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van der Velpen IF, Yaqub A, Vernooij MW, Perry M, Vernooij-Dassen MJF, Ghanbari M, Ikram MA, Melis RJF. Sex-differences in the association of social health and marital status with blood-based immune and neurodegeneration markers in a cohort of community-dwelling older adults. Brain Behav Immun 2024; 120:71-81. [PMID: 38782212 DOI: 10.1016/j.bbi.2024.05.031] [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: 12/12/2023] [Revised: 04/24/2024] [Accepted: 05/20/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND The immune system has been proposed to play a role in the link between social health and all-cause dementia risk. We explored cross-sectional and longitudinal associations between social health, immune system balance and plasma neurodegeneration markers in community-dwelling older adults, and explored whether the balance between innate and adaptive immunity mediates associations between social health and both cognition and total brain volume. METHODS Social health markers (social support, marital status, loneliness) were measured in the Rotterdam Study between 2002-2008. Immune system cell counts and balance were assessed repeatedly from 2002 to 2016 using white blood-cell-based indices and individual counts (granulocyte-to-lymphocyte ratio (GLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII)). Plasma neurodegeneration biomarkers (amyloid-β40, amyloid-β42, total tau and neurofilament light chain) were measured once from blood samples collected between 2002-2008. Global cognitive function and total brain volume (MRI) were measured at the follow-up visit between 2009-2014. We used linear mixed models to study longitudinal associations and performed causal mediation analyses. RESULTS In 8374 adults (mean age 65.7, 57 % female), never married participants (n = 394) had higher GLR, PLR and SII compared to married peers at baseline and during follow-up, indicating imbalance towards innate immunity. Being never married was associated with higher plasma amyloid-β40, and being widowed or divorced with higher plasma total tau levels at baseline. Widowed or divorced males, but not females, had higher GLR, PLR and SII at baseline. Higher social support was associated with lower PLR in females, but higher PLR in males. Loneliness was not associated with any of the immune system balance ratios. Never married males had higher levels of all plasma neurodegeneration markers at baseline. Immune system balance did not mediate associations between social health and cognition or total brain volume, but does interact with marital status. CONCLUSION This study indicates that marital status is associated with blood-based immune system markers toward innate immunity and higher levels of plasma neurodegeneration markers. This is particularly evident for never married or previously married male older adults compared to married or female peers.
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Affiliation(s)
- Isabelle F van der Velpen
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands; Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Amber Yaqub
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands; Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Marieke Perry
- Department of Geriatric Medicine, Radboudumc Alzheimer Center, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, the Netherlands
| | | | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
| | - René J F Melis
- Department of Geriatric Medicine, Radboudumc Alzheimer Center, Radboud University Medical Center, Nijmegen, the Netherlands; Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands.
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Hofe IV, Stricker BH, Vernooij MW, Ikram MK, Ikram MA, Wolters FJ. Benzodiazepine use in relation to long-term dementia risk and imaging markers of neurodegeneration: a population-based study. BMC Med 2024; 22:266. [PMID: 38951846 PMCID: PMC11218055 DOI: 10.1186/s12916-024-03437-5] [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: 11/11/2023] [Accepted: 05/22/2024] [Indexed: 07/03/2024] Open
Abstract
BACKGROUND Benzodiazepine use is common, particularly in older adults. Benzodiazepines have well-established acute adverse effects on cognition, but long-term effects on neurodegeneration and dementia risk remain uncertain. METHODS We included 5443 cognitively healthy (MMSE ≥ 26) participants from the population-based Rotterdam Study (57.4% women, mean age 70.6 years). Benzodiazepine use from 1991 until baseline (2005-2008) was derived from pharmacy dispensing records, from which we determined drug type and cumulative dose. Benzodiazepine use was defined as prescription of anxiolytics (ATC-code: N05BA) or sedative-hypnotics (ATC-code: N05CD) between inception of pharmacy records and study baseline. Cumulative dose was calculated as the sum of the defined daily doses for all prescriptions. We determined the association with dementia risk until 2020 using Cox regression. Among 4836 participants with repeated brain MRI, we further determined the association of benzodiazepine use with changes in neuroimaging markers using linear mixed models. RESULTS Of all 5443 participants, 2697 (49.5%) had used benzodiazepines at any time in the 15 years preceding baseline, of whom 1263 (46.8%) used anxiolytics, 530 (19.7%) sedative-hypnotics, and 904 (33.5%) used both; 345 (12.8%) participants were still using at baseline assessment. During a mean follow-up of 11.2 years, 726 participants (13.3%) developed dementia. Overall, use of benzodiazepines was not associated with dementia risk compared to never use (HR [95% CI]: 1.06 [0.90-1.25]), irrespective of cumulative dose. Risk estimates were somewhat higher for any use of anxiolytics than for sedative-hypnotics (HR 1.17 [0.96-1.41] vs 0.92 [0.70-1.21]), with strongest associations for high cumulative dose of anxiolytics (HR [95% CI] 1.33 [1.04-1.71]). In imaging analyses, current use of benzodiazepine was associated cross-sectionally with lower brain volumes of the hippocampus, amygdala, and thalamus and longitudinally with accelerated volume loss of the hippocampus and to a lesser extent amygdala. However, imaging findings did not differ by type of benzodiazepines or cumulative dose. CONCLUSIONS In this population-based sample of cognitively healthy adults, overall use of benzodiazepines was not associated with increased dementia risk, but potential class-dependent adverse effects and associations with subclinical markers of neurodegeneration may warrant further investigation.
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Affiliation(s)
- Ilse Vom Hofe
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Bruno H Stricker
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Radiology & Nuclear Medicine, Alzheimer Centre Erasmus MC, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - M Kamran Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Frank J Wolters
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.
- Department of Radiology & Nuclear Medicine, Alzheimer Centre Erasmus MC, Erasmus University Medical Center, Rotterdam, The Netherlands.
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Nguyen Ho PT, Hoepel SJW, Rodriguez-Ayllon M, Luik AI, Vernooij MW, Neitzel J. Sleep, 24-Hour Activity Rhythms, and Subsequent Amyloid-β Pathology. JAMA Neurol 2024:2820395. [PMID: 38913396 PMCID: PMC11197458 DOI: 10.1001/jamaneurol.2024.1755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 04/15/2024] [Indexed: 06/25/2024]
Abstract
Importance Sleep disturbances are common among older adults and have been associated with the development of Alzheimer disease (AD), such as amyloid-β (Aβ) pathology. For effective AD prevention, it is essential to pinpoint the specific disturbances in sleep and the underlying 24-hour activity rhythms that confer the highest risk of Aβ deposition. Objective To determine the associations of 24-hour activity rhythms and sleep with Aβ deposition in adults without dementia, to evaluate whether disrupted 24-hour activity and sleep may precede Aβ deposition, and to assess the role of the apolipoprotein E ε4 (APOE4) genotype. Design, Setting, and Participants This was an observational cohort study using data from the Rotterdam Study. Of 639 participants without dementia who underwent Aβ positron emission tomography (PET) from September 2018 to November 2021, 319 were included in the current study. Exclusion criteria were no APOE genotyping and no valid actigraphy data at the baseline visits from 2004 to 2006 or from 2012 to 2014. The mean (SD) follow-up was 7.8 (2.4) years. Data were analyzed from March 2023 to April 2024. Exposures Actigraphy (7 days and nights, objective sleep, and 24-hour activity rhythms), sleep diaries (self-reported sleep), Aβ42/40, phosphorylated tau (p-tau)181 and p-tau217 plasma assays, 18F-florbetaben PET (mean standard uptake value ratio [SUVR] in a large cortical region of interest), and APOE4 genotype. Main Outcomes and Measures Association of objective and self-reported sleep and 24-hour activity rhythms at baseline with brain Aβ PET burden at follow-up. Results The mean (range) age in the study population was 61.5 (48-80) years at baseline and 69.2 (60-88) years at follow-up; 150 (47%) were women. Higher intradaily variability at baseline, an indicator of fragmented 24-hour activity rhythms, was associated with higher Aβ PET burden at follow-up (β, 0.15; bootstrapped 95% CI, 0.04 to 0.26; bootstrapped P = .02, false discovery rate [FDR] P = .048). APOE genotype modified this association, which was stronger in APOE4 carriers (β, 0.38; bootstrapped 95% CI, 0.05 to 0.64; bootstrapped P = .03) compared to noncarriers (β, 0.07; bootstrapped 95% CI, -0.04 to 0.18; bootstrapped P = .19). The findings remained largely similar after excluding participants with AD pathology at baseline, suggesting that a fragmented 24-hour activity rhythm may have preceded Aβ deposition. No other objective or self-reported measure of sleep was associated with Aβ. Conclusions and Relevance Among community-dwelling adults included in this study, higher fragmentation of the 24-hour activity rhythms was associated with greater subsequent Aβ burden, especially in APOE4 carriers. These results suggest that rest-activity fragmentation could represent a modifiable risk factor for AD.
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Affiliation(s)
- Phuong Thuy Nguyen Ho
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, the Netherlands
| | - Sanne J. W. Hoepel
- Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, the Netherlands
| | - Maria Rodriguez-Ayllon
- Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, the Netherlands
| | - Annemarie I. Luik
- Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, the Netherlands
- Trimbos Institute—the Netherlands Institute of Mental Health and Addiction, Utrecht, the Netherlands
| | - Meike W. Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, the Netherlands
| | - Julia Neitzel
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, the Netherlands
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
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Miller LR, Bickel MA, Vance ML, Vaden H, Nagykaldi D, Nyul-Toth A, Bullen EC, Gautam T, Tarantini S, Yabluchanskiy A, Kiss T, Ungvari Z, Conley SM. Vascular smooth muscle cell-specific Igf1r deficiency exacerbates the development of hypertension-induced cerebral microhemorrhages and gait defects. GeroScience 2024; 46:3481-3501. [PMID: 38388918 PMCID: PMC11009188 DOI: 10.1007/s11357-024-01090-7] [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: 12/05/2023] [Accepted: 02/03/2024] [Indexed: 02/24/2024] Open
Abstract
Cerebrovascular fragility and cerebral microhemorrhages (CMH) contribute to age-related cognitive impairment, mobility defects, and vascular cognitive impairment and dementia, impairing healthspan and reducing quality of life in the elderly. Insulin-like growth factor 1 (IGF-1) is a key vasoprotective growth factor that is reduced during aging. Circulating IGF-1 deficiency leads to the development of CMH and other signs of cerebrovascular dysfunction. Here our goal was to understand the contribution of IGF-1 signaling on vascular smooth muscle cells (VSMCs) to the development of CMH and associated gait defects. We used an inducible VSMC-specific promoter and an IGF-1 receptor (Igf1r) floxed mouse line (Myh11-CreERT2 Igf1rf/f) to knockdown Igf1r. Angiotensin II in combination with L-NAME-induced hypertension was used to elicit CMH. We observed that VSMC-specific Igf1r knockdown mice had accelerated development of CMH, and subsequent associated gait irregularities. These phenotypes were accompanied by upregulation of a cluster of pro-inflammatory genes associated with VSMC maladaptation. Collectively our findings support an essential role for VSMCs as a target for the vasoprotective effects of IGF-1, and suggest that VSMC dysfunction in aging may contribute to the development of CMH.
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Affiliation(s)
- Lauren R Miller
- Department of Cell Biology, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd, BMSB 553, Oklahoma City, OK, 73104, USA
- Currently at: Cardiovascular Biology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104, USA
| | - Marisa A Bickel
- Department of Cell Biology, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd, BMSB 553, Oklahoma City, OK, 73104, USA
| | - Michaela L Vance
- Department of Cell Biology, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd, BMSB 553, Oklahoma City, OK, 73104, USA
| | - Hannah Vaden
- Department of Cell Biology, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd, BMSB 553, Oklahoma City, OK, 73104, USA
| | - Domonkos Nagykaldi
- Department of Cell Biology, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd, BMSB 553, Oklahoma City, OK, 73104, USA
| | - Adam Nyul-Toth
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
- International Training Program in Geroscience, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary
| | - Elizabeth C Bullen
- Department of Cell Biology, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd, BMSB 553, Oklahoma City, OK, 73104, USA
| | - Tripti Gautam
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
| | - Stefano Tarantini
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
- The Peggy and Charles Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Health Promotion Sciences, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Andriy Yabluchanskiy
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
| | - Tamas Kiss
- Pediatric Center, Semmelweis University, Budapest, Hungary
- Eötvös Loránd Research Network and Semmelweis University Cerebrovascular and Neurocognitive Disorders Research Group, Budapest, Hungary
| | - Zoltan Ungvari
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
- International Training Program in Geroscience, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary
- The Peggy and Charles Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Shannon M Conley
- Department of Cell Biology, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd, BMSB 553, Oklahoma City, OK, 73104, USA.
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA.
