of Neurology, St George's University Hospitals NHS Foundation Trust, London, SW17 0QT, UK
| | - Andrew J Larner
- Cognitive Function Clinic, Walton Centre for Neurology and Neurosurgery, Liverpool, L9 7LJ, UK
| | - Timothy R Nicholson
- Institute of Psychiatry Psychology and Neuroscience, King's College London, SE5 8AF, UK
| | | | - Norman Poole
- Department of Neurology, St George's University Hospitals NHS Foundation Trust, London, SW17 0QT, UK
| | - Gary Price
- University College London Hospitals NHS Foundation Trust, NW1 2BU, UK
| | - Jason P Price
- Department of Neuropsychology, South Tees Hospitals NHS Foundation Trust, TS4 3BW, UK
| | - Markus Reuber
- Department of Neuroscience, Medical School, The University of Sheffield, S10 2TN, UK
| | - Craig Ritchie
- Centre for Clinical Brain Sciences, The University of Edinburgh, EH16 4SB, UK
| | - Martin N Rossor
- Dementia Research Centre, Department of Neurodegenerative Diseases, UCL Queen Square Institute of Neurology, WC1E 6BT, UK
| | - Jonathan M Schott
- Dementia Research Centre, Department of Neurodegenerative Diseases, UCL Queen Square Institute of Neurology, WC1E 6BT, UK
| | - Tiago Teodoro
- Neuroscience Research Centre, St George's, University of London, SW17 0RE, UK.,Instituto de Medicina Molecular, Universidade de Lisbon, 1649-028 Lisboa, Portugal
| | - Annalena Venneri
- Department of Neuroscience, Medical School, The University of Sheffield, S10 2TN, UK
| | - Jon Stone
- Centre for Clinical Brain Sciences, The University of Edinburgh, EH16 4SB, UK
| | - Alan J Carson
- Centre for Clinical Brain Sciences, The University of Edinburgh, EH16 4SB, UK
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79
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Buegler M, Harms R, Balasa M, Meier IB, Exarchos T, Rai L, Boyle R, Tort A, Kozori M, Lazarou E, Rampini M, Cavaliere C, Vlamos P, Tsolaki M, Babiloni C, Soricelli A, Frisoni G, Sanchez-Valle R, Whelan R, Merlo-Pich E, Tarnanas I. Digital biomarker-based individualized prognosis for people at risk of dementia. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2020; 12:e12073. [PMID: 32832589 PMCID: PMC7437401 DOI: 10.1002/dad2.12073] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 06/30/2020] [Indexed: 12/23/2022]
Abstract
Background Research investigating treatments and interventions for cognitive decline fail due to difficulties in accurately recognizing behavioral signatures in the presymptomatic stages of the disease. For this validation study, we took our previously constructed digital biomarker‐based prognostic models and focused on generalizability and robustness of the models. Method We validated prognostic models characterizing subjects using digital biomarkers in a longitudinal, multi‐site, 40‐month prospective study collecting data in memory clinics, general practitioner offices, and home environments. Results Our models were able to accurately discriminate between healthy subjects and individuals at risk to progress to dementia within 3 years. The model was also able to differentiate between people with or without amyloid neuropathology and classify fast and slow cognitive decliners with a very good diagnostic performance. Conclusion Digital biomarker prognostic models can be a useful tool to assist large‐scale population screening for the early detection of cognitive impairment and patient monitoring over time.
