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Baneshi MR, Dobson A, Mishra GD. Transition between cardiometabolic conditions and body weight among women: which paths increase the risk of diabetes and cardiovascular diseases? J Hum Hypertens 2024; 38:611-619. [PMID: 38866978 PMCID: PMC11329370 DOI: 10.1038/s41371-024-00923-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 06/02/2024] [Accepted: 06/07/2024] [Indexed: 06/14/2024]
Abstract
Previous studies investigated the association of body weight and hypertension with risk of incident cardiometabolic multimorbidity. Our aim was to estimate the risk of diabetes and cardiovascular disease later in life for subjects with different progression patterns of overweight, obesity, and hypertension in mid-life. This was a prospective cohort study in which data from 12,784 participants in the Australian Longitudinal Study on Women's Health were used. Multistate model was used to study the progression pattern of overweight, obesity, hypertension, diabetes, and cardiovascular disease over the life course. The cumulative incidence of diabetes and cardiovascular disease up to the age of 73 was estimated for women with different patterns of other conditions. The six most common paths and corresponding cumulative incidences for diabetes were overweight 5.1%, obesity 11.5%, hypertension 6.9%, progression from overweight to obesity 8.2%, overweight and hypertension 12.1%, and obesity and hypertension 36.8%. For women with diabetes and other conditions, the cumulative incidence of cardiovascular disease (heart disease or stroke) as the next immediate condition was 22.4%. The corresponding figure for women who only had a report of diabetes but did not have high body weight or hypertension was 8.3%. The higher risk of transition from healthy state to a cardiometabolic condition was associated with low education, income stress, smoking, not drinking alcohol (compared to low drinkers), physical inactivity, and high perceived stress. Women with obesity and hypertension in middle-age had a substantially higher risk of developing diabetes and cardiovascular disease than women without these potentially preventable conditions.
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Affiliation(s)
- Mohammad R Baneshi
- The University of Queensland, Australian Women and Girls' Health Research Centre, School of Public Health, Herston Road, Herston, QLD, Australia.
| | - Annette Dobson
- The University of Queensland, Australian Women and Girls' Health Research Centre, School of Public Health, Herston Road, Herston, QLD, Australia
| | - Gita D Mishra
- The University of Queensland, Australian Women and Girls' Health Research Centre, School of Public Health, Herston Road, Herston, QLD, Australia
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Wei J, Lohman MC, Brown MJ, Hardin JW, Yang CH, Merchant AT, Friedman DB. Modifiable and Non-Modifiable Risk Factors for Dementia Among Non-Hispanic White and Black Populations Aged 50-64 in the United States, 2006-2016. J Geriatr Psychiatry Neurol 2024:8919887241267315. [PMID: 39037016 DOI: 10.1177/08919887241267315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
Abstract
BACKGROUND AND OBJECTIVES Non-Hispanic Black populations (NHB) have a significantly higher prevalence of dementia than non-Hispanic Whites in the U.S., and the underlying risk factors may play a role in this racial disparity. We aimed to calculate risk scores for dementia among non-Hispanic White (NHW) and non-Hispanic Black populations aged 50-64 years over a period of 10 years, and to estimate potential differences of scores between NHW and NHB. RESEARCH DESIGN AND METHODS The Health and Retirement Study from 2006 to 2016 was used to calculate the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) risk score, a validated score for predicting dementia risk. Weighted average CAIDE score, as well as CAIDE score for modifiable factors hypertension, obese, hypercholesterolemia, physical inactivity), and non-modifiable factors (age, sex, education) were calculated for adults aged 50-64 years with normal cognition for 2006-2008, 2010-2012, 2014-2016. The associations of race with CAIDE score and elevated CAIDE score were examined. RESULTS A total of 10,871 participants were included in the analysis. The CAIDE score showed declining trends for NHB from 2006 to 2016, while NHB consistently had a higher total CAIDE score and CAIDE score for modifiable factors from 2006 to 2016, but not for non-modifiable factors. DISCUSSION AND IMPLICATIONS NHB had a higher level of dementia risk factors than NHW among adults aged 50-64 years in the U.S. from 2006 to 2016, and the difference is attributable to modifiable risk factors, which holds promise for risk reduction of dementia.
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Affiliation(s)
- Jingkai Wei
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- The Office for the Study of Aging, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Matthew C Lohman
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- The Office for the Study of Aging, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Monique J Brown
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- The Office for the Study of Aging, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - James W Hardin
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Chih-Hsiang Yang
- The Office for the Study of Aging, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Anwar T Merchant
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- The Office for the Study of Aging, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Daniela B Friedman
- The Office for the Study of Aging, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
- Department of Health Promotion, Education, Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
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Putri AF, Utomo DH, Tunjung WAS, Putri WA. Analysis of the anti-Alzheimer potential of bioactive compounds from Citrus hystrix DC. peel, leaf, and essential oil by network pharmacology. Heliyon 2024; 10:e33496. [PMID: 39050443 PMCID: PMC11267028 DOI: 10.1016/j.heliyon.2024.e33496] [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: 02/21/2024] [Revised: 05/30/2024] [Accepted: 06/21/2024] [Indexed: 07/27/2024] Open
Abstract
Alzheimer's disease (AD) is the most known neurodegenerative disease, and its prevalence is predicted to increase significantly. Discovering novel drugs and treatments for AD is urgently needed. Drugs from natural products have been preferred lately due to their high potential and low toxicity. Citrus hystrix DC. (kaffir lime; KL) is one such herbal plant that is found abundantly in Southeast Asia with many biological activities. In this study, the potential of bioactive compounds from KL peel, leaf, and essential oil as anti-AD agents was explored using network pharmacology. First, the compounds were identified with KNApSAcK database and related literature. Subsequently, the targets of each corresponding compound were determined with SEA Search Server and Swiss Target Prediction, while the proteins associated with AD were identified using OMIM, GenCLiP3, and DisGeNET. Furthermore, a protein-protein interaction network and a compound-target interaction network were constructed to identify the most crucial proteins and compounds in the network by employing Cytoscape v3.9.1. The study continued with pathway enrichment analysis using STRING v1.7.1, molecular docking with PyRx and SwissDock, and molecular dynamics simulation with YASARA for further confirmation. Our results showed that almost all the secondary metabolites of KL targeted AD-associated genes, with oxypeucedanin and citrusoside A showing the highest anti-AD potential and targeting essential genes, EGFR and MAPK14, respectively. These targets were associated with inflammatory and oxidative stress pathways, indicating the potential mechanism of KL in attenuating AD clinical manifestation.
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Affiliation(s)
- Adhisa Fathirisari Putri
- Faculty of Biology, Universitas Gadjah Mada, Jl. Teknika Selatan, Sekip Utara, Yogyakarta, 55281, Indonesia
- Bioinformatics Research Center, INBIO-Indonesia, Malang, 65162, Indonesia
| | - Didik Huswo Utomo
- Bioinformatics Research Center, INBIO-Indonesia, Malang, 65162, Indonesia
- Biosystem Education Center, Brawijaya University, Malang, 65145, Indonesia
| | - Woro Anindito Sri Tunjung
- Faculty of Biology, Universitas Gadjah Mada, Jl. Teknika Selatan, Sekip Utara, Yogyakarta, 55281, Indonesia
| | - Wahyu Aristyaning Putri
- Faculty of Biology, Universitas Gadjah Mada, Jl. Teknika Selatan, Sekip Utara, Yogyakarta, 55281, Indonesia
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Trares K, Wiesenfarth M, Stocker H, Perna L, Petrera A, Hauck SM, Beyreuther K, Brenner H, Schöttker B. Addition of inflammation-related biomarkers to the CAIDE model for risk prediction of all-cause dementia, Alzheimer's disease and vascular dementia in a prospective study. Immun Ageing 2024; 21:23. [PMID: 38570813 PMCID: PMC10988812 DOI: 10.1186/s12979-024-00427-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 03/20/2024] [Indexed: 04/05/2024]
Abstract
BACKGROUND It is of interest whether inflammatory biomarkers can improve dementia prediction models, such as the widely used Cardiovascular Risk Factors, Aging and Dementia (CAIDE) model. METHODS The Olink Target 96 Inflammation panel was assessed in a nested case-cohort design within a large, population-based German cohort study (n = 9940; age-range: 50-75 years). All study participants who developed dementia over 20 years of follow-up and had complete CAIDE variable data (n = 562, including 173 Alzheimer's disease (AD) and 199 vascular dementia (VD) cases) as well as n = 1,356 controls were selected for measurements. 69 inflammation-related biomarkers were eligible for use. LASSO logistic regression and bootstrapping were utilized to select relevant biomarkers and determine areas under the curve (AUCs). RESULTS The CAIDE model 2 (including Apolipoprotein E (APOE) ε4 carrier status) predicted all-cause dementia, AD, and VD better than CAIDE model 1 (without APOE ε4) with AUCs of 0.725, 0.752 and 0.707, respectively. Although 20, 7, and 4 inflammation-related biomarkers were selected by LASSO regression to improve CAIDE model 2, the AUCs did not increase markedly. CAIDE models 1 and 2 generally performed better in mid-life (50-64 years) than in late-life (65-75 years) sub-samples of our cohort, but again, inflammation-related biomarkers did not improve their predictive abilities. CONCLUSIONS Despite a lack of improvement in dementia risk prediction, the selected inflammation-related biomarkers were significantly associated with dementia outcomes and may serve as a starting point to further elucidate the pathogenesis of dementia.
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Affiliation(s)
- Kira Trares
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Im Neuenheimer Feld 581, Heidelberg, 69120, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Hannah Stocker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Im Neuenheimer Feld 581, Heidelberg, 69120, Germany
| | - Laura Perna
- Department of Genes and Environment, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, Munich, 80804, Germany
- Division of Mental Health of Older Adults, Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Agnese Petrera
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Stefanie M Hauck
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Konrad Beyreuther
- Network Aging Research, Heidelberg University, Bergheimer Straße 20, Heidelberg, 69115, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Im Neuenheimer Feld 581, Heidelberg, 69120, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Im Neuenheimer Feld 581, Heidelberg, 69120, Germany.
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Liu L, Gracely EJ, Zhao X, Gliebus GP, May NS, Volpe SL, Shi J, DiMaria-Ghalili RA, Eisen HJ. Association of multiple metabolic and cardiovascular markers with the risk of cognitive decline and mortality in adults with Alzheimer's disease and AD-related dementia or cognitive decline: a prospective cohort study. Front Aging Neurosci 2024; 16:1361772. [PMID: 38628973 PMCID: PMC11020085 DOI: 10.3389/fnagi.2024.1361772] [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: 12/26/2023] [Accepted: 02/26/2024] [Indexed: 04/19/2024] Open
Abstract
Background and objectives There is a scarcity of data stemming from large-scale epidemiological longitudinal studies focusing on potentially preventable and controllable risk factors for Alzheimer's disease (AD) and AD-related dementia (ADRD). This study aimed to examine the effect of multiple metabolic factors and cardiovascular disorders on the risk of cognitive decline and AD/ADRD. Methods We analyzed a cohort of 6,440 participants aged 45-84 years at baseline. Multiple metabolic and cardiovascular disorder factors included the five components of the metabolic syndrome [waist circumference, high blood pressure (HBP), elevated glucose and triglyceride (TG) concentrations, and reduced high-density lipoprotein cholesterol (HDL-C) concentrations], C-reactive protein (CRP), fibrinogen, interleukin-6 (IL-6), factor VIII, D-dimer, and homocysteine concentrations, carotid intimal-medial thickness (CIMT), and urine albumin-to-creatinine ratio (ACR). Cognitive decline was defined using the Cognitive Abilities Screening Instrument (CASI) score, and AD/ADRD cases were classified using clinical diagnoses. Results Over an average follow-up period of 13 years, HBP and elevated glucose, CRP, homocysteine, IL-6, and ACR concentrations were significantly associated with the risk of mortality in the individuals with incident AD/ADRD or cognitive decline. Elevated D-dimer and homocysteine concentrations, as well as elevated ACR were significantly associated with incident AD/ADRD. Elevated homocysteine and ACR were significantly associated with cognitive decline. A dose-response association was observed, indicating that an increased number of exposures to multiple risk factors corresponded to a higher risk of mortality in individuals with cognitive decline or with AD/ADRD. Conclusion Findings from our study reaffirm the significance of preventable and controllable factors, including HBP, hyperglycemia, elevated CRP, D-dimer, and homocysteine concentrations, as well as, ACR, as potential risk factors for cognitive decline and AD/ADRD.