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Yaqub A, Vojinovic D, Vernooij MW, Slagboom PE, Ghanbari M, Beekman M, van der Grond J, Hankemeier T, van Duijn CM, Ikram MA, Ahmad S. Plasma trimethylamine N-oxide (TMAO): associations with cognition, neuroimaging, and dementia. Alzheimers Res Ther 2024; 16:113. [PMID: 38769578 PMCID: PMC11103865 DOI: 10.1186/s13195-024-01480-1] [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: 10/23/2023] [Accepted: 05/13/2024] [Indexed: 05/22/2024]
Abstract
BACKGROUND The gut-derived metabolite Trimethylamine N-oxide (TMAO) and its precursors - betaine, carnitine, choline, and deoxycarnitine - have been associated with an increased risk of cardiovascular disease, but their relation to cognition, neuroimaging markers, and dementia remains uncertain. METHODS In the population-based Rotterdam Study, we used multivariable regression models to study the associations between plasma TMAO, its precursors, and cognition in 3,143 participants. Subsequently, we examined their link to structural brain MRI markers in 2,047 participants, with a partial validation in the Leiden Longevity Study (n = 318). Among 2,517 participants, we assessed the risk of incident dementia using multivariable Cox proportional hazard models. Following this, we stratified the longitudinal associations by medication use and sex, after which we conducted a sensitivity analysis for individuals with impaired renal function. RESULTS Overall, plasma TMAO was not associated with cognition, neuroimaging markers or incident dementia. Instead, higher plasma choline was significantly associated with poor cognition (adjusted mean difference: -0.170 [95% confidence interval (CI) -0.297;-0.043]), brain atrophy and more markers of cerebral small vessel disease, such as white matter hyperintensity volume (0.237 [95% CI: 0.076;0.397]). By contrast, higher carnitine concurred with lower white matter hyperintensity volume (-0.177 [95% CI: -0.343;-0.010]). Only among individuals with impaired renal function, TMAO appeared to increase risk of dementia (hazard ratio (HR): 1.73 [95% CI: 1.16;2.60]). No notable differences were observed in stratified analyses. CONCLUSIONS Plasma choline, as opposed to TMAO, was found to be associated with cognitive decline, brain atrophy, and markers of cerebral small vessel disease. These findings illustrate the complexity of relationships between TMAO and its precursors, and emphasize the need for concurrent study to elucidate gut-brain mechanisms.
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Affiliation(s)
- Amber Yaqub
- Department of Epidemiology, Erasmus MC, University Medical Center, PO Box 2040, Rotterdam, CA, 3000, the Netherlands
| | - Dina Vojinovic
- Department of Epidemiology, Erasmus MC, University Medical Center, PO Box 2040, Rotterdam, CA, 3000, the Netherlands
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC, University Medical Center, PO Box 2040, Rotterdam, CA, 3000, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - P Eline Slagboom
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, University Medical Center, PO Box 2040, Rotterdam, CA, 3000, the Netherlands
| | - Marian Beekman
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Thomas Hankemeier
- Division of Analytical Biosciences, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands
| | | | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center, PO Box 2040, Rotterdam, CA, 3000, the Netherlands.
| | - Shahzad Ahmad
- Department of Epidemiology, Erasmus MC, University Medical Center, PO Box 2040, Rotterdam, CA, 3000, the Netherlands
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vom Hofe I, Stricker BH, Vernooij MW, Ikram MK, Ikram MA, Wolters FJ. Antidepressant use in relation to dementia risk, cognitive decline, and brain atrophy. Alzheimers Dement 2024; 20:3378-3387. [PMID: 38561253 PMCID: PMC11095425 DOI: 10.1002/alz.13807] [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: 06/08/2023] [Revised: 02/01/2024] [Accepted: 02/26/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION We aimed to assess the effect of antidepressant use on dementia risk, cognitive decline, and brain atrophy. METHODS In this prospective cohort study, we included 5511 dementia-free participants (Mini-Mental State Examination [MMSE] > 25) of the Rotterdam study (57.5% women, mean age 70.6 years). Antidepressant use was extracted from pharmacy records from 1991 until baseline (2002-2008). Incident dementia was monitored from baseline until 2018, with repeated cognitive assessment and magnetic resonance imaging (MRI) every 4 years. RESULTS Compared to never use, any antidepressant use was not associated with dementia risk (hazard ratio [HR] 1.14, 95% confidence interval [CI] 0.92-1.41), or with accelerated cognitive decline or atrophy of white and gray matter. Compared to never use, dementia risk was somewhat higher with tricyclic antidepressants (HR 1.36, 95% CI 1.01-1.83) than with selective serotonin reuptake inhibitors (HR 1.12, 95% CI 0.81-1.54), but without dose-response relationships, accelerated cognitive decline, or atrophy in either group. DISCUSSION Antidepressant medication in adults without indication of cognitive impairment was not consistently associated with long-term adverse cognitive effects. HIGHLIGHTS Antidepressant medications are frequently prescribed, especially among older adults. In this study, antidepressant use was not associated with long-term dementia risk. Antidepressant use was not associated with cognitive decline or brain atrophy. Our results support safe prescription in an older, cognitively healthy population.
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Affiliation(s)
- Ilse vom Hofe
- Department of EpidemiologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Bruno H. Stricker
- Department of EpidemiologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Meike W. Vernooij
- Department of EpidemiologyErasmus University Medical CenterRotterdamThe Netherlands
- Department of Radiology & Nuclear Medicine and Alzheimer Centre Erasmus MCErasmus University Medical CenterRotterdamThe Netherlands
| | - M. Kamran Ikram
- Department of EpidemiologyErasmus University Medical CenterRotterdamThe Netherlands
- Department of NeurologyErasmus University Medical CenterRotterdamThe Netherlands
| | - M. Arfan Ikram
- Department of EpidemiologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Frank J. Wolters
- Department of EpidemiologyErasmus University Medical CenterRotterdamThe Netherlands
- Department of Radiology & Nuclear Medicine and Alzheimer Centre Erasmus MCErasmus University Medical CenterRotterdamThe Netherlands
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Wulms N, Kugel H, Cnyrim C, Tenberge A, Schwindt W, Dannlowski U, Berger K, Sundermann B, Minnerup H. Cerebral MRI in a prospective cohort study on depression and atherosclerosis: the BiDirect sample, processing pipelines, and analysis tools. Eur Radiol Exp 2024; 8:16. [PMID: 38332362 PMCID: PMC10853141 DOI: 10.1186/s41747-023-00415-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/23/2023] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND The use of cerebral magnetic resonance imaging (MRI) in observational studies has increased exponentially in recent years, making it critical to provide details about the study sample, image processing, and extracted imaging markers to validate and replicate study results. This article reviews the cerebral MRI dataset from the now-completed BiDirect cohort study, as an update and extension of the feasibility report published after the first two examination time points. METHODS We report the sample and flow of participants spanning four study sessions and twelve years. In addition, we provide details on the acquisition protocol; the processing pipelines, including standardization and quality control methods; and the analytical tools used and markers available. RESULTS All data were collected from 2010 to 2021 at a single site in Münster, Germany, starting with a population of 2,257 participants at baseline in 3 different cohorts: a population-based cohort (n = 911 at baseline, 672 with MRI data), patients diagnosed with depression (n = 999, 736 with MRI data), and patients with manifest cardiovascular disease (n = 347, 52 with MRI data). During the study period, a total of 4,315 MRI sessions were performed, and over 535 participants underwent MRI at all 4 time points. CONCLUSIONS Images were converted to Brain Imaging Data Structure (a standard for organizing and describing neuroimaging data) and analyzed using common tools, such as CAT12, FSL, Freesurfer, and BIANCA to extract imaging biomarkers. The BiDirect study comprises a thoroughly phenotyped study population with structural and functional MRI data. RELEVANCE STATEMENT The BiDirect Study includes a population-based sample and two patient-based samples whose MRI data can help answer numerous neuropsychiatric and cardiovascular research questions. KEY POINTS • The BiDirect study included characterized patient- and population-based cohorts with MRI data. • Data were standardized to Brain Imaging Data Structure and processed with commonly available software. • MRI data and markers are available upon request.
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Affiliation(s)
- Niklas Wulms
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
| | - Harald Kugel
- Clinic of Radiology Radiology, University Hospital Muenster, Münster, Germany
| | - Christian Cnyrim
- Department of Clinical Radiology, Klinikum Ibbenbueren, Ibbenbueren, Germany
| | - Anja Tenberge
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Wolfram Schwindt
- Clinic of Radiology Radiology, University Hospital Muenster, Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Benedikt Sundermann
- Clinic of Radiology Radiology, University Hospital Muenster, Münster, Germany
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus, University of Oldenburg, Oldenburg, Germany
- Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
| | - Heike Minnerup
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
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Ikram MA, Kieboom BCT, Brouwer WP, Brusselle G, Chaker L, Ghanbari M, Goedegebure A, Ikram MK, Kavousi M, de Knegt RJ, Luik AI, van Meurs J, Pardo LM, Rivadeneira F, van Rooij FJA, Vernooij MW, Voortman T, Terzikhan N. The Rotterdam Study. Design update and major findings between 2020 and 2024. Eur J Epidemiol 2024; 39:183-206. [PMID: 38324224 DOI: 10.1007/s10654-023-01094-1] [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: 07/21/2023] [Accepted: 12/14/2023] [Indexed: 02/08/2024]
Abstract
The Rotterdam Study is a population-based cohort study, started in 1990 in the district of Ommoord in the city of Rotterdam, the Netherlands, with the aim to describe the prevalence and incidence, unravel the etiology, and identify targets for prediction, prevention or intervention of multifactorial diseases in mid-life and elderly. The study currently includes 17,931 participants (overall response rate 65%), aged 40 years and over, who are examined in-person every 3 to 5 years in a dedicated research facility, and who are followed-up continuously through automated linkage with health care providers, both regionally and nationally. Research within the Rotterdam Study is carried out along two axes. First, research lines are oriented around diseases and clinical conditions, which are reflective of medical specializations. Second, cross-cutting research lines transverse these clinical demarcations allowing for inter- and multidisciplinary research. These research lines generally reflect subdomains within epidemiology. This paper describes recent methodological updates and main findings from each of these research lines. Also, future perspective for coming years highlighted.
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Affiliation(s)
- M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands.
| | - Brenda C T Kieboom
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Willem Pieter Brouwer
- Department of Hepatology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Guy Brusselle
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
- Department of Pulmonology, University Hospital Ghent, Ghent, Belgium
| | - Layal Chaker
- Department of Epidemiology, and Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - André Goedegebure
- Department of Otorhinolaryngology and Head & Neck Surgery, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - M Kamran Ikram
- Department of Epidemiology, and Department of Neurology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Rob J de Knegt
- Department of Hepatology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Joyce van Meurs
- Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Luba M Pardo
- Department of Dermatology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Fernando Rivadeneira
- Department of Medicine, and Department of Oral & Maxillofacial Surgery, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Frank J A van Rooij
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, and Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Trudy Voortman
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Natalie Terzikhan
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
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9
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van Arendonk J, Wolters FJ, Neitzel J, Vinke EJ, Vernooij MW, Ghanbari M, Ikram MA. Plasma neurofilament light chain in relation to 10-year change in cognition and neuroimaging markers: a population-based study. GeroScience 2024; 46:57-70. [PMID: 37535203 PMCID: PMC10828339 DOI: 10.1007/s11357-023-00876-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 07/10/2023] [Indexed: 08/04/2023] Open
Abstract
Neurofilament light chain (NfL) is a promising biomarker for risk stratification and disease monitoring of dementia, but its utility in the preclinical disease stage remains uncertain. We determined the association of plasma NfL with (change in) neuroimaging markers and cognition in the population-based Rotterdam Study, using linear and logistic regression and mixed-effects models. Plasma NfL levels were measured using the Simoa NF-light™ assay in 4705 dementia-free participants (mean age 71.9 years, 57% women), who underwent cognitive assessment and brain MRI with repeated assessments over a 10-year follow-up period. Higher plasma NfL was associated with worse cognitive performance at baseline (g-factor: β = - 0.12 (- 0.15; - 0.09), p < 0.001), and accelerated cognitive decline during follow-up on the Stroop color naming task (β = 0.04 (0.02; 0.06), p < 0.001), with a smaller trend for decline in global cognition (g-factor β = - 0.02 (- 0.04; 0.00), p = 0.044). In the subset of 975 participants with brain MRI, higher NfL was associated with poorer baseline white matter integrity (e.g., global mean diffusivity: β = 0.12 (0.06; 0.19), p < 0.001), with similar trends for volume of white matter hyperintensities (β = 0.09 (0.02; 0.16), p = 0.011) and presence of lacunes (OR = 1.55 (1.13; 2.14), p = 0.007). Plasma NfL was not associated with volumes or thickness of the total gray matter, hippocampus, or Alzheimer signature regions. In conclusion, higher plasma NfL levels are associated with cognitive decline and larger burden of primarily white matter pathology in the general population.
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Affiliation(s)
- Joyce van Arendonk
- Department of Radiology and Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, PO Box 2040, Rotterdam, 3000 CA, the Netherlands
| | - Frank J Wolters
- Department of Radiology and Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, PO Box 2040, Rotterdam, 3000 CA, the Netherlands
| | - Julia Neitzel
- Department of Radiology and Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, PO Box 2040, Rotterdam, 3000 CA, the Netherlands
- Department of Epidemiology, Harvard T.H Chan School of Public Health, Boston, MA, USA
| | - Elisabeth J Vinke
- Department of Radiology and Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, PO Box 2040, Rotterdam, 3000 CA, the Netherlands
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, PO Box 2040, Rotterdam, 3000 CA, the Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, PO Box 2040, Rotterdam, 3000 CA, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, PO Box 2040, Rotterdam, 3000 CA, the Netherlands.
