1
|
Luo M, Sun M, Wang T, Wei J, Ruan X, Chen K, Ou J, Chen Y, Qin J. Type 2 diabetes, glycaemic traits, structural brain capacity and cognitive function: A Mendelian randomization analysis. Diabetes Obes Metab 2024; 26:3618-3632. [PMID: 38925590 DOI: 10.1111/dom.15702] [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: 01/30/2024] [Revised: 05/23/2024] [Accepted: 05/23/2024] [Indexed: 06/28/2024]
Abstract
AIM To estimate the causal associations of type 2 diabetes and glycaemic traits with cognitive function, and to determine the potential mediating role of various brain imaging-derived phenotypes (IDPs) using Mendelian randomization (MR) analysis. METHODS Using publicly available summary data, we performed a series of univariable and multivariable MR analysis to infer causality. Two-step MR analysis was then conducted in turn to evaluate the potential mediating role of each brain IDP. RESULTS There was no evidence of causal associations between type 2 diabetes and cognitive function outcomes. Each 1-SD unit higher genetically predicted fasting proinsulin was associated with worse cognitive performance, as evidenced by both univariable (beta: -0.022; 95% confidence interval [CI] -0.038, -0.007) and multivariable MR analysis (beta: -0.027; 95% CI -0.048, -0.005). In addition, the univariable MR analysis identified several causal associations between fasting proinsulin and brain IDPs, and between brain IDPs and cognitive performance. The inverse association of genetically predicted fasting proinsulin with cognitive performance did not attenuate after adjusting for each of the brain IDPs in multivariable MR analysis. CONCLUSIONS The present MR study provided credible evidence for the causal association between genetically predicted fasting proinsulin and cognitive function, informing a potential diagnosis and intervention target for patients with cognitive impairment. No significant brain IDP included in this study was identified as lying on the causal pathway from fasting proinsulin to cognitive performance. Future research involving more specific and granular brain IDP or other brain process is warranted to explore the potential biological mechanism underlying the association.
Collapse
Affiliation(s)
- Manjun Luo
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Mengting Sun
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Tingting Wang
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Jianhui Wei
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Xiaorui Ruan
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Kebin Chen
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Jun Ou
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Yige Chen
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
| | - Jiabi Qin
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Central South University, Changsha, China
| |
Collapse
|
2
|
Grasset L, Frison E, Helmer C, Catheline G, Chêne G, Dufouil C. Understanding the relationship between type-2 diabetes, MRI markers of neurodegeneration and small vessel disease, and dementia risk: a mediation analysis. Eur J Epidemiol 2024; 39:409-417. [PMID: 38190014 PMCID: PMC11101545 DOI: 10.1007/s10654-023-01080-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 11/03/2023] [Indexed: 01/09/2024]
Abstract
To explore to which extent neurodegeneration and cerebral small vessel disease (SVD) could mediate the association between type-2 diabetes and higher dementia risk. The analytical sample consisted in 2228 participants, out of the Three-City study, aged 65 and older, free of dementia at baseline who underwent brain MRI. Diabetes was defined by medication intake or fasting or non-fasting elevated glucose levels. Dementia status was assessed every 2 to 3 years, during up to 12 years of follow-up. Brain parenchymal fraction (BPF) and white matter hyperintensities volume (WMHV) were selected as markers of neurodegeneration and cerebral SVD respectively. We performed a mediation analysis of the effect of baseline BPF and WMHV (mediators) on the association between diabetes and dementia risk using linear and Cox models adjusted for age, sex, education level, hypertension, hypercholesterolemia, BMI, smoking and alcohol drinking status, APOE-ε4 status, and study site. At baseline, 8.8% of the participants had diabetes. Diabetes (yes vs. no) was associated with higher WMHV (βdiab = 0.193, 95% CI 0.040; 0.346) and lower BPF (βdiab = -0.342, 95% CI -0.474; -0.210), as well as with an increased risk of dementia over 12 years of follow-up (HRdiab = 1.65, 95% CI 1.04; 2.60). The association between diabetes status and dementia risk was statistically mediated by higher WMHV (HRdiab=1.05, 95% CI 1.01; 1.11, mediated part = 10.8%) and lower BPF (HRdiab = 1.12, 95% CI 1.05; 1.20, mediated part = 22.9%). This study showed that both neurodegeneration and cerebral SVD statistically explained almost 30% of the association between diabetes and dementia.
Collapse
Affiliation(s)
- Leslie Grasset
- University of Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR 1219, CIC1401-EC, F-33000, Bordeaux, France.
- INSERM U1219, University of Bordeaux, 146 rue Léo Saignat, 33077, Bordeaux cedex, France.
| | - Eric Frison
- University of Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR 1219, CIC1401-EC, F-33000, Bordeaux, France
- Service d'Information Médicale, CHU Bordeaux, Bordeaux, France
| | - Catherine Helmer
- University of Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR 1219, CIC1401-EC, F-33000, Bordeaux, France
| | - Gwénaëlle Catheline
- INCIA, EPHE, CNRS, Université PSL, University of Bordeaux, 33076, Bordeaux, France
| | - Geneviève Chêne
- University of Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR 1219, CIC1401-EC, F-33000, Bordeaux, France
- Pole de sante publique Centre Hospitalier Universitaire (CHU) de Bordeaux, 33000, Bordeaux, France
| | - Carole Dufouil
- University of Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR 1219, CIC1401-EC, F-33000, Bordeaux, France
- Pole de sante publique Centre Hospitalier Universitaire (CHU) de Bordeaux, 33000, Bordeaux, France
| |
Collapse
|
3
|
Newby D, Orgeta V, Marshall CR, Lourida I, Albertyn CP, Tamburin S, Raymont V, Veldsman M, Koychev I, Bauermeister S, Weisman D, Foote IF, Bucholc M, Leist AK, Tang EYH, Tai XY, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia prevention. Alzheimers Dement 2023; 19:5952-5969. [PMID: 37837420 PMCID: PMC10843720 DOI: 10.1002/alz.13463] [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: 04/12/2023] [Revised: 08/01/2023] [Accepted: 08/07/2023] [Indexed: 10/16/2023]
Abstract
INTRODUCTION A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding. METHODS ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field. RESULTS Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics. DISCUSSION ML is not yet widely used but has considerable potential to enhance precision in dementia prevention. HIGHLIGHTS Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.
Collapse
Affiliation(s)
- Danielle Newby
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Vasiliki Orgeta
- Division of Psychiatry, University College London, London, W1T 7BN, UK
| | - Charles R Marshall
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, E1 4NS, UK
- Department of Neurology, Royal London Hospital, London, E1 1BB, UK
| | - Ilianna Lourida
- Population Health Sciences Institute, Newcastle University, Newcastle, NE2 4AX, UK
- University of Exeter Medical School, Exeter, EX1 2HZ, UK
| | - Christopher P Albertyn
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, 37129, Italy
| | - Vanessa Raymont
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Michele Veldsman
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK
- Department of Experimental Psychology, University of Oxford, Oxford, OX2 6GG, UK
| | - Ivan Koychev
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Sarah Bauermeister
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - David Weisman
- Abington Neurological Associates, Abington, PA 19001, USA
| | - Isabelle F Foote
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, E1 4NS, UK
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, BT48 7JL, UK
| | - Anja K Leist
- Institute for Research on Socio-Economic Inequality (IRSEI), Department of Social Sciences, University of Luxembourg, L-4365, Luxembourg
| | - Eugene Y H Tang
- Population Health Sciences Institute, Newcastle University, Newcastle, NE2 4AX, UK
| | - Xin You Tai
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, OX3 9DU, UK
- Division of Clinical Neurology, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, OX3 9DU, UK
| | | | - David J. Llewellyn
- University of Exeter Medical School, Exeter, EX1 2HZ, UK
- The Alan Turing Institute, London, NW1 2DB, UK
| | | |
Collapse
|
4
|
Andrews R, Shpitser I, Didelez V, Chaves P, Lopez O, Carlson M. Examining the Causal Mediating Role of Cardiovascular Disease on the Effect of Subclinical Cardiovascular Disease on Cognitive Impairment via Separable Effects. J Gerontol A Biol Sci Med Sci 2023; 78:1172-1178. [PMID: 36869806 PMCID: PMC10329225 DOI: 10.1093/gerona/glad077] [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: 09/09/2022] [Indexed: 03/05/2023] Open
Abstract
BACKGROUND An important epidemiological question is understanding how vascular risk factors contribute to cognitive impairment. Using data from the Cardiovascular Health Cognition Study, we investigated how subclinical cardiovascular disease (sCVD) relates to cognitive impairment risk and the extent to which the hypothesized risk is mediated by the incidence of clinically manifested cardiovascular disease (CVD), both overall and within apolipoprotein E-4 (APOE-4) subgroups. METHODS We adopted a novel "separable effects" causal mediation framework that assumes that sCVD has separably intervenable atherosclerosis-related components. We then ran several mediation models, adjusting for key covariates. RESULTS We found that sCVD increased overall risk of cognitive impairment (risk ratio [RR] = 1.21, 95% confidence interval [CI]: 1.03, 1.44); however, there was little or no mediation by incident clinically manifested CVD (indirect effect RR = 1.02, 95% CI: 1.00, 1.03). We also found attenuated effects among APOE-4 carriers (total effect RR = 1.09, 95% CI: 0.81, 1.47; indirect effect RR = 0.99, 95% CI: 0.96, 1.01) and stronger findings among noncarriers (total effect RR = 1.29, 95% CI: 1.05, 1.60; indirect effect RR = 1.02, 95% CI: 1.00, 1.05). In secondary analyses restricting cognitive impairment to only incident dementia cases, we found similar effect patterns. CONCLUSIONS We found that the effect of sCVD on cognitive impairment does not seem to be mediated by CVD, both overall and within APOE-4 subgroups. Our results were critically assessed via sensitivity analyses, and they were found to be robust. Future work is needed to fully understand the relationship between sCVD, CVD, and cognitive impairment.
Collapse
Affiliation(s)
- Ryan M Andrews
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
- Department of Biometry and Data Science, Leibniz Institute for Prevention Research and Epidemiology—BIPS, Bremen, Germany
| | - Ilya Shpitser
- Department of Mental Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Vanessa Didelez
- Department of Biometry and Data Science, Leibniz Institute for Prevention Research and Epidemiology—BIPS, Bremen, Germany
- Department of Mathematics and Computer Science, University of Bremen, Bremen, Germany
| | - Paulo H M Chaves
- Department of Translational Medicine, Division of Internal Medicine, Florida International University, Miami, Florida, USA
| | - Oscar L Lopez
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michelle C Carlson
- Department of Mental Health, Johns Hopkins University School of Public Health, Baltimore, Maryland, USA
| |
Collapse
|
5
|
Chen H, Young A, Oxtoby NP, Barkhof F, Alexander DC, Altmann A. Transferability of Alzheimer's disease progression subtypes to an independent population cohort. Neuroimage 2023; 271:120005. [PMID: 36907283 DOI: 10.1016/j.neuroimage.2023.120005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/22/2023] [Accepted: 03/06/2023] [Indexed: 03/13/2023] Open
Abstract
In the past, methods to subtype or biotype patients using brain imaging data have been developed. However, it is unclear whether and how these trained machine learning models can be successfully applied to population cohorts to study the genetic and lifestyle factors underpinning these subtypes. This work, using the Subtype and Stage Inference (SuStaIn) algorithm, examines the generalisability of data-driven Alzheimer's disease (AD) progression models. We first compared SuStaIn models trained separately on Alzheimer's disease neuroimaging initiative (ADNI) data and an AD-at-risk population constructed from the UK Biobank dataset. We further applied data harmonization techniques to remove cohort effects. Next, we built SuStaIn models on the harmonized datasets, which were then used to subtype and stage subjects in the other harmonized dataset. The first key finding is that three consistent atrophy subtypes were found in both datasets, which match the previously identified subtype progression patterns in AD: 'typical', 'cortical' and 'subcortical'. Next, the subtype agreement was further supported by high consistency in individuals' subtypes and stage assignment based on the different models: more than 92% of the subjects, with reliable subtype assignment in both ADNI and UK Biobank dataset, were assigned to an identical subtype under the model built on the different datasets. The successful transferability of AD atrophy progression subtypes across cohorts capturing different phases of disease development enabled further investigations of associations between AD atrophy subtypes and risk factors. Our study showed that (1) the average age is highest in the typical subtype and lowest in the subcortical subtype; (2) the typical subtype is associated with statistically more-AD-like cerebrospinal fluid biomarkers values in comparison to the other two subtypes; and (3) in comparison to the subcortical subtype, the cortical subtype subjects are more likely to associate with prescription of cholesterol and high blood pressure medications. In summary, we presented cross-cohort consistent recovery of AD atrophy subtypes, showing how the same subtypes arise even in cohorts capturing substantially different disease phases. Our study opened opportunities for future detailed investigations of atrophy subtypes with a broad range of early risk factors, which will potentially lead to a better understanding of the disease aetiology and the role of lifestyle and behaviour on AD.
Collapse
Affiliation(s)
- Hanyi Chen
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering and Department of Computer Science, University College London, UK
| | - Alexandra Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering and Department of Computer Science, University College London, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering and Department of Computer Science, University College London, UK; Queen Square Institute of Neurology, University College London, UK; Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, The Netherlands
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering and Department of Computer Science, University College London, UK
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering and Department of Computer Science, University College London, UK.
| | | |
Collapse
|
6
|
Tariq H, Ramakrishnan M, Gupta A. Insights into Cognitive Brain Health in Chronic Kidney Disease. GERONTOLOGY & GERIATRICS : RESEARCH 2022; 8:1074. [PMID: 37671071 PMCID: PMC10478617 DOI: 10.26420/gerontolgeriatrres.2022.1074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
Cognitive impairment and Chronic Kidney Disease (CKD) are common in older adults. With advances in medicine, the average lifespan is expected to increase, further increasing the prevalence of both conditions. The mechanisms underlying cognitive impairment in CKD are unclear. While mild-moderately low estimated glomerular filtration rate (eGFR) may not be associated with cognitive impairment, severely decreased eGFR and albuminuria do. Patients on dialysis have a high prevalence of cognitive impairment. Cognitive function improves after kidney transplantation. However, some residual cognitive deficits persist after transplantation, indicating that restoring the kidney function alone may not be enough to restore cognitive function, and other etiological factors may play a role. Albuminuria, another marker of CKD is also associated with cognitive impairment. However, albuminuria is often undiagnosed. Improving early identification and management of patients with albuminuria may be a good population-based dementia prevention strategy. Other factors associated with cognitive impairment in CKD include anemia and other metabolic derangements commonly observed in CKD. In this article, we reviewed the prevalence of cognitive impairment in CKD, the potential mechanisms underlying cognitive impairment in CKD, andthecurrent evidence on the association between cognitive impairment and eGFR and albuminuria.
Collapse
Affiliation(s)
- H Tariq
- Division of Nephrology and Hypertension, Department of Internal Medicine, University of Kansas Medical Center, USA
| | - M Ramakrishnan
- Division of Nephrology and Hypertension, Department of Internal Medicine, University of Kansas Medical Center, USA
| | - A Gupta
- Division of Nephrology and Hypertension, Department of Internal Medicine, University of Kansas Medical Center, USA
| |
Collapse
|