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Affiliation(s)
- Longjian Liu
- Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA, United States
| | - Edward J. Gracely
- Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA, United States
- Department of Family, Community & Preventive Medicine, College of Medicine, Drexel University, Philadelphia, PA, United States
| | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Gediminas P. Gliebus
- Department of Neurology, College of Medicine, Drexel University Philadelphia, Philadelphia, PA, United States
| | - Nathalie S. May
- Department of Medicine, College of Medicine, Drexel University, Philadelphia, PA, United States
| | - Stella L. Volpe
- Department of Human Nutrition, Foods, and Exercise, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
| | - Jingyi Shi
- Department of Mathematics and Statistics, Mississippi State University, Starkville, MS, United States
| | - Rose Ann DiMaria-Ghalili
- Doctoral Nursing Department, Nutrition Science Department, College of Nursing and Health Professions, Drexel University, Philadelphia, PA, United States
| | - Howard J. Eisen
- Clinical Research for the Advanced Cardiac and Pulmonary Vascular Disease Program, Thomas Jefferson University Hospital, Philadelphia, PA, United States
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Ma J, Chen H, Zou C, Yang G. Association evaluations of oral anticoagulants with dementia risk based on genomic and real-world data. Prog Neuropsychopharmacol Biol Psychiatry 2024; 130:110929. [PMID: 38154516 DOI: 10.1016/j.pnpbp.2023.110929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/20/2023] [Accepted: 12/23/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND Several observational studies have suggested that oral anticoagulants (OACs) might reduce the risk of dementia in the elderly, but the evidence is inconclusive. And the consistency of this relationship across different OAC classes and dementia subtypes is still uncertain. METHODS To comprehensively evaluate this association, we applied Mendelian randomization (MR) combined with pharmacovigilance analysis. MR was used to assess the associations between genetic proxies for three target genes of OACs (VKORC1, F2, and F10) and dementia, including Alzheimer's disease (AD) and vascular dementia (VaD). This genetic analysis was supplemented with real-world pharmacovigilance data, employing disproportionality analysis for more reliable causal inference. RESULTS Increased expression of the VKORC1 gene was strongly associated with increased risk of dementia, especially for AD (OR = 1.28, 95% CI = 1.14-1.43; p value < 0.001). Based on pharmacovigilance data, vitamin K antagonists (VKAs, inhibitors targeting VKORC1) exhibited a protective effect against dementia risk (ROR = 0.43, 95% CI = 0.28-0.67). Additional sensitivity analyses, including different MR models and cohorts, supported these results. Conversely, no strong causal associations of genetically proxied F2 and F10 target genes with dementia and its subtypes were found. CONCLUSIONS This study reveals that the inhibition of genetically proxied VKORC1 expression or VKAs exposure is associated with a reduced risk of Alzheimer's dementia. However, there is little evidence to support similar associations with direct oral anticoagulants (F2 inhibitors and F10 inhibitors). Further research is warranted to clinically validate our findings.
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Affiliation(s)
- Junlong Ma
- Center of Clinical Pharmacology, Third Xiangya Hospital, Central South University, Changsha 410013, Hunan, China; Hubei Provincial Clinical Research Center for Umbilical Cord Blood Hematopoietic Stem Cells, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, China
| | - Heng Chen
- Department of Pharmacy, The First Hospital of Changsha, Changsha 410013, Hunan, China
| | - Chan Zou
- Center of Clinical Pharmacology, Third Xiangya Hospital, Central South University, Changsha 410013, Hunan, China
| | - Guoping Yang
- Center of Clinical Pharmacology, Third Xiangya Hospital, Central South University, Changsha 410013, Hunan, China.
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Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, Commodore-Mensah Y, Currie ME, Elkind MSV, Evenson KR, Generoso G, Heard DG, Hiremath S, Johansen MC, Kalani R, Kazi DS, Ko D, Liu J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Perman SM, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Tsao CW, Urbut SM, Van Spall HGC, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Palaniappan LP. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation 2024; 149:e347-e913. [PMID: 38264914 DOI: 10.1161/cir.0000000000001209] [Citation(s) in RCA: 124] [Impact Index Per Article: 124.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
BACKGROUND The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Brain J, Kafadar AH, Errington L, Kirkley R, Tang EY, Akyea RK, Bains M, Brayne C, Figueredo G, Greene L, Louise J, Morgan C, Pakpahan E, Reeves D, Robinson L, Salter A, Siervo M, Tully PJ, Turnbull D, Qureshi N, Stephan BC. What's New in Dementia Risk Prediction Modelling? An Updated Systematic Review. Dement Geriatr Cogn Dis Extra 2024; 14:49-74. [PMID: 39015518 PMCID: PMC11250535 DOI: 10.1159/000539744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 06/07/2024] [Indexed: 07/18/2024] Open
Abstract
Introduction Identifying individuals at high risk of dementia is critical to optimized clinical care, formulating effective preventative strategies, and determining eligibility for clinical trials. Since our previous systematic reviews in 2010 and 2015, there has been a surge in dementia risk prediction modelling. The aim of this study was to update our previous reviews to explore, and critically review, new developments in dementia risk modelling. Methods MEDLINE, Embase, Scopus, and Web of Science were searched from March 2014 to June 2022. Studies were included if they were population- or community-based cohorts (including electronic health record data), had developed a model for predicting late-life incident dementia, and included model performance indices such as discrimination, calibration, or external validation. Results In total, 9,209 articles were identified from the electronic search, of which 74 met the inclusion criteria. We found a substantial increase in the number of new models published from 2014 (>50 new models), including an increase in the number of models developed using machine learning. Over 450 unique predictor (component) variables have been tested. Nineteen studies (26%) undertook external validation of newly developed or existing models, with mixed results. For the first time, models have also been developed in low- and middle-income countries (LMICs) and others validated in racial and ethnic minority groups. Conclusion The literature on dementia risk prediction modelling is rapidly evolving with new analytical developments and testing in LMICs. However, it is still challenging to make recommendations about which one model is the most suitable for routine use in a clinical setting. There is an urgent need to develop a suitable, robust, validated risk prediction model in the general population that can be widely implemented in clinical practice to improve dementia prevention.
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Affiliation(s)
- Jacob Brain
- Institute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK
- Freemasons Foundation Centre for Men’s Health, Discipline of Medicine, School of Psychology, The University of Adelaide, Adelaide, SA, Australia
| | - Aysegul Humeyra Kafadar
- Institute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK
| | - Linda Errington
- Walton Library, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Rachael Kirkley
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Eugene Y.H. Tang
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Ralph K. Akyea
- PRISM Group, Centre for Academic Primary Care, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Manpreet Bains
- Nottingham Centre for Public Health and Epidemiology, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Carol Brayne
- Cambridge Public Health, University of Cambridge, Cambridge, UK
| | | | - Leanne Greene
- Exeter Clinical Trials Unit, Department of Health and Community Sciences, University of Exeter Medical School, Exeter, UK
| | - Jennie Louise
- Women’s and Children’s Hospital Research Centre and South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Catharine Morgan
- Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, UK
| | - Eduwin Pakpahan
- Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne, UK
| | - David Reeves
- School for Health Sciences, University of Manchester, Manchester, UK
| | - Louise Robinson
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Amy Salter
- School of Public Health, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Mario Siervo
- School of Population Health, Curtin University, Perth, WA, Australia
- Dementia Centre of Excellence, Curtin enAble Institute, Faculty of Health Sciences, Curtin University, Perth, WA, Australia
| | - Phillip J. Tully
- Freemasons Foundation Centre for Men’s Health, Discipline of Medicine, School of Psychology, The University of Adelaide, Adelaide, SA, Australia
- Faculty of Medicine and Health, School of Psychology, University of New England, Armidale, NSW, Australia
| | - Deborah Turnbull
- Freemasons Foundation Centre for Men’s Health, Discipline of Medicine, School of Psychology, The University of Adelaide, Adelaide, SA, Australia
| | - Nadeem Qureshi
- PRISM Group, Centre for Academic Primary Care, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Blossom C.M. Stephan
- Institute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK
- Dementia Centre of Excellence, Curtin enAble Institute, Faculty of Health Sciences, Curtin University, Perth, WA, Australia
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Ding H, Liu C, Li Y, Ang TFA, Devine S, Liu Y, Au R, Doraiswamy PM. Sex-specific blood biomarkers linked to memory changes in middle-aged adults: The Framingham Heart Study. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12569. [PMID: 38545543 PMCID: PMC10966919 DOI: 10.1002/dad2.12569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 02/02/2024] [Accepted: 02/13/2024] [Indexed: 06/06/2024]
Abstract
The relationship between sex-specific blood biomarkers and memory changes in middle-aged adults remains unclear. We aimed to investigate this relationship using the data from the Framingham Heart Study (FHS). We conducted association analysis, partial correlation analysis, and causal dose-response curves using blood biomarkers and other data from 793 middle-aged participants (≤ 60 years) from the FHS Offspring Cohort. The results revealed associations of adiponectin and fasting blood glucose with midlife memory change, along with a U-shaped relationship of high-density lipoprotein cholesterol with memory change. No significant associations were found for the other blood biomarkers (e.g., amyloid beta protein 42) with memory change. To our knowledge, this is the first sex-specific network analysis of blood biomarkers related to midlife memory change in a prospective cohort study. Our findings highlight the importance of targeting cardiometabolic risks and the need to validate midlife-specific biomarkers that can accelerate the development of primary preventive strategies.
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Affiliation(s)
- Huitong Ding
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Chunyu Liu
- Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
| | - Yi Li
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
| | - Ting Fang Alvin Ang
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Slone Epidemiology CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Sherral Devine
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Yulin Liu
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Rhoda Au
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Slone Epidemiology CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMassachusettsUSA
| | - P. Murali Doraiswamy
- Neurocognitive Disorders ProgramDepartments of Psychiatry and Medicineand the Duke Institute for Brain SciencesDuke University School of MedicineDurhamNorth CarolinaUSA
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10
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Vishwanath S, Hopper I, Chowdhury E, Wolfe R, Freak-Poli R, Reid CM, Tonkin AM, Murray AM, Shah RC, Chong TTJ, Woods RL, McNeil J, Orchard SG, Nelson MR, Steves CJ, Ryan J. Cardiovascular Disease Risk Scores and Incident Dementia and Cognitive Decline in Older Men and Women. Gerontology 2023; 70:143-154. [PMID: 37984339 PMCID: PMC10866179 DOI: 10.1159/000535284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 11/11/2023] [Indexed: 11/22/2023] Open
Abstract
INTRODUCTION Risk factors for cardiovascular disease (CVD) also increase the risk of dementia. However, whether commonly used CVD risk scores are associated with dementia risk in older adults who do not have a history of CVD, and potential gender differences in this association, remains unclear. The aim of this study was to determine whether CVD risk scores are prospectively associated with cognitive decline and dementia in initially healthy older men and women. METHODS A total of19,114 participants from a prospective cohort of individuals aged 65+ years without known CVD or dementia were recruited. The atherosclerotic cardiovascular disease risk score (ASCVDRS), Systematic Coronary Risk Evaluation 2-Older Persons (SCORE2-OP), and the Framingham risk score (FRS) were calculated at baseline. Risk of dementia (according to DSM-IV criteria) and cognitive decline (defined as a >1.5 standard deviation decline in global cognition, episodic memory, psychomotor speed, or verbal fluency from the previous year) were assessed using hazard ratio. RESULTS Over a median follow-up of 6.4 years, 850 individuals developed dementia and 4,352 cognitive decline. Men and women in the highest ASCVDRS tertile had a 41% (95% CI 1.08, 1.85) and 45% (1.11, 1.89) increased risk of dementia compared to the lowest tertile, respectively. Likewise, men and women in the highest SCORE2-OP tertile had a 64% (1.24, 2.16) and 60% (1.22, 2.11) increased risk of dementia compared to the lowest tertile, respectively. Findings were similar, but the risk was slightly lesser when examining risk of cognitive decline for both ASCVDRS and SCORE2-OP. However, FRS was only associated with the risk of cognitive decline among women (highest vs. lowest tertiles: 1.13 [1.01-1.26]). CONCLUSION These findings suggest the utility of the ASCVDRS and SCORE2-OP in clinical practice, to not only assess future risk of CVD, but also as potential early indicators of cognitive impairment, even in relatively healthy older men and women.
