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Ke TM, Lophatananon A, Muir KR. Strengthening the Evidence for a Causal Link between Type 2 Diabetes Mellitus and Pancreatic Cancer: Insights from Two-Sample and Multivariable Mendelian Randomization. Int J Mol Sci 2024; 25:4615. [PMID: 38731833 PMCID: PMC11082974 DOI: 10.3390/ijms25094615] [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: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 05/13/2024] Open
Abstract
This two-sample Mendelian randomization (MR) study was conducted to investigate the causal associations between type 2 diabetes mellitus (T2DM) and the risk of pancreatic cancer (PaCa), as this causal relationship remains inconclusive in existing MR studies. The selection of instrumental variables for T2DM was based on two genome-wide association study (GWAS) meta-analyses from European cohorts. Summary-level data for PaCa were extracted from the FinnGen and UK Biobank databases. Inverse variance weighted (IVW) and four other robust methods were employed in our MR analysis. Various sensitivity analyses and multivariable MR approaches were also performed to enhance the robustness of our findings. In the IVW and Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) analyses, the odds ratios (ORs) for each 1-unit increase in genetically predicted log odds of T2DM were approximately 1.13 for PaCa. The sensitivity tests and multivariable MR supported the causal link between T2DM and PaCa without pleiotropic effects. Therefore, our analyses suggest a causal relationship between T2DM and PaCa, shedding light on the potential pathophysiological mechanisms of T2DM's impact on PaCa. This finding underscores the importance of T2DM prevention as a strategy to reduce the risk of PaCa.
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Affiliation(s)
| | | | - Kenneth R. Muir
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK; (T.-M.K.); (A.L.)
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2
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Tian C, Ye Z, McCoy RG, Pan Y, Bi C, Gao S, Ma Y, Chen M, Yu J, Lu T, Hong LE, Kochunov P, Ma T, Chen S, Liu S. The causal effect of HbA1c on white matter brain aging by two-sample Mendelian randomization analysis. Front Neurosci 2024; 17:1335500. [PMID: 38274506 PMCID: PMC10808780 DOI: 10.3389/fnins.2023.1335500] [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: 11/09/2023] [Accepted: 12/28/2023] [Indexed: 01/27/2024] Open
Abstract
Background Poor glycemic control with elevated levels of hemoglobin A1c (HbA1c) is associated with increased risk of cognitive impairment, with potentially varying effects between sexes. However, the causal impact of poor glycemic control on white matter brain aging in men and women is uncertain. Methods We used two nonoverlapping data sets from UK Biobank cohort: gene-outcome group (with neuroimaging data, (N = 15,193; males/females: 7,101/8,092)) and gene-exposure group (without neuroimaging data, (N = 279,011; males/females: 122,638/156,373)). HbA1c was considered the exposure and adjusted "brain age gap" (BAG) was calculated on fractional anisotropy (FA) obtained from brain imaging as the outcome, thereby representing the difference between predicted and chronological age. The causal effects of HbA1c on adjusted BAG were studied using the generalized inverse variance weighted (gen-IVW) and other sensitivity analysis methods, including Mendelian randomization (MR)-weighted median, MR-pleiotropy residual sum and outlier, MR-using mixture models, and leave-one-out analysis. Results We found that for every 6.75 mmol/mol increase in HbA1c, there was an increase of 0.49 (95% CI = 0.24, 0.74; p-value = 1.30 × 10-4) years in adjusted BAG. Subgroup analyses by sex and age revealed significant causal effects of HbA1c on adjusted BAG, specifically among men aged 60-73 (p-value = 2.37 × 10-8). Conclusion Poor glycemic control has a significant causal effect on brain aging, and is most pronounced among older men aged 60-73 years, which provides insights between glycemic control and the susceptibility to age-related neurodegenerative diseases.
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Affiliation(s)
- Cheng Tian
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Rozalina G. McCoy
- Division of Endocrinology, Diabetes, & Nutrition, Department of Medicine, School of Medicine, University of Maryland, Baltimore, MD, United States
- University of Maryland Institute for Health Computing, Bethesda, MD, United States
| | - Yezhi Pan
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Chuan Bi
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Si Gao
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Yizhou Ma
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Mo Chen
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Jiaao Yu
- Department of Mathematics, University of Maryland, College Park, MD, United States
| | - Tong Lu
- Department of Mathematics, University of Maryland, College Park, MD, United States
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, United States
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Song Liu
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
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3
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Bettencourt C, Skene N, Bandres-Ciga S, Anderson E, Winchester LM, Foote IF, Schwartzentruber J, Botia JA, Nalls M, Singleton A, Schilder BM, Humphrey J, Marzi SJ, Toomey CE, Kleifat AA, Harshfield EL, Garfield V, Sandor C, Keat S, Tamburin S, Frigerio CS, Lourida I, Ranson JM, Llewellyn DJ. Artificial intelligence for dementia genetics and omics. Alzheimers Dement 2023; 19:5905-5921. [PMID: 37606627 PMCID: PMC10841325 DOI: 10.1002/alz.13427] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 08/23/2023]
Abstract
Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.
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Affiliation(s)
- Conceicao Bettencourt
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK
| | - Nathan Skene
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Sara Bandres-Ciga
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Emma Anderson
- Department of Mental Health of Older People, Division of Psychiatry, University College London, London, UK
| | | | - Isabelle F Foote
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
| | - Jeremy Schwartzentruber
- Open Targets, Cambridge, UK
- Wellcome Sanger Institute, Cambridge, UK
- Illumina Artificial Intelligence Laboratory, Illumina Inc, Foster City, California, USA
| | - Juan A Botia
- Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
| | - Mike Nalls
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
- Data Tecnica International LLC, Washington, DC, USA
| | - Andrew Singleton
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Brian M Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Jack Humphrey
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Sarah J Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Christina E Toomey
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London, UK
- The Francis Crick Institute, London, UK
| | - Ahmad Al Kleifat
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Eric L Harshfield
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Victoria Garfield
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, UK
| | - Cynthia Sandor
- UK Dementia Research Institute. School of Medicine, Cardiff University, Cardiff, UK
| | - Samuel Keat
- UK Dementia Research Institute. School of Medicine, Cardiff University, Cardiff, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, Neurology Section, University of Verona, Verona, Italy
| | - Carlo Sala Frigerio
- UK Dementia Research Institute, Queen Square Institute of Neurology, University College London, London, UK
| | | | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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Paz V, Dashti HS, Burgess S, Garfield V. Selection of genetic instruments in Mendelian randomisation studies of sleep traits. Sleep Med 2023; 112:342-351. [PMID: 37956646 PMCID: PMC7615498 DOI: 10.1016/j.sleep.2023.10.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/22/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023]
Abstract
This review explores the criteria used for the selection of genetic instruments of sleep traits in the context of Mendelian randomisation studies. This work was motivated by the fact that instrument selection is the most important decision when designing a Mendelian randomisation study. As far as we are aware, no review has sought to address this to date, even though the number of these studies is growing rapidly. The review is divided into the following sections which are essential for genetic instrument selection: 1) Single-gene region vs polygenic analysis; 2) Polygenic analysis: biologically-vs statistically-driven approaches; 3) P-value; 4) Linkage disequilibrium clumping; 5) Sample overlap; 6) Type of exposure; 7) Total (R2) and average strength (F-statistic) metrics; 8) Number of single-nucleotide polymorphisms; 9) Minor allele frequency and palindromic variants; 10) Confounding. Our main aim is to discuss how instrumental choice impacts analysis and compare the strategies that Mendelian randomisation studies of sleep traits have used. We hope that our review will enable more researchers to take a more considered approach when selecting genetic instruments for sleep exposures.