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Affiliation(s)
| | | | - Mircea Balasa
- Global Brain Health Institute San Francisco, California USA
| | | | - Themis Exarchos
- Bioinformatics and Human Electrophysiology Laboratory Corfu Greece
| | - Laura Rai
- Trinity College Institute of Neuroscience College Green, Dublin Ireland
| | - Rory Boyle
- Trinity College Institute of Neuroscience College Green, Dublin Ireland
| | - Adria Tort
- Institut d'Investigació Biomèdica August Pi i Sunyer Carrer del Rosselló, Barcelona Spain
| | - Maha Kozori
- Greek Association for Alzheimer's Disease and Related Disorders, Thessaloniki Greece
| | - Eutuxia Lazarou
- Greek Association for Alzheimer's Disease and Related Disorders, Thessaloniki Greece
| | | | | | | | - Magda Tsolaki
- 1st Department of Neurology AHEPA University Hospital, Thessaloniki Greece.,Information Technologies Institute Centre for Research and Technology Hellas (CERTH); Aristotle University of Thessaloniki, Thermi Greece
| | - Claudio Babiloni
- Department of Physiology and Pharmacology University of Rome, Roma Italy.,San Raffaele Cassino, Cassino (FR), Italy
| | - Andrea Soricelli
- 1st Department of Neurology AHEPA University Hospital, Thessaloniki Greece.,University of Naples Parthenope, Napoli Italy
| | - Giovanni Frisoni
- University of Geneva, Geneva Switzerland.,Laboratory of Neuroimaging and Alzheimer's Epidemiology IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia Italy.,Memory Clinic and LANVIE, Geneva Switzerland.,University of Brescia, Brescia Italy
| | - Raquel Sanchez-Valle
- IDIBAPS Neurological Tissue Bank Hospital Clinic, Barcelona Spain.,Institut d'Investigació Biomèdica August Pi i Sunyer, Barcelona Spain.,Alzheimer's Disease and Other Cognitive Disorders Unit Hospital Clínic Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona Spain
| | - Robert Whelan
- Trinity College Institute of Neuroscience College Green, Dublin Ireland
| | | | - Ioannis Tarnanas
- Altoida Inc. Houston, Texas USA.,Global Brain Health Institute San Francisco, California USA.,Hellenic Initiative Against Alzheimer's Disease, Johns Hopkins Precision Medicine Center, Baltimore, Maryland, United States and BiHeLab, Ionian University, Kerkira, Greece
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80
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Mattke S, Cho SK, Bittner T, Hlávka J, Hanson M. Blood-based biomarkers for Alzheimer's pathology and the diagnostic process for a disease-modifying treatment: Projecting the impact on the cost and wait times. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12081. [PMID: 32832590 PMCID: PMC7434228 DOI: 10.1002/dad2.12081] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/09/2020] [Accepted: 07/13/2020] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Concerns have been raised about the limited health system capacity for identification of patients who are eligible for a disease-modifying Alzheimer's treatment (DMT). Blood-based biomarker (BBBM) tests are a promising tool to improve triaging at the primary care level. We projected their impact on cost of and wait times during the diagnostic process. METHODS We compare four scenarios for triaging patients at the primary care level from the perspective of the U.S. health care system: (1) cognitive test only (Mini Mental State Examination [MMSE]), (2) BBBM test only, (3) MMSE followed by BBBM if positive, and (4) BBBM followed by MMSE if positive. RESULTS Referring patients to dementa specialists based on MMSE or BBBM results alone would continuously require more specialist appointments than projected to be available until 2050. Combining MMSE and BBBM would eliminate wait lists after the first 3 years and reduce average annual cost by $400 to 700 million, while increasing correctly identified cases by about 120,000 per year. DISCUSSION The combination BBBM with MMSE is projected to increase the efficiency and value of the triage process for DMT eligibility.