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10
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Mooldijk SS, Lu T, Waqas K, Chen J, Vernooij MW, Ikram MK, Zillikens MC, Ikram MA. Skin autofluorescence, reflecting accumulation of advanced glycation end products, and the risk of dementia in a population-based cohort. Sci Rep 2024; 14:1256. [PMID: 38218902 PMCID: PMC10787742 DOI: 10.1038/s41598-024-51703-6] [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: 10/21/2022] [Accepted: 01/08/2024] [Indexed: 01/15/2024] Open
Abstract
Conditions such as hyperglycemia and oxidative stress lead to the formation of advanced glycation end products (AGEs), which are harmful compounds that have been implicated in dementia. Within the Rotterdam Study, we measured skin AGEs as skin autofluorescence, reflecting long-term accumulation of AGEs, and determined their association with the risk of dementia and with brain magnetic resonance imaging (MRI) measures. Skin autofluorescence was measured between 2013 and 2016 in 2922 participants without dementia. Of these, 1504 also underwent brain MRI, on which measures of brain atrophy and cerebral small vessel disease were assessed. All participants were followed for the incidence of dementia until 2020. Of 2922 participants (mean age 72.6 years, 57% women), 123 developed dementia. Higher skin autofluorescence (per standard deviation) was associated with an increased risk of dementia (hazard ratio 1.21 [95% confidence interval 1.01-1.46]) and Alzheimer's disease (1.19 [0.97-1.47]), independently of age and other studied potential confounders. Stronger effects were seen in apolipoprotein E (APOE) ε4 carriers (1.34 [0.98-1.82]) and in participants with diabetes (1.35 [0.94-1.94]). Participants with higher skin autofluorescence levels also had smaller total brain volumes and smaller hippocampus volumes on MRI, and they had more often lacunes. These results suggest that AGEs may be involved in dementia pathophysiology.
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Affiliation(s)
- Sanne S Mooldijk
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Tianqi Lu
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Komal Waqas
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jinluan Chen
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - M Kamran Ikram
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
- Department of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - M Carola Zillikens
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
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11
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de Jong JJA, Jansen JFA, Vergoossen LWM, Schram MT, Stehouwer CDA, Wildberger JE, Linden DEJ, Backes WH. Effect of Magnetic Resonance Image Quality on Structural and Functional Brain Connectivity: The Maastricht Study. Brain Sci 2024; 14:62. [PMID: 38248277 PMCID: PMC10813868 DOI: 10.3390/brainsci14010062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/27/2023] [Accepted: 01/04/2024] [Indexed: 01/23/2024] Open
Abstract
In population-based cohort studies, magnetic resonance imaging (MRI) is vital for examining brain structure and function. Advanced MRI techniques, such as diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI), provide insights into brain connectivity. However, biases in MRI data acquisition and processing can impact brain connectivity measures and their associations with demographic and clinical variables. This study, conducted with 5110 participants from The Maastricht Study, explored the relationship between brain connectivity and various image quality metrics (e.g., signal-to-noise ratio, head motion, and atlas-template mismatches) that were obtained from dMRI and rs-fMRI scans. Results revealed that in particular increased head motion (R2 up to 0.169, p < 0.001) and reduced signal-to-noise ratio (R2 up to 0.013, p < 0.001) negatively impacted structural and functional brain connectivity, respectively. These image quality metrics significantly affected associations of overall brain connectivity with age (up to -59%), sex (up to -25%), and body mass index (BMI) (up to +14%). Associations with diabetes status, educational level, history of cardiovascular disease, and white matter hyperintensities were generally less affected. This emphasizes the potential confounding effects of image quality in large population-based neuroimaging studies on brain connectivity and underscores the importance of accounting for it.
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Affiliation(s)
- Joost J. A. de Jong
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Jacobus F. A. Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Laura W. M. Vergoossen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Miranda T. Schram
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Cardiovascular Disease (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
- Heart and Vascular Centre, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
| | - Coen D. A. Stehouwer
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Cardiovascular Disease (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
- Heart and Vascular Centre, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
| | - Joachim E. Wildberger
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Cardiovascular Disease (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - David E. J. Linden
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Walter H. Backes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
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12
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Sudre CH, Van Wijnen K, Dubost F, Adams H, Atkinson D, Barkhof F, Birhanu MA, Bron EE, Camarasa R, Chaturvedi N, Chen Y, Chen Z, Chen S, Dou Q, Evans T, Ezhov I, Gao H, Girones Sanguesa M, Gispert JD, Gomez Anson B, Hughes AD, Ikram MA, Ingala S, Jaeger HR, Kofler F, Kuijf HJ, Kutnar D, Lee M, Li B, Lorenzini L, Menze B, Molinuevo JL, Pan Y, Puybareau E, Rehwald R, Su R, Shi P, Smith L, Tillin T, Tochon G, Urien H, van der Velden BHM, van der Velpen IF, Wiestler B, Wolters FJ, Yilmaz P, de Groot M, Vernooij MW, de Bruijne M. Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021. Med Image Anal 2024; 91:103029. [PMID: 37988921 DOI: 10.1016/j.media.2023.103029] [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: 07/01/2022] [Revised: 08/09/2023] [Accepted: 11/13/2023] [Indexed: 11/23/2023]
Abstract
Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.
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Affiliation(s)
- Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, United Kingdom; Centre for Medical Image Computing, University College London, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - Kimberlin Van Wijnen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Florian Dubost
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Hieab Adams
- Department of Clinical Genetics and Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - David Atkinson
- Centre for Medical Imaging, University College London, London, United Kingdom
| | - Frederik Barkhof
- Centre for Medical Image Computing, University College London, London, United Kingdom; Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Mahlet A Birhanu
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Esther E Bron
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Robin Camarasa
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Nish Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, United Kingdom
| | - Yuan Chen
- Department of Radiology, University of Massachusetts Medical School, Worcester, USA
| | - Zihao Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shuai Chen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Qi Dou
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, China
| | - Tavia Evans
- Department of Clinical Genetics and Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Ivan Ezhov
- Department of Informatics, Technische Universitat Munchen, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany
| | - Haojun Gao
- Department of Radiology, Zhejiang University, Hangzhou, China
| | | | - Juan Domingo Gispert
- Barcelonaß Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain; Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Barcelona, Spain
| | | | - Alun D Hughes
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, United Kingdom
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - H Rolf Jaeger
- Institute of Neurology, University College London, London, United Kingdom
| | - Florian Kofler
- Department of Informatics, Technische Universitat Munchen, Munich, Germany; Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Denis Kutnar
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Bo Li
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Luigi Lorenzini
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Bjoern Menze
- Department of Informatics, Technische Universitat Munchen, Munich, Germany; Department of Quantitative Biomedicine, University of Zurich, Switzerland
| | - Jose Luis Molinuevo
- Barcelonaß Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; H. Lundbeck A/S, Copenhagen, Denmark
| | - Yiwei Pan
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | | | - Rafael Rehwald
- Institute of Neurology, University College London, London, United Kingdom
| | - Ruisheng Su
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Pengcheng Shi
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | | | - Therese Tillin
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, United Kingdom
| | | | - Hélène Urien
- ISEP-Institut Supérieur d'Électronique de Paris, Issy-les-Moulineaux, France
| | | | - Isabelle F van der Velpen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany
| | - Frank J Wolters
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Pinar Yilmaz
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Marius de Groot
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; GlaxoSmithKline Research, Stevenage, United Kingdom
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
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13
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Yaqub A, Khan SR, Vernooij MW, van Hagen PM, Peeters RP, Ikram MA, Chaker L, Dalm VASH. Serum immunoglobulins and biomarkers of dementia: a population-based study. Alzheimers Res Ther 2023; 15:194. [PMID: 37936180 PMCID: PMC10629143 DOI: 10.1186/s13195-023-01333-3] [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: 09/20/2022] [Accepted: 10/15/2023] [Indexed: 11/09/2023]
Abstract
BACKGROUND Inflammation plays a key role in the development of dementia, but its link to early biomarkers, particularly those in plasma or neuroimaging, remains elusive. This study aimed to investigate the association between serum immunoglobulins and biomarkers of dementia. METHODS Between 1997 and 2009, serum immunoglobulins (IgA, IgG and IgM) were measured in dementia-free participants of the population-based Rotterdam Study. A random subset of participants had assessment of biomarkers in plasma (total tau (t-tau), neurofilament light chain (NfL), amyloid-β40 (Aβ-40), amyloid-β42 (Aβ-42), while another subset of participants underwent neuroimaging to quantify brain volume, white matter structural integrity and markers of cerebral small vessel disease. Linear regression models were constructed to determine cross-sectional associations between IgA, IgG, IgM and biomarkers of dementia, with adjustment for potential confounders. Multiple testing correction was applied using the false discovery rate. As a sensitivity analysis, we re-ran the models for participants within the reference range of immunoglobulins, excluding those using immunomodulating drugs, and conducted a stratified analysis by APOE-ε4 carriership and sex. RESULTS Of 8,768 participants with serum immunoglobulins, 3,455 participants (65.8 years [interquartile range (IQR): 61.5-72.0], 57.2% female) had plasma biomarkers available and 3,139 participants (57.4 years [IQR: 52.7-60.7], 54.4% female) had neuroimaging data. Overall, no associations between serum immunoglobulins and biomarkers of dementia remained significant after correction for multiple testing. However, several suggestive associations were noted: higher serum IgA levels concurred with lower plasma levels of Aβ-42 (standardized adjusted mean difference: -0.015 [95% confidence interval (CI): -0.029--0.002], p = 2.8 × 10-2), and a lower total brain volume, mainly driven by less gray matter (-0.027 [-0.046--0.008], p = 6.0 × 10-3) and more white matter hyperintensities (0.047 [0.016 - 0.077], p = 3.0 × 10-3). In sensitivity analyses, higher IgM was linked to lower t-tau, Aβ-40, and Aβ-42, but also a loss of white matter microstructural integrity. Stratified analyses indicate that these associations potentially differ between carriers and non-carriers of the APOE-ε4 allele and men and women. CONCLUSIONS While associations between serum immunoglobulins and early markers of dementia could not be established in this population-based sample, it may be valuable to consider factors such as APOE-ε4 allele carriership and sex in future investigations.
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Affiliation(s)
- Amber Yaqub
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Samer R Khan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Allergy & Clinical Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - P Martin van Hagen
- Department of Internal Medicine, Division of Allergy & Clinical Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Robin P Peeters
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Endocrinology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Layal Chaker
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Endocrinology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Virgil A S H Dalm
- Department of Internal Medicine, Division of Allergy & Clinical Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
- Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
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Costanzo A, van der Velpen IF, Ikram MA, Vernooij-Dassen MJ, Niessen WJ, Vernooij MW, Kas MJ. Social Health Is Associated With Tract-Specific Brain White Matter Microstructure in Community-Dwelling Older Adults. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2023; 3:1003-1011. [PMID: 37881589 PMCID: PMC10593878 DOI: 10.1016/j.bpsgos.2022.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/19/2022] [Accepted: 08/08/2022] [Indexed: 11/15/2022] Open
Abstract
Background Poor social health has been linked to a risk of neuropsychiatric disorders. Neuroimaging studies have shown associations between social health and global white matter microstructural integrity. We aimed to identify which white matter tracts are involved in these associations. Methods Social health markers (loneliness, perceived social support, and partnership status) and white matter microstructural integrity of 15 white matter tracts (identified with probabilistic tractography after diffusion magnetic resonance imaging) were collected for 3352 participants (mean age 58.4 years, 54.9% female) from 2002 to 2008 in the Rotterdam Study. Cross-sectional associations were studied using multivariable linear regression. Results Loneliness was associated with higher mean diffusivity (MD) in the superior thalamic radiation and the parahippocampal part of the cingulum (standardized mean difference for both tracts: 0.21, 95% CI, 0.09 to 0.34). Better perceived social support was associated with lower MD in the forceps minor (standardized mean difference per point increase in social support: -0.06, 95% CI, -0.09 to -0.03), inferior fronto-occipital fasciculus, and uncinate fasciculus. In male participants, better perceived social support was associated with lower MD in the forceps minor, and not having a partner was associated with lower fractional anisotropy in the forceps minor. Loneliness was associated with higher MD in the superior thalamic radiation in female participants only. Conclusions Social health was associated with tract-specific white matter microstructure. Loneliness was associated with lower integrity of limbic and sensorimotor tracts, whereas better perceived social support was associated with higher integrity of association and commissural tracts, indicating that social health domains involve distinct neural pathways of the brain.