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Affiliation(s)
- Swarna Vishwanath
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia,
| | - Ingrid Hopper
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Cardiology and General Medicine Unit, Alfred Health, Melbourne, Victoria, Australia
| | - Enayet Chowdhury
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- GenesisCare, Leabrook, South Australia, Australia
| | - Rory Wolfe
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Rosanne Freak-Poli
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Christopher M Reid
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- School of Public Health, Curtin University, Bentley, Washington, Australia
| | - Andrew M Tonkin
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Anne M Murray
- Berman Center for Outcomes and Clinical Research, Hennepin Healthcare Research Institute, Minneapolis, Minnesota, USA
- Department of Medicine, Division of Geriatrics Hennepin Healthcare, University of Minnesota, Minneapolis, Minnesota, USA
| | - Raj C Shah
- Department of Family and Preventive Medicine and the Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Trevor T-J Chong
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
- Department of Clinical Neurosciences, St. Vincent's Hospital, Melbourne, Victoria, Australia
| | - Robyn L Woods
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - John McNeil
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Suzanne G Orchard
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Mark R Nelson
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Ageing and Health, Guy's and St Thomas's NHS Foundation Trust, London, UK
| | - Joanne Ryan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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11
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Jia R, Wang Q, Huang H, Yang Y, Chung YF, Liang T. Cardiovascular disease risk models and dementia or cognitive decline: a systematic review. Front Aging Neurosci 2023; 15:1257367. [PMID: 37904838 PMCID: PMC10613491 DOI: 10.3389/fnagi.2023.1257367] [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: 07/12/2023] [Accepted: 09/11/2023] [Indexed: 11/01/2023] Open
Abstract
Background Health cognitive promotion and protection is a critical topic. With the world's aging population and rising life expectancy, there will be many people living with highly age-related dementia illnesses. Cardiovascular disease (CVD) and dementia share the same risk factors, such as unhealthy lifestyles and metabolic factors. These recognized risks associated with CVD and dementia frequently co-occur. CVD risk models may have a close association with dementia and cognitive decline. So, this systematic review aimed to determine whether CVD risk models were connected with dementia or cognitive decline and compare the predictive ability of various models. Methods PubMed, Web of Science, PsychINFO, Embase, Cochrane Library, CNKI, Sinomed, and WanFang were searched from 1 January 2014 until 16 February 2023. Only CVD risk models were included. We used the Newcastle-Ottawa scale (NOS) for the quality assessment of included cohort studies and the Agency for Healthcare Research and Quality (AHRQ) for cross-sectional studies. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement's guidelines were followed in this systematic study. Results In all, 9,718 references were screened, of which 22 articles were included. A total of 15 CVD risk models were summarized. Except for the Cardiovascular Health in Ambulatory Care Research Team (CANHEART) health index, the other 14 CVD risk models were associated with dementia and cognitive decline. In comparison, different CVD risk models and domain-specific cognitive function correlation variation depended on cohort characteristics, risk models, cognitive function tests, and study designs. Moreover, it needed to be clarified when comparing the predicting performance of different CVD risk models. Conclusion It is significant for public health to improve disease risk prediction and prevention and mitigate the potential adverse effects of the heart on the brain. More cohort studies are warranted to prove the correlation between CVD risk models and cognitive function. Moreover, further studies are encouraged to compare the efficacy of CVD risk models in predicting cognitive disorders.
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Affiliation(s)
- Ruirui Jia
- School of Nursing, Lanzhou University, Lanzhou, China
| | - Qing Wang
- School of Nursing, Lanzhou University, Lanzhou, China
- School of Nursing, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hengyi Huang
- School of Nursing, Lanzhou University, Lanzhou, China
| | - Yanli Yang
- School of Nursing, Lanzhou University, Lanzhou, China
| | | | - Tao Liang
- School of Nursing, Lanzhou University, Lanzhou, China
- School of Nursing, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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12
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Skallevold HE, Rokaya N, Wongsirichat N, Rokaya D. Importance of oral health in mental health disorders: An updated review. J Oral Biol Craniofac Res 2023; 13:544-552. [PMID: 37396968 PMCID: PMC10314291 DOI: 10.1016/j.jobcr.2023.06.003] [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: 12/30/2022] [Revised: 06/05/2023] [Accepted: 06/14/2023] [Indexed: 07/04/2023] Open
Abstract
Background Mental disorders are indeed an expanding threat, which requires raised awareness, education, prevention, and treatment initiatives nationally and globally. This review presents an updated review on the relationships between oral health and mental health disorders and the importance of oral health in mental health disorders. Method A literature search was done regarding mental disorders and oral health approaches in Google Scholar and PubMed from the year 1995 until 2023. All the English-language papers were evaluated based on the inclusion criteria. Publications included original research papers, review articles and book chapters. Results Common mental disorders include depression, anxiety, bipolar disorder, Schizophrenia, dementia, and alcohol and drug use disorders. The interplay of oral health and mental disorders involves dysregulated microbiome, translocated bacteria, and systemic inflammation, among others. Conclusion There is a complex relationship between mental disorders and oral diseases. Various oral health problems are associated with mental health problems. The interplay of oral health and mental disorders involves dysregulated microbiome, translocated bacteria, and systemic inflammation, among others. Mental health nurses including physicians and dental professionals should be involved in the oral health care of mental health disorder patients. Therefore, multidisciplinary should be involved in the care of mental health disorders, and they should consider oral health care as an essential part of their care for patients with mental health disorders. Future investigations should strive to elucidate the exact biological relationships, to develop new directions for treatment.
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Affiliation(s)
- Hans Erling Skallevold
- Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Chulalongkorn University, Bangkok, 10330, Thailand
| | | | - Natthamet Wongsirichat
- Faculty of Dentistry, Bangkok Thonburi University, 16/10 Taweewatana, Bangkok, 10170, Thailand
| | - Dinesh Rokaya
- Department of Clinical Dentistry, Walailak University International College of Dentistry, Bangkok, 10400, Thailand
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13
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Wang Y, Xu H, Geng Z, Geng G, Zhang F. Dementia and the history of disease in older adults in community. BMC Public Health 2023; 23:1555. [PMID: 37582737 PMCID: PMC10428616 DOI: 10.1186/s12889-023-16494-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 08/09/2023] [Indexed: 08/17/2023] Open
Abstract
INTRODUCTION Many studies have revealed the effect of medical history on dementia. The aim of this study was to explore the relationship between the history of disease and onset of dementia. METHODS This was a multi-center, cross-sectional study, with 2595 older adults enrolled. The onset of dementia was evaluated with Revised Hasegawa Dementia Scale (HDS-R). The diagnosed diseases after the age of 40 of the participants were investigated, including respiratory system diseases, digestive system diseases, cardiovascular diseases, endocrine disorders, genitourinary system diseases, nervous system disease, sensory system diseases, dental/oral diseases, bone/joint diseases and mental illnesses. RESULTS Data of 2458 older adults were analyzed. Univariate analysis showed that diabetes, thyroid disease, mental illness, hearing loss, stroke, dental/oral disease, Denture use, fracture/osteoporosis, kidney disease and number of diseases were risk factors for dementia. After controlling for demographic sociological variables, diabetes, dental/oral disease, and denture use were independent risk factors for dementia. Thyroid disease (P = 0.313), mental illnesses (P = 0.067), hearing loss (P = 0.595), stroke (P = 0.538), fractures/osteoporosis (P = 0.069), kidney disease (P = 0.168) were no longer significant to dementia. CONCLUSION Diabetes, dental/oral disease and denture use were main risk factors for dementia.
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Affiliation(s)
- Yuan Wang
- Medical College of Nantong University, 19 QiXiu Road, Nantong City, Jiangsu Province, China
| | - Honglian Xu
- Nantong North Rehabilitation Hospital, Nantong City, Jiangsu Province, China
| | - Zihan Geng
- Medical College of Nantong University, 19 QiXiu Road, Nantong City, Jiangsu Province, China
| | - Guiling Geng
- Medical College of Nantong University, 19 QiXiu Road, Nantong City, Jiangsu Province, China
| | - Feng Zhang
- Medical College of Nantong University, 19 QiXiu Road, Nantong City, Jiangsu Province, China.
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14
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Juul Rasmussen I, Frikke-Schmidt R. Modifiable cardiovascular risk factors and genetics for targeted prevention of dementia. Eur Heart J 2023; 44:2526-2543. [PMID: 37224508 PMCID: PMC10481783 DOI: 10.1093/eurheartj/ehad293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 02/22/2023] [Accepted: 05/04/2023] [Indexed: 05/26/2023] Open
Abstract
Dementia is a major global challenge for health and social care in the 21st century. A third of individuals >65 years of age die with dementia, and worldwide incidence numbers are projected to be higher than 150 million by 2050. Dementia is, however, not an inevitable consequence of old age; 40% of dementia may theoretically be preventable. Alzheimer's disease (AD) accounts for approximately two-thirds of dementia cases and the major pathological hallmark of AD is accumulation of amyloid-β. Nevertheless, the exact pathological mechanisms of AD remain unknown. Cardiovascular disease and dementia share several risk factors and dementia often coexists with cerebrovascular disease. In a public health perspective, prevention is crucial, and it is suggested that a 10% reduction in prevalence of cardiovascular risk factors could prevent more than nine million dementia cases worldwide by 2050. Yet this assumes causality between cardiovascular risk factors and dementia and adherence to the interventions over decades for a large number of individuals. Using genome-wide association studies, the entire genome can be scanned for disease/trait associated loci in a hypothesis-free manner, and the compiled genetic information is not only useful for pinpointing novel pathogenic pathways but also for risk assessments. This enables identification of individuals at high risk, who likely will benefit the most from a targeted intervention. Further optimization of the risk stratification can be done by adding cardiovascular risk factors. Additional studies are, however, highly needed to elucidate dementia pathogenesis and potential shared causal risk factors between cardiovascular disease and dementia.
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Affiliation(s)
- Ida Juul Rasmussen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej 9, DK-2100 Copenhagen, Denmark
| | - Ruth Frikke-Schmidt
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej 9, DK-2100 Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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15
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Mohanannair Geethadevi G, Quinn TJ, George J, Anstey KJ, Bell JS, Sarwar MR, Cross AJ. Multi-domain prognostic models used in middle-aged adults without known cognitive impairment for predicting subsequent dementia. Cochrane Database Syst Rev 2023; 6:CD014885. [PMID: 37265424 PMCID: PMC10239281 DOI: 10.1002/14651858.cd014885.pub2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
BACKGROUND Dementia, a global health priority, has no current cure. Around 50 million people worldwide currently live with dementia, and this number is expected to treble by 2050. Some health conditions and lifestyle behaviours can increase or decrease the risk of dementia and are known as 'predictors'. Prognostic models combine such predictors to measure the risk of future dementia. Models that can accurately predict future dementia would help clinicians select high-risk adults in middle age and implement targeted risk reduction. OBJECTIVES Our primary objective was to identify multi-domain prognostic models used in middle-aged adults (aged 45 to 65 years) for predicting dementia or cognitive impairment. Eligible multi-domain prognostic models involved two or more of the modifiable dementia predictors identified in a 2020 Lancet Commission report and a 2019 World Health Organization (WHO) report (less education, hearing loss, traumatic brain injury, hypertension, excessive alcohol intake, obesity, smoking, depression, social isolation, physical inactivity, diabetes mellitus, air pollution, poor diet, and cognitive inactivity). Our secondary objectives were to summarise the prognostic models, to appraise their predictive accuracy (discrimination and calibration) as reported in the development and validation studies, and to identify the implications of using dementia prognostic models for the management of people at a higher risk for future dementia. SEARCH METHODS We searched MEDLINE, Embase, PsycINFO, CINAHL, and ISI Web of Science Core Collection from inception until 6 June 2022. We performed forwards and backwards citation tracking of included studies using the Web of Science platform. SELECTION CRITERIA: We included development and validation studies of multi-domain prognostic models. The minimum eligible follow-up was five years. Our primary outcome was an incident clinical diagnosis of dementia based on validated diagnostic criteria, and our secondary outcome was dementia or cognitive impairment determined by any other method. DATA COLLECTION AND ANALYSIS Two review authors independently screened the references, extracted data using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), and assessed risk of bias and applicability of included studies using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We synthesised the C-statistics of models that had been externally validated in at least three comparable studies. MAIN RESULTS: We identified 20 eligible studies; eight were development studies and 12 were validation studies. There were 14 unique prognostic models: seven models with validation studies and seven models with development-only studies. The models included a median of nine predictors (range 6 to 34); the median number of modifiable predictors was five (range 2 to 11). The most common modifiable predictors in externally validated models were diabetes, hypertension, smoking, physical activity, and obesity. In development-only models, the most common modifiable predictors were obesity, diabetes, hypertension, and smoking. No models included hearing loss or air pollution as predictors. Nineteen studies had a high risk of bias according to the PROBAST assessment, mainly because of inappropriate analysis methods, particularly lack of reported calibration measures. Applicability concerns were low for 12 studies, as their population, predictors, and outcomes were consistent with those of interest for this review. Applicability concerns were high for nine studies, as they lacked baseline cognitive screening or excluded an age group within the range of 45 to 65 years. Only one model, Cardiovascular Risk Factors, Ageing, and Dementia (CAIDE), had been externally validated in multiple studies, allowing for meta-analysis. The CAIDE model included eight predictors (four modifiable predictors): age, education, sex, systolic blood pressure, body mass index (BMI), total cholesterol, physical activity and APOEƐ4 status. Overall, our confidence in the prediction accuracy of CAIDE was very low; our main reasons for downgrading the certainty of the evidence were high risk of bias across all the studies, high concern of applicability, non-overlapping confidence intervals (CIs), and a high degree of heterogeneity. The summary C-statistic was 0.71 (95% CI 0.66 to 0.76; 3 studies; very low-certainty evidence) for the incident clinical diagnosis of dementia, and 0.67 (95% CI 0.61 to 0.73; 3 studies; very low-certainty evidence) for dementia or cognitive impairment based on cognitive scores. Meta-analysis of calibration measures was not possible, as few studies provided these data. AUTHORS' CONCLUSIONS We identified 14 unique multi-domain prognostic models used in middle-aged adults for predicting subsequent dementia. Diabetes, hypertension, obesity, and smoking were the most common modifiable risk factors used as predictors in the models. We performed meta-analyses of C-statistics for one model (CAIDE), but the summary values were unreliable. Owing to lack of data, we were unable to meta-analyse the calibration measures of CAIDE. This review highlights the need for further robust external validations of multi-domain prognostic models for predicting future risk of dementia in middle-aged adults.