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Affiliation(s)
- Soeren Mattke
- Center for Economic and Social ResearchUniversity of Southern CaliforniaLos AngelesUSA
| | - Sang Kyu Cho
- Leonard D. Schaeffer Center for Health Policy and EconomicsUniversity of Southern CaliforniaLos AngelesUSA
| | | | - Jakub Hlávka
- Leonard D. Schaeffer Center for Health Policy and EconomicsUniversity of Southern CaliforniaLos AngelesUSA
| | - Mark Hanson
- Center for Economic and Social ResearchUniversity of Southern CaliforniaLos AngelesUSA
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81
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Burnham SC, Fandos N, Fowler C, Pérez-Grijalba V, Dore V, Doecke JD, Shishegar R, Cox T, Fripp J, Rowe C, Sarasa M, Masters CL, Pesini P, Villemagne VL. Longitudinal evaluation of the natural history of amyloid-β in plasma and brain. Brain Commun 2020; 2:fcaa041. [PMID: 32954297 PMCID: PMC7425352 DOI: 10.1093/braincomms/fcaa041] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 02/26/2020] [Accepted: 03/04/2020] [Indexed: 01/03/2023] Open
Abstract
Plasma amyloid-β peptide concentration has recently been shown to have high accuracy to predict amyloid-β plaque burden in the brain. These amyloid-β plasma markers will allow wider screening of the population and simplify and reduce screening costs for therapeutic trials in Alzheimer's disease. The aim of this study was to determine how longitudinal changes in blood amyloid-β track with changes in brain amyloid-β. Australian Imaging, Biomarker and Lifestyle study participants with a minimum of two assessments were evaluated (111 cognitively normal, 7 mild cognitively impaired, 15 participants with Alzheimer's disease). Amyloid-β burden in the brain was evaluated through PET and was expressed in Centiloids. Total protein amyloid-β 42/40 plasma ratios were determined using ABtest® assays. We applied our method for obtaining natural history trajectories from short term data to measures of total protein amyloid-β 42/40 plasma ratios and PET amyloid-β. The natural history trajectory of total protein amyloid-β 42/40 plasma ratios appears to approximately mirror that of PET amyloid-β, with both spanning decades. Rates of change of 7.9% and 8.8%, were observed for total protein amyloid-β 42/40 plasma ratios and PET amyloid-β, respectively. The trajectory of plasma amyloid-β preceded that of brain amyloid-β by a median value of 6 years (significant at 88% confidence interval). These findings, showing the tight association between changes in plasma and brain amyloid-β, support the use of plasma total protein amyloid-β 42/40 plasma ratios as a surrogate marker of brain amyloid-β. Also, that plasma total protein amyloid-β 42/40 plasma ratios has potential utility in monitoring trial participants, and as an outcome measure.
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Affiliation(s)
- Samantha C Burnham
- The Australian e-Health Research Centre, CSIRO Health & Biosecurity, Parkville, VIC 3052, Australia
- Centre of Excellence for Alzheimer’s Disease Research and Care, School of Medical Sciences, Edith Cowan University, Joondalup, WA 6027, Australia
| | | | - Christopher Fowler
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC 3010, Australia
| | | | - Vincent Dore
- The Australian e-Health Research Centre, CSIRO Health & Biosecurity, Parkville, VIC 3052, Australia
- Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, VIC 3084, Australia
| | - James D Doecke
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Herston 4029, Australia
| | - Rosita Shishegar
- The Australian e-Health Research Centre, CSIRO Health & Biosecurity, Parkville, VIC 3052, Australia
| | - Timothy Cox
- The Australian e-Health Research Centre, CSIRO Health & Biosecurity, Parkville, VIC 3052, Australia
| | - Jurgen Fripp
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Herston 4029, Australia
| | - Christopher Rowe
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC 3010, Australia
- Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, VIC 3084, Australia
- Department of Medicine, The University of Melbourne, Parkville, VIC 3052, Australia
| | | | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC 3010, Australia
| | | | - Victor L Villemagne
- Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, VIC 3084, Australia
- Department of Medicine, The University of Melbourne, Parkville, VIC 3052, Australia
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82
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Rosenberg A, Solomon A, Jelic V, Hagman G, Bogdanovic N, Kivipelto M. Progression to dementia in memory clinic patients with mild cognitive impairment and normal β-amyloid. ALZHEIMERS RESEARCH & THERAPY 2019; 11:99. [PMID: 31805990 PMCID: PMC6896336 DOI: 10.1186/s13195-019-0557-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 11/13/2019] [Indexed: 12/13/2022]
Abstract