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Affiliation(s)
- Andrea Costanzo
- Groningen Institute for Evolutionary Life Sciences, Faculty of Science and Engineering, University of Groningen, Groningen, the Netherlands
| | - Isabelle F. van der Velpen
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | | | - Wiro J. Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Meike W. Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Martien J. Kas
- Groningen Institute for Evolutionary Life Sciences, Faculty of Science and Engineering, University of Groningen, Groningen, the Netherlands
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15
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de Crom TOE, Blekkenhorst L, Vernooij MW, Ikram MK, Voortman T, Ikram MA. Dietary nitrate intake in relation to the risk of dementia and imaging markers of vascular brain health: a population-based study. Am J Clin Nutr 2023; 118:352-359. [PMID: 37536866 DOI: 10.1016/j.ajcnut.2023.05.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 08/05/2023] Open
Abstract
BACKGROUND Nitric oxide is a free radical that can be produced from dietary nitrate and positively affects cardiovascular health. With cardiovascular health playing an important role in the etiology of dementia, we hypothesized a link between dietary nitrate intake and the risk of dementia. OBJECTIVES This study aimed to find the association of total, vegetable, and nonvegetable dietary nitrate intake with the risk of dementia and imaging markers of vascular brain health, such as total brain volume, global cerebral perfusion, white matter hyperintensity volume, microbleeds, and lacunar infarcts. METHODS Between 1990 and 2009, dietary intake was assessed using food-frequency questionnaires in 9543 dementia-free participants (mean age, 64 y; 58% female) from the prospective population-based Rotterdam Study. Participants were followed up for incidence dementia until January 2020. We used Cox models to determine the association between dietary nitrate intake and incident dementia. Using linear mixed models and logistic regression models, we assessed the association of dietary nitrate intake with changes in imaging markers across 3 consecutive examination rounds (mean interval between images 4.6 y). RESULTS Participants median dietary nitrate consumption was 85 mg/d (interquartile range, 55 mg/d), derived on average for 81% from vegetable sources. During a mean follow-up of 14.5 y, 1472 participants developed dementia. A higher intake of total and vegetable dietary nitrate was associated with a lower risk of dementia per 50-mg/d increase [hazard ratio (HR): 0.92; 95% confidence interval (CI): 0.87, 0.98; and HR: 0.92; 95% CI: 0.86, 0.97, respectively] but not with changes in neuroimaging markers. No association between nonvegetable dietary nitrate intake and the risk of dementia (HR: 1.15; 95% CI: 0.64, 2.07) or changes in neuroimaging markers were observed. CONCLUSIONS A higher dietary nitrate intake from vegetable sources was associated with a lower risk of dementia. We found no evidence that this association was driven by vascular brain health.
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Affiliation(s)
- Tosca O E de Crom
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Lauren Blekkenhorst
- Nutrition and Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Royal Perth Hospital Research Foundation, Perth, Western Australia, Australia
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands; Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, Netherlands
| | - M Kamran Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands; Department of Neurology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Trudy Voortman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands.
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16
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Özel F, Hilal S, de Feijter M, van der Velpen I, Direk N, Ikram MA, Vernooij MW, Luik AI. Associations of neuroimaging markers with depressive symptoms over time in middle-aged and elderly persons. Psychol Med 2023; 53:4355-4363. [PMID: 35534463 PMCID: PMC10388307 DOI: 10.1017/s003329172200112x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 03/03/2022] [Accepted: 04/04/2022] [Indexed: 01/30/2023]
Abstract
BACKGROUND Cerebrovascular disease is regarded as a potential cause of late-life depression. Yet, evidence for associations of neuroimaging markers of vascular brain disease with depressive symptoms is inconclusive. We examined the associations of neuroimaging markers and depressive symptoms in a large population-based study of middle-aged and elderly persons over time. METHODS A total of 4943 participants (mean age = 64.6 ± 11.1 years, 55.7% women) from the Rotterdam Study were included. At baseline, total brain volume, gray matter volume, white matter volume, white matter hyperintensities volume, cortical infarcts, lacunar infarcts, microbleeds, white matter fractional anisotropy, and mean diffusivity (MD) were measured with a brain MRI (1.5T). Depressive symptoms were assessed twice with the Center for Epidemiologic Studies Depression scale (median follow-up time: 5.5 years, IQR = 0.9). To assess temporal associations of neuroimaging markers and depressive symptoms, linear mixed models were used. RESULTS A smaller total brain volume (β = -0.107, 95% CI -0.192 to -0.022), larger white matter hyperintensities volume (β = 0.047, 95% CI 0.010-0.084), presence of cortical infarcts (β = 0.194, 95% CI 0.047-0.341), and higher MD levels (β = 0.060, 95% CI 0.022-0.098) were cross-sectionally associated with more depressive symptoms. Longitudinal analyses showed that small total brain volume (β = -0.091, 95% CI -0.167 to -0.015) and presence of cortical infarcts (β = 0.168, 95% CI 0.022-0.314) were associated with increasing depressive symptoms over time. After stratification on age, effect sizes were more pronounced at older ages. CONCLUSIONS Neuroimaging markers of white matter microstructural damage were associated with depressive symptoms longitudinally in this study of middle-aged and elderly persons. These associations were more pronounced at older ages, providing evidence for the role of white matter structure in late-life depressive symptomatology.
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Affiliation(s)
- Fatih Özel
- Department of Organismal Biology, Uppsala University, Uppsala, Sweden
| | - Saima Hilal
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Maud de Feijter
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Isabelle van der Velpen
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Nese Direk
- Istanbul Faculty of Medicine, Department of Psychiatry, Istanbul University, Istanbul, Turkey
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Meike W. Vernooij
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Annemarie I. Luik
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
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17
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van der Velpen IF, de Feijter M, Raina R, Özel F, Perry M, Ikram MA, Vernooij MW, Luik AI. Psychosocial health modifies associations between HPA-axis function and brain structure in older age. Psychoneuroendocrinology 2023; 153:106106. [PMID: 37028139 DOI: 10.1016/j.psyneuen.2023.106106] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 01/21/2023] [Accepted: 03/31/2023] [Indexed: 04/09/2023]
Abstract
BACKGROUND Dysregulation of the negative feedback loop of the hypothalamic-pituitary-adrenal (HPA) axis may have damaging effects on the brain, potentially under influence of psychosocial health factors. We studied associations between functioning of the negative feedback loop of HPA-axis, measured with a very low-dose dexamethasone suppression test (DST), and brain structure in middle-aged and older adults, and whether these associations were modified by psychosocial health. METHODS From 2006 to 2008, 1259 participants (mean age 57.6 ± 6.4, 59.6 % female) of the population-based Rotterdam Study completed a very low-dose DST (0.25 mg) and underwent magnetic resonance imaging (MRI) of the brain. Self-reported psychosocial health (depressive symptoms, loneliness, marital status, perceived social support) were assessed in the same time period. Multivariable linear and logistic regression were used to study cross-sectional associations between cortisol response and brain volumetrics, cerebral small vessel disease markers and white matter structural integrity. To assess the effect of psychosocial health on these associations, analyses were further stratified for psychosocial health markers. RESULTS Cortisol response was not associated with markers of global brain structure in the overall study sample. However, in participants with clinically relevant depressive symptoms, a diminished cortisol response was associated with smaller white matter volume (mean difference: - 1.00 mL, 95 %CI = - 1.89;- 0.10) and smaller white matter hyperintensity volume (mean difference: - 0.03 mL (log), 95 %CI = - 0.05;0.00). In participants with low/moderate perceived social support compared to those with high social support, a diminished cortisol response was associated with larger gray matter volume (mean difference: 0.70 mL, 95 %CI = 0.01;1.39) and higher fractional anisotropy (standardized mean difference 0.03, 95 %CI = 0.00;0.06). CONCLUSION Diminished function of the HPA-axis is differently associated with brain structure in community-dwelling middle-aged and older adults with clinically relevant depressive symptoms or suboptimal social support, but not in adults without depressive symptoms or with optimal social support.
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Affiliation(s)
- Isabelle F van der Velpen
- Department of Epidemiology, Erasmus MC University Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands; Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands
| | - Maud de Feijter
- Department of Epidemiology, Erasmus MC University Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands
| | - Rutika Raina
- Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA 30322, USA; OPEN Health, 4350 East-West Highway, Suite 1100, Bethesda, MD 20814, USA
| | - Fatih Özel
- Department of Organismal Biology, Uppsala University, Norbyvägen 18 A, 752 36 Uppsala, Sweden
| | - Marieke Perry
- Department of Geriatric Medicine, Radboudumc Alzheimer Center, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Primary and Community Care, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands; Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus MC University Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands; Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC University Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands.
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18
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Ellström K, Abul-Kasim K, Siennicki-Lantz A, Elmståhl S. Associations of carotid artery flow parameters with MRI markers of cerebral small vessel disease and patterns of brain atrophy. J Stroke Cerebrovasc Dis 2023; 32:106981. [PMID: 36657270 DOI: 10.1016/j.jstrokecerebrovasdis.2023.106981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 01/04/2023] [Accepted: 01/06/2023] [Indexed: 01/19/2023] Open
Abstract
OBJECTIVES A growing body of evidence links age related brain pathologies to systemic vascular processes. We aimed to study the prevalence and interrelations between magnetic resonance imaging (MRI) markers of cerebral small vessel disease and patterns of brain atrophy, and their association to carotid duplex ultrasound flow parameters. MATERIALS AND METHODS We investigated a population based randomised cohort of older adults (n=391) aged 70-87, part of the Swedish Good Aging in Skåne Study. Peak systolic and end diastolic velocities of the carotid arteries were measured by ultrasound, and resistivity- and pulsatility indexes were calculated. Subjects with increased peak systolic velocity indicating carotid stenosis were excluded from analysis. Nine MRI findings were rated by visual scales: white matter changes, pontine white matter changes, microbleeds, lacunar infarctions, medial temporal lobe atrophy, global cortical atrophy, parietal atrophy, precuneus atrophy and central atrophy. RESULTS MRI pathologies were found in 80% of subjects. Mean end diastolic velocity in common carotid arteries was inversely associated with white matter hyperintensities (OR=0.92; p=0.004), parietal lobe atrophy (OR=0.94; p=0.039), global cortical atrophy (OR=0.90; p=0.013), precuneus atrophy (OR=0.94; p=0.022), "number of CSV pathologies" (β=-0.07; p<0.001) and "MRI-burden score" (β=-0.11; p<0.001), after adjustment for age and sex. The latter three were also associated with pulsatility and resistivity indexes. CONCLUSIONS Low carotid end diastolic velocity, as well as increased carotid resistivity and pulsatility, were associated with signs of cerebral small vessel disease and patterns of brain atrophy, indicating a vascular component in the process of brain aging.
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Affiliation(s)
- Katarina Ellström
- Department of Clinical Sciences in Malmö, Division of Geriatric Medicine, Skåne University Hospital, Lund University, Jan Waldenströms gata 35, pl13, Malmö SE 205 02, Sweden.
| | - Kasim Abul-Kasim
- Department of Clinical Sciences Lund, Division of Diagnostic Radiology, Lund University, Sweden
| | - Arkadiusz Siennicki-Lantz
- Department of Clinical Sciences in Malmö, Division of Geriatric Medicine, Skåne University Hospital, Lund University, Jan Waldenströms gata 35, pl13, Malmö SE 205 02, Sweden
| | - Sölve Elmståhl
- Department of Clinical Sciences in Malmö, Division of Geriatric Medicine, Skåne University Hospital, Lund University, Jan Waldenströms gata 35, pl13, Malmö SE 205 02, Sweden
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19
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Yilmaz P, Alferink LJM, Cremers LGM, Murad SD, Niessen WJ, Ikram MA, Vernooij MW. Subclinical liver traits are associated with structural and hemodynamic brain imaging markers. Liver Int 2023; 43:1256-1268. [PMID: 36801835 DOI: 10.1111/liv.15549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/16/2023] [Accepted: 02/18/2023] [Indexed: 02/23/2023]
Abstract
BACKGROUND & AIMS Impaired liver function affects brain health and therefore understanding potential mechanisms for subclinical liver disease is essential. We assessed the liver-brain associations using liver measures with brain imaging markers, and cognitive measures in the general population. METHODS Within the population-based Rotterdam Study, liver serum and imaging measures (ultrasound and transient elastography), metabolic dysfunction-associated fatty liver disease (MAFLD), non-alcoholic fatty liver disease (NAFLD) and fibrosis phenotypes, and brain structure were determined in 3493 non-demented and stroke-free participants in 2009-2014. This resulted in subgroups of n = 3493 for MAFLD (mean age 69 ± 9 years, 56% ♀), n = 2938 for NAFLD (mean age 70 ± 9 years, 56% ♀) and n = 2252 for fibrosis (mean age 65 ± 7 years, 54% ♀). Imaging markers of small vessel disease and neurodegeneration, cerebral blood flow (CBF) and brain perfusion (BP) were acquired from brain MRI (1.5-tesla). General cognitive function was assessed by Mini-Mental State Examination and the g-factor. Multiple linear and logistic regression models were used for liver-brain associations and adjusted for age, sex, intracranial volume, cardiovascular risk factors and alcohol use. RESULTS Higher gamma-glutamyltransferase (GGT) levels were significantly associated with smaller total brain volume (TBV, standardized mean difference (SMD), -0.02, 95% confidence interval (CI) (-0.03 to -0.01); p = 8.4·10-4 ), grey matter volumes, and lower CBF and BP. Liver serum measures were not related to small vessel disease markers, nor to white matter microstructural integrity or general cognition. Participants with ultrasound-based liver steatosis had a higher fractional anisotropy (FA, SMD 0.11, 95% CI (0.04 to 0.17), p = 1.5·10-3 ) and lower CBF and BP. MAFLD and NAFLD phenotypes were associated with alterations in white matter microstructural integrity (NAFLD ~ FA, SMD 0.14, 95% CI (0.07 to 0.22), p = 1.6·10-4 ; NAFLD ~ mean diffusivity, SMD -0.12, 95% CI (-0.18 to -0.05), p = 4.7·10-4 ) and also with lower CBF and BP (MAFLD ~ CBF, SMD -0.13, 95% CI (-0.20 to -0.06), p = 3.1·10-4 ; MAFLD ~ BP, SMD -0.12, 95% CI (-0.20 to -0.05), p = 1.6·10-3 ). Furthermore, fibrosis phenotypes were related to TBV, grey and white matter volumes. CONCLUSIONS Presence of liver steatosis, fibrosis and elevated serum GGT are associated with structural and hemodynamic brain markers in a population-based cross-sectional setting. Understanding the hepatic role in brain changes can target modifiable factors and prevent brain dysfunction.
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Affiliation(s)
- Pinar Yilmaz
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Louise J M Alferink
- Departments of Gastroenterology and Hepatology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Lotte G M Cremers
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Sarwa D Murad
- Departments of Gastroenterology and Hepatology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
- Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
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20
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Yang Y, Knol MJ, Wang R, Mishra A, Liu D, Luciano M, Teumer A, Armstrong N, Bis JC, Jhun MA, Li S, Adams HHH, Aziz NA, Bastin ME, Bourgey M, Brody JA, Frenzel S, Gottesman RF, Hosten N, Hou L, Kardia SLR, Lohner V, Marquis P, Maniega SM, Satizabal CL, Sorond FA, Valdés Hernández MC, van Duijn CM, Vernooij MW, Wittfeld K, Yang Q, Zhao W, Boerwinkle E, Levy D, Deary IJ, Jiang J, Mather KA, Mosley TH, Psaty BM, Sachdev PS, Smith JA, Sotoodehnia N, DeCarli CS, Breteler MMB, Ikram MA, Grabe HJ, Wardlaw J, Longstreth WT, Launer LJ, Seshadri S, Debette S, Fornage M. Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI. Brain 2023; 146:492-506. [PMID: 35943854 PMCID: PMC9924914 DOI: 10.1093/brain/awac290] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/23/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Cerebral white matter hyperintensities on MRI are markers of cerebral small vessel disease, a major risk factor for dementia and stroke. Despite the successful identification of multiple genetic variants associated with this highly heritable condition, its genetic architecture remains incompletely understood. More specifically, the role of DNA methylation has received little attention. We investigated the association between white matter hyperintensity burden and DNA methylation in blood at ∼450 000 cytosine-phosphate-guanine (CpG) sites in 9732 middle-aged to older adults from 14 community-based studies. Single CpG and region-based association analyses were carried out. Functional annotation and integrative cross-omics analyses were performed to identify novel genes underlying the relationship between DNA methylation and white matter hyperintensities. We identified 12 single CpG and 46 region-based DNA methylation associations with white matter hyperintensity burden. Our top discovery single CpG, cg24202936 (P = 7.6 × 10-8), was associated with F2 expression in blood (P = 6.4 × 10-5) and co-localized with FOLH1 expression in brain (posterior probability = 0.75). Our top differentially methylated regions were in PRMT1 and in CCDC144NL-AS1, which were also represented in single CpG associations (cg17417856 and cg06809326, respectively). Through Mendelian randomization analyses cg06809326 was putatively associated with white matter hyperintensity burden (P = 0.03) and expression of CCDC144NL-AS1 possibly mediated this association. Differentially methylated region analysis, joint epigenetic association analysis and multi-omics co-localization analysis consistently identified a role of DNA methylation near SH3PXD2A, a locus previously identified in genome-wide association studies of white matter hyperintensities. Gene set enrichment analyses revealed functions of the identified DNA methylation loci in the blood-brain barrier and in the immune response. Integrative cross-omics analysis identified 19 key regulatory genes in two networks related to extracellular matrix organization, and lipid and lipoprotein metabolism. A drug-repositioning analysis indicated antihyperlipidaemic agents, more specifically peroxisome proliferator-activated receptor-alpha, as possible target drugs for white matter hyperintensities. Our epigenome-wide association study and integrative cross-omics analyses implicate novel genes influencing white matter hyperintensity burden, which converged on pathways related to the immune response and to a compromised blood-brain barrier possibly due to disrupted cell-cell and cell-extracellular matrix interactions. The results also suggest that antihyperlipidaemic therapy may contribute to lowering risk for white matter hyperintensities possibly through protection against blood-brain barrier disruption.
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Affiliation(s)
- Yunju Yang
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science at Houston, Houston, TX 77030, USA
| | - Maria J Knol
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
| | - Ruiqi Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Aniket Mishra
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, F-33000 Bordeaux, France
| | - Dan Liu
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Michelle Luciano
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald 17475, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald 17475, Germany
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, 15-269, Poland
| | - Nicola Armstrong
- Mathematics and Statistics, Curtin University, 6845 Perth, Australia
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 02115, USA
| | - Min A Jhun
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA
| | - Shuo Li
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Hieab H H Adams
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
| | - Nasir Ahmad Aziz
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurology, Faculty of Medicine, University of Bonn, 53127 Bonn, Germany
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - Mathieu Bourgey
- Canadian Centre for Computational Genomics, McGill University, Montréal, Quebec, Canada H3A 0G1
- Department for Human Genetics, McGill University Genome Centre, McGill University, Montréal, Quebec, Canada H3A 0G1
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 02115, USA
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald 17475, Germany
| | - Rebecca F Gottesman
- Stroke Branch, National Institutes of Neurological Disorders and Stroke, Bethesda, MD 20814, USA
| | - Norbert Hosten
- Department of Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA
| | - Valerie Lohner
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Pascale Marquis
- Canadian Centre for Computational Genomics, McGill University, Montréal, Quebec, Canada H3A 0G1
- Department for Human Genetics, McGill University Genome Centre, McGill University, Montréal, Quebec, Canada H3A 0G1
| | - Susana Muñoz Maniega
- Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases and Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX 78229, USA
- The Framingham Heart Study, Framingham, MA 01701, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02115, USA
| | - Farzaneh A Sorond
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Maria C Valdés Hernández
- Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
- Nuffield Department of Population Health, Oxford University, Oxford, OX3 7LF, UK
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald 17475, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, 17475 Rostock, Germany
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
- The Framingham Heart Study, Framingham, MA 01701, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, University of Texas Health Science at Houston, Houston, TX 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Daniel Levy
- The Framingham Heart Study, Framingham, MA 01701, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | - Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia
- Neuroscience Research Australia, Sydney, NSW 2031, Australia
| | - Thomas H Mosley
- The Memory Impairment Neurodegenerative Dementia (MIND) Research Center, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 02115, USA
- Department of Epidemiology, University of Washington, Seattle, WA 98104, USA
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia
- Neuropsychiatric Institute, The Prince of Wales Hospital, University of New South Wales, Randwick, NSW 2031, Australia
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 02115, USA
| | - Charles S DeCarli
- Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA 95816, USA
| | - Monique M B Breteler
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, 53127 Bonn, Germany
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald 17475, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, 17475 Rostock, Germany
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - W T Longstreth
- Department of Epidemiology, University of Washington, Seattle, WA 98104, USA
- Department of Neurology, University of Washington, Seattle, WA 98104, USA
| | - Lenore J Launer
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases and Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX 78229, USA
- The Framingham Heart Study, Framingham, MA 01701, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02115, USA
| | - Stephanie Debette
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, F-33000 Bordeaux, France
- Department of Neurology, Boston University School of Medicine, Boston, MA 02115, USA
- CHU de Bordeaux, Department of Neurology, F-33000 Bordeaux, France
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science at Houston, Houston, TX 77030, USA
- Human Genetics Center, School of Public Health, University of Texas Health Science at Houston, Houston, TX 77030, USA
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21
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Arinze JT, Vinke EJ, Verhamme KMC, de Ridder MAJ, Stricker B, Ikram MK, Brusselle G, Vernooij MW. Chronic Cough-Related Differences in Brain Morphometry in Adults: A Population-Based Study. Chest 2023:S0012-3692(23)00187-3. [PMID: 36781103 DOI: 10.1016/j.chest.2023.02.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 02/13/2023] Open
Abstract
BACKGROUND Individuals with cough hypersensitivity have increased central neural responses to tussive stimuli, which may result in maladaptive morphometric changes in the central cough processing systems. RESEARCH QUESTION Are the volumes of the brain regions implicated in cough hypersensitivity different in adults with chronic cough compared with adults without chronic cough? STUDY DESIGN AND METHODS Between 2009 and 2014, participants in the Rotterdam Study, a population-based cohort, underwent brain MRI and were interviewed for chronic cough, which was defined as daily coughing for at least 3 months. Regional brain volumes were quantified with the use of parcellation software. Based on literature review, we identified and studied seven brain regions that previously had been associated with altered functional brain activity in chronic cough. The relationship between chronic cough and regional brain volumes was investigated with the use of multivariable regression models. RESULTS Chronic cough was prevalent in 9.6% (No. = 349) of the 3,620 study participants (mean age, 68.5 ± 9.0 years; 54.6% women). Participants with chronic cough had significantly smaller anterior cingulate cortex volume than participants without chronic cough (mean difference, -126.16 mm3; 95% CI, -245.67 to -6.66; P = .039). Except for anterior cingulate cortex, there were no significant difference in the volume of other brain regions based on chronic cough status. The volume difference in the anterior cingulate cortex was more pronounced in the left hemisphere (mean difference, -88.11 mm3; 95% CI, -165.16 to -11.06; P = .025) and in men (mean difference, -242.58 mm3; 95% CI, -428.60 to -56.55; P = .011). INTERPRETATION Individuals with chronic cough have a smaller volume of the anterior cingulate cortex, which is a brain region involved in cough suppression. CLINICAL TRIAL REGISTRATION The Netherlands National Trial Registry (NTR; www.trialregister.nl) and the World Health Organization's International Clinical Trials Registry Platform (ICTRP; www.who.int/ictrp/network/primary/en/) under the joint catalogue number NTR6831.
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Affiliation(s)
- Johnmary T Arinze
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium; Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Elisabeth J Vinke
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Katia M C Verhamme
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Maria A J de Ridder
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Bruno Stricker
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - M K Ikram
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Guy Brusselle
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium; Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Respiratory Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.
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22
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van der Velpen IF, Vlasov V, Evans TE, Ikram MK, Gutman BA, Roshchupkin GV, Adams HH, Vernooij MW, Ikram MA. Subcortical brain structures and the risk of dementia in the Rotterdam Study. Alzheimers Dement 2023; 19:646-657. [PMID: 35633518 DOI: 10.1002/alz.12690] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 04/05/2022] [Accepted: 04/10/2022] [Indexed: 11/07/2022]
Abstract
INTRODUCTION Volumetric and morphological changes in subcortical brain structures are present in persons with dementia, but it is unknown if these changes occur prior to diagnosis. METHODS Between 2005 and 2016, 5522 Rotterdam Study participants (mean age: 64.4) underwent cerebral magnetic resonance imaging (MRI) and were followed for development of dementia until 2018. Volume and shape measures were obtained for seven subcortical structures. RESULTS During 12 years of follow-up, 272 dementia cases occurred. Mean volumes of thalamus (hazard ratio [HR] per standard deviation [SD] decrease 1.94, 95% confidence interval [CI]: 1.55-2.43), amygdala (HR 1.66, 95% CI: 1.44-1.92), and hippocampus (HR 1.64, 95% CI: 1.43-1.88) were strongly associated with dementia risk. Associations for accumbens, pallidum, and caudate volumes were less pronounced. Shape analyses identified regional surface changes in the amygdala, limbic thalamus, and caudate. DISCUSSION Structure of the amygdala, thalamus, hippocampus, and caudate is associated with risk of dementia in a large population-based cohort of older adults.
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Affiliation(s)
- Isabelle F van der Velpen
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Vanja Vlasov
- Interventional Neuroscience Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg
| | - Tavia E Evans
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Mohammad Kamran Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Neurology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Boris A Gutman
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Gennady V Roshchupkin
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Hieab H Adams
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Mohammad Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
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23
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Zijlmans JL, Riemens MS, Vernooij MW, Ikram MA, Luik AI. Sleep, 24-Hour Activity Rhythms, and Cognitive Reserve: A Population-Based Study. J Alzheimers Dis 2023; 91:663-672. [PMID: 36463444 PMCID: PMC9912716 DOI: 10.3233/jad-220714] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
BACKGROUND The cognitive reserve hypothesis aims to explain individual differences in susceptibility to the functional impact of dementia-related pathology. Previous research suggested that poor subjective sleep may be associated with a lower cognitive reserve. OBJECTIVE The objective was to investigate if actigraphy-estimated sleep and 24-hour activity rhythms are associated with cognitive reserve. METHODS This cross-sectional study included 1,002 participants from the Rotterdam Study (mean age: 65.0 years, standard deviation (SD): 7.1) who were assessed with actigraphy, five cognitive tests, and brain-MRI between 2009- 2014. Sleep and 24-hour activity rhythms were measured using actigraphy (mean days: 6.7, SD: 0.5). Cognitive reserve was defined as a latent variable that captures variance across cognitive tests, while adjusting for age, sex, education, total brain volume, intracranial volume, and white matter hyperintensity volume. Associations of sleep and 24-hour activity rhythms with cognitive reserve were assessed using structural equation models. RESULTS Longer sleep onset latency (adjusted mean difference: - 0.16, 95% CI: - 0.24; - 0.08) and lower sleep efficiency (0.14, 95% CI: 0.05; 0.22) were associated with lower cognitive reserve. Total sleep time and wake after sleep onset were not significantly associated with cognitive reserve. After mutual adjustment, only the association of longer sleep onset latency remained significant (- 0.12, 95% CI: - 0.20; - 0.04). The 24-hour activity rhythm was not significantly associated with cognitive reserve. CONCLUSION In conclusion, our study suggests that longer sleep onset latency is particularly associated with lower cognitive reserve. Future longitudinal work is needed to assess whether shortening the sleep onset latency could enhance cognitive reserve, in order to limit the susceptibility to the functional impact of dementia-related pathology.
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Affiliation(s)
- Jend L. Zijlmans
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Mariska S. Riemens
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Meike W. Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands,
Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Annemarie I. Luik
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands,Correspondence to: Annemarie I. Luik, Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, PO Box 2040, 3000 CA Rotterdam, the Netherlands. Tel.: +31 10 703 21 83; E-mail:
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24
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Evans TE, Knol MJ, Schwingenschuh P, Wittfeld K, Hilal S, Ikram MA, Dubost F, van Wijnen KMH, Katschnig P, Yilmaz P, de Bruijne M, Habes M, Chen C, Langer S, Völzke H, Ikram MK, Grabe HJ, Schmidt R, Adams HHH, Vernooij MW. Determinants of Perivascular Spaces in the General Population: A Pooled Cohort Analysis of Individual Participant Data. Neurology 2023; 100:e107-e122. [PMID: 36253103 PMCID: PMC9841448 DOI: 10.1212/wnl.0000000000201349] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 08/19/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Perivascular spaces (PVS) are emerging markers of cerebral small vessel disease (CSVD), but research on their determinants has been hampered by conflicting results from small single studies using heterogeneous rating methods. In this study, we therefore aimed to identify determinants of PVS burden in a pooled analysis of multiple cohort studies using 1 harmonized PVS rating method. METHODS Individuals from 10 population-based cohort studies with adult participants from the Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement consortium and the UK Biobank were included. On MRI scans, we counted PVS in 4 brain regions (mesencephalon, hippocampus, basal ganglia, and centrum semiovale) according to a uniform and validated rating protocol, both manually and automated using a deep learning algorithm. As potential determinants, we considered demographics, cardiovascular risk factors, APOE genotypes, and other imaging markers of CSVD. Negative binomial regression models were used to examine the association between these determinants and PVS counts. RESULTS In total, 39,976 individuals were included (age range 20-96 years). The average count of PVS in the 4 regions increased from the age 20 years (0-1 PVS) to 90 years (2-7 PVS). Men had more mesencephalic PVS (OR [95% CI] = 1.13 [1.08-1.18] compared with women), but less hippocampal PVS (0.82 [0.81-0.83]). Higher blood pressure, particularly diastolic pressure, was associated with more PVS in all regions (ORs between 1.04-1.05). Hippocampal PVS showed higher counts with higher high-density lipoprotein cholesterol levels (1.02 [1.01-1.02]), glucose levels (1.02 [1.01-1.03]), and APOE ε4-alleles (1.02 [1.01-1.04]). Furthermore, white matter hyperintensity volume and presence of lacunes were associated with PVS in multiple regions, but most strongly with the basal ganglia (1.13 [1.12-1.14] and 1.10 [1.09-1.12], respectively). DISCUSSION Various factors are associated with the burden of PVS, in part regionally specific, which points toward a multifactorial origin beyond what can be expected from PVS-related risk factor profiles. This study highlights the power of collaborative efforts in population neuroimaging research.
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Affiliation(s)
- Tavia E Evans
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Maria J Knol
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Petra Schwingenschuh
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Katharina Wittfeld
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Saima Hilal
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - M Arfan Ikram
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Florian Dubost
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Kimberlin M H van Wijnen
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Petra Katschnig
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Pinar Yilmaz
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Marleen de Bruijne
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Mohamad Habes
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Christopher Chen
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Sönke Langer
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Henry Völzke
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - M Kamran Ikram
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Hans J Grabe
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Reinhold Schmidt
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Hieab H H Adams
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile.
| | - Meike W Vernooij
- From the Departments of Clinical Genetics (T.E.E., M.J.K., H.H.H.A.), Radiology and Nuclear Medicine (T.E.E., F.D., K.M.H.W., P.Y., M.B., H.H.H.A., M.W.V.), Epidemiology (M.J.K., M.A.I., P.Y., M.K.I., M.W.V.), and Neurology (M.K.I.), Erasmus MC, Rotterdam, the Netherlands; Department of Neurology (P.S., P.K., R.S.), Medical University of Graz, Austria; German Center for Neurodegenerative Diseases (DZNE) (K.W., M.H., H.J.G.), Site Rostock/Greifswald; Department of Psychiatry and Psychotherapy (K.W., H.J.G.) and Institute of Diagnostic Radiology and Neuroradiology (S.L.), University Medicine Greifswald, Germany; Department of Pharmacology (S.H., C.C.), National University of Singapore; Memory Aging & Cognition Centre (MACC) (S.H., C.C., M.K.I.), National University Health System, Singapore; Saw Swee Hock School of Public Health (S.H.), National University of Singapore; Department of Biomedical Data Sciences (F.D.), Stanford University, CA; J. Philip Kistler Stroke Research Center (P.Y.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston; The Machine Learning Section (M.B.), Department of Computer Science, University of Copenhagen, Denmark; Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) (M.H.), Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), TX; and Latin American Brain Health (BrainLat) (H.H.H.A.), Universidad Adolfo Ibáñez, Santiago, Chile
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Zijlmans JL, Vernooij MW, Ikram MA, Luik AI. The role of cognitive and brain reserve in late-life depressive events: The Rotterdam Study. J Affect Disord 2023; 320:211-217. [PMID: 36183828 DOI: 10.1016/j.jad.2022.09.145] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 09/09/2022] [Accepted: 09/27/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Cognitive and brain reserve aim to explain individual differences in susceptibility to dementia and may also affect the risk of late-life depressive events. We assessed whether higher cognitive and brain reserve are associated with lower risk of a late-life depressive event. METHODS This study included 4509 participants from the population-based Rotterdam Study (mean age: 63.4 ± 10.2 years, 55 % women) between 2005 and 2019. Participants completed cognitive testing and brain-MRI at baseline. Cognitive reserve was defined as the common variance across cognitive tests, while adjusting for demographic factors and brain MRI-markers. Brain reserve was defined as total brain volume divided by intracranial volume. Depressive events (depressive symptoms/depressive syndrome/major depressive disorder) were repeatedly measured (follow-up: 6.6 ± 3.9 years) with validated questionnaires, clinical interviews, and follow-up of medical records. Hazard ratios (HR) with 95 % confidence intervals (CI) were estimated using Cox-regressions. RESULTS Higher cognitive (HR: 0.91/SD, 95%CI: 0.84; 1.00) and brain reserve (HR: 0.88/SD, 95%CI: 0.77; 1.00) were associated with a lower risk of a depressive event after adjustment for baseline depressive symptoms. These associations attenuated when participants with clinically relevant depressive symptoms at baseline were excluded (HR: 0.95/SD, 95%CI: 0.86; 1.05, HR: 0.89/SD, 95%CI: 0.76; 1.03, respectively). LIMITATIONS No data was available on depression in early-life and the number of participants with major depressive disorder was relatively low (n = 105). CONCLUSIONS Higher cognitive and brain reserve may be protective factors for late-life depressive events, particularly in those who have experienced clinical relevant depressive symptoms before. Further research is needed to determine whether cognitive and brain reserve could be used as targets for the prevention of late-life depression.
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Affiliation(s)
- Jendé L Zijlmans
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
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Hofman A, Rodriguez-Ayllon M, Vernooij MW, Croll PH, Luik AI, Neumann A, Niessen WJ, Ikram MA, Voortman T, Muetzel RL. Physical activity levels and brain structure in middle-aged and older adults: a bidirectional longitudinal population-based study. Neurobiol Aging 2023; 121:28-37. [DOI: 10.1016/j.neurobiolaging.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 10/04/2022] [Accepted: 10/05/2022] [Indexed: 11/11/2022]
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de Vreede DK, Bessems JHJM, Dremmen MHG, Vernooij MW, van der Lugt A, Oei EHG. The prevalence of incidental findings on pelvis MRI of 8-13-year-old children. Pediatr Res 2022:10.1038/s41390-022-02259-6. [PMID: 36207540 DOI: 10.1038/s41390-022-02259-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 07/21/2022] [Accepted: 07/28/2022] [Indexed: 11/09/2022]
Abstract
BACKGROUND The prevalence and clinical relevance of incidental findings (IF(s)) on imaging assessing the pelvis in children has not been well documented. METHODS Three-thousand two-hundred thirty-one children (mean age 10.2 (range 8.6-12.9) years) were evaluated with MRI of the hips, pelvis, and lumbar spine, as part of a prospective population-based pediatric cohort study. Scans were reviewed by trained medical staff for abnormalities. IFs were categorized by clinical relevance and need for further clinical evaluation. RESULTS 8.3% (n = 267) of children featured at least one IF. One or more musculoskeletal IFs were found in 7.9% (n = 254) of children, however, only 0.8% (n = 2) of musculoskeletal IFs required clinical evaluation. Most frequent abnormalities were simple bone cysts 6.0% (n = 195), chondroid lesions 0.6% (n = 20), and perineural cysts 0.5% (n = 15). Intra-abdominal IFs were detected in 0.5% (n = 17) of children, with over half (n = 9) of these requiring evaluation. The three most common intra-abdominal IFs were a duplex collecting system 0.09% (n = 3), significant ascites 0.06% (n = 2), and hydroureteronephrosis 0.06% (n = 2). CONCLUSIONS IFs on MRI of the lower abdominal and hip region are relatively common in children aged 8-13 years, most of these can be confidently categorized as clinically irrelevant without the need for additional clinical or radiologic follow up. IMPACT Our research contributes greatly to the knowledge of the prevalence of (asymptomatic) pathology in children. We evaluated MR images of 3231 children, covering hip joints, pelvic skeleton, lower and mid-abdomen, and lumbar and lower thoracic spine as part of a population study. One or more musculoskeletal incidental finding were found in 7.9% of children. Most of these can be confidently categorized as clinically irrelevant without the need for additional follow up. However 0.8% of musculoskeletal findings required further evaluation. Intra-abdominal incidental findings were detected in 0.5% of children, with over half of the abdominal and urogenital findings requiring further evaluation.
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Affiliation(s)
- Desirée K de Vreede
- Erasmus MC, Department of Radiology, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Zuid-Holland, The Netherlands.
| | - Johannes H J M Bessems
- Erasmus MC, Department of Orthopedics, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Zuid-Holland, The Netherlands
| | - Marjolein H G Dremmen
- Erasmus MC, Department of Radiology, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Zuid-Holland, The Netherlands
| | - Meike W Vernooij
- Erasmus MC, Department of Radiology, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Zuid-Holland, The Netherlands
| | - Aad van der Lugt
- Erasmus MC, Department of Radiology, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Zuid-Holland, The Netherlands
| | - Edwin H G Oei
- Erasmus MC, Department of Radiology, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Zuid-Holland, The Netherlands
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Geiger MA, Flumignan RLG, Sobreira ML, Avelar WM, Fingerhut C, Stein S, Guillaumon AT. Carotid Plaque Composition and the Importance of Non-Invasive in Imaging Stroke Prevention. Front Cardiovasc Med 2022; 9:885483. [PMID: 35651908 PMCID: PMC9149096 DOI: 10.3389/fcvm.2022.885483] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/27/2022] [Indexed: 12/24/2022] Open
Abstract
Luminal stenosis has been the standard feature for the current management strategies in patients with atherosclerotic carotid disease. Histological and imaging studies show considerable differences between plaques with identical degrees of stenosis. They indicate that specific plaque characteristics like Intraplaque hemorrhage, Lipid Rich Necrotic Core, Plaque Inflammation, Thickness and Ulceration are responsible for the increased risk of ischemic events. Intraplaque hemorrhage is defined by the accumulation of blood components within the plaque, Lipid Rich Necrotic Core is composed of macrophages loaded with lipid, Plaque Inflammation is defined as the process of atherosclerosis itself and Plaque thickness and Ulceration are defined as morphological features. Advances in imaging methods like Magnetic Resonance Imaging, Ultrasound, Computed Tomography and Positron Emission Tomography have enabled a more detailed characterization of the plaque, and its vulnerability is linked to these characteristics, changing the management of these patients based only on the degree of plaque stenosis. Studies like Rotterdam, ARIC, PARISK, CAPIAS and BIOVASC were essential to evaluate and prove the relevance of these characteristics with cerebrovascular symptoms. A better approach for the prevention of stroke is needed. This review summarizes the more frequent carotid plaque features and the available validation from recent studies with the latest evidence.
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Affiliation(s)
- Martin Andreas Geiger
- Division of Vascular Surgery, Department of Surgery, Universidade Estadual de Campinas—UNICAMP, São Paulo, Brazil
- *Correspondence: Martin Andreas Geiger
| | - Ronald Luiz Gomes Flumignan
- Division of Vascular and Endovascular Surgery, Department of Surgery, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Marcone Lima Sobreira
- Division of Vascular and Endovascular Surgery, Department of Surgery and Orthopedics, Botucatu Medical School, Universidade Estadual Paulista (UNESP), São Paulo, Brazil
| | - Wagner Mauad Avelar
- Department of Neurology, Universidade Estadual de Campinas—UNICAMP, São Paulo, Brazil
| | - Carla Fingerhut
- Division of Radiology, Department of Anesthesiology and Radiology, Universidade Estadual de Campinas—UNICAMP, São Paulo, Brazil
| | - Sokrates Stein
- Division of Vascular Surgery, Department of Surgery, Universidade Estadual de Campinas—UNICAMP, São Paulo, Brazil
| | - Ana Terezinha Guillaumon
- Division of Vascular Surgery, Department of Surgery, Universidade Estadual de Campinas—UNICAMP, São Paulo, Brazil
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Ramusino MC, Vitali P, Anzalone N, Melazzini L, Lombardo FP, Farina LM, Bernini S, Costa A. Vascular Lesions and Brain Atrophy in Alzheimer's, Vascular and Mixed Dementia: An Optimized 3T MRI Protocol Reveals Distinctive Radiological Profiles. Curr Alzheimer Res 2022; 19:449-457. [PMID: 35726416 DOI: 10.2174/1567205019666220620112831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 05/10/2022] [Accepted: 05/17/2022] [Indexed: 12/31/2022]
Abstract
BACKGROUND Vascular lesions may be a common finding also in Alzheimer's dementia, but their role on cognitive status is uncertain. OBJECTIVE The study aims to investigate their distribution in patients with Alzheimer's, vascular or mixed dementia and detect any distinctive neuroradiological profiles. METHODS Seventy-six subjects received a diagnosis of Alzheimer's (AD=32), vascular (VD=26) and mixed (MD=18) dementia. Three independent raters assessed the brain images acquired with an optimized 3T MRI protocol (including (3D FLAIR, T1, SWI, and 2D coronal T2 sequences) using semiquantitative scales for vascular lesions (periventricular lesions (PVL), deep white matter lesions (DWML), deep grey matter lesions (DGML), enlarged perivascular spaces (PVS), and microbleeds (MB)) and brain atrophy (medial temporal atrophy (MTA), posterior atrophy (PA), global cortical atrophy- frontal (GCA-F) and Evans' index). RESULTS Raters reached a good-to-excellent agreement for all scales (ICC ranging from 0.78-0.96). A greater number of PVL (p<0.001), DWML (p<0.001), DGML (p=0.010), and PVS (p=0.001) was observed in VD compared to AD, while MD showed a significant greater number of PVL (p=0.001), DWML (p=0.002), DGML (p=0.018), and deep and juxtacortical MB (p=0.006 and p<0.001, respectively). Comparing VD and MD, VD showed a higher number of PVS in basal ganglia and centrum semiovale (p=0.040), while MD showed more deep and juxtacortical MB (p=0.042 and p=0.022, respectively). No significant difference was observed in scores of cortical atrophy scales and Evans' index among the three groups. CONCLUSION The proposed MRI protocol represents a useful advancement in the diagnostic assessment of patients with cognitive impairment by more accurately detecting vascular lesions, mainly microbleeds, without a significant increase in time and resource expenditure. Our findings confirm that white and grey matter lesions predominate in vascular and mixed dementia, whereas deep and juxtacortical microbleeds predominate in mixed dementia, suggesting that cerebral amyloid angiopathy could be the main underlying pathology.
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Affiliation(s)
- Matteo Cotta Ramusino
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Paolo Vitali
- Department of Biomedical Sciences for Health, University of Milan, and Unit of Radiology, IRCCS Policlinico San Donato, Milan, Italy
| | | | - Luca Melazzini
- Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Francesca Paola Lombardo
- Advanced Imaging and Radiomics Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Lisa Maria Farina
- Advanced Imaging and Radiomics Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Sara Bernini
- Laboratory of Neuropsychology, IRCCS Mondino Foundation, Pavia, Italy
| | - Alfredo Costa
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
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30
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Fani L, Roa Dueñas O, Bos D, Vernooij MW, Klaver CCW, Ikram MK, Peeters RP, Ikram MA, Chaker L. Thyroid Status and Brain Circulation: The Rotterdam Study. J Clin Endocrinol Metab 2022; 107:e1293-e1302. [PMID: 34634119 PMCID: PMC8851919 DOI: 10.1210/clinem/dgab744] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Indexed: 11/28/2022]
Abstract
CONTEXT Whether thyroid dysfunction is related to altered brain circulation in the general population remains unknown. OBJECTIVE We determined the association of thyroid hormones with different markers of brain circulation within community-dwelling elderly people. METHODS This was a population-based study of 3 subcohorts of the Rotterdam Study, starting in 1989, 2000, and 2006. A total of 5142 participants (mean age, 63.8 years; 55.4% women), underwent venipuncture to measure serum thyroid-stimulating hormone (TSH) and free thyroxine (FT4). Between 2005 and 2015, all participants underwent phase-contrast brain magnetic resonance imaging to assess global brain perfusion (mL of blood flow/100 mL of brain/minute). Arteriolar retinal calibers were assessed using digitized images of stereoscopic fundus color transparencies in 3105 participants as markers of microcirculation. We investigated associations of TSH, FT4 with brain circulation measures using (non)linear regression models. RESULTS FT4 (in pmol/L) levels had an inverse U-shaped association with global brain perfusion, such that high and low levels of FT4 were associated with lower global brain perfusion than middle levels of FT4. The difference in global brain perfusion between high FT4 levels (25 pmol/L) and middle FT4 levels (FT4 = 15 pmol/L; P nonlinearity = .002) was up to -2.44 mL (95% CI -4.31; -0.56). Higher and lower levels of FT4, compared with middle FT4 levels, were associated with arteriolar retinal vessels (mean difference up to -2.46 µm, 95% CI -4.98; 0.05 for lower FT4). CONCLUSION These results suggest that thyroid dysfunction could lead to brain diseases such as stroke or dementia through suboptimal brain circulation that is potentially modifiable.
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Affiliation(s)
- Lana Fani
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Oscar Roa Dueñas
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Daniel Bos
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, The Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, The Netherlands
| | - Caroline C W Klaver
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Ophthalmology, Erasmus MC, the Netherlands
| | - M Kamran Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Neurology, Erasmus MC, The Netherlands
| | - Robin P Peeters
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Layal Chaker
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, The Netherlands
- Correspondence: Layal Chaker, MD, PhD, Department of Epidemiology, Erasmus MC University Medical Center, Dr. Molewaterplein 40, PO Box 2040, 3000CA Rotterdam, The Netherlands.
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31
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Mangesius S, Haider L, Lenhart L, Steiger R, Prados Carrasco F, Scherfler C, Gizewski ER. Qualitative and Quantitative Comparison of Hippocampal Volumetric Software Applications: Do All Roads Lead to Rome? Biomedicines 2022; 10:biomedicines10020432. [PMID: 35203641 PMCID: PMC8962257 DOI: 10.3390/biomedicines10020432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/30/2022] [Accepted: 02/10/2022] [Indexed: 02/06/2023] Open
Abstract
Brain volumetric software is increasingly suggested for clinical routine. The present study quantifies the agreement across different software applications. Ten cases with and ten gender- and age-adjusted healthy controls without hippocampal atrophy (median age: 70; 25–75% range: 64–77 years and 74; 66–78 years) were retrospectively selected from a previously published cohort of Alzheimer’s dementia patients and normal ageing controls. Hippocampal volumes were computed based on 3 Tesla T1-MPRAGE-sequences with FreeSurfer (FS), Statistical-Parametric-Mapping (SPM; Neuromorphometrics and Hammers atlases), Geodesic-Information-Flows (GIF), Similarity-and-Truth-Estimation-for-Propagated-Segmentations (STEPS), and Quantib™. MTA (medial temporal lobe atrophy) scores were manually rated. Volumetric measures of each individual were compared against the mean of all applications with intraclass correlation coefficients (ICC) and Bland–Altman plots. Comparing against the mean of all methods, moderate to low agreement was present considering categorization of hippocampal volumes into quartiles. ICCs ranged noticeably between applications (left hippocampus (LH): from 0.42 (STEPS) to 0.88 (FS); right hippocampus (RH): from 0.36 (Quantib™) to 0.86 (FS). Mean differences between individual methods and the mean of all methods [mm3] were considerable (LH: FS −209, SPM-Neuromorphometrics −820; SPM-Hammers −1474; Quantib™ −680; GIF 891; STEPS 2218; RH: FS −232, SPM-Neuromorphometrics −745; SPM-Hammers −1547; Quantib™ −723; GIF 982; STEPS 2188). In this clinically relevant sample size with large spread in data ranging from normal aging to severe atrophy, hippocampal volumes derived by well-accepted applications were quantitatively different. Thus, interchangeable use is not recommended.
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Affiliation(s)
- Stephanie Mangesius
- Department of Neuroradiology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria; (S.M.); (L.L.); (R.S.); (E.R.G.)
- Neuroimaging Core Facility, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
| | - Lukas Haider
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, University College London Institute of Neurology, Russell Square House, Russell Square 10-12, London WC1B 5EH, UK;
- Department of Biomedical Imaging and Image Guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
- Correspondence:
| | - Lukas Lenhart
- Department of Neuroradiology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria; (S.M.); (L.L.); (R.S.); (E.R.G.)
- Neuroimaging Core Facility, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
| | - Ruth Steiger
- Department of Neuroradiology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria; (S.M.); (L.L.); (R.S.); (E.R.G.)
- Neuroimaging Core Facility, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
| | - Ferran Prados Carrasco
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, University College London Institute of Neurology, Russell Square House, Russell Square 10-12, London WC1B 5EH, UK;
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, Malet Place Engineering Building, Gower Street, London WC1E 6BT, UK
- e-Health Centre, Universitat Oberta de Catalunya, Rambla del Poblenou 156, 08018 Barcelona, Spain
| | - Christoph Scherfler
- Department of Neurology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria;
| | - Elke R. Gizewski
- Department of Neuroradiology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria; (S.M.); (L.L.); (R.S.); (E.R.G.)
- Neuroimaging Core Facility, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
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Yaqub A, Darweesh SKL, Dommershuijsen LJ, Vernooij MW, Ikram MK, Wolters FJ, Ikram MA. Risk factors, neuroimaging correlates and prognosis of the motoric cognitive risk syndrome: a population-based comparison with mild cognitive impairment. Eur J Neurol 2022; 29:1587-1599. [PMID: 35147272 PMCID: PMC9306517 DOI: 10.1111/ene.15281] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 02/08/2022] [Indexed: 11/27/2022]
Abstract
Background and purpose This study was undertaken to compare risk factors, neuroimaging characteristics and prognosis between two clinical prodromes of dementia, namely, the motoric cognitive risk syndrome (MCRS) and mild cognitive impairment (MCI). Methods Between 2009 and 2015, dementia‐free participants of the population‐based Rotterdam Study were classified with a dementia prodrome if they had subjective cognitive complaints and scored >1 SD below the population mean of gait speed (MCRS) or >1.5 SD below the population mean of cognitive test scores (MCI). Using multinomial logistic regression models, we determined cross‐sectional associations of risk factors and structural neuroimaging markers with MCRS and MCI, followed by subdistribution hazard models, to determine risk of incident dementia until 2016. Results Of 3025 included participants (mean age = 70.4 years, 54.7% women), 231 had MCRS (7.6%), 132 had MCI (4.4%), and 62 (2.0%) fulfilled criteria for both. Although many risk factors were shared, a higher body mass index predisposed to MCRS, whereas male sex and hypercholesterolemia were associated with MCI only. Gray matter volumes, hippocampal volumes, white matter hyperintensities, and structural white matter integrity were worse in both MCRS and MCI. During a mean follow‐up of 3.9 years, 71 individuals developed dementia and 200 died. Five‐year cumulative risk of dementia was 7.0% (2.5%–11.5%) for individuals with MCRS, versus 13.3% (5.8%–20.8%) with MCI and only 2.3% (1.5%–3.1%) in unaffected individuals. Conclusions MCRS is associated with imaging markers of neurodegeneration and risk of dementia, even in the absence of MCI, highlighting the potential of motor function assessment in early risk stratification for dementia.
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Affiliation(s)
- Amber Yaqub
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Sirwan K L Darweesh
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.,Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
| | | | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.,Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - M Kamran Ikram
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.,Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Frank J Wolters
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.,Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
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33
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van der Velpen IF, Melis RJF, Hussainali RF, Perry M, Vernooij-Dassen MJF, Ikram MA, Luik AI, Vernooij MW. Determinants of social health trajectories during the COVID-19 pandemic in older adults: the Rotterdam Study. Int Psychogeriatr 2022:1-15. [PMID: 35086605 DOI: 10.1017/s1041610221002891] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVES The coronavirus disease-2019 (COVID-19) pandemic and accompanying lockdown restrictions impacted social life significantly. We studied associations of sociodemographic factors, mental and social health markers, and brain structure with social health trajectories during the COVID-19 pandemic. DESIGN Prospective longitudinal population-based cohort study. SETTING Community-dwelling inhabitants of Rotterdam, the Netherlands. PARTICIPANTS Repeated questionnaires including questions on social health were sent to Rotterdam Study participants from April 2020 onwards. Social health data at study baseline were available for 5017 participants (mean age: 68.7 ± 11.3; 56.9% women). MEASUREMENTS Determinants were assessed in routine Rotterdam Study follow-up (1990-2020), including global brain volumes in a subset of participants (N = 1720). We applied linear mixed models and generalized estimating equations to quantify associations between determinants and trajectories of loneliness, perceived social isolation and social connectedness over three time points from April 22nd to July 31st 2020. RESULTS Loneliness prevalence was 27.9% in April 2020 versus 12.6% prepandemic. Social isolation (baseline mean 4.7 ± 2.4) and loneliness scores (baseline mean 4.9 ± 1.5) decreased over time, whereas social connectedness trajectories remained stable. Depressive symptoms, female sex, prepandemic loneliness, living alone, and not owning a pet were independently associated with lower social connectedness and higher social isolation and loneliness at COVID-19 baseline, but recovery of social health was similar for all determinants. Larger intracranial volume was associated with higher social connectedness. CONCLUSIONS Despite baseline differences for specific determinants, older adults showed similar recovery of loneliness and social isolation alongside stable social connectedness over time during the pandemic. Social health is multidimensional, especially during a global health crisis.
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Affiliation(s)
- Isabelle F van der Velpen
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - René J F Melis
- Department of Geriatric Medicine, Radboudumc Alzheimer Center, Radboud University Medical Center, Nijmegen, The Netherlands
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Rowina F Hussainali
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Generation R Study Group, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Obstetrics and Gynecology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Marieke Perry
- Department of Geriatric Medicine, Radboudumc Alzheimer Center, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
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Oerlemans AJM, Barendregt DMH, Kooijman SC, Bunnik EM. Impact of incidental findings on young adult participants in brain imaging research: an interview study. Eur Radiol 2022; 32:3839-3845. [DOI: 10.1007/s00330-021-08474-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/25/2021] [Accepted: 11/19/2021] [Indexed: 01/02/2023]
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35
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Bridging gaps between images and data: a systematic update on imaging biobanks. Eur Radiol 2022; 32:3173-3186. [PMID: 35001159 DOI: 10.1007/s00330-021-08431-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/01/2021] [Accepted: 10/22/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND OBJECTIVE The systematic collection of medical images combined with imaging biomarkers and patient non-imaging data is the core concept of imaging biobanks, a key element for fuelling the development of modern precision medicine. Our purpose is to review the existing image repositories fulfilling the criteria for imaging biobanks. METHODS Pubmed, Scopus and Web of Science were searched for articles published in English from January 2010 to July 2021 using a combination of the terms: "imaging" AND "biobanks" and "imaging" AND "repository". Moreover, the Community Research and Development Information Service (CORDIS) database ( https://cordis.europa.eu/projects ) was searched using the terms: "imaging" AND "biobanks", also including collections, projects, project deliverables, project publications and programmes. RESULTS Of 9272 articles retrieved, only 54 related to biobanks containing imaging data were finally selected, of which 33 were disease-oriented (61.1%) and 21 population-based (38.9%). Most imaging biobanks were European (26/54, 48.1%), followed by American biobanks (20/54, 37.0%). Among disease-oriented biobanks, the majority were focused on neurodegenerative (9/33, 27.3%) and oncological diseases (9/33, 27.3%). The number of patients enrolled ranged from 240 to 3,370,929, and the imaging modality most frequently involved was MRI (40/54, 74.1%), followed by CT (20/54, 37.0%), PET (13/54, 24.1%), and ultrasound (12/54, 22.2%). Most biobanks (38/54, 70.4%) were accessible under request. CONCLUSIONS Imaging biobanks can serve as a platform for collecting, sharing and analysing medical images integrated with imaging biomarkers, biological and clinical data. A relatively small proportion of current biobanks also contain images and can thus be classified as imaging biobanks. KEY POINTS • Imaging biobanks are a powerful tool for large-scale collection and processing of medical images integrated with imaging biomarkers and patient non-imaging data. • Most imaging biobanks retrieved were European, disease-oriented and accessible under user request. • While many biobanks have been developed so far, only a relatively small proportion of them are imaging biobanks.
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36
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Aleknaviciute J, Evans TE, Aribas E, de Vries MW, Steegers EAP, Ikram MA, Tiemeier H, Kavousi M, Vernooij MW, Kushner SA. Long-term association of pregnancy and maternal brain structure: the Rotterdam Study. Eur J Epidemiol 2022; 37:271-281. [PMID: 34989970 PMCID: PMC9110529 DOI: 10.1007/s10654-021-00818-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 10/29/2021] [Indexed: 02/06/2023]
Abstract
The peripartum period is the highest risk interval for the onset or exacerbation of psychiatric illness in women’s lives. Notably, pregnancy and childbirth have been associated with short-term structural and functional changes in the maternal human brain. Yet the long-term effects of pregnancy on maternal brain structure remain unknown. We investigated a large population-based cohort to examine the association between parity and brain structure. In total, 2,835 women (mean age 65.2 years; all free from dementia, stroke, and cortical brain infarcts) from the Rotterdam Study underwent magnetic resonance imaging (1.5 T) between 2005 and 2015. Associations of parity with global and lobar brain tissue volumes, white matter microstructure, and markers of vascular brain disease were examined using regression models. We found that parity was associated with a larger global gray matter volume (β = 0.14, 95% CI = 0.09–0.19), a finding that persisted following adjustment for sociodemographic factors. A non-significant dose-dependent relationship was observed between a higher number of childbirths and larger gray matter volume. The gray matter volume association with parity was globally proportional across lobes. No associations were found regarding white matter volume or integrity, nor with markers of cerebral small vessel disease. The current findings suggest that pregnancy and childbirth are associated with robust long-term changes in brain structure involving a larger global gray matter volume that persists for decades. Future studies are warranted to further investigate the mechanism and physiological relevance of these differences in brain morphology.
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Affiliation(s)
- Jurate Aleknaviciute
- Department of Psychiatry, Erasmus MC, University Medical Center Rotterdam, 's Gravendijkwal 230, 3000 CA, Rotterdam, The Netherlands
| | - Tavia E Evans
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Wytemaweg 90, 3015 CN, Rotterdam, The Netherlands.,Department of Radiology, Erasmus MC, University Medical Center Rotterdam, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands
| | - Elif Aribas
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Wytemaweg 90, 3015 CN, Rotterdam, The Netherlands
| | - Merel W de Vries
- Department of Psychiatry, Erasmus MC, University Medical Center Rotterdam, 's Gravendijkwal 230, 3000 CA, Rotterdam, The Netherlands
| | - Eric A P Steegers
- Department of Obstetrics and Gynecology, Erasmus MC-Sophia Children's Hospital, University Medical Center Rotterdam, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands
| | - Mohammad Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Wytemaweg 90, 3015 CN, Rotterdam, The Netherlands
| | - Henning Tiemeier
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Child Psychiatry, Sophia Children's Hospital, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Wytemaweg 90, 3015 CN, Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Radiology, Erasmus MC, University Medical Center Rotterdam, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands. .,Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Wytemaweg 90, 3015 CN, Rotterdam, The Netherlands.
| | - Steven A Kushner
- Department of Psychiatry, Erasmus MC, University Medical Center Rotterdam, 's Gravendijkwal 230, 3000 CA, Rotterdam, The Netherlands
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37
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Zijlmans JL, Lamballais S, Vernooij MW, Ikram MA, Luik AI. Sociodemographic, Lifestyle, Physical, and Psychosocial Determinants of Cognitive Reserve. J Alzheimers Dis 2021; 85:701-713. [PMID: 34864674 PMCID: PMC8842775 DOI: 10.3233/jad-215122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Cognitive reserve aims to explain individual differences in the susceptibility to the functional impact of dementia in the presence of equal amount of neuropathological damage. It is thought to be shaped by a combination of innate individual differences and lifetime exposures. Which determinants are associated with cognitive reserve remains unknown. OBJECTIVE The objective of this study was to investigate the associations of sociodemographic, lifestyle, physical, and psychosocial determinants with cognitive reserve, and potential sex differences. METHODS This cross-sectional study included 4,309 participants from the Rotterdam Study (mean age 63.9±10.7) between 2006-2016. Participants completed five cognitive tests and a brain MRI-scan. Cognitive reserve was defined as a latent variable that captures variance common across five cognitive tests, while adjusting for demographic and MRI-inferred neuropathological factors. The associations of potential determinants and cognitive reserve, adjusted for relevant confounders, were assessed with structural equation models. RESULTS Current smoking (adjusted mean difference: -0.31, 95%confidence interval -0.42; -0.19), diabetes mellitus (-0.25, -0.40; -0.10) and depressive symptoms (-0.07/SD, -0.12; -0.03) were associated with a lower cognitive reserve whereas alcohol use (0.07/SD, 0.03; 0.12) was associated with higher cognitive reserve. Only smoking was associated with cognitive reserve in both men and women. Employment, alcohol use, diabetes, history of cancer, COPD, and depressive symptoms were only associated with cognitive reserve in women. CONCLUSION Our study found that current smoking, diabetes mellitus, and depressive symptoms were associated with a lower cognitive reserve, whereas more alcohol use was associated with a higher cognitive reserve, but with clear differences between men and women.
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Affiliation(s)
- Jendé L Zijlmans
- Department of Epidemiology, Erasmus MC University Medical Centre, Rotterdam, the Netherlands
| | - Sander Lamballais
- Department of Clinical Genetics, Erasmus MC University Medical Centre, Rotterdam, the Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Centre, Rotterdam, the Netherlands.,Department of Radiology and nuclear medicine, Erasmus University Medical Centre, Rotterdam, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Centre, Rotterdam, the Netherlands
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus MC University Medical Centre, Rotterdam, the Netherlands
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38
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Pawlak MA, Knol MJ, Vernooij MW, Ikram MA, Adams HHH, Evans TE. Neural correlates of orbital telorism. Cortex 2021; 145:315-326. [PMID: 34781092 DOI: 10.1016/j.cortex.2021.10.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 08/30/2021] [Accepted: 10/01/2021] [Indexed: 12/23/2022]
Abstract
Orbital telorism, the interocular distance, is clinically informative and in extremes is considered a minor physical anomaly. While its extremes, hypo- and hypertelorism, have been linked to disorders often related to cognitive ability, little is known about the neural correlates of normal variation of telorism within the general population. We derived measures of orbital telorism from cranial magnetic resonance imaging (MRI) by calculating the distance between the eyeball center of gravity in two population-based datasets (N = 5,653, N = 29,824; mean age 64.66, 63.75 years). This measure was found to be related to grey matter tissue density within numerous regions of the brain, including, but surprisingly not limited to, the frontal regions, in both positive and negative directions. Additionally, telorism was related to several cognitive functions, such as Purdue pegboard test (Beta, P-value (CI95%) -.02, 1.63 × 10-7 (-.03:-.01)) and fluid intelligence (.02, 4.75 × 10-6 (.01:0.02)), with some relationships driven by individuals with a smaller orbital telorism. This is reflective of the higher prevalence of hypotelorism in developmental disorders, specifically those that accompany lower cognitive lower functioning. This study suggests, despite previous links only made in clinical extremes, that orbital telorism holds some relation to structural brain development and cognitive function in the general population. This relationship is likely driven by shared developmental periods.
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Affiliation(s)
- Mikolaj A Pawlak
- Department of Neurology and Cerebrovascular Disorders Poznan University of Medical Sciences, Poznan, Poland; Department of Clinical Genetics, Erasmus MC, Rotterdam, CE, the Netherlands
| | - Maria J Knol
- Department of Epidemiology, Erasmus MC, Rotterdam, CE, the Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC, Rotterdam, CE, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, CE, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, Rotterdam, CE, the Netherlands
| | - Hieab H H Adams
- Department of Clinical Genetics, Erasmus MC, Rotterdam, CE, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, CE, the Netherlands
| | - T E Evans
- Department of Clinical Genetics, Erasmus MC, Rotterdam, CE, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, CE, the Netherlands.
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Mooldijk SS, Licher S, Vernooij MW, Ikram MK, Ikram MA. Seasonality of cognitive function in the general population: the Rotterdam Study. GeroScience 2021; 44:281-291. [PMID: 34750718 PMCID: PMC8810929 DOI: 10.1007/s11357-021-00485-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 10/31/2021] [Indexed: 11/23/2022] Open
Abstract
Seasonal variation in cognitive function and underlying cerebral hemodynamics in humans has been suggested, but not consistently shown in previous studies. We assessed cognitive function in 10,276 participants from the population-based Rotterdam Study, aged 45 years and older without dementia, at baseline and at subsequent visits between 1999 and 2016. Seasonality of five cognitive test scores and of a summary measure of global cognition were determined, as well as of brain perfusion. Using linkage with medical records, we also examined whether a seasonal variation was present in clinical diagnoses of dementia. We found a seasonal variation of global cognition (0.05 standard deviations [95% confidence interval: 0.02–0.08]), the Stroop reading task, the Purdue Pegboard test, and of the delayed world learning test, with the best performance in summer months. In line with these findings, there were fewer dementia diagnoses of dementia in spring and summer than in winter and fall. We found no seasonal variation in brain perfusion. These findings support seasonality of cognition, albeit not explained by brain perfusion.
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Affiliation(s)
- Sanne S Mooldijk
- Department of Epidemiology, Erasmus University Medical Centre, PO Box 2040, 3000, CA, Rotterdam, The Netherlands
| | - Silvan Licher
- Department of Epidemiology, Erasmus University Medical Centre, PO Box 2040, 3000, CA, Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus University Medical Centre, PO Box 2040, 3000, CA, Rotterdam, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - M Kamran Ikram
- Department of Epidemiology, Erