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Subramaniapillai S, Schindler LS, Redmond P, Bastin ME, Wardlaw JM, Valdés Hernández M, Maniega SM, Aribisala B, Westlye LT, Coath W, Groves J, Cash DM, Barnes J, James SN, Sudre CH, Barkhof F, Richards M, Corley J, Russ TC, Cox SR, Schott JM, Cole JH, de Lange AMG. Sex-Dependent Effects of Cardiometabolic Health and APOE4 on Brain Age: A Longitudinal Cohort Study. Neurology 2024; 103:e209744. [PMID: 39173100 DOI: 10.1212/wnl.0000000000209744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2024] Open
Abstract
BACKGROUND AND OBJECTIVES The aging population is growing faster than all other demographic strata. With older age comes a greater risk of health conditions such as obesity and high blood pressure (BP). These cardiometabolic risk factors (CMRs) exhibit prominent sex differences in midlife and aging, yet their influence on brain health in females vs males is largely unexplored. In this study, we investigated sex differences in relationships between BP, body mass index (BMI), and brain age over time and tested for interactions with APOE ε4 genotype (APOE4), a known genetic risk factor of Alzheimer disease. METHODS The sample included participants from 2 United Kingdom-based longitudinal birth cohorts, the Lothian Birth Cohort (1936) and Insight 46 (1946). Participants with MRI data from at least 1 time point were included to evaluate sex differences in associations between CMRs and brain age. The open-access software package brainageR 2.1 was used to estimate brain age for each participant. Linear mixed-effects models were used to assess the relationships between brain age, BMI, BP, and APOE4 status (i.e., carrier vs noncarrier) in males and females over time. RESULTS The combined sample comprised 1,120 participants (48% female) with a mean age (SD) of 73 (0.72) years in the Lothian Birth Cohort and 71 (0.68) years in Insight 46 at the time point 1 assessment. Approximately 30% of participants were APOE4 carriers. Higher systolic and diastolic BP was significantly associated with older brain age in females only (β = 0.43-0.56, p < 0.05). Among males, higher BMI was associated with older brain age across time points and APOE4 groups (β = 0.72-0.77, p < 0.05). In females, higher BMI was linked to older brain age among APOE4 noncarriers (β = 0.68-0.99, p < 0.05), whereas higher BMI was linked to younger brain age among carriers, particularly at the last time point (β = -1.75, p < 0.05). DISCUSSION This study indicates sex-dependent and time-dependent relationships between CMRs, APOE4 status, and brain age. Our findings highlight the necessity of sex-stratified analyses to elucidate the role of CMRs in individual aging trajectories, providing a basis for developing personalized preventive interventions.
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Affiliation(s)
- Sivaniya Subramaniapillai
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Louise S Schindler
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Paul Redmond
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Mark E Bastin
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Joanna M Wardlaw
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Maria Valdés Hernández
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Susana Muñoz Maniega
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Benjamin Aribisala
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Lars T Westlye
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - William Coath
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - James Groves
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - David M Cash
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Josephine Barnes
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Sarah-Naomi James
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Carole H Sudre
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Frederik Barkhof
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Marcus Richards
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Janie Corley
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Tom C Russ
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Simon R Cox
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Jonathan M Schott
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - James H Cole
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Ann-Marie G de Lange
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
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Moran C, Herson J, Than S, Collyer T, Beare R, Syed S, Srikanth V. Interactions between age, sex and visceral adipose tissue on brain ageing. Diabetes Obes Metab 2024; 26:3821-3829. [PMID: 38899555 PMCID: PMC11300145 DOI: 10.1111/dom.15727] [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: 02/18/2024] [Revised: 06/03/2024] [Accepted: 06/04/2024] [Indexed: 06/21/2024]
Abstract
AIM To examine the associations between visceral adipose tissue (VAT) and brain structural measures at midlife and explore how these associations may be affected by age, sex and cardiometabolic factors. METHODS We used abdominal and brain magnetic resonance imaging data from a population-based cohort of people at midlife in the UK Biobank. Regression modelling was applied to study associations of VAT volume with total brain volume (TBV), grey matter volume (GMV), white matter volume, white matter hyperintensity volume (WMHV) and total hippocampal volume (THV), and whether these associations were altered by age, sex or cardiometabolic factors. RESULTS Complete data were available for 17 377 participants (mean age 63 years, standard deviation = 12, 53% female). Greater VAT was associated with lower TBV, GMV and THV (P < .001). We found an interaction between VAT and sex on TBV (P < .001), such that the negative association of VAT with TBV was greater in men (β = -2.89, 95% confidence interval [CI] -2.32 to -10.15) than in women (β = -1.32, 95% CI -0.49 to -3.14), with similar findings for GMV. We also found an interaction between VAT and age (but not sex) on WMHV (P < .001). The addition of other cardiometabolic factors or measures of physical activity resulted in little change to the models. CONCLUSIONS VAT volume is associated with poorer brain health in midlife and this relationship is greatest in men and those at younger ages.