Background Determination of β-amyloid (Aβ) positivity and likelihood of underlying Alzheimer’s disease (AD) relies on dichotomous biomarker cut-off values. Individuals with mild cognitive impairment (MCI) and Aβ within the normal range may still have a substantial risk of developing dementia, primarily of Alzheimer type. Their prognosis, as well as predictors of clinical progression, are not fully understood. The aim of this study was to explore the associations of cerebrospinal fluid (CSF) biomarkers (Aβ42, total tau, phosphorylated tau) and other characteristics, including modifiable vascular factors, with the risk of progression to dementia among patients with MCI and normal CSF Aβ42. Methods Three hundred eighteen memory clinic patients with CSF and clinical data, and at least 1-year follow-up, were included. Patients had normal CSF Aβ42 levels based on clinical cut-offs. Cox proportional hazard models with age as time scale and adjusted for sex, education, and cognition (Mini-Mental State Examination) were used to investigate predictors of progression to dementia and Alzheimer-type dementia. Potential predictors included CSF biomarkers, cognitive performance (verbal learning and memory), apolipoprotein E (APOE) ε4 genotype, medial temporal lobe atrophy, family history of dementia, depressive symptoms, and vascular factors, including the Cardiovascular Risk Factors, Aging and Dementia (CAIDE) risk score. Predictive performance of patient characteristics was further explored with Harrell C statistic. Results Lower normal Aβ42 and higher total tau and phosphorylated tau were associated with higher dementia risk, and the association was not driven by Aβ42 values close to cut-off. Additional predictors included poorer cognition, APOE ε4 genotype, higher systolic blood pressure, and lower body mass index, but not the CAIDE dementia risk score. Aβ42 individually and in combination with other CSF biomarkers improved the risk prediction compared to age and cognition alone. Medial temporal lobe atrophy or vascular factors did not increase the predictive performance. Conclusions Possibility of underlying AD pathology and increased dementia risk should not be ruled out among MCI patients with CSF Aβ42 within the normal range. While cut-offs may be useful in clinical practice to identify high-risk individuals, personalized risk prediction tools incorporating continuous biomarkers may be preferable among individuals with intermediate risk. The role of modifiable vascular factors could be explored in this context.
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Affiliation(s)
- Anna Rosenberg
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.
| | - Alina Solomon
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Vesna Jelic
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Clinic for Cognitive Disorders, Theme Aging, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Göran Hagman
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Clinic for Cognitive Disorders, Theme Aging, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Nenad Bogdanovic
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Clinic for Cognitive Disorders, Theme Aging, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Miia Kivipelto
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Clinic for Cognitive Disorders, Theme Aging, Karolinska University Hospital-Huddinge, Stockholm, Sweden.,Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland.,The Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
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83
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Burnham SC, Loi SM, Doecke J, Fedyashov V, Dore V, Villemagne VL, Masters CL. The dawn of robust individualised risk models for dementia. Lancet Neurol 2019; 18:985-987. [PMID: 31526626 DOI: 10.1016/s1474-4422(19)30353-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 09/02/2019] [Indexed: 01/06/2023]
Affiliation(s)
- Samantha C Burnham
- The Australian e-Health Research Centre, CSIRO Health & Biosecurity, Parkville, VIC 3052, Australia; Centre of Excellence for Alzheimer's Disease Research & Care, School of Medical Sciences, Edith Cowan University, Joondalup, WA, Australia.
| | - Samantha M Loi
- Neuropsychiatry Unit, NorthWestern Mental Health, Royal Melbourne Hospital, Parkville, VIC, Australia; Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia
| | - James Doecke
- The Australian e-Health Research Centre, CSIRO, Herston, QLD, Australia
| | - Victor Fedyashov
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia; ARC Training Centre in Cognitive Computing for Medical Technologies, The University of Melbourne, Parkville, VIC, Australia
| | - Vincent Dore
- The Australian e-Health Research Centre, CSIRO Health & Biosecurity, Parkville, VIC 3052, Australia; Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, VIC, Australia
| | - Victor L Villemagne
- Department of Medicine, The University of Melbourne, Parkville, VIC, Australia; Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, VIC, Australia
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
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