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Affiliation(s)
| | - Terry J Quinn
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Johnson George
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia
- Faculty of Medicine, Nursing and Health Sciences, School of Public Health and Preventive Medicine, Melbourne, Australia
| | - Kaarin J Anstey
- School of Psychology, The University of New South Wales, Sydney, Australia
- Ageing Futures Institute, The University of New South Wales, Sydney, Australia
| | - J Simon Bell
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia
| | - Muhammad Rehan Sarwar
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia
| | - Amanda J Cross
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia
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16
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Kivimäki M, Livingston G, Singh-Manoux A, Mars N, Lindbohm JV, Pentti J, Nyberg ST, Pirinen M, Anderson EL, Hingorani AD, Sipilä PN. Estimating Dementia Risk Using Multifactorial Prediction Models. JAMA Netw Open 2023; 6:e2318132. [PMID: 37310738 PMCID: PMC10265307 DOI: 10.1001/jamanetworkopen.2023.18132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 04/27/2023] [Indexed: 06/14/2023] Open
Abstract
Importance The clinical value of current multifactorial algorithms for individualized assessment of dementia risk remains unclear. Objective To evaluate the clinical value associated with 4 widely used dementia risk scores in estimating 10-year dementia risk. Design, Setting, and Participants This prospective population-based UK Biobank cohort study assessed 4 dementia risk scores at baseline (2006-2010) and ascertained incident dementia during the following 10 years. Replication with a 20-year follow-up was based on the British Whitehall II study. For both analyses, participants who had no dementia at baseline, had complete data on at least 1 dementia risk score, and were linked to electronic health records from hospitalizations or mortality were included. Data analysis was conducted from July 5, 2022, to April 20, 2023. Exposures Four existing dementia risk scores: the Cardiovascular Risk Factors, Aging and Dementia (CAIDE)-Clinical score, the CAIDE-APOE-supplemented score, the Brief Dementia Screening Indicator (BDSI), and the Australian National University Alzheimer Disease Risk Index (ANU-ADRI). Main Outcomes and Measures Dementia was ascertained from linked electronic health records. To evaluate how well each score predicted the 10-year risk of dementia, concordance (C) statistics, detection rate, false-positive rate, and the ratio of true to false positives were calculated for each risk score and for a model including age alone. Results Of 465 929 UK Biobank participants without dementia at baseline (mean [SD] age, 56.5 [8.1] years; range, 38-73 years; 252 778 [54.3%] female participants), 3421 were diagnosed with dementia at follow-up (7.5 per 10 000 person-years). If the threshold for a positive test result was calibrated to achieve a 5% false-positive rate, all 4 risk scores detected 9% to 16% of incident dementia and therefore missed 84% to 91% (failure rate). The corresponding failure rate was 84% for a model that included age only. For a positive test result calibrated to detect at least half of future incident dementia, the ratio of true to false positives ranged between 1 to 66 (for CAIDE-APOE-supplemented) and 1 to 116 (for ANU-ADRI). For age alone, the ratio was 1 to 43. The C statistic was 0.66 (95% CI, 0.65-0.67) for the CAIDE clinical version, 0.73 (95% CI, 0.72-0.73) for the CAIDE-APOE-supplemented, 0.68 (95% CI, 0.67-0.69) for BDSI, 0.59 (95% CI, 0.58-0.60) for ANU-ADRI, and 0.79 (95% CI, 0.79-0.80) for age alone. Similar C statistics were seen for 20-year dementia risk in the Whitehall II study cohort, which included 4865 participants (mean [SD] age, 54.9 [5.9] years; 1342 [27.6%] female participants). In a subgroup analysis of same-aged participants aged 65 (±1) years, discriminatory capacity of risk scores was low (C statistics between 0.52 and 0.60). Conclusions and Relevance In these cohort studies, individualized assessments of dementia risk using existing risk prediction scores had high error rates. These findings suggest that the scores were of limited value in targeting people for dementia prevention. Further research is needed to develop more accurate algorithms for estimation of dementia risk.
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Affiliation(s)
- Mika Kivimäki
- Department of Mental Health of Older People, UCL Brain Sciences, University College London, London, United Kingdom
- Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Gill Livingston
- Department of Mental Health of Older People, UCL Brain Sciences, University College London, London, United Kingdom
| | - Archana Singh-Manoux
- Department of Mental Health of Older People, UCL Brain Sciences, University College London, London, United Kingdom
- Université Paris Cité, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, Paris, France
| | - Nina Mars
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Joni V. Lindbohm
- Department of Mental Health of Older People, UCL Brain Sciences, University College London, London, United Kingdom
- Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Jaana Pentti
- Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Solja T. Nyberg
- Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Matti Pirinen
- Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Emma L. Anderson
- Department of Mental Health of Older People, UCL Brain Sciences, University College London, London, United Kingdom
- MRC Integrative Epidemiology Unit and Population Health Sciences, University of Bristol Medical School, Bristol, United Kingdom
| | - Aroon D. Hingorani
- Institute of Cardiovascular Sciences, University College London, London, United Kingdom
| | - Pyry N. Sipilä
- Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
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17
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Ma YH, Shen LX, Li YZ, Leng Y, Yang L, Chen SD, He XY, Zhang YR, Chen RJ, Feng JF, Tan L, Dong Q, Suckling J, David Smith A, Cheng W, Yu JT. Lung function and risk of incident dementia: A prospective cohort study of 431,834 individuals. Brain Behav Immun 2023; 109:321-330. [PMID: 36796705 DOI: 10.1016/j.bbi.2023.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 01/26/2023] [Accepted: 02/10/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Whether lung function prospectively affects cognitive brain health independent of their overlapping factors remains largely unknown. This study aimed to investigate the longitudinal association between decreased lung function and cognitive brain health and to explore underlying biological and brain structural mechanisms. METHODS This population-based cohort included 43,1834 non-demented participants with spirometry from the UK Biobank. Cox proportional hazard models were fitted to estimate the risk of incident dementia for individuals with low lung function. Mediation models were regressed to explore the underlying mechanisms driven by inflammatory markers, oxygen-carrying indices, metabolites, and brain structures. FINDINGS During a follow-up of 3,736,181 person-years (mean follow-up 8.65 years), 5,622 participants (1.30 %) developed all-cause dementia, which consisted of 2,511 Alzheimer's dementia (AD) and 1,308 Vascular Dementia (VD) cases. Per unit decrease in lung function measure was each associated with increased risk for all-cause dementia (forced expiratory volume in 1 s [liter]: hazard ratio [HR, 95 %CI], 1.24 [1.14-1.34], P = 1.10 × 10-07; forced vital capacity [liter]: 1.16 [1.08-1.24], P = 2.04 × 10-05; peak expiratory flow [liter/min]: 1.0013 [1.0010-1.0017], P = 2.73 × 10-13). Low lung function generated similar hazard estimates for AD and VD risks. As underlying biological mechanisms, systematic inflammatory markers, oxygen-carrying indices, and specific metabolites mediated the effects of lung function on dementia risks. Besides, brain grey and white matter patterns mostly affected in dementia were substantially changed with lung function. INTERPRETATION Life-course risk for incident dementia was modulated by individual lung function. Maintaining optimal lung function is useful for healthy aging and dementia prevention.
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Affiliation(s)
- Ya-Hui Ma
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China; Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Ling-Xiao Shen
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yu-Zhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yue Leng
- Department of Psychiatry, University of California, San Francisco, CA, USA
| | - Liu Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Xiao-Yu He
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Ren-Jie Chen
- School of Public Health, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, United Kingdom; School of Mathematical Sciences, Fudan University, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - A David Smith
- Department of Pharmacology, University of Oxford, Oxford OX1 3QT, United Kingdom
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China.
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18
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Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Beaton AZ, Boehme AK, Buxton AE, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Fugar S, Generoso G, Heard DG, Hiremath S, Ho JE, Kalani R, Kazi DS, Ko D, Levine DA, Liu J, Ma J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Virani SS, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association. Circulation 2023; 147:e93-e621. [PMID: 36695182 DOI: 10.1161/cir.0000000000001123] [Citation(s) in RCA: 1288] [Impact Index Per Article: 1288.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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19
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Ebinger JE, Driver MP, Botting P, Wang M, Cheng S, Tan ZS. Association of blood pressure variability during acute care hospitalization and incident dementia. Front Neurol 2023; 14:1085885. [PMID: 36824417 PMCID: PMC9941567 DOI: 10.3389/fneur.2023.1085885] [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: 10/31/2022] [Accepted: 01/17/2023] [Indexed: 02/10/2023] Open
Abstract
Background and objectives Recognized as a potential risk factor for Alzheimer's disease and related dementias (ADRD), blood pressure variability (BPV) could be leveraged to facilitate identification of at-risk individuals at a population level. Granular BPV data are available during acute care hospitalization periods for potentially high-risk patients, but the incident ADRD risk association with BPV measured in this setting is unknown. Our objective was to evaluate the relation of BPV, measured during acute care hospitalization, and incidence of ADRD. Methods We retrospectively studied adults, without a prior ADRD diagnosis, who were admitted to a large quaternary care medical center in Southern California between January 1, 2013 and December 31, 2019. For all patients, determined BPV, calculated as variability independent of the mean (VIM), using blood pressure readings obtained as part of routine clinical care. We used multivariable Cox proportional hazards regression to examine the association between BP VIM during hospitalization and the development of incident dementia, determined by new ICD-9/10 coding or the new prescription of dementia medication, occurring at least 2 years after the index hospitalization. Results Of 81,892 adults hospitalized without a prior ADRD diagnosis, 2,442 (2.98%) went on to develop ADRD (2.6 to 5.2 years after hospitalization). In multivariable-adjusted Cox models, both systolic (HR 1.05, 95% CI 1.00-1.09) and diastolic (1.06, 1.02-1.10) VIM were associated with incident ADRD. In pre-specified stratified analyses, the VIM associations with incident ADRD were most pronounced in individuals over age 60 years and among those with renal disease or hypertension. Results were similar when repeated to include incident ADRD diagnoses made at least 1 or 3 years after index hospitalization. Discussion We found that measurements of BPV from acute care hospitalizations can be used to identify individuals at risk for developing a diagnosis of ADRD within approximately 5 years. Use of the readily accessible BPV measure may allow healthcare systems to risk stratify patients during periods of intense patient-provider interaction and, in turn, facilitate engagement in ADRD screening programs.
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Affiliation(s)
- Joseph E. Ebinger
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States,*Correspondence: Joseph E. Ebinger ✉
| | - Matthew P. Driver
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Patrick Botting
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Minhao Wang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Zaldy S. Tan
- Department of Neurology and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States,David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
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20
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Heneghan A, Deng F, Wells K, Ritchie K, Muniz-Terrera G, Ritchie CW, Lawlor B, Naci L. Modifiable Lifestyle Activities Affect Cognition in Cognitively Healthy Middle-Aged Individuals at Risk for Late-Life Alzheimer's Disease. J Alzheimers Dis 2023; 91:833-846. [PMID: 36502318 DOI: 10.3233/jad-220267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND It is now acknowledged that Alzheimer's disease (AD) processes are present decades before the onset of clinical symptoms, but it remains unknown whether lifestyle factors can protect against these early AD processes in mid-life. OBJECTIVE We asked whether modifiable lifestyle activities impact cognition in middle-aged individuals who are cognitively healthy, but at risk for late life AD. Participants (40-59 years) completed cognitive and clinical assessments at baseline (N = 206) and two years follow-up (N = 174). METHODS Mid-life activities were measured with the Lifetime of Experiences Questionnaire. We assessed the impact of lifestyle activities, known risk factors for sporadic late-onset AD (Apolipoprotein E ɛ4 allele status, family history of dementia, and the Cardiovascular Risk Factors Aging and Dementia score), and their interactions on cognition. RESULTS More frequent engagement in physically, socially, and intellectually stimulating activities was associated with better cognition (verbal, spatial, and relational memory), at baseline and follow-up. Critically, more frequent engagement in these activities was associated with stronger cognition (verbal and visuospatial functions, and conjunctive short-term memory binding) in individuals with family history of dementia. Impaired visuospatial function is one of the earliest cognitive deficits in AD and has previously associated with increased AD risk in this cohort. Additionally, conjunctive memory functions have been found impaired in the pre-symptomatic stages of AD. CONCLUSION These findings suggest that modifiable lifestyle activities offset cognitive decrements due to AD risk in mid-life and support the targeting of modifiable lifestyle activities for the prevention of AD.
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Affiliation(s)
- Amy Heneghan
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland.,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Feng Deng
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland.,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Katie Wells
- Edinburgh Dementia Prevention, University of Edinburgh, Edinburgh, UK
| | - Karen Ritchie
- Edinburgh Dementia Prevention, University of Edinburgh, Edinburgh, UK.,INSERM and University of Montpellier, Montpellier, France
| | | | - Craig W Ritchie
- Edinburgh Dementia Prevention, University of Edinburgh, Edinburgh, UK
| | - Brian Lawlor
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland.,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland.,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
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21
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Geethadevi GM, Peel R, Bell JS, Cross AJ, Hancock S, Ilomaki J, Tang T, Attia J, George J. Validity of three risk prediction models for dementia or cognitive impairment in Australia. Age Ageing 2022; 51:6964931. [PMID: 36585910 PMCID: PMC9804251 DOI: 10.1093/ageing/afac307] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND no studies have compared the predictive validity of different dementia risk prediction models in Australia. OBJECTIVES (i) to investigate the predictive validity of the Australian National University-Alzheimer's Disease Risk Index (ANU-ADRI), LIfestyle for BRAin Health (LIBRA) Index and cardiovascular risk factors, ageing and dementia study (CAIDE) models for predicting probable dementia/cognitive impairment in an Australian cohort. (ii) To develop and assess the predictive validity of a new hybrid model combining variables from the three models. METHODS the Hunter Community Study (HCS) included 3,306 adults aged 55-85 years with a median follow-up of 7.1 years. Probable dementia/cognitive impairment was defined using Admitted Patient Data Collection, dispensing of cholinesterase inhibitors or memantine, or a cognitive test. Model validity was assessed by calibration and discrimination. A hybrid model was developed using deep neural network analysis, a machine learning method. RESULTS 120 (3.6%) participants developed probable dementia/cognitive impairment. Mean calibration by ANU-ADRI, LIBRA, CAIDE and the hybrid model was 19, 0.5, 4.7 and 3.4%, respectively. The discrimination of the models was 0.65 (95% CI 0.60-0.70), 0.65 (95% CI 0.60-0.71), 0.54 (95% CI 0.49-0.58) and 0.80 (95% CI 0.78-0.83), respectively. CONCLUSION ANU-ADRI and LIBRA were better dementia prediction tools than CAIDE for identification of high-risk individuals in this cohort. ANU-ADRI overestimated and LIBRA underestimated the risk. The new hybrid model had a higher predictive performance than the other models but it needs to be validated independently in longitudinal studies.
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Affiliation(s)
- Gopisankar M Geethadevi
- Faculty of Pharmacy and Pharmaceutical Sciences, Centre for Medicine Use and Safety, Monash University, Melbourne, VIC, Australia
| | - Roseanne Peel
- School of Medicine and Public Health and Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia
| | - J Simon Bell
- Faculty of Pharmacy and Pharmaceutical Sciences, Centre for Medicine Use and Safety, Monash University, Melbourne, VIC, Australia
| | - Amanda J Cross
- Faculty of Pharmacy and Pharmaceutical Sciences, Centre for Medicine Use and Safety, Monash University, Melbourne, VIC, Australia
| | - Stephen Hancock
- School of Medicine and Public Health and Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia
| | - Jenni Ilomaki
- Faculty of Pharmacy and Pharmaceutical Sciences, Centre for Medicine Use and Safety, Monash University, Melbourne, VIC, Australia
| | - Titus Tang
- Data Science and Artificial Intelligence Platform, Monash University, Melbourne, VIC, Australia
| | - John Attia
- School of Medicine and Public Health and Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia
| | - Johnson George
- Address correspondence to: Johnson George. Tel: +61399039178;
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22
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Wang ZT, Fu Y, Zhang YR, Chen SD, Huang SY, Yang L, Li HQ, Ou YN, Feng JF, Dong Q, Cheng W, Tan L, Wang HF, Yu JT. Modified dementia risk score as a tool for the prediction of dementia: a prospective cohort study of 239745 participants. Transl Psychiatry 2022; 12:509. [PMID: 36496374 PMCID: PMC9741578 DOI: 10.1038/s41398-022-02269-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 12/13/2022] Open
Abstract
Based on risk profiles, several approaches for predicting dementia risk have been developed. Predicting the risk of dementia with accuracy is a significant clinical challenge. The goal was to create a modified dementia risk score (MDRS) based on a big sample size. A total of 239,745 participants from UK Biobank were studied (mean follow-up of 8.7 years). The score value of each risk factor was estimated according to the β coefficient in the logistic regression model. The total dementia risk score was the sum of each risk score. Kaplan Meier survival curves and Cox proportional hazards analyses were used to assess the associations between total score and dementia. Among all participants included, 3531 incident cases of all-cause dementia (ACD), 1729 cases of Alzheimer's disease (AD), and 925 cases of vascular dementia (VD) were identified. Several vascular risk factors (physical activity, current smoking status, and glycemic status) and depressive symptoms were found to be significantly related to dementia risk. The modified dementia risk scores predicted dementia well (model 1, area under curve 0.810; model 2, area under curve 0.832). In model 1, the cut-off value for high risk (HR) was 81 or higher, and in model 2 (including the APOE4), it was 98 or higher. According to Kaplan-Meier survival analyses, patients in the HR group had faster clinical progression (p < 0.0001) in either model 1 or 2. Cox regression analyses for HR versus low risk (LR) revealed that the Hazard radio for ACD was 7.541 (6.941 to 8.193) in model 1 and 8.348 (7.727 to 9.019) in model 2. MDRS is appropriate for dementia primary prevention, and may help quickly identify individuals with elevated risk of dementia.
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Affiliation(s)
- Zuo-Teng Wang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ya-Ru Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shu-Yi Huang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Liu Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Hong-Qi Li
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China.
| | - Hui-Fu Wang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China.
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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23
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Desai RJ, Mahesri M, Lee SB, Varma VR, Loeffler T, Schilcher I, Gerhard T, Segal JB, Ritchey ME, Horton DB, Kim SC, Schneeweiss S, Thambisetty M. No association between initiation of phosphodiesterase-5 inhibitors and risk of incident Alzheimer's disease and related dementia: results from the Drug Repurposing for Effective Alzheimer's Medicines study. Brain Commun 2022; 4:fcac247. [PMID: 36330433 PMCID: PMC9598543 DOI: 10.1093/braincomms/fcac247] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 07/11/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022] Open
Abstract
We evaluated the hypothesis that phosphodiesterase-5 inhibitors, including sildenafil and tadalafil, may be associated with reduced incidence of Alzheimer's disease and related dementia using a patient-level cohort study of Medicare claims and cell culture-based phenotypic assays. We compared incidence of Alzheimer's disease and related dementia after phosphodiesterase-5 inhibitor initiation versus endothelin receptor antagonist initiation among patients with pulmonary hypertension after controlling for 76 confounding variables through propensity score matching. Across four separate analytic approaches designed to address specific types of biases including informative censoring, reverse causality, and outcome misclassification, we observed no evidence for a reduced risk of Alzheimer's disease and related dementia with phosphodiesterase-5 inhibitors;hazard ratio (95% confidence interval): 0.99 (0.69-1.43), 1.00 (0.71-1.42), 0.67 (0.43-1.06), and 1.15 (0.57-2.34). We also did not observe evidence that sildenafil ameliorated molecular abnormalities relevant to Alzheimer's disease in most cell culture-based phenotypic assays. These results do not provide support to the hypothesis that phosphodiesterase-5 inhibitors are promising repurposing candidates for Alzheimer's disease and related dementia.
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Affiliation(s)
- Rishi J Desai
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital & Harvard Medical School, Boston, MA 02115, USA
| | - Mufaddal Mahesri
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital & Harvard Medical School, Boston, MA 02115, USA
| | - Su Been Lee
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital & Harvard Medical School, Boston, MA 02115, USA
| | - Vijay R Varma
- Clinical & Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA
| | - Tina Loeffler
- QPS Austria GmbH, Parkring 12, 8074 Grambach, Austria
| | | | - Tobias Gerhard
- Rutgers Center for Pharmacoepidemiology and Treatment Science, New Brunswick, NJ 08901, USA
- Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Jodi B Segal
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Mary E Ritchey
- Rutgers Center for Pharmacoepidemiology and Treatment Science, New Brunswick, NJ 08901, USA
| | - Daniel B Horton
- Rutgers Center for Pharmacoepidemiology and Treatment Science, New Brunswick, NJ 08901, USA
- Rutgers Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08901, USA
| | - Seoyoung C Kim
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital & Harvard Medical School, Boston, MA 02115, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital & Harvard Medical School, Boston, MA 02115, USA
| | - Madhav Thambisetty
- Clinical & Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA
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24
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Machado-Fragua MD, Landré B, Chen M, Fayosse A, Dugravot A, Kivimaki M, Sabia S, Singh-Manoux A. Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study. BMC Med 2022; 20:334. [PMID: 36163029 PMCID: PMC9513883 DOI: 10.1186/s12916-022-02519-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 08/08/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predicting dementia. We examined the role of midlife serum metabolites using a machine learning approach and determined whether the selected metabolites improved prediction accuracy beyond the effect of age. METHODS Five thousand three hundred seventy-four participants from the Whitehall II study, mean age 55.8 (standard deviation (SD) 6.0) years in 1997-1999 when 233 metabolites were quantified using nuclear magnetic resonance metabolomics. Participants were followed for a median 21.0 (IQR 20.4, 21.7) years for clinically-diagnosed dementia (N=329). Elastic net penalized Cox regression with 100 repetitions of nested cross-validation was used to select models that improved prediction accuracy for incident dementia compared to an age-only model. Risk scores reflecting the frequency with which predictors appeared in the selected models were constructed, and their predictive accuracy was examined using Royston's R2, Akaike's information criterion, sensitivity, specificity, C-statistic and calibration. RESULTS Sixteen of the 100 models had a better c-statistic compared to an age-only model and 15 metabolites were selected at least once in all 16 models with glucose present in all models. Five risk scores, reflecting the frequency of selection of metabolites, and a 1-SD increment in all five risk scores was associated with higher dementia risk (HR between 3.13 and 3.26). Three of these, constituted of 4, 5 and 15 metabolites, had better prediction accuracy (c-statistic from 0.788 to 0.796) compared to an age-only model (c-statistic 0.780), all p<0.05. CONCLUSIONS Although there was robust evidence for the role of glucose in dementia, metabolites measured in midlife made only a modest contribution to dementia prediction once age was taken into account.
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Affiliation(s)
- Marcos D Machado-Fragua
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France.
| | - Benjamin Landré
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France
| | - Mathilde Chen
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France
| | - Aurore Fayosse
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France
| | - Aline Dugravot
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Séverine Sabia
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France.,Department of Epidemiology and Public Health, University College London, London, UK
| | - Archana Singh-Manoux
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France.,Department of Epidemiology and Public Health, University College London, London, UK
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25
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Huang M, Gao X, Zhao R, Dong C, Gu Z, Gao J. Development and validation of a nomogram for predicting mild cognitive impairment in middle-aged and elderly people. Asian J Psychiatr 2022; 75:103224. [PMID: 35870309 DOI: 10.1016/j.ajp.2022.103224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 07/13/2022] [Accepted: 07/16/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Mild cognitive impairment (MCI) is a clinical cognitive impairment state between dementia and normal aging. Early identification of MCI is beneficial, and it can delay the development of dementia. We aimed to develop and validate a prediction model to predict MCI of middle-aged and elderly people (aged 45 years and over). METHODS According to 478 middle-aged and elderly people (48-85 years old) from a cross-sectional study, we developed and validated a predictive nomogram. The least absolute shrinkage and selection operator (LASSO) regression model and multivariate logistic regression analysis were used to select variables and develop a prediction model. The performance of the nomogram was evaluated in terms of its discriminative power, calibration, and decision curve analysis (DCA). RESULTS The predictive nomogram was composed of the following: age, gender, education level, residence, and reading. The model showed good discrimination power (area under receiver-operating characteristic (ROC) curve was 0.8704) and good calibration. Similar results were seen in 10-fold cross-validation. The nomogram showed clinically useful in DCA analysis. CONCLUSION This predictive nomogram provides researchers with a practical tool for predicting MCI. The variables included in this nomogram were readily available. The population used for this nomogram was middle-aged and elderly people.
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Affiliation(s)
- Mengli Huang
- School of Public Health, Nantong University, Nantong 226001, China; Research Center of Gerontology and Longevity, Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Xingxing Gao
- Nantong University Medical School, Nantong 226001, China
| | - Rui Zhao
- Research Center of Gerontology and Longevity, Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Chen Dong
- Research Center of Gerontology and Longevity, Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China; Nantong University Medical School, Nantong 226001, China
| | - Zhifeng Gu
- Research Center of Gerontology and Longevity, Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Jianlin Gao
- School of Public Health, Nantong University, Nantong 226001, China; Research Center of Gerontology and Longevity, Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China; Nantong University Medical School, Nantong 226001, China.
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van der Veere P, Hammami I, Buck G, Greenland M, Offer A, Nunn M, Whiteley W, Bulbulia R, Collins R, Armitage J, Mafham M, Parish S. Weight loss in a cardiovascular trial population identifies people at future risk of dementia. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12352. [PMID: 36092692 PMCID: PMC9428278 DOI: 10.1002/dad2.12352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/11/2022] [Indexed: 11/09/2022]
Abstract
Introduction Populations at increased risk of dementia need to be identified for well-powered trials of preventive interventions. Weight loss, which often occurs in pre-clinical dementia, could identify a population at sufficiently high dementia risk. Methods In 12,975 survivors in the Heart Protection Study statin trial of people with, or at high risk of, cardiovascular disease, the association of weight change over 5 years during the trial with post-trial dementia recorded in electronic hospital admission and death records (n = 784) was assessed, after adjustment for age, sex, treatment allocation, and deprivation measures. Results Among the 60% without substantial weight gain (≤2 kg weight gain), each 1 kg weight loss was associated with a risk ratio for dementia of 1.04 (95% confidence interval, 1.02-1.07). Weight loss ≥4 kg and cognitive function below the mean identified participants aged ≥67 years with a 13% 10-year dementia risk. Discussion The combination of weight loss and high vascular risk identified individuals at high risk of dementia who could be recruited to dementia prevention trials.
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Affiliation(s)
- Pieter van der Veere
- Clinical Trial Service Unit and Epidemiological Studies UnitNuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Imen Hammami
- Clinical Trial Service Unit and Epidemiological Studies UnitNuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Georgina Buck
- Clinical Trial Service Unit and Epidemiological Studies UnitNuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Melanie Greenland
- Clinical Trial Service Unit and Epidemiological Studies UnitNuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Alison Offer
- Clinical Trial Service Unit and Epidemiological Studies UnitNuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Michelle Nunn
- Clinical Trial Service Unit and Epidemiological Studies UnitNuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - William Whiteley
- Clinical Trial Service Unit and Epidemiological Studies UnitNuffield Department of Population HealthUniversity of OxfordOxfordUK,Centre for Clinical Brain SciencesUniversity of EdinburghUK
| | - Richard Bulbulia
- Clinical Trial Service Unit and Epidemiological Studies UnitNuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Rory Collins
- Clinical Trial Service Unit and Epidemiological Studies UnitNuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Jane Armitage
- Clinical Trial Service Unit and Epidemiological Studies UnitNuffield Department of Population HealthUniversity of OxfordOxfordUK,MRC Population Health Research UnitNuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Marion Mafham
- Clinical Trial Service Unit and Epidemiological Studies UnitNuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Sarah Parish
- Clinical Trial Service Unit and Epidemiological Studies UnitNuffield Department of Population HealthUniversity of OxfordOxfordUK,MRC Population Health Research UnitNuffield Department of Population HealthUniversity of OxfordOxfordUK
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27
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Guo R, Ou YN, Hu HY, Ma YH, Tan L, Yu JT. The Association Between Osteoarthritis with Risk of Dementia and Cognitive Impairment: A Meta-Analysis and Systematic Review. J Alzheimers Dis 2022; 89:1159-1172. [DOI: 10.3233/jad-220568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: The relationship between osteoarthritis (OA) and risk of dementia and cognitive impairment (CIM) has long been debated; however, uncertainties still persist. Objective: The aim of our present meta-analysis and systematic review was to roundly illuminate the association between OA and the risk of dementia and CIM. Methods: We identified relevant studies by searching PubMed, Embase, and Web of Science up to October 2021. The relative risk (RR) or odds ratio (OR) with 95% confidence interval (CI) were aggregated using random-effects methods. Credibility of each meta-analysis was assessed. Meta-regression and subgroup analyses were conducted. Publication bias was explored using funnel plot. Results: Of 21,925 identified literatures, 8 were eligible for inclusion in the systematic review and 19 observational studies involving 724,351 individuals were included in the meta-analysis. The risk of developing dementia and CIM among OA patients was demonstrated in 11 prospective studies (RR = 1.42, 95% CI = 1.07–1.86, I2 = 98.9%, p < 0.001), 2 retrospective cohort studies (RR = 1.35, 95% CI = 1.19–1.52, I2 = 61.0%, p = 0.109), 3 retrospective case-control studies (OR = 1.21, 95% CI = 0.96–1.53, I2 = 95.2%, p < 0.001), and 4 cross-sectional studies (OR = 1.51, 95% CI = 1.09–2.09, I2 = 75.8%, p = 0.006). Meta-regression analyses did not find any valid moderators. Heterogeneity in subgroup analyses for population age, OA location, year of publication, outcome type, adjusted for BMI, depression, and comorbidity decreased to zero. No significant evidence of publication bias was found. Conclusion: OA associated with an increased risk of dementia and CIM. Effective interventions in OA patients may decrease new incidence of dementia or CIM.
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Affiliation(s)
- Rong Guo
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - He-Ying Hu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ya-Hui Ma
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
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28
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Scheerbaum P, Book S, Jank M, Hanslian E, DellO'ro M, Schneider J, Scheuermann JS, Bösl S, Jeitler M, Kessler C, Graessel E. Computerised cognitive training tools and online nutritional group counselling for people with mild cognitive impairment: study protocol of a completely digital, randomised, controlled trial. BMJ Open 2022; 12:e060473. [PMID: 35777882 PMCID: PMC9252202 DOI: 10.1136/bmjopen-2021-060473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 06/08/2022] [Indexed: 11/12/2022] Open
Abstract
INTRODUCTION People with mild cognitive impairment (MCI) are at increased risk of decreasing cognitive functioning. Computerised cognitive training (CCT) and nutrition have been shown to improve the cognitive capacities of people with MCI. For each variable, we developed two kinds of interventions specialised for people with MCI (CCT: 'individualised' CCT; nutrition: a whole-food, plant-based diet). Additionally, there are two kinds of active control measures (CCT: 'basic' CCT; nutrition: a healthy diet following the current guidelines of the German Nutrition Society). The aim of this study is to investigate the effects of the two interventions on cognition in people with MCI in a 2×2 randomised controlled trial with German participants. METHODS AND ANALYSIS Participants will be community-dwelling individuals with a psychometric diagnosis of MCI based on the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination. With N=200, effects with an effect size of f≥0.24 (comparable to Cohen's d≥0.48) can be detected. Screening, baseline, t6 and t12 testing will be conducted via a videoconferencing assessment, telephone, and online survey. Participants will be randomly allocated to one of four groups and will receive a combination of CCT and online nutritional counselling. The CCT can be carried out independently at home on a computer, laptop, or tablet. Nutrition counselling includes 12 online group sessions every fortnight for 1.5 hours. The treatment phase is 6 months with follow-ups after six and 12 months after baseline. ETHICS AND DISSEMINATION All procedures were approved by the Friedrich-Alexander-Universität Erlangen-Nürnberg Ethics Committee (Ref. 21-318-1-B). Written informed consent will be obtained from all participants. Results will be published in peer-reviewed scientific journals, conference presentations. TRIAL REGISTRATION NUMBER ISRCTN10560738.
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Affiliation(s)
- Petra Scheerbaum
- Centre of Health Services Research in Medicine, Department of Psychiatry and Psychotherapy, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Stephanie Book
- Centre of Health Services Research in Medicine, Department of Psychiatry and Psychotherapy, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Michael Jank
- Centre of Health Services Research in Medicine, Department of Psychiatry and Psychotherapy, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Genesis Mediware GmbH, Hersbruck, Germany
| | - Etienne Hanslian
- Institute of Social Medicine, Epidemiology and Health Economics, Charite University Hospital, Berlin, Germany
| | - Melanie DellO'ro
- Institute of Social Medicine, Epidemiology and Health Economics, Charite University Hospital, Berlin, Germany
| | - Julia Schneider
- Institute of Social Medicine, Epidemiology and Health Economics, Charite University Hospital, Berlin, Germany
| | - Julia-Sophia Scheuermann
- Centre of Health Services Research in Medicine, Department of Psychiatry and Psychotherapy, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sophia Bösl
- Centre of Health Services Research in Medicine, Department of Psychiatry and Psychotherapy, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Michael Jeitler
- Institute of Social Medicine, Epidemiology and Health Economics, Charite University Hospital, Berlin, Germany
| | - Christian Kessler
- Institute of Social Medicine, Epidemiology and Health Economics, Charite University Hospital, Berlin, Germany
| | - Elmar Graessel
- Centre of Health Services Research in Medicine, Department of Psychiatry and Psychotherapy, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Han X, Wang Y, Jiang Z, Li Y, Dong Y, Cong L, Hou T, Liang Y, Laukka EJ, Du Y, Qiu C. Association of Cardiovascular Health Metrics with Dementia in Rural Chinese Older Adults: A Population-Based Study. Clin Interv Aging 2022; 17:947-956. [PMID: 35711676 PMCID: PMC9196277 DOI: 10.2147/cia.s356910] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 05/27/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose We explore the associations of individual and composite cardiovascular health metrics with all-cause dementia, Alzheimer’s disease, and vascular dementia among rural-dwelling older adults and the potential age variations in their associations. Patients and Methods This community-based cross-sectional study included 4980 older adults (age ≥65 years; 57.23% women) from the baseline examination of MIND-China. In March–September 2018, data were collected via face-to-face interviews, clinical examinations, and laboratory test. We defined six cardiovascular health metrics according to the modified American Heart Association’s recommendations. We diagnosed dementia and its subtypes following the international criteria. Data were analyzed using logistic regression models. Results Of all the participants, 250 were diagnosed with dementia, including 165 with Alzheimer’s disease and 75 with vascular dementia. Ideal composite global cardiovascular health metrics (vs poor composite metrics) were associated with a multi-adjusted odds ratio (95% confidence interval) of 0.62 (0.42–0.93) for dementia, 0.88 (0.52–1.48) for Alzheimer’s disease, and 0.31 (0.16–0.60) for vascular dementia. Moreover, ideal biological cardiovascular health metrics were associated with multi-adjusted odds ratio of 0.52 (0.28–0.95) for dementia and 0.21 (0.06–0.77) for vascular dementia in young–old adults (65–74 years), whereas ideal behavioral cardiovascular health metrics were associated with multi-adjusted odds ratio of 0.48 (0.26–0.89) for dementia and 0.16 (0.06–0.43) for vascular dementia in old–old adults (≥75 years). Conclusion Our results suggest that ideal cardiovascular health metrics are cross-sectionally associated with a low likelihood of dementia and vascular dementia among rural-dwelling older Chinese adults. The associations vary with age, components of cardiovascular health metrics, and dementia subtypes.
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Affiliation(s)
- Xiaolei Han
- Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, People's Republic of China.,Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China
| | - Yongxiang Wang
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China.,Shandong Provincial Clinical Research Center for Neurological Diseases, Jinan, Shandong Province, People's Republic of China
| | - Ziying Jiang
- Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, People's Republic of China
| | - Yuanjing Li
- Aging Research Center and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet-Stockholm University, Stockholm, Sweden
| | - Yi Dong
- Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, People's Republic of China
| | - Lin Cong
- Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, People's Republic of China.,Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China.,Shandong Provincial Clinical Research Center for Neurological Diseases, Jinan, Shandong Province, People's Republic of China
| | - Tingting Hou
- Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, People's Republic of China.,Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China.,Shandong Provincial Clinical Research Center for Neurological Diseases, Jinan, Shandong Province, People's Republic of China
| | - Yajun Liang
- Aging Research Center and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet-Stockholm University, Stockholm, Sweden.,Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Erika J Laukka
- Aging Research Center and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet-Stockholm University, Stockholm, Sweden
| | - Yifeng Du
- Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, People's Republic of China.,Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China.,Shandong Provincial Clinical Research Center for Neurological Diseases, Jinan, Shandong Province, People's Republic of China
| | - Chengxuan Qiu
- Department of Neurology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, People's Republic of China.,Aging Research Center and Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet-Stockholm University, Stockholm, Sweden
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30
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Desai RJ, Varma VR, Gerhard T, Segal J, Mahesri M, Chin K, Horton DB, Kim SC, Schneeweiss S, Thambisetty M. Comparative Risk of Alzheimer Disease and Related Dementia Among Medicare Beneficiaries With Rheumatoid Arthritis Treated With Targeted Disease-Modifying Antirheumatic Agents. JAMA Netw Open 2022; 5:e226567. [PMID: 35394510 PMCID: PMC8994126 DOI: 10.1001/jamanetworkopen.2022.6567] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
IMPORTANCE Cytokine signaling, including tumor necrosis factor (TNF) and interleukin (IL)-6, through the Janus-kinase (JAK)-signal transducer and activator of transcription pathway, was hypothesized to attenuate the risk of Alzheimer disease and related dementia (ADRD) in the Drug Repurposing for Effective Alzheimer Medicines (DREAM) initiative based on multiomics phenotyping. OBJECTIVE To evaluate the association between treatment with tofacitinib, tocilizumab, or TNF inhibitors compared with abatacept and risk of incident ADRD. DESIGN, SETTING, AND PARTICIPANTS This cohort study was conducted among US Medicare fee-for-service patients with rheumatoid arthritis aged 65 years and older from 2007 to 2017. Patients were categorized into 3 cohorts based on initiation of tofacitinib (a JAK inhibitor), tocilizumab (an IL-6 inhibitor), or TNF inhibitors compared with a common comparator abatacept (a T-cell activation inhibitor). Analyses were conducted from August 2020 to August 2021. MAIN OUTCOMES AND MEASURES The main outcome was onset of ADRD based on diagnosis codes evaluated in 4 alternative analysis schemes: (1) an as-treated follow-up approach, (2) an as-started follow-up approach incorporating a 6-month induction period, (3) incorporating a 6-month symptom to diagnosis period to account for misclassification of ADRD onset, and (4) identifying ADRD through symptomatic prescriptions and diagnosis codes. Hazard ratios (HRs) with 95% CIs were calculated from Cox proportional hazard regression after adjustment for 79 preexposure characteristics through propensity score matching. RESULTS After 1:1 propensity score matching to patients using abatacept, a total of 22 569 propensity score-matched patient pairs, including 4224 tofacitinib pairs (mean [SD] age 72.19 [5.65] years; 6945 [82.2%] women), 6369 tocilizumab pairs (mean [SD] age 72.01 [5.46] years; 10 105 [79.4%] women), and 11 976 TNF inhibitor pairs (mean [SD] age 72.67 [5.91] years; 19 710 [82.3%] women), were assessed. Incidence rates of ADRD varied from 2 to 18 per 1000 person-years across analyses schemes. There were no statistically significant associations of ADRD with tofacitinib (analysis 1: HR, 0.90 [95% CI, 0.55-1.51]; analysis 2: HR, 0.78 [95% CI, 0.53-1.13]; analysis 3: HR, 1.29 [95% CI, 0.72-2.33]; analysis 4: HR, 0.50 [95% CI, 0.21-1.20]), tocilizumab (analysis 1: HR, 0.82 [95% CI, 0.55-1.21]; analysis 2: HR, 1.05 [95% CI, 0.81-1.35]; analysis 3: HR, 1.21 [95% CI, 0.75-1.96]; analysis 4: HR, 0.78 [95% CI, 0.44-1.39]), or TNF inhibitors (analysis 1: HR, 0.93 [95% CI, 0.72-1.20]; analysis 2: HR, 1.02 [95% CI, 0.86-1.20]; analysis 3: HR, 1.13 [95% CI, 0.86-1.48]; analysis 4: 0.90 [95% CI, 0.60-1.37]) compared with abatacept. Results from prespecified subgroup analysis by age, sex, and baseline cardiovascular disease were consistent except in patients with cardiovascular disease, for whom there was a potentially lower risk of ADRD with TNF inhibitors vs abatacept, but only in analyses 2 and 4 (analysis 1: HR, 0.76 [95% CI, 0.50-1.16]; analysis 2: HR, 0.74 [95% CI, 0.56-0.99]; analysis 3: HR, 1.03 [95% CI, 0.65-1.61]; analysis 4: HR, 0.45 [95% CI, 0.21-0.98]). CONCLUSIONS AND RELEVANCE This cohort study did not find any association of risk of ADRD in patients treated with tofacitinib, tocilizumab, or TNF inhibitors compared with abatacept.
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Affiliation(s)
- Rishi J. Desai
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Vijay R. Varma
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
| | - Tobias Gerhard
- Center for Pharmacoepidemiology and Treatment Science, Ernest Mario School of Pharmacy, Rutgers University, New Brunswick, New Jersey
| | - Jodi Segal
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Mufaddal Mahesri
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Kristyn Chin
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Daniel B. Horton
- Center for Pharmacoepidemiology and Treatment Science, Ernest Mario School of Pharmacy, Rutgers University, New Brunswick, New Jersey
| | - Seoyoung C. Kim
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Madhav Thambisetty
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
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Tseng WYI, Hsu YC, Kao TW. Brain Age Difference at Baseline Predicts Clinical Dementia Rating Change in Approximately Two Years. J Alzheimers Dis 2022; 86:613-627. [PMID: 35094993 DOI: 10.3233/jad-215380] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND The Clinical Dementia Rating (CDR) has been widely used to assess dementia severity, but it is limited in predicting dementia progression, thus unable to advise preventive measures to those who are at high risk. OBJECTIVE Predicted age difference (PAD) was proposed to predict CDR change. METHODS All diffusion magnetic resonance imaging and CDR scores were obtained from the OASIS-3 databank. A brain age model was trained by a machine learning algorithm using the imaging data of 258 cognitively healthy adults. Two diffusion indices, i.e., mean diffusivity and fractional anisotropy, over the whole brain white matter were extracted to serve as the features for model training. The validated brain age model was applied to a longitudinal cohort of 217 participants who had CDR = 0 (CDR0), 0.5 (CDR0.5), and 1 (CDR1) at baseline. Participants were grouped according to different baseline CDR and their subsequent CDR in approximately 2 years of follow-up. PAD was compared between different groups with multiple comparison correction. RESULTS PADs were significantly different among participants with different baseline CDRs. PAD in participants with relatively stable CDR0.5 was significantly smaller than PAD in participants who had CDR0.5 at baseline but converted to CDR1 in the follow-up. Similarly, participants with relatively stable CDR0 had significantly smaller PAD than those who were CDR0 at baseline but converted to CDR0.5 in the follow-up. CONCLUSION Our results imply that PAD might be a potential imaging biomarker for predicting CDR outcomes in patients with CDR0 or CDR0.5.
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Affiliation(s)
- Wen-Yih Isaac Tseng
- AcroViz Inc. Taipei, Taiwan (R.O.C.).,Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan (R.O.C.).,Molecular Imaging Center, National Taiwan University, Taipei, Taiwan (R.O.C.)
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Validation of Neuroimaging-based Brain Age Gap as a Mediator between Modifiable Risk Factors and Cognition. Neurobiol Aging 2022; 114:61-72. [DOI: 10.1016/j.neurobiolaging.2022.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 03/08/2022] [Accepted: 03/09/2022] [Indexed: 11/17/2022]
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Shang X, Zhu Z, Zhang X, Huang Y, Zhang X, Liu J, Wang W, Tang S, Yu H, Ge Z, Yang X, He M. Association of a wide range of chronic diseases and apolipoprotein E4 genotype with subsequent risk of dementia in community-dwelling adults: A retrospective cohort study. EClinicalMedicine 2022; 45:101335. [PMID: 35299656 PMCID: PMC8921546 DOI: 10.1016/j.eclinm.2022.101335] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/15/2022] [Accepted: 02/17/2022] [Indexed: 11/27/2022] Open
Abstract
Background Identifying independent and interactive associations of a wide range of diseases and multimorbidity and apolipoprotein E4 (APOE4) with dementia may help promote cognitive health. The main aim of the present study was to investigate associations of such diseases and their multimorbidity with incident dementia. Methods In this retrospective cohort study, we included 471,485 individuals of European ancestry from the UK Biobank, aged 38-73 years at baseline (2006-10). Dementia was identified using inpatient records and death registers. The follow-up period was between March 16, 2006, and Jan 31, 2021. Findings During a median follow-up of 11·9 years, 6189 cases of incident all-cause dementia (503 young-onset cases, 5686 late-onset cases) were documented. In multivariable-adjusted analysis, 33 out of 63 major diseases were associated with an increased risk of dementia. The hazard ratio (HR [95% CI]) ranged from 1·12 (1·06-1·19) for obesity to 14·22 (12·33-16·18) for Parkinson's disease. In addition to conventional diseases, respiratory disorders, musculoskeletal disorders, digestive disorders, painful conditions, and chronic kidney disease were associated with increased dementia risk. A larger HR for dementia was observed for a larger number of diseases (3·97 [3·51-4·48] for ≥6 diseases versus no disease). These individual diseases and multimorbidity were more predictive of young-onset dementia than of late-onset dementia. Dementia risk score incorporating multimorbidity, age, and APOE4 status had strong prediction performance (area under the curve [95% CI]: 82·2% [81·7-82·7%]). APOE4 was more predictive of late-onset dementia (HR [95% CI]: 2·90 [2·75-3·06]) than of young-onset dementia (1·26 [1·03-1·54]). Associations of painful conditions, depression, obesity, diabetes, stroke, Parkinson's disease, high cholesterol, and their multimorbidity with incident dementia were stronger among non-APOE4 carriers. Interpretation Besides conventional diseases, numerous diseases are associated with an increased risk of dementia. These individual diseases and multimorbidity are more predictive of young-onset dementia, whereas APOE4 is more predictive of late-onset dementia. Individual diseases and multimorbidity are stronger predictors of dementia in non-APOE4 carriers. Although multiple risk factors have been adjusted for in the analysis, potential confounding from unknown factors may have biased the associations. Funding The Fundamental Research Funds of the State Key Laboratory of Ophthalmology, Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou, China (Z012014075), Science and Technology Program of Guangzhou, China (202,002,020,049).
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Key Words
- AD, Alzheimer's disease
- APOE4, apolipoprotein E4
- AUC, area under the curve
- Apolipoprotein E4
- BMI, body mass index
- CAIDE, Cardiovascular Risk Factors, Aging, and Incidence of Dementia
- CI, confidence interval
- CKD, chronic kidney disease
- COPD, chronic obstructive pulmonary disease
- Dementia
- FRS, Framingham Heart Study
- HDL-C, high-density lipoprotein cholesterol
- HIV, human immunodeficiency virus
- HR, hazard ratio
- HbA1c, Glycosylated haemoglobin
- ICD, international classification diseases
- IQR, interquartile range
- Interaction
- LDL-C, low-density lipoprotein cholesterol
- Late-onset dementia
- Major chronic diseases
- Multimorbidity
- PAR, Population attributable risk
- ROC, receiver operating characteristic curve
- SD, standard deviation
- VD, vascular dementia
- Young-onset dementia
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Affiliation(s)
- Xianwen Shang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Centre for Eye Research Australia, The University of Melbourne, Level 7, 32 Gisborne Street, Melbourne, VIC 3002, Australia
| | - Zhuoting Zhu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Centre for Eye Research Australia, The University of Melbourne, Level 7, 32 Gisborne Street, Melbourne, VIC 3002, Australia
| | - Xueli Zhang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou 510080, China
| | - Yu Huang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xiayin Zhang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jiahao Liu
- Melbourne School of Population and Global Health, University of Melbourne, Melbourne VIC 3010, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Shulin Tang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou 510080, China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou 510080, China
| | - Zongyuan Ge
- Monash e-Research Center, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Center, Monash University, Melbourne VIC 3800, Australia
| | - Xiaohong Yang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou 510080, China
| | - Mingguang He
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou 510080, China
- Centre for Eye Research Australia, The University of Melbourne, Level 7, 32 Gisborne Street, Melbourne, VIC 3002, Australia
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
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Ibarrondo O, Huerta JM, Amiano P, Andreu-Reinón ME, Mokoroa O, Ardanaz E, Larumbe R, Colorado-Yohar SM, Navarro-Mateu F, Chirlaque MD, Mar J. Dementia Risk Score for a Population in Southern Europe Calculated Using Competing Risk Models. J Alzheimers Dis 2022; 86:1751-1762. [PMID: 35253747 PMCID: PMC9108562 DOI: 10.3233/jad-215211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Dementia prevention can be addressed if the intervention is applied early. Objective: The objective of this study was to develop and validate competing risk models to predict the late risk of dementia based on variables assessed in middle age in a southern European population. Methods: We conducted a prospective observational study of the EPIC-Spain cohort that included 25,015 participants. Dementia cases were identified from electronic health records and validated by neurologists. Data were gathered on sociodemographic characteristics and cardiovascular risk factors. To stratify dementia risk, Fine and Gray competing risk prediction models were constructed for the entire sample and for over-55-year-olds. Risk scores were calculated for low (the 30% of the sample with the lowest risk), moderate (> 30% –60%), and high (> 60% –100%) risk. Results: The 755 cases of dementia identified represented a cumulative incidence of 3.1% throughout the study period. The AUC of the model for over-55-year-olds was much higher (80.8%) than the overall AUC (68.5%) in the first 15 years of follow-up and remained that way in the subsequent follow-up. The weight of the competing risk of death was greater than that of dementia and especially when the entire population was included. Conclusion: This study presents the first dementia risk score calculated in a southern European population in mid-life and followed up for 20 years. The score makes it feasible to achieve the early identification of individuals in a southern European population who could be targeted for the prevention of dementia based on the intensive control of risk factors.
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Affiliation(s)
- Oliver Ibarrondo
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Spain
- Biodonostia Health Research Institute, Epidemiology and Public Health Area, San Sebastián, Spain
| | - José María Huerta
- Murcia Biomedical Research Institute (IMIB-Arrixaca), Murcia, Spain
- Department of Epidemiology. Murcia Regional Health Council, Murcia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Pilar Amiano
- Biodonostia Health Research Institute, Epidemiology and Public Health Area, San Sebastián, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Ministry of Health of the Basque Government, Sub-Directorate for Public Health and Addictions of Gipuzkoa, SanSebastián, Spain
| | - María Encarnación Andreu-Reinón
- Murcia Biomedical Research Institute (IMIB-Arrixaca), Murcia, Spain
- Section of Neurology, Department of Internal Medicine, Rafael Méndez Hospital, Murcian Health Service, Lorca, Spain
| | - Olatz Mokoroa
- Biodonostia Health Research Institute, Epidemiology and Public Health Area, San Sebastián, Spain
- Ministry of Health of the Basque Government, Sub-Directorate for Public Health and Addictions of Gipuzkoa, SanSebastián, Spain
| | - Eva Ardanaz
- Public Health Institute of Navarra, IdiSNA, Pamplona, Spain
- Neuroepigenetics Laboratory, Navarrabiomed, Public University of Navarre (UPNA), Navarre, Spain
| | - Rosa Larumbe
- Public Health Institute of Navarra, IdiSNA, Pamplona, Spain
- Neuroepigenetics Laboratory, Navarrabiomed, Public University of Navarre (UPNA), Navarre, Spain
- Department of Neurology, Complejo Hospitalario deNavarra, Pamplona, Spain
| | - Sandra M. Colorado-Yohar
- Murcia Biomedical Research Institute (IMIB-Arrixaca), Murcia, Spain
- Department of Epidemiology. Murcia Regional Health Council, Murcia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Research Group on Demography and Health, National Faculty of Public Health, University of Antioquia, Medellín, Colombia
| | - Fernando Navarro-Mateu
- Murcia Biomedical Research Institute (IMIB-Arrixaca), Murcia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Unidad deDocencia, Investigación y Formación en Salud Mental(UDIF-SM), Murcian Health Service, IMIB-Arrixaca, Murcia, Spain
| | - María Dolores Chirlaque
- Murcia Biomedical Research Institute (IMIB-Arrixaca), Murcia, Spain
- Department of Epidemiology. Murcia Regional Health Council, Murcia, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Health and Social Sciences, University of Murcia, Murcia, Spain
| | - Javier Mar
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Spain
- Biodonostia Health Research Institute, Epidemiology and Public Health Area, San Sebastián, Spain
- Kronikgune Health Services Research Institute, Barakaldo, Spain
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Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, Boehme AK, Buxton AE, Carson AP, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Ferguson JF, Generoso G, Ho JE, Kalani R, Khan SS, Kissela BM, Knutson KL, Levine DA, Lewis TT, Liu J, Loop MS, Ma J, Mussolino ME, Navaneethan SD, Perak AM, Poudel R, Rezk-Hanna M, Roth GA, Schroeder EB, Shah SH, Thacker EL, VanWagner LB, Virani SS, Voecks JH, Wang NY, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation 2022; 145:e153-e639. [PMID: 35078371 DOI: 10.1161/cir.0000000000001052] [Citation(s) in RCA: 2489] [Impact Index Per Article: 1244.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Wang L, Li P, Hou M, Zhang X, Cao X, Li H. Construction of a risk prediction model for Alzheimer's disease in the elderly population. BMC Neurol 2021; 21:271. [PMID: 34233656 PMCID: PMC8262052 DOI: 10.1186/s12883-021-02276-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 06/09/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Dementia is one of the greatest global health and social care challenges of the twenty-first century. The etiology and pathogenesis of Alzheimer's disease (AD) as the most common type of dementia remain unknown. In this study, a simple nomogram was drawn to predict the risk of AD in the elderly population. METHODS Nine variables affecting the risk of AD were obtained from 1099 elderly people through clinical data and questionnaires. Least Absolute Shrinkage Selection Operator (LASSO) regression analysis was used to select the best predictor variables, and multivariate logistic regression analysis was used to construct the prediction model. In this study, a graphic tool including 9 predictor variables (nomogram-see precise definition in the text) was drawn to predict the risk of AD in the elderly population. In addition, calibration diagram, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to verify the model. RESULTS Six predictors namely sex, age, economic status, health status, lifestyle and genetic risk were identified by LASSO regression analysis of nine variables (body mass index, marital status and education level were excluded). The area under the ROC curve in the training set was 0.822, while that in the validation set was 0.801, suggesting that the model built with these 6 predictors showed moderate predictive ability. The DCA curve indicated that a nomogram could be applied clinically if the risk threshold was between 30 and 40% (30 to 42% in the validation set). CONCLUSION The inclusion of sex, age, economic status, health status, lifestyle and genetic risk into the risk prediction nomogram could improve the ability of the prediction model to predict AD risk in the elderly patients.
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Affiliation(s)
- Lingling Wang
- Department of Neurology, People's Hospital of Xinjiang Uygur Autonomous Region, NO.91 Tianchi Road, Tianshan District, Urumqi, Xinjiang, 830001, Uygur Autonomous Region, China
| | - Ping Li
- Department of Nursing, People's Hospital of Xinjiang Uygur Autonomous Region, Uygur Autonomous Region, Xinjiang, 830001, China
| | - Ming Hou
- Department of Nursing, People's Hospital of Xinjiang Uygur Autonomous Region, Uygur Autonomous Region, Xinjiang, 830001, China
| | - Xiumin Zhang
- Department of Nursing, People's Hospital of Xinjiang Uygur Autonomous Region, Uygur Autonomous Region, Xinjiang, 830001, China
| | - Xiaolin Cao
- Department of Nursing, People's Hospital of Xinjiang Uygur Autonomous Region, Uygur Autonomous Region, Xinjiang, 830001, China
| | - Hongyan Li
- Department of Neurology, People's Hospital of Xinjiang Uygur Autonomous Region, NO.91 Tianchi Road, Tianshan District, Urumqi, Xinjiang, 830001, Uygur Autonomous Region, China.
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Makino K, Lee S, Bae S, Chiba I, Harada K, Katayama O, Shinkai Y, Shimada H. Absolute Cardiovascular Disease Risk Is Associated With the Incidence of Non-amnestic Cognitive Impairment in Japanese Older Adults. Front Aging Neurosci 2021; 13:685683. [PMID: 34194318 PMCID: PMC8236854 DOI: 10.3389/fnagi.2021.685683] [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: 03/25/2021] [Accepted: 05/20/2021] [Indexed: 12/03/2022] Open
Abstract
Background The estimated absolute cardiovascular disease (CVD) risk level is known to be a useful surrogate marker for future cognitive impairment; however, evidence regarding its predictive validity in terms of cognitive subtypes is limited. We aimed to examine subtype-dependent differences in the associations between absolute CVD risk and the incidence of cognitive impairment in a community-dwelling older Japanese cohort. Methods and Results This study comprised 1,641 cognitively intact older Japanese participants without CVDs at baseline. We estimated absolute CVD risk using WHO region-specific risk estimation charts and included age, sex, diabetes mellitus, smoking, systolic blood pressure, and total cholesterol at baseline, and the CVD risk level was stratified into the three following risk categories: low (<10%), moderate (10 to <20%), and high (≥20%). Objective cognitive screening was performed using a multicomponent neurocognitive test at baseline and follow-up, and the incidence of cognitive impairment over 48 ± 2 months was determined. The incidence of cognitive impairment in low-, moderate-, and high-CVD risk participants was 1.2, 3.0, and 5.4%, respectively, for amnestic subtypes and 5.8, 10.1, and 14.0%, respectively, for non-amnestic subtypes. After adjusting for potential confounding factors, the absolute CVD risk level was significantly associated with non-amnestic impairment but not with amnestic impairment. Conclusions The absolute CVD risk estimated using region-specific risk estimation charts in old age is useful to predict incidence of cognitive impairment. Strategies to screen populations at risk of cognitive impairment and to prevent progression to dementia should be cognitive subtype-specific.
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Affiliation(s)
- Keitaro Makino
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan.,Japan Society for the Promotion of Science, Chiyoda-ku, Japan
| | - Sangyoon Lee
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Seongryu Bae
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Ippei Chiba
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Kenji Harada
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Osamu Katayama
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Yohei Shinkai
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Hiroyuki Shimada
- Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan
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Affiliation(s)
- Elham Mahmoudi
- Department of Family Medicine, University of Michigan Medical School, Ann Arbor
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor
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