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Affiliation(s)
- Chris Moran
- Peninsula Clinical School, Central Clinical School, Monash University, PO Box 52, Frankston VIC 3199, Australia
- Department of Geriatric Medicine, Peninsula Health, 24 Separation Street Mornington VIC 3931, Australia
- National Centre for Healthy Ageing, PO Box 52, Frankston VIC 3199, Australia
- School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne 3004, Victoria, Australia
- Department of Home, Acute and Community, Alfred Health, 260 Kooyong Rd, Caulfield VIC 3162, Australia
| | - Jarin Herson
- Department of Geriatric Medicine, Peninsula Health, 24 Separation Street Mornington VIC 3931, Australia
| | - Stephanie Than
- Peninsula Clinical School, Central Clinical School, Monash University, PO Box 52, Frankston VIC 3199, Australia
- Department of Geriatric Medicine, Peninsula Health, 24 Separation Street Mornington VIC 3931, Australia
- National Centre for Healthy Ageing, PO Box 52, Frankston VIC 3199, Australia
- Department of Geriatric Medicine, Western Health, 160 Gordon Street, Footscray 3011, Australia
| | - Taya Collyer
- Peninsula Clinical School, Central Clinical School, Monash University, PO Box 52, Frankston VIC 3199, Australia
- National Centre for Healthy Ageing, PO Box 52, Frankston VIC 3199, Australia
| | - Richard Beare
- Peninsula Clinical School, Central Clinical School, Monash University, PO Box 52, Frankston VIC 3199, Australia
- National Centre for Healthy Ageing, PO Box 52, Frankston VIC 3199, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, 50 Flemington Rd, Parkville VIC 3052, Australia
| | - Sarah Syed
- Department of Home, Acute and Community, Alfred Health, 260 Kooyong Rd, Caulfield VIC 3162, Australia
| | - Velandai Srikanth
- Peninsula Clinical School, Central Clinical School, Monash University, PO Box 52, Frankston VIC 3199, Australia
- Department of Geriatric Medicine, Peninsula Health, 24 Separation Street Mornington VIC 3931, Australia
- National Centre for Healthy Ageing, PO Box 52, Frankston VIC 3199, Australia
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Paula DP, Camacho M, Barbosa O, Marques L, Harter Griep R, da Fonseca MJM, Barreto S, Lekadir K. Sex and population differences in the cardiometabolic continuum: a machine learning study using the UK Biobank and ELSA-Brasil cohorts. BMC Public Health 2024; 24:2131. [PMID: 39107721 PMCID: PMC11304673 DOI: 10.1186/s12889-024-19395-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 04/08/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND The temporal relationships across cardiometabolic diseases (CMDs) were recently conceptualized as the cardiometabolic continuum (CMC), sequence of cardiovascular events that stem from gene-environmental interactions, unhealthy lifestyle influences, and metabolic diseases such as diabetes, and hypertension. While the physiological pathways linking metabolic and cardiovascular diseases have been investigated, the study of the sex and population differences in the CMC have still not been described. METHODS We present a machine learning approach to model the CMC and investigate sex and population differences in two distinct cohorts: the UK Biobank (17,700 participants) and the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) (7162 participants). We consider the following CMDs: hypertension (Hyp), diabetes (DM), heart diseases (HD: angina, myocardial infarction, or heart failure), and stroke (STK). For the identification of the CMC patterns, individual trajectories with the time of disease occurrence were clustered using k-means. Based on clinical, sociodemographic, and lifestyle characteristics, we built multiclass random forest classifiers and used the SHAP methodology to evaluate feature importance. RESULTS Five CMC patterns were identified across both sexes and cohorts: EarlyHyp, FirstDM, FirstHD, Healthy, and LateHyp, named according to prevalence and disease occurrence time that depicted around 95%, 78%, 75%, 88% and 99% of individuals, respectively. Within the UK Biobank, more women were classified in the Healthy cluster and more men in all others. In the EarlyHyp and LateHyp clusters, isolated hypertension occurred earlier among women. Smoking habits and education had high importance and clear directionality for both sexes. For ELSA-Brasil, more men were classified in the Healthy cluster and more women in the FirstDM. The diabetes occurrence time when followed by hypertension was lower among women. Education and ethnicity had high importance and clear directionality for women, while for men these features were smoking, alcohol, and coffee consumption. CONCLUSIONS There are clear sex differences in the CMC that varied across the UK and Brazilian cohorts. In particular, disadvantages regarding incidence and the time to onset of diseases were more pronounced in Brazil, against woman. The results show the need to strengthen public health policies to prevent and control the time course of CMD, with an emphasis on women.
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Affiliation(s)
- Daniela Polessa Paula
- National School of Statistical Sciences, Brazilian Institute of Geography and Statistics, Rio de Janeiro, Brazil.
- Institute of Mathematics and Statistics, University of the Rio de Janeiro State, Rio de Janeiro, Brazil.
| | - Marina Camacho
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Odaleia Barbosa
- Institute of Nutrition, University of the Rio de Janeiro State, Rio de Janeiro, Brazil
| | - Larissa Marques
- Coordination of Information and Communication (CINCO - PEIC), Oswaldo Cruz Foundation, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Rosane Harter Griep
- Health and Environmental Education Laboratory, Oswaldo Cruz Institute (IOC), Rio de Janeiro, RJ, Brazil
| | | | - Sandhi Barreto
- Postgraduate Program in Public Health, School of Medicine & Clinical Hospital, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Karim Lekadir
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
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Crestol A, de Lange AMG, Schindler L, Subramaniapillai S, Nerland S, Oppenheimer H, Westlye LT, Andreassen OA, Agartz I, Tamnes CK, Barth C. Linking menopause-related factors, history of depression, APOE ε4, and proxies of biological aging in the UK biobank cohort. Horm Behav 2024; 164:105596. [PMID: 38944998 DOI: 10.1016/j.yhbeh.2024.105596] [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: 02/13/2024] [Revised: 06/14/2024] [Accepted: 06/18/2024] [Indexed: 07/02/2024]
Abstract
In a subset of females, postmenopausal status has been linked to accelerated aging and neurological decline. A complex interplay between reproductive-related factors, mental disorders, and genetics may influence brain function and accelerate the rate of aging in the postmenopausal phase. Using multiple regressions corrected for age, in this preregistered study we investigated the associations between menopause-related factors (i.e., menopausal status, menopause type, age at menopause, and reproductive span) and proxies of cellular aging (leukocyte telomere length, LTL) and brain aging (white and gray matter brain age gap, BAG) in 13,780 females from the UK Biobank (age range 39-82). We then determined how these proxies of aging were associated with each other, and evaluated the effects of menopause-related factors, history of depression (= lifetime broad depression), and APOE ε4 genotype on BAG and LTL, examining both additive and interactive relationships. We found that postmenopausal status and older age at natural menopause were linked to longer LTL and lower BAG. Surgical menopause and longer natural reproductive span were also associated with longer LTL. BAG and LTL were not significantly associated with each other. The greatest variance in each proxy of biological aging was most consistently explained by models with the addition of both lifetime broad depression and APOE ε4 genotype. Overall, this study demonstrates a complex interplay between menopause-related factors, lifetime broad depression, APOE ε4 genotype, and proxies of biological aging. However, results are potentially influenced by a disproportionate number of healthier participants among postmenopausal females. Future longitudinal studies incorporating heterogeneous samples are an essential step towards advancing female health.