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Affiliation(s)
- Valentina Paz
- Instituto de Psicología Clínica, Facultad de Psicología, Universidad de la República, Tristán Narvaja, 1674, Montevideo, 11200, Uruguay; MRC Unit for Lifelong Health & Ageing, Institute of Cardiovascular Science, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
| | - Hassan S Dashti
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, 185 Cambridge Street, Boston, MA, 02114, USA; Broad Institute, 415 Main Street, Cambridge, MA, 02142, USA; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Edwards 4-410C, Boston, MA, 02114, USA
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK; Department of Public Health and Primary Care, University of Cambridge, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK
| | - Victoria Garfield
- MRC Unit for Lifelong Health & Ageing, Institute of Cardiovascular Science, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK
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Ranglani S, Ward J, Sattar N, Strawbridge RJ, Lyall DM. Testing for associations between HbA1c levels, polygenic risk and brain health in UK Biobank (N = 39 283). Diabetes Obes Metab 2023; 25:3136-3143. [PMID: 37435691 DOI: 10.1111/dom.15207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 06/09/2023] [Accepted: 06/18/2023] [Indexed: 07/13/2023]
Abstract
AIM To investigate whether continuous HbA1c levels and HbA1c-polygenic risk scores (HbA1c-PRS) are significantly associated with worse brain health independent of type 2 diabetes (T2D) diagnosis (vs. not), by examining brain structure and cognitive test score phenotypes. METHODS Using UK Biobank data (n = 39 283), we tested whether HbA1c levels and/or HbA1c-PRS were associated with cognitive test scores and brain imaging phenotypes. We adjusted for confounders of age, sex, Townsend deprivation score, level of education, genotyping chip, eight genetic principal components, smoking, alcohol intake frequency, cholesterol medication, body mass index, T2D and apolipoprotein (APOE) e4 dosage. RESULTS We found an association between higher HbA1c levels and poorer performance on symbol digit substitution scores (standardized beta [β] = -0.022, P = .001) in the fully adjusted model. We also found an association between higher HbA1c levels and worse brain MRI phenotypes of grey matter (GM; fully-adjusted β = -0.026, P < .001), whole brain volume (β = -0.072, P = .0113) and a general factor of frontal lobe GM (β = -0.022, P < .001) in partially and fully adjusted models. HbA1c-PRS were significantly associated with GM volume in the fully adjusted model (β = -0.010, P = .0113); however, when adjusted for HbA1c levels, the association was not significant. CONCLUSIONS Our findings suggest that measured HbA1c is associated with poorer cognitive health, and that HbA1c-PRS do not add significant information to this.
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Affiliation(s)
- Sanskar Ranglani
- School of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
| | - Joey Ward
- School of Health & Wellbeing, University of Glasgow, Glasgow, UK
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Sciences, University of Glasgow, Glasgow, UK
| | - Rona J Strawbridge
- School of Health & Wellbeing, University of Glasgow, Glasgow, UK
- Division of Cardiovascular Medicine, Department of Medicine Solna, Karolinska Institutet, Solna, Sweden
- HDR-UK, London, UK
| | - Donald M Lyall
- School of Health & Wellbeing, University of Glasgow, Glasgow, UK
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6
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Hu J, Fang M, Pike JR, Lutsey PL, Sharrett AR, Wagenknecht LE, Hughes TM, Seegmiller JC, Gottesman RF, Mosley TH, Coresh J, Selvin E. Prediabetes, intervening diabetes and subsequent risk of dementia: the Atherosclerosis Risk in Communities (ARIC) study. Diabetologia 2023; 66:1442-1449. [PMID: 37221246 PMCID: PMC10467356 DOI: 10.1007/s00125-023-05930-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 02/28/2023] [Indexed: 05/25/2023]
Abstract
AIMS/HYPOTHESIS The aim of this work was to evaluate whether the association of prediabetes with dementia is explained by the intervening onset of diabetes. METHODS Among participants of the Atherosclerosis Risk in Communities (ARIC) study we defined baseline prediabetes as HbA1c 39-46 mmol/mol (5.7-6.4%) and subsequent incident diabetes as a self-reported physician diagnosis or use of diabetes medication. Incident dementia was ascertained via active surveillance and adjudicated. We quantified the association of prediabetes with dementia risk before and after accounting for the subsequent development of diabetes among ARIC participants without diabetes at baseline (1990-1992; participants aged 46-70 years). We also evaluated whether age at diabetes diagnosis modified the risk of dementia. RESULTS Among 11,656 participants without diabetes at baseline, 2330 (20.0%) had prediabetes. Before accounting for incident diabetes, prediabetes was significantly associated with the risk of dementia (HR 1.12 [95% CI 1.01, 1.24]). After accounting for incident diabetes, the association was attenuated and non-significant (HR 1.05 [95% CI 0.94, 1.16]). Earlier age of onset of diabetes had the strongest association with dementia: HR 2.92 (95% CI 2.06, 4.14) for onset before 60 years; HR 1.73 (95% CI 1.47, 2.04) for onset at 60-69 years; and HR 1.23 (95% CI 1.08, 1.40) for onset at 70-79 years. CONCLUSIONS/INTERPRETATION Prediabetes is associated with dementia risk but this risk is explained by the subsequent development of diabetes. Earlier age of onset of diabetes substantially increases dementia risk. Preventing or delaying progression of prediabetes to diabetes will reduce dementia burden.
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Affiliation(s)
- Jiaqi Hu
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Michael Fang
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - James R Pike
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Pamela L Lutsey
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - A Richey Sharrett
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Lynne E Wagenknecht
- Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Timothy M Hughes
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Jesse C Seegmiller
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Rebecca F Gottesman
- Stroke Branch, National Institute of Neurological Disorders and Stroke, Baltimore, MD, USA
| | - Thomas H Mosley
- The MIND Center, University of Mississippi School of Medicine, Jackson, MS, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA.
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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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8
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Mina T, Yew YW, Ng HK, Sadhu N, Wansaicheong G, Dalan R, Low DYW, Lam BCC, Riboli E, Lee ES, Ngeow J, Elliott P, Griva K, Loh M, Lee J, Chambers J. Adiposity impacts cognitive function in Asian populations: an epidemiological and Mendelian Randomization study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2023; 33:100710. [PMID: 36851942 PMCID: PMC9957736 DOI: 10.1016/j.lanwpc.2023.100710] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/19/2023] [Accepted: 01/26/2023] [Indexed: 02/15/2023]
Abstract
Background Obesity and related metabolic disturbances including diabetes, hypertension and hyperlipidemia predict future cognitive decline. Asia has a high prevalence of both obesity and metabolic disease, potentially amplifying the future burden of dementia in the region. We aimed to investigate the impact of adiposity and metabolic risk on cognitive function in Asian populations, using an epidemiological analysis and a two-sample Mendelian Randomization (MR) study. Methods The Health for Life in Singapore (HELIOS) Study is a population-based cohort of South-East-Asian men and women in Singapore, aged 30-84 years. We analyzed 8769 participants with metabolic and cognitive data collected between 2018 and 2021. Whole-body fat mass was quantified with Dual X-Ray Absorptiometry (DEXA). Cognition was assessed using a computerized cognitive battery. An index of general cognition ' g ' was derived through factor analysis. We tested the relationship of fat mass indices and metabolic measures with ' g ' using regression approaches. We then performed inverse-variance-weighted MR of adiposity and metabolic risk factors on ' g ', using summary statistics for genome-wide association studies of BMI, visceral adipose tissue (VAT), waist-hip-ratio (WHR), blood pressure, HDL cholesterol, triglycerides, fasting glucose, HbA1c, and general cognition. Findings Participants were 58.9% female, and aged 51.4 (11.3) years. In univariate analysis, all 29 adiposity and metabolic measures assessed were associated with ' g ' at P < 0.05. In multivariable analyses, reduced ' g ' was consistently associated with increased visceral fat mass index and lower HDL cholesterol (P < 0.001), but not with blood pressure, triglycerides, or glycemic indices. The reduction in ' g ' associated with 1SD higher visceral fat, or 1SD lower HDL cholesterol, was equivalent to a 0.7 and 0.9-year increase in chronological age respectively (P < 0.001). Inverse variance MR analyses showed that reduced ' g ' is associated with genetically determined elevation of VAT, BMI and WHR (all P < 0.001). In contrast, MR did not support a causal role for blood pressure, lipid, or glycemic indices on cognition. Interpretation We show an independent relationship between adiposity and cognition in a multi-ethnic Asian population. MR analyses suggest that both visceral adiposity and raised BMI are likely to be causally linked to cognition. Our findings have important implications for preservation of cognitive health, including further motivation for action to reverse the rising burden of obesity in the Asia-Pacific region. Funding The Nanyang Technological University-the Lee Kong Chian School of Medicine, National Healthcare Group, National Medical Research Council, Ministry of Education, Singapore.
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Affiliation(s)
- Theresia Mina
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore
| | - Yik Weng Yew
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,National Skin Centre, Research Division, 1 Mandalay Rd, 308205, Singapore
| | - Hong Kiat Ng
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore
| | - Nilanjana Sadhu
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore
| | - Gervais Wansaicheong
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Department of Diagnostic Radiology, Tan Tock Seng Hospital (TTSH), 11 Jalan Tan Tock Seng, 308433, Singapore
| | - Rinkoo Dalan
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Department of Endocrinology, TTSH, Singapore
| | - Dorrain Yan Wen Low
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore
| | - Benjamin Chih Chiang Lam
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Khoo Teck Puat Hospital, Integrated Care for Obesity & Diabetes, 90 Yishun Central, 768828, Singapore
| | - Elio Riboli
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, 152 Medical School, St Mary's Campus, London, W2 1NY, United Kingdom
| | - Eng Sing Lee
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Clinical Research Unit, National Healthcare Group Polyclinic, 3 Fusionopolis Link, Nexus@one-north, #05-10, 138543, Singapore
| | - Joanne Ngeow
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Division of Medical Oncology, National Cancer Centre, 11 Hospital Drive, 169610, Singapore
| | - Paul Elliott
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, 152 Medical School, St Mary's Campus, London, W2 1NY, United Kingdom
| | - Konstadina Griva
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore
| | - Marie Loh
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,National Skin Centre, Research Division, 1 Mandalay Rd, 308205, Singapore
| | - Jimmy Lee
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Research Division, Institute of Mental Health, 539747, Singapore
| | - John Chambers
- Nanyang Technological University Lee Kong Chian School of Medicine, Level 18 Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore.,Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, 152 Medical School, St Mary's Campus, London, W2 1NY, United Kingdom
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9
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Litkowski EM, Logue MW, Zhang R, Charest BR, Lange EM, Hokanson JE, Lynch JA, Vujkovic M, Phillips LS, Hauger RL, Lange LA, Raghavan S. Mendelian randomization study of diabetes and dementia in the Million Veteran Program. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.07.23286526. [PMID: 36945581 PMCID: PMC10029030 DOI: 10.1101/2023.03.07.23286526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
INTRODUCTION Diabetes and dementia are diseases of high healthcare burden worldwide. Individuals with diabetes have 1.4 to 2.2 times higher risk of dementia. Our objective was to evaluate evidence of causality between these two common diseases. METHODS We conducted a one-sample Mendelian randomization (MR) analysis in the U.S. Department of Veterans Affairs Million Veteran program. The study included 334,672 participants ≥65 years of age with type 2 diabetes and dementia case-control status and genotype data. RESULTS For each standard deviation increase in genetically-predicted diabetes, we found increased odds of three dementia diagnoses in non-Hispanic White participants (all-cause: OR=1.07[1.05-1.08], P =3.40E-18; vascular: OR=1.11[1.07-1.15], P =3.63E-09, Alzheimer's: OR=1.06[1.02-1.09], P =6.84E-04) and non-Hispanic Black participants (all-cause: OR=1.06[1.02-1.10], P =3.66E-03, vascular: OR=1.11[1.04-1.19], P =2.20E-03, Alzheimer's: OR=1.12 [1.02-1.23], P =1.60E-02) but not in Hispanic participants (all P >.05). DISCUSSION We found evidence of causality between diabetes and dementia using a one-sample MR study, with access to individual level data, overcoming limitations of prior studies utilizing two-sample MR techniques.
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Affiliation(s)
- Elizabeth M Litkowski
- VA Eastern Colorado Healthcare System, Aurora, CO, 80045 USA
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Mark W Logue
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA, 02301, USA
- Boston University Schools of Medicine and Public Health, Boston, MA, 02118, USA
| | - Rui Zhang
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA, 02301, USA
| | | | - Ethan M Lange
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - John E Hokanson
- Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Julie A Lynch
- Salt Lake City VA, VA Informatics & Computing Infrastructure, Salt Lake City, UT, 84148, USA
- University of Utah, School of Medicine, Salt Lake City, UT, 84132, USA
| | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lawrence S Phillips
- Atlanta VA Health Care System, Decatur, GA, 30033, USA
- Division of Endocrinology and Metabolism, Department of Medicine, Emory University School of Medicine, Atlanta, GA, 30307, USA
| | - Richard L Hauger
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, 92161, USA
- Center for Behavior Genetics of Aging, School of Medicine, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Leslie A Lange
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Sridharan Raghavan
- VA Eastern Colorado Healthcare System, Aurora, CO, 80045 USA
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
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10
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Anderson EL, Williams DM. Drug target Mendelian randomisation: are we really instrumenting drug use? Diabetologia 2023; 66:1156-1158. [PMID: 36795331 PMCID: PMC9932405 DOI: 10.1007/s00125-023-05875-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 12/15/2022] [Indexed: 02/17/2023]
Affiliation(s)
- Emma L Anderson
- Department of Mental Health of Older People, Division of Psychiatry, University College London, London, UK.
| | - Dylan M Williams
- MRC Unit for Lifelong Health and Ageing, Population Science & Experimental Medicine, University College London, London, UK
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11
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Affiliation(s)
- Gian Paolo Fadini
- Department of Medicine, Division of Metabolic Diseases, University of Padova, Via Giustiniani 2, 35128 Padua, Italy
| | - Mario Luca Morieri
- Department of Medicine, Division of Metabolic Diseases, University of Padova, Via Giustiniani 2, 35128 Padua, Italy
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12
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Garfield V, Salzmann A, Burgess S, Chaturvedi N. A Guide for Selection of Genetic Instruments in Mendelian Randomization Studies of Type 2 Diabetes and HbA1c: Toward an Integrated Approach. Diabetes 2023; 72:175-183. [PMID: 36669000 PMCID: PMC7614590 DOI: 10.2337/db22-0110] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 10/24/2022] [Indexed: 01/21/2023]
Abstract
In this study we examine the instrument selection strategies currently used throughout the type 2 diabetes and HbA1c Mendelian randomization (MR) literature. We then argue for a more integrated and thorough approach, providing a framework to do this in the context of HbA1c and diabetes. We conducted a literature search for MR studies that have instrumented diabetes and/or HbA1c. We also used data from the UK Biobank (UKB) (N = 349,326) to calculate instrument strength metrics that are key in MR studies (the F statistic for average strength and R2 for total strength) with two different methods ("individual-level data regression" and Cragg-Donald formula). We used a 157-single nucleotide polymorphism (SNP) instrument for diabetes and a 51-SNP instrument (with partition into glycemic and erythrocytic as well) for HbA1c. Our literature search yielded 48 studies for diabetes and 22 for HbA1c. Our UKB empirical examples showed that irrespective of the method used to calculate metrics of strength and whether the instrument was the main one or included partition by function, the HbA1c genetic instrument is strong in terms of both average and total strength. For diabetes, a 157-SNP instrument was shown to have good average strength and total strength, but these were both substantially lesser than those of the HbA1c instrument. We provide a careful set of five recommendations to researchers who wish to genetically instrument type 2 diabetes and/or HbA1c. In MR studies of glycemia, investigators should take a more integrated approach when selecting genetic instruments, and we give specific guidance on how to do this.
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Affiliation(s)
- Victoria Garfield
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London
| | - Antoine Salzmann
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London
| | - Stephen Burgess
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, MRC Biostatistics Unit, University of Cambridge, UK
| | - Nish Chaturvedi
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London
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13
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Li J, Yang M, Luo P, Wang G, Dong B, Xu P. Type 2 diabetes and glycemic traits are not causal factors of delirium: A two-sample mendelian randomization analysis. Front Genet 2023; 14:1087878. [PMID: 36896238 PMCID: PMC9988945 DOI: 10.3389/fgene.2023.1087878] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 01/20/2023] [Indexed: 02/23/2023] Open
Abstract
This study aims to explore the genetic causal association between type 2 diabetes (T2D) and glycemic traits (fasting glucose [FG], fasting insulin [FI], and glycated hemoglobin [HbA1c]) on delirium using Mendelian randomization (MR). Genome-wide association studies (GWAS) summary data for T2D and glycemic traits were obtained from the IEU OpenGWAS database. GWAS summary data for delirium were obtained from the FinnGen Consortium. All the participants were of European ancestry. In addition, we used T2D, FG, FI, and HbA1c as exposures and delirium as outcomes. A random-effects variance-weighted model (IVW), MR Egger, weighted median, simple mode, and weighted mode were used to perform MR analysis. In addition, MR-IVW and MR-Egger analyses were used to detect heterogeneity in the MR results. Horizontal pleiotropy was detected using MR-Egger regression and MR pleiotropy residual sum and outliers (MR-PRESSO). MR-PRESSO was also used to assess outlier single nucleotide polymorphisms (SNPs). The "leave one out" analysis was used to investigate whether the MR analysis results were influenced by a single SNP and evaluate the robustness of the results. In this study, we conducted a two-sample MR analysis, and there was no evidence of a genetic causal association between T2D and glycemic traits (T2D, FG, FI, and HbA1c) on delirium (all p > 0.05). The MR-IVW and MR-Egger tests showed no heterogeneity in our MR results (all p values >0.05). In addition, The MR-Egger and MR-PRESSO tests showed no horizontal pleiotropy in our MR results (all p > 0.05). The MR-PRESSO results also showed that there were no outliers during the MR analysis. In addition, the "leave one out" test did not find that the SNPs included in the analysis could affect the stability of the MR results. Therefore, our study did not support the causal effects of T2D and glycemic traits (FG, FI, and HbA1c) on delirium risk.
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Affiliation(s)
- Jing Li
- Department of Anesthesiology, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Mingyi Yang
- Department of Joint Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Pan Luo
- Department of Joint Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Gang Wang
- Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Buhuai Dong
- Department of Anesthesiology, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Peng Xu
- Department of Joint Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
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14
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Boujelbane MA, Trabelsi K, Jahrami HA, Masmoudi L, Ammar A, Khacharem A, Boukhris O, Puce L, Garbarino S, Scoditti E, Khanfir S, Msaad A, Msaad A, Akrout S, Hakim A, Bragazzi NL, Bryk K, Glenn JM, Chtourou H. Time-restricted feeding and cognitive function in sedentary and physically active elderly individuals: Ramadan diurnal intermittent fasting as a model. Front Nutr 2022; 9:1041216. [DOI: 10.3389/fnut.2022.1041216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 10/13/2022] [Indexed: 11/11/2022] Open
Abstract
ObjectivesThis study aimed to investigate the effects of Ramadan diurnal intermittent fasting (RDIF) on cognitive performance, sleep quality, daytime sleepiness, and insomnia in physically active and sedentary elderly individuals.MethodsA total of 58 participants (62.93 ± 3.99 years) were assigned to one of the following two groups: a sedentary group (control group) who observed Ramadan (n = 32) and a physically active group (n = 26) who continued to train while observing Ramadan. Participants were assessed 2 weeks before Ramadan and during the fourth week of Ramadan. On each occasion, participants completed a digital assessment of their cognitive performance and responded to the Pittsburgh sleep quality index (PSQI), the insomnia severity index (ISI) and the Epworth sleepiness scale (ESS) questionnaires to assess sleep parameters.ResultsCompared to before Ramadan, performance in executive function (p = 0.035), attention (p = 0.005), inhibition (p = 0.02), associative memory (p = 0.041), and recognition memory (p = 0.025) increased significantly during Ramadan in the physically active group. For the sedentary group, associative learning performance decreased (p = 0.041), whilst performances in the remaining domains remained unchanged during Ramadan. Global PSQI, ISI, and ESS scores indicated both groups suffered from poor sleep quality and excessive daytime sleepiness, with significantly higher negative effects of RDIF observed in the sedentary group.ConclusionOlder adults who continue to train at least three times per week during Ramadan may improve their cognitive performance, despite the impairment of sleep quality. Future studies in older adults during Ramadan including objective measures of sleep (e.g., polysomnography, actigraphy) and brain function (e.g., functional magnetic resonance imaging) are warranted.
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15
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Savelieff MG, Chen KS, Elzinga SE, Feldman EL. Diabetes and dementia: Clinical perspective, innovation, knowledge gaps. J Diabetes Complications 2022; 36:108333. [PMID: 36240668 PMCID: PMC10076101 DOI: 10.1016/j.jdiacomp.2022.108333] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/30/2022] [Indexed: 10/31/2022]
Abstract
The world faces a pandemic-level prevalence of type 2 diabetes. In parallel with this massive burden of metabolic disease is the growing prevalence of dementia as the population ages. The two health issues are intertwined. The Lancet Commission on dementia prevention, intervention, and care was convened to tackle the growing global concern of dementia by identifying risk factors. It concluded, along with other studies, that diabetes as well as obesity and the metabolic syndrome more broadly, which are frequently comorbid, raise the risk of developing dementia. Type 2 diabetes is a modifiable risk factor; however, it is uncertain whether anti-diabetic drugs mitigate risk of developing dementia. Reasons are manifold but constitute a critical knowledge gap in the field. This review outlines studies of type 2 diabetes on risk of dementia, illustrating key concepts. Moreover, it identifies knowledge gaps, reviews strategies to help fill these gaps, and concludes with a series of recommendations to mitigate risk and advance understanding of type 2 diabetes and dementia.
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Affiliation(s)
- Masha G Savelieff
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kevin S Chen
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Sarah E Elzinga
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Eva L Feldman
- NeuroNetwork for Emerging Therapies, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA.
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16
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Litkowski EM, Logue MW, Zhang R, Charest BR, Lange EM, Hokanson JE, Lynch JA, Vujkovic M, Phillips LS, Lange LA, Hauger RL, Raghavan S. A Diabetes Genetic Risk Score Is Associated With All-Cause Dementia and Clinically Diagnosed Vascular Dementia in the Million Veteran Program. Diabetes Care 2022; 45:2544-2552. [PMID: 36041056 PMCID: PMC9679262 DOI: 10.2337/dc22-0105] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 07/15/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Diabetes and dementia are diseases of high health care burden worldwide, and studies have shown that diabetes is associated with an increased relative risk of dementia. We set out to examine whether type 2 diabetes-associated genetic variants were associated with dementia and whether they differed by race/ethnicity or clinical dementia diagnosis. RESEARCH DESIGN AND METHODS We evaluated associations of two type 2 diabetes genetic risk scores (GRS and GRS-nonAPOE: a score without rs429358, a variant associated with Alzheimer disease [AD]) with three classifications of clinical dementia diagnoses in the Million Veteran Program (MVP): all-cause dementia, vascular dementia (VaD), and AD. We conducted our analysis stratified by European (EUR), African (AFR), and Hispanic (HIS) races/ethnicities. RESULTS In EUR, we found associations of the GRS with all-cause dementia (odds ratio [OR] 1.06, P = 1.60e-07) and clinically diagnosed VaD (OR 1.12, P = 5.2e-05) but not with clinically diagnosed AD (OR 1.02, P = 0.43). The GRS was not associated with any dementia outcome in AFR or HIS. When testing with GRS-nonAPOE, we found that effect size estimates in EUR increased and P values decreased for all-cause dementia (OR 1.08, P = 2.6e-12), for VaD (OR 1.14, P = 7.2e-07), and for AD (OR 1.06, P = 0.018). For AFR, the association of GRS-nonAPOE and clinically diagnosed VaD (OR 1.15, P = 0.016) was statistically significant. There were no significant findings for HIS. CONCLUSIONS We found evidence suggesting shared genetic pathogenesis of diabetes with all-cause dementia and clinically diagnosed VaD.
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Affiliation(s)
- Elizabeth M. Litkowski
- VA Eastern Colorado Healthcare System, Aurora, CO
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO
- Department of Epidemiology, University of Colorado, Aurora, CO
| | - Mark W. Logue
- Behavioral Sciences Division, National Center for PTSD, VA Boston Healthcare System, Boston
- Boston University Schools of Medicine and Public Health, Boston, MA
| | - Rui Zhang
- Behavioral Sciences Division, National Center for PTSD, VA Boston Healthcare System, Boston
| | | | - Ethan M. Lange
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO
| | | | - Julie A. Lynch
- VA Informatics & Computing Infrastructure, VA Salt Lake City Healthcare System, Salt Lake City, UT
- School of Medicine, University of Utah, Salt Lake City, UT
| | - Marijana Vujkovic
- Corporal Michael J. Crescenz Department of Veterans Affairs Medical Center, Philadelphia, PA
- Department of Medicine (M.V.), University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Lawrence S. Phillips
- VA Atlanta Healthcare System, Decatur, GA
- Division of Endocrinology and Metabolism, Department of Medicine, Emory University School of Medicine, Atlanta, GA
| | - Leslie A. Lange
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO
- Department of Epidemiology, University of Colorado, Aurora, CO
| | - Richard L. Hauger
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego
- Center for Behavior Genetics of Aging, School of Medicine, University of California, San Diego, La Jolla, CA
| | - Sridharan Raghavan
- VA Eastern Colorado Healthcare System, Aurora, CO
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO
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17
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Srikanth V. Type 2 Diabetes, Glycemia, and Brain Health: The Complexity of Causality. Diabetes 2021; 70:2187-2189. [PMID: 34593540 DOI: 10.2337/dbi21-0018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Velandai Srikanth
- Peninsula Clinical School, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
- Department of Medicine, Peninsula Health, Frankston Hospital, Melbourne, Australia
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