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Anderson EL, Davies NM, Korologou-Linden R, Kivimäki M. Dementia prevention: the Mendelian randomisation perspective. J Neurol Neurosurg Psychiatry 2024; 95:384-390. [PMID: 37967935 DOI: 10.1136/jnnp-2023-332293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/25/2023] [Indexed: 11/17/2023]
Abstract
Understanding the causes of Alzheimer's disease and related dementias remains a challenge. Observational studies investigating dementia risk factors are limited by the pervasive issues of confounding, reverse causation and selection biases. Conducting randomised controlled trials for dementia prevention is often impractical due to the long prodromal phase and the inability to randomise many potential risk factors. In this essay, we introduce Mendelian randomisation as an alternative approach to examine factors that may prevent or delay Alzheimer's disease. Mendelian randomisation is a causal inference method that has successfully identified risk factors and treatments in various other fields. However, applying this method to dementia risk factors has yielded unexpected findings. Here, we consider five potential explanations and provide recommendations to enhance causal inference from Mendelian randomisation studies on dementia. By employing these strategies, we can better understand factors affecting dementia risk.
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Affiliation(s)
- Emma Louise Anderson
- Mental Health of Older People, Division of Psychiatry, University College London, London, UK
| | - Neil M Davies
- Epidemiology & Applied Clinical Research, Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, UK
| | | | - Mika Kivimäki
- Mental Health of Older People, Division of Psychiatry, University College London, London, UK
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Clark K, Fu W, Liu CL, Ho PC, Wang H, Lee WP, Chou SY, Wang LS, Tzeng JY. The prediction of Alzheimer's disease through multi-trait genetic modeling. Front Aging Neurosci 2023; 15:1168638. [PMID: 37577355 PMCID: PMC10416111 DOI: 10.3389/fnagi.2023.1168638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 06/26/2023] [Indexed: 08/15/2023] Open
Abstract
To better capture the polygenic architecture of Alzheimer's disease (AD), we developed a joint genetic score, MetaGRS. We incorporated genetic variants for AD and 24 other traits from two independent cohorts, NACC (n = 3,174, training set) and UPitt (n = 2,053, validation set). One standard deviation increase in the MetaGRS is associated with about 57% increase in the AD risk [hazard ratio (HR) = 1.577, p = 7.17 E-56], showing little difference from the HR for AD GRS alone (HR = 1.579, p = 1.20E-56), suggesting similar utility of both models. We also conducted APOE-stratified analyses to assess the role of the e4 allele on risk prediction. Similar to that of the combined model, our stratified results did not show a considerable improvement of the MetaGRS. Our study showed that the prediction power of the MetaGRS significantly outperformed that of the reference model without any genetic information, but was effectively equivalent to the prediction power of the AD GRS.
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Affiliation(s)
- Kaylyn Clark
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Wei Fu
- Department of Health Management and Systems Sciences, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, United States
| | - Chia-Lun Liu
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Pei-Chuan Ho
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, United States
| | - Hui Wang
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Wan-Ping Lee
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Shin-Yi Chou
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Economics, Lehigh University, Bethlehem, PA, United States
- National Bureau of Economic Research, Cambridge, MA, United States
| | - Li-San Wang
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jung-Ying Tzeng
- Department of Pathology and Laboratory Medicine, Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Statistics, North Carolina State University, Raleigh, NC, United States
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, United States
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Anstey KJ, Zheng L, Peters R, Kootar S, Barbera M, Stephen R, Dua T, Chowdhary N, Solomon A, Kivipelto M. Dementia Risk Scores and Their Role in the Implementation of Risk Reduction Guidelines. Front Neurol 2022; 12:765454. [PMID: 35058873 PMCID: PMC8764151 DOI: 10.3389/fneur.2021.765454] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 12/07/2021] [Indexed: 12/24/2022] Open
Abstract
Dementia prevention is a global health priority. In 2019, the World Health Organisation published its first evidence-based guidelines on dementia risk reduction. We are now at the stage where we need effective tools and resources to assess dementia risk and implement these guidelines into policy and practice. In this paper we review dementia risk scores as a means to facilitate this process. Specifically, we (a) discuss the rationale for dementia risk assessment, (b) outline some conceptual and methodological issues to consider when reviewing risk scores, (c) evaluate some dementia risk scores that are currently in use, and (d) provide some comments about future directions. A dementia risk score is a weighted composite of risk factors that reflects the likelihood of an individual developing dementia. In general, dementia risks scores have a wide range of implementations and benefits including providing early identification of individuals at high risk, improving risk perception for patients and physicians, and helping health professionals recommend targeted interventions to improve lifestyle habits to decrease dementia risk. A number of risk scores for dementia have been published, and some are widely used in research and clinical trials e.g., CAIDE, ANU-ADRI, and LIBRA. However, there are some methodological concerns and limitations associated with the use of these risk scores and more research is needed to increase their effectiveness and applicability. Overall, we conclude that, while further refinement of risk scores is underway, there is adequate evidence to use these assessments to implement guidelines on dementia risk reduction.
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Affiliation(s)
- Kaarin J Anstey
- School of Psychology, University of New South Wales, Sydney, NSW, Australia.,Neuroscience Research Australia, Randwick, NSW, Australia
| | - Lidan Zheng
- School of Psychology, University of New South Wales, Sydney, NSW, Australia.,Neuroscience Research Australia, Randwick, NSW, Australia
| | - Ruth Peters
- School of Psychology, University of New South Wales, Sydney, NSW, Australia.,Neuroscience Research Australia, Randwick, NSW, Australia
| | - Scherazad Kootar
- School of Psychology, University of New South Wales, Sydney, NSW, Australia.,Neuroscience Research Australia, Randwick, NSW, Australia
| | - Mariagnese Barbera
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,The Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, United Kingdom
| | - Ruth Stephen
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Tarun Dua
- Brain Health Unit, Department of Mental Health and Substance Use, World Health Organization, Geneva, Switzerland
| | - Neerja Chowdhary
- Brain Health Unit, Department of Mental Health and Substance Use, World Health Organization, Geneva, Switzerland
| | - Alina Solomon
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,The Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, United Kingdom.,Division of Clinical Geriatrics, Department of Neurobiology, Center for Alzheimer's Research, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Miia Kivipelto
- The Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, United Kingdom.,Division of Clinical Geriatrics, Department of Neurobiology, Center for Alzheimer's Research, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden.,Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden.,Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
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Licinio J, Wong ML. Molecular Psychiatry special issue: advances in Alzheimer's disease. Mol Psychiatry 2021; 26:5467-5470. [PMID: 35027660 DOI: 10.1038/s41380-021-01434-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 12/23/2021] [Indexed: 11/10/2022]
Affiliation(s)
- Julio Licinio
- State University of New York, Upstate Medical University, Syracuse, NY, 13210, USA.
| | - Ma-Li Wong
- State University of New York, Upstate Medical University, Syracuse, NY, 13210, USA
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