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Affiliation(s)
- Arielle Crestol
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; Center for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Ann-Marie G de Lange
- Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Louise Schindler
- Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Sivaniya Subramaniapillai
- Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway
| | - Stener Nerland
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Hannah Oppenheimer
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Center for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo & Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Center for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo & Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo & Oslo University Hospital, Oslo, Norway; Department of Clinical Neuroscience, Centre for Psychiatry Research, Stockholm Health Care Services, Karolinska Institute, Stockholm County Council, Stockholm, Sweden; Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Christian K Tamnes
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Claudia Barth
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway.
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5
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Wu F, Liu Y, Lin C, Haghbin N, Xia L, Li Y, Chen T, Qiu H, Jiang W, Li J, Lin J. Correlation between fat-to-muscle mass ratio and cognitive impairment in elderly patients with type 2 diabetes mellitus: a cross-sectional study. BMC Geriatr 2024; 24:352. [PMID: 38637745 PMCID: PMC11027286 DOI: 10.1186/s12877-024-04941-2] [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: 09/05/2023] [Accepted: 04/02/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Fat to muscle mass ratio (FMR), a novel index integrating fat and muscle composition, has garnered attention in age-related conditions such as type 2 diabetes mellitus (T2DM) and neurodegenerative diseases. Despite this research on the relationship between FMR and cognitive impairment (CI) in T2DM remains scarce. This study aimed to investigate the sex-specific association between FMR and CI in elderly T2DM patients. METHODS A total of 768 elderly (> 60 years) T2DM in-patients (356 men and 412 women) were recruited from the Department of Endocrinology at Tianjin Nankai University affiliated hospital. Bioelectrical Impedance Analysis (BIA) was used to assess body composition, and Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive performance. T2DM patients were categorized into normal cognitive function (NC) and cognitive impairment (CI) groups based on MoCA scores and stratified by sex. Binary logistic regression was employed to examine the association between FMR and CI. RESULTS Among the participants, 42.7% of men and 56.3% of women experienced cognitive deterioration. Women with CI exhibited lower body mass index (BMI) and skeletal muscle mass index (SMI), while men with cognitive disorders showed lower SMI, FMR, and higher fat mass index (FMI). FMR was consistently unrelated to cognition in females, irrespective of adjustment made. However, in males, FMR was significantly associated with an increasing risk of cognitive dysfunction after adjusting for demographic and clinical variables (OR: 1.175, 95% CI: 1.045-1.320, p = 0.007). Furthermore, for each 0.1 increase in FMR, the incidence of CI rose by 31.1% after additional adjustment for BMI. In males, the prevalence of CI increased sequentially across FMR quartiles (p < 0.05). CONCLUSION Elderly T2DM men with high FMR had unfavorable cognitive function. FMR is independently associated with an increased risk of CI in male T2DM patients regardless of BMI.
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Affiliation(s)
- Fan Wu
- Department of Endocrinology, Tianjin Union Medical Center, Nankai University Affiliated Hospital, Tianjin, China
| | - Yanlan Liu
- Department of Endocrinology, Tianjin Union Medical Center, Nankai University Affiliated Hospital, Tianjin, China
| | - Chenying Lin
- Department of Endocrinology, Tianjin Union Medical Center, Nankai University Affiliated Hospital, Tianjin, China
- Tianjin Union Medical Center, Tianjin Medical University, Tianjin, China
| | - Nahal Haghbin
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Longfei Xia
- Department of Endocrinology, Tianjin Union Medical Center, Nankai University Affiliated Hospital, Tianjin, China
- Tianjin Union Medical Center, Tianjin Medical University, Tianjin, China
| | - Yaoshuang Li
- Department of Endocrinology, Tianjin Union Medical Center, Nankai University Affiliated Hospital, Tianjin, China
- Tianjin Union Medical Center, Tianjin Medical University, Tianjin, China
| | - Tong Chen
- Department of Endocrinology, Tianjin Union Medical Center, Nankai University Affiliated Hospital, Tianjin, China
- Tianjin Union Medical Center, School of Medicine, Nankai University, Tianjin, China
| | - Huina Qiu
- Department of Endocrinology, Tianjin Union Medical Center, Nankai University Affiliated Hospital, Tianjin, China
| | - Weiran Jiang
- Eastman Institute for Oral Health, University of Rochester Medical Center, Rochester, NY, USA
| | - Jingbo Li
- Department of Endocrinology, Tianjin Union Medical Center, Nankai University Affiliated Hospital, Tianjin, China.
| | - Jingna Lin
- Department of Endocrinology, Tianjin Union Medical Center, Nankai University Affiliated Hospital, Tianjin, China.
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Beck D, de Lange AG, Gurholt TP, Voldsbekk I, Maximov II, Subramaniapillai S, Schindler L, Hindley G, Leonardsen EH, Rahman Z, van der Meer D, Korbmacher M, Linge J, Leinhard OD, Kalleberg KT, Engvig A, Sønderby I, Andreassen OA, Westlye LT. Dissecting unique and common variance across body and brain health indicators using age prediction. Hum Brain Mapp 2024; 45:e26685. [PMID: 38647042 PMCID: PMC11034003 DOI: 10.1002/hbm.26685] [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: 12/29/2023] [Revised: 03/21/2024] [Accepted: 04/04/2024] [Indexed: 04/25/2024] Open
Abstract
Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.
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Affiliation(s)
- Dani Beck
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Mental Health and Substance AbuseDiakonhjemmet HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ann‐Marie G. de Lange
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Tiril P. Gurholt
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Irene Voldsbekk
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ivan I. Maximov
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Sivaniya Subramaniapillai
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Louise Schindler
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Guy Hindley
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Esten H. Leonardsen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Zillur Rahman
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Max Korbmacher
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Jennifer Linge
- AMRA Medical ABLinköpingSweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | - Olof D. Leinhard
- AMRA Medical ABLinköpingSweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | | | - Andreas Engvig
- Department of Endocrinology, Obesity and Preventive Medicine, Section of Preventive CardiologyOslo University HospitalOsloNorway
| | - Ida Sønderby
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Medical GeneticsOslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
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7
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Barth C, Galea LA, Jacobs EG, Lee BH, Westlye LT, de Lange AMG. Menopausal hormone therapy and the female brain: leveraging neuroimaging and prescription registry data from the UK Biobank cohort. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.08.24305450. [PMID: 38645009 PMCID: PMC11030497 DOI: 10.1101/2024.04.08.24305450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Background and Objectives Menopausal hormone therapy (MHT) is generally thought to be neuroprotective, yet results have been inconsistent. Here, we present a comprehensive study of MHT use and brain characteristics in middle- to older aged females from the UK Biobank, assessing detailed MHT data, APOE ε4 genotype, and tissue-specific gray (GM) and white matter (WM) brain age gap (BAG), as well as hippocampal and white matter hyperintensity (WMH) volumes. Methods A total of 19,846 females with magnetic resonance imaging data were included (current-users = 1,153, 60.1 ± 6.8 years; past-users = 6,681, 67.5 ± 6.2 years; never-users = 12,012, mean age 61.6 ± 7.1 years). For a sub-sample (n = 538), MHT prescription data was extracted from primary care records. Brain measures were derived from T1-, T2- and diffusion-weighted images. We fitted regression models to test for associations between the brain measures and MHT variables including user status, age at initiation, dosage and duration, formulation, route of administration, and type (i.e., bioidentical vs synthetic), as well as active ingredient (e.g., estradiol hemihydrate). We further tested for differences in brain measures among MHT users with and without a history of hysterectomy ± bilateral oophorectomy and examined associations by APOE ε4 status. Results We found significantly higher GM and WM BAG (i.e., older brain age relative to chronological age) as well as smaller left and right hippocampus volumes in current MHT users, not past users, compared to never-users. Effects were modest, with the largest effect size indicating a group difference of 0.77 years (~9 months) for GM BAG. Among MHT users, we found no significant associations between age at MHT initiation and brain measures. Longer duration of use and older age at last use post menopause was associated with higher GM and WM BAG, larger WMH volume, and smaller left and right hippocampal volumes. MHT users with a history of hysterectomy ± bilateral oophorectomy showed lower GM BAG relative to MHT users without such history. Although we found smaller hippocampus volumes in carriers of two APOE ε4 alleles compared to non-carriers, we found no interactions with MHT variables. In the sub-sample with prescription data, we found no significant associations between detailed MHT variables and brain measures after adjusting for multiple comparisons. Discussion Our results indicate that population-level associations between MHT use, and female brain health might vary depending on duration of use and past surgical history. Future research is crucial to establish causality, dissect interactions between menopause-related neurological changes and MHT use, and determine individual-level implications to advance precision medicine in female health care.
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Affiliation(s)
- Claudia Barth
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Liisa A.M. Galea
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Emily G. Jacobs
- Psychological and Brain Sciences, University of California Santa Barbara, CA, USA
| | - Bonnie H. Lee
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Lars T. Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ann-Marie G. de Lange
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Department of Psychiatry, University of Oxford, Oxford, UK
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8
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Okudzhava L, Schulz S, Fischi‐Gomez E, Girard G, Machann J, Koch PJ, Thiran J, Münte TF, Heldmann M. White adipose tissue distribution and amount are associated with increased white matter connectivity. Hum Brain Mapp 2024; 45:e26654. [PMID: 38520361 PMCID: PMC10960552 DOI: 10.1002/hbm.26654] [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: 10/31/2023] [Revised: 02/09/2024] [Accepted: 02/27/2024] [Indexed: 03/25/2024] Open
Abstract
Obesity represents a significant public health concern and is linked to various comorbidities and cognitive impairments. Previous research indicates that elevated body mass index (BMI) is associated with structural changes in white matter (WM). However, a deeper characterization of body composition is required, especially considering the links between abdominal obesity and metabolic dysfunction. This study aims to enhance our understanding of the relationship between obesity and WM connectivity by directly assessing the amount and distribution of fat tissue. Whole-body magnetic resonance imaging (MRI) was employed to evaluate total adipose tissue (TAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT), while MR liver spectroscopy measured liver fat content in 63 normal-weight, overweight, and obese males. WM connectivity was quantified using microstructure-informed tractography. Connectome-based predictive modeling was used to predict body composition metrics based on WM connectomes. Our analysis revealed a positive dependency between BMI, TAT, SAT, and WM connectivity in brain regions involved in reward processing and appetite regulation, such as the insula, nucleus accumbens, and orbitofrontal cortex. Increased connectivity was also observed in cognitive control and inhibition networks, including the middle frontal gyrus and anterior cingulate cortex. No significant associations were found between WM connectivity and VAT or liver fat. Our findings suggest that altered neural communication between these brain regions may affect cognitive processes, emotional regulation, and reward perception in individuals with obesity, potentially contributing to weight gain. While our study did not identify a link between WM connectivity and VAT or liver fat, further investigation of the role of various fat depots and metabolic factors in brain networks is required to advance obesity prevention and treatment approaches.
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Affiliation(s)
- Liana Okudzhava
- Department of NeurologyUniversity of LübeckLübeckGermany
- Center of Brain, Behavior and MetabolismUniversity of LübeckLübeckGermany
| | - Stephanie Schulz
- Department of NeurologyUniversity of LübeckLübeckGermany
- Center of Brain, Behavior and MetabolismUniversity of LübeckLübeckGermany
| | - Elda Fischi‐Gomez
- CIBM Center for Biomedical ImagingLausanneSwitzerland
- Radiology DepartmentLausanne University and University Hospital (CHUV)LausanneSwitzerland
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Gabriel Girard
- CIBM Center for Biomedical ImagingLausanneSwitzerland
- Department of Computer ScienceUniversité de SherbrookeSherbrookeQuebecCanada
| | - Jürgen Machann
- Section on Experimental Radiology, Department of RadiologyEberhard‐Karls UniversityTübingenGermany
- German Center for Diabetes Research (DZD)NeuherbergGermany
- Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center MunichUniversity of TübingenTübingenGermany
| | - Philipp J. Koch
- Department of NeurologyUniversity of LübeckLübeckGermany
- Center of Brain, Behavior and MetabolismUniversity of LübeckLübeckGermany
| | - Jean‐Philippe Thiran
- CIBM Center for Biomedical ImagingLausanneSwitzerland
- Radiology DepartmentLausanne University and University Hospital (CHUV)LausanneSwitzerland
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Thomas F. Münte
- Department of NeurologyUniversity of LübeckLübeckGermany
- Center of Brain, Behavior and MetabolismUniversity of LübeckLübeckGermany
| | - Marcus Heldmann
- Department of NeurologyUniversity of LübeckLübeckGermany
- Center of Brain, Behavior and MetabolismUniversity of LübeckLübeckGermany
- Institute of Psychology IIUniversity of LübeckLübeckGermany
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9
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Korbmacher M, van der Meer D, Beck D, de Lange AMG, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Brain asymmetries from mid- to late life and hemispheric brain age. Nat Commun 2024; 15:956. [PMID: 38302499 PMCID: PMC10834516 DOI: 10.1038/s41467-024-45282-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/19/2024] [Indexed: 02/03/2024] Open
Abstract
The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N=48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway.
- Mohn Medical Imaging and Visualization Centre (MMIV), Bergen, Norway.
| | - Dennis van der Meer
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Dani Beck
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ann-Marie G de Lange
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre (MMIV), Bergen, Norway
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A Andreassen
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway.
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10
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Schindler LS, Subramaniapillai S, Ambikairajah A, Barth C, Crestol A, Voldsbekk I, Beck D, Gurholt TP, Topiwala A, Suri S, Ebmeier KP, Andreassen OA, Draganski B, Westlye LT, de Lange AMG. Cardiometabolic health across menopausal years is linked to white matter hyperintensities up to a decade later. Front Glob Womens Health 2023; 4:1320640. [PMID: 38213741 PMCID: PMC10783171 DOI: 10.3389/fgwh.2023.1320640] [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: 10/12/2023] [Accepted: 12/06/2023] [Indexed: 01/13/2024] Open
Abstract
Introduction The menopause transition is associated with several cardiometabolic risk factors. Poor cardiometabolic health is further linked to microvascular brain lesions, which can be detected as white matter hyperintensities (WMHs) using T2-FLAIR magnetic resonance imaging (MRI) scans. Females show higher risk for WMHs post-menopause, but it remains unclear whether changes in cardiometabolic risk factors underlie menopause-related increase in brain pathology. Methods In this study, we assessed whether cross-sectional measures of cardiometabolic health, including body mass index (BMI) and waist-to-hip ratio (WHR), blood lipids, blood pressure, and long-term blood glucose (HbA1c), as well as longitudinal changes in BMI and WHR, differed according to menopausal status at baseline in 9,882 UK Biobank females (age range 40-70 years, n premenopausal = 3,529, n postmenopausal = 6,353). Furthermore, we examined whether these cardiometabolic factors were associated with WMH outcomes at the follow-up assessment, on average 8.78 years after baseline. Results Postmenopausal females showed higher levels of baseline blood lipids (HDL β = 0.14, p < 0.001, LDL β = 0.20, p < 0.001, triglycerides β = 0.12, p < 0.001) and HbA1c (β = 0.24, p < 0.001) compared to premenopausal women, beyond the effects of age. Over time, BMI increased more in the premenopausal compared to the postmenopausal group (β = -0.08, p < 0.001), while WHR increased to a similar extent in both groups (β = -0.03, p = 0.102). The change in WHR was however driven by increased waist circumference only in the premenopausal group. While the group level changes in BMI and WHR were in general small, these findings point to distinct anthropometric changes in pre- and postmenopausal females over time. Higher baseline measures of BMI, WHR, triglycerides, blood pressure, and HbA1c, as well as longitudinal increases in BMI and WHR, were associated with larger WMH volumes (β range = 0.03-0.13, p ≤ 0.002). HDL showed a significant inverse relationship with WMH volume (β = -0.27, p < 0.001). Discussion Our findings emphasise the importance of monitoring cardiometabolic risk factors in females from midlife through the menopause transition and into the postmenopausal phase, to ensure improved cerebrovascular outcomes in later years.
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Affiliation(s)
- Louise S. Schindler
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Sivaniya Subramaniapillai
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ananthan Ambikairajah
- Discipline of Psychology, Faculty of Health, University of Canberra, Canberra, Australia
- National Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia
| | - Claudia Barth
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Arielle Crestol
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Irene Voldsbekk
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dani Beck
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tiril P. Gurholt
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anya Topiwala
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Sana Suri
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Klaus P. Ebmeier
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Bogdan Draganski
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Lars T. Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ann-Marie G. de Lange
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
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11
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Vigneshwaran V, Wilms M, Forkert ND. The causal link between cardiometabolic risk factors and gray matter atrophy: An exploratory study. Heliyon 2023; 9:e21567. [PMID: 38027770 PMCID: PMC10661200 DOI: 10.1016/j.heliyon.2023.e21567] [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: 07/26/2023] [Revised: 10/04/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Although gray matter atrophy is commonly observed with aging, it is highly variable, even among healthy people of the same age. This raises the question of what other factors may contribute to gray matter atrophy. Previous studies have reported that risk factors for cardiometabolic diseases are associated with accelerated brain aging. However, these studies were primarily based on standard correlation analyses, which do not unveil a causal relationship. While randomized controlled trials are typically required to investigate true causality, in this work, we investigated an alternative method by exploring data-driven causal discovery and inference techniques on observational data. Accordingly, this feasibility study used clinical and quantified gray matter volume data from 22,793 subjects from the UK biobank cohort without any known neurological disease. Our method identified that age, sex, body mass index (BMI), body fat percentage (BFP), and smoking exhibit a causal relationship with gray matter volume. Interventions on the causal network revealed that higher BMI and BFP values significantly increased the chance of gray matter atrophy in males, whereas this was not the case in females.
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Affiliation(s)
- Vibujithan Vigneshwaran
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Matthias Wilms
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Pediatrics, University of Calgary, Calgary, AB, Canada
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Nils D. Forkert
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
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12
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García-García I, Donica O, Cohen AA, Gonseth Nusslé S, Heini A, Nusslé S, Pichard C, Rietschel E, Tanackovic G, Folli S, Draganski B. Maintaining brain health across the lifespan. Neurosci Biobehav Rev 2023; 153:105365. [PMID: 37604360 DOI: 10.1016/j.neubiorev.2023.105365] [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: 03/14/2023] [Revised: 07/24/2023] [Accepted: 08/17/2023] [Indexed: 08/23/2023]
Abstract
Across the lifespan, the human body and brain endure the impact of a plethora of exogenous and endogenous factors that determine the health outcome in old age. The overwhelming inter-individual variance spans between progressive frailty with loss of autonomy to largely preserved physical, cognitive, and social functions. Understanding the mechanisms underlying the diverse aging trajectories can inform future strategies to maintain a healthy body and brain. Here we provide a comprehensive overview of the current literature on lifetime factors governing brain health. We present the growing body of evidence that unhealthy alimentary regime, sedentary behaviour, sleep pathologies, cardio-vascular risk factors, and chronic inflammation exert their harmful effects in a cumulative and gradual manner, and that timely and efficient intervention could promote healthy and successful aging. We discuss the main effects and interactions between these risk factors and the resulting brain health outcomes to follow with a description of current strategies aiming to eliminate, treat, or counteract the risk factors. We conclude that the detailed insights about modifiable risk factors could inform personalized multi-domain strategies for brain health maintenance on the background of increased longevity.
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Affiliation(s)
- Isabel García-García
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Centre for Research in Neurosciences, Lausanne University Hospital, University of Lausanne, Switzerland; Clinique la Prairie, Montreux, Switzerland
| | | | - Armand Aaron Cohen
- Department of Geriatrics and Rehabilitation, Hadassah University Medical Center Mount Scopus, Jerusalem, Israel
| | | | | | | | - Claude Pichard
- Nutrition Unit, University Hospital of Geneva, Geneva, Switzerland
| | | | | | | | - Bogdan Draganski
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Centre for Research in Neurosciences, Lausanne University Hospital, University of Lausanne, Switzerland; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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13
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Korbmacher M, Gurholt TP, de Lange AMG, van der Meer D, Beck D, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Bio-psycho-social factors' associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants. Front Psychol 2023; 14:1117732. [PMID: 37359862 PMCID: PMC10288151 DOI: 10.3389/fpsyg.2023.1117732] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/27/2023] [Indexed: 06/28/2023] Open
Abstract
Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6-82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions' influence on brain age in future studies.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
| | - Tiril P. Gurholt
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Ann-Marie G. de Lange
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Dani Beck
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
| | - Arvid Lundervold
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I. Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
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14
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Zhao B, Li T, Fan Z, Yang Y, Shu J, Yang X, Wang X, Luo T, Tang J, Xiong D, Wu Z, Li B, Chen J, Shan Y, Tomlinson C, Zhu Z, Li Y, Stein JL, Zhu H. Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images. Science 2023; 380:abn6598. [PMID: 37262162 DOI: 10.1126/science.abn6598] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 04/11/2023] [Indexed: 06/03/2023]
Abstract
Cardiovascular health interacts with cognitive and mental health in complex ways, yet little is known about the phenotypic and genetic links of heart-brain systems. We quantified heart-brain connections using multiorgan magnetic resonance imaging (MRI) data from more than 40,000 subjects. Heart MRI traits displayed numerous association patterns with brain gray matter morphometry, white matter microstructure, and functional networks. We identified 80 associated genomic loci (P < 6.09 × 10-10) for heart MRI traits, which shared genetic influences with cardiovascular and brain diseases. Genetic correlations were observed between heart MRI traits and brain-related traits and disorders. Mendelian randomization suggests that heart conditions may causally contribute to brain disorders. Our results advance a multiorgan perspective on human health by revealing heart-brain connections and shared genetic influences.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tianyou Luo
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jiarui Tang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Di Xiong
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhenyi Wu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Jie Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Chalmer Tomlinson
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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15
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Calderón-Garcidueñas L, Hernández-Luna J, Aiello-Mora M, Brito-Aguilar R, Evelson PA, Villarreal-Ríos R, Torres-Jardón R, Ayala A, Mukherjee PS. APOE Peripheral and Brain Impact: APOE4 Carriers Accelerate Their Alzheimer Continuum and Have a High Risk of Suicide in PM 2.5 Polluted Cities. Biomolecules 2023; 13:927. [PMID: 37371506 DOI: 10.3390/biom13060927] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/22/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2023] Open
Abstract
This Review emphasizes the impact of APOE4-the most significant genetic risk factor for Alzheimer's disease (AD)-on peripheral and neural effects starting in childhood. We discuss major mechanistic players associated with the APOE alleles' effects in humans to understand their impact from conception through all life stages and the importance of detrimental, synergistic environmental exposures. APOE4 influences AD pathogenesis, and exposure to fine particulate matter (PM2.5), manufactured nanoparticles (NPs), and ultrafine particles (UFPs) associated with combustion and friction processes appear to be major contributors to cerebrovascular dysfunction, neuroinflammation, and oxidative stress. In the context of outdoor and indoor PM pollution burden-as well as Fe, Ti, and Al alloys; Hg, Cu, Ca, Sn, and Si UFPs/NPs-in placenta and fetal brain tissues, urban APOE3 and APOE4 carriers are developing AD biological disease hallmarks (hyperphosphorylated-tau (P-tau) and amyloid beta 42 plaques (Aβ42)). Strikingly, for Metropolitan Mexico City (MMC) young residents ≤ 40 y, APOE4 carriers have 4.92 times higher suicide odds and 23.6 times higher odds of reaching Braak NFT V stage versus APOE4 non-carriers. The National Institute on Aging and Alzheimer's Association (NIA-AA) framework could serve to test the hypothesis that UFPs and NPs are key players for oxidative stress, neuroinflammation, protein aggregation and misfolding, faulty complex protein quality control, and early damage to cell membranes and organelles of neural and vascular cells. Noninvasive biomarkers indicative of the P-tau and Aβ42 abnormal protein deposits are needed across the disease continuum starting in childhood. Among the 21.8 million MMC residents, we have potentially 4 million APOE4 carriers at accelerated AD progression. These APOE4 individuals are prime candidates for early neuroprotective interventional trials. APOE4 is key in the development of AD evolving from childhood in highly polluted urban centers dominated by anthropogenic and industrial sources of pollution. APOE4 subjects are at higher early risk of AD development, and neuroprotection ought to be implemented. Effective reductions of PM2.5, UFP, and NP emissions from all sources are urgently needed. Alzheimer's Disease prevention ought to be at the core of the public health response and physicians-scientist minority research be supported.
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Affiliation(s)
- Lilian Calderón-Garcidueñas
- College of Health, The University of Montana, Missoula, MT 59812, USA
- Universidad del Valle de México, Mexico City 14370, Mexico
| | | | - Mario Aiello-Mora
- Otorrinolaryngology Department, Instituto Nacional de Cardiología, Mexico City 14080, Mexico
| | | | - Pablo A Evelson
- Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires C1113 AAD, Argentina
| | | | - Ricardo Torres-Jardón
- Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Alberto Ayala
- Sacramento Metropolitan Air Quality Management District, Sacramento, CA 95814, USA
- West Virginia University, Morgantown, WV 26506, USA
| | - Partha S Mukherjee
- Interdisciplinary Statistical Research Unit, Indian Statistical Institute, Kolkata 700108, India
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16
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Trofimova O, Latypova A, DiDomenicantonio G, Lutti A, de Lange AMG, Kliegel M, Stringhini S, Marques-Vidal P, Vaucher J, Vollenweider P, Strippoli MPF, Preisig M, Kherif F, Draganski B. Topography of associations between cardiovascular risk factors and myelin loss in the ageing human brain. Commun Biol 2023; 6:392. [PMID: 37037939 PMCID: PMC10086032 DOI: 10.1038/s42003-023-04741-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/21/2023] [Indexed: 04/12/2023] Open
Abstract
Our knowledge of the mechanisms underlying the vulnerability of the brain's white matter microstructure to cardiovascular risk factors (CVRFs) is still limited. We used a quantitative magnetic resonance imaging (MRI) protocol in a single centre setting to investigate the cross-sectional association between CVRFs and brain tissue properties of white matter tracts in a large community-dwelling cohort (n = 1104, age range 46-87 years). Arterial hypertension was associated with lower myelin and axonal density MRI indices, paralleled by higher extracellular water content. Obesity showed similar associations, though with myelin difference only in male participants. Associations between CVRFs and white matter microstructure were observed predominantly in limbic and prefrontal tracts. Additional genetic, lifestyle and psychiatric factors did not modulate these results, but moderate-to-vigorous physical activity was linked to higher myelin content independently of CVRFs. Our findings complement previously described CVRF-related changes in brain water diffusion properties pointing towards myelin loss and neuroinflammation rather than neurodegeneration.
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Affiliation(s)
- Olga Trofimova
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Adeliya Latypova
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Giulia DiDomenicantonio
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ann-Marie G de Lange
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Matthias Kliegel
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Silvia Stringhini
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
- Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Julien Vaucher
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Marie-Pierre F Strippoli
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Martin Preisig
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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17
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Patel A, Chad JA, Chen JJ. Is adiposity associated with white matter microstructural health and intelligence differently in males and females? Obesity (Silver Spring) 2023; 31:1011-1023. [PMID: 36883598 DOI: 10.1002/oby.23686] [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: 08/22/2022] [Revised: 10/27/2022] [Accepted: 11/24/2022] [Indexed: 03/09/2023]
Abstract
OBJECTIVE The role of vascular risk factors in age-related brain degeneration has long been the subject of intense study, but the role of obesity remains understudied. Given known sex differences in fat storage and usage, this study investigates sex differences in the association between adiposity and white matter microstructural integrity, an important early marker of brain degeneration. METHODS This study assesses the associations between adiposity (abdominal fat ratio and liver proton density fat fraction) and brain health (measures of intelligence and white matter microstructure using diffusion-tensor imaging [DTI]) in a group of UK Biobank participants. RESULTS This study finds that intelligence and DTI metrics are indeed associated with adiposity differently in males and females. These sex differences are distinct from those in the associations of DTI metrics with age and blood pressure. CONCLUSIONS Taken together, these findings suggest that there are inherent sex-driven differences in how brain health is associated with obesity.
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Affiliation(s)
- Arjun Patel
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
| | - Jordan A Chad
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - J Jean Chen
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
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18
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Espeland MA, Howard M, Bennett W, Robusto BA, Yasar S, Hugenschmidt CE, Luchsinger JA, Bahnson J, Yassine H, Johnson KC, Cook D, Hayden KM. Associations between cognitive function and endogenous levels of estradiol and testosterone in adults with type 2 diabetes. J Diabetes Complications 2022; 36:108268. [PMID: 35926332 PMCID: PMC10162709 DOI: 10.1016/j.jdiacomp.2022.108268] [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: 05/14/2022] [Revised: 07/22/2022] [Accepted: 07/22/2022] [Indexed: 10/16/2022]
Abstract
AIMS To assess associations that endogenous estradiol and testosterone levels have with cognitive function in older adults with Type 2 diabetes mellitus (T2DM). METHODS We use data from the Look AHEAD clinical trial of behavioral weight loss. Endogenous estradiol and total testosterone levels were determined using stored serum from 996 individuals, mean age 69 years, at two times (averaging 4 years apart) during years 8-18 of follow-up. One to four standardized assessments of attention, executive function, memory, and verbal fluency were collected during this follow-up. Mixed effects models and multiple imputation were used to assess associations that estradiol and total testosterone levels had with body mass index and cognitive function. RESULTS Estradiol levels were not associated with cognitive function in either sex. Total testosterone levels were not associated with cognitive function in women, but greater total testosterone levels were associated with better verbal fluency in men (p < 0.001), most strongly among those carrying the APOE-e4 allele (interaction p = 0.02). The weight loss intervention left a legacy of relatively lower cognitive functioning among women, which was not mediated by current levels of sex hormones. CONCLUSIONS Behavioral weight loss intervention does not affect cognitive functioning through mechanisms related to estradiol or testosterone. CLINICALTRIALS gov Identifier: NCT00017953.
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Affiliation(s)
- Mark A Espeland
- Sticht Center for Healthy Aging and Alzheimer's Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC, USA.
| | - Marjorie Howard
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA.
| | - Wendy Bennett
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| | - Brian A Robusto
- Sticht Center for Healthy Aging and Alzheimer's Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA.
| | - Sevil Yasar
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| | - Christina E Hugenschmidt
- Sticht Center for Healthy Aging and Alzheimer's Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA.
| | - Jose A Luchsinger
- Department of Medicine, Columbia University Medical Center, New York, NY, USA.
| | - Judy Bahnson
- Sticht Center for Healthy Aging and Alzheimer's Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA.
| | - Hussein Yassine
- Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Karen C Johnson
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA.
| | - Delilah Cook
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Kathleen M Hayden
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC, USA.
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19
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Schindler LS, Subramaniapillai S, Barth C, van der Meer D, Pedersen ML, Kaufmann T, Maximov II, Linge J, Leinhard OD, Beck D, Gurholt TP, Voldsbekk I, Suri S, Ebmeier KP, Draganski B, Andreassen OA, Westlye LT, de Lange AMG. Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women. Neuroimage Clin 2022; 36:103239. [PMID: 36451350 PMCID: PMC9668664 DOI: 10.1016/j.nicl.2022.103239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.
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Affiliation(s)
- Louise S Schindler
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway.
| | - Sivaniya Subramaniapillai
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway
| | - Claudia Barth
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, The Netherlands
| | - Mads L Pedersen
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry and Psychotherapy, University of Tübingen, Germany
| | - Ivan I Maximov
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Jennifer Linge
- AMRA Medical AB, Linköping, Sweden; Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Olof Dahlqvist Leinhard
- AMRA Medical AB, Linköping, Sweden; Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Dani Beck
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tiril P Gurholt
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Irene Voldsbekk
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sana Suri
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | | | - Bogdan Draganski
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Dept. of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ann-Marie G de Lange
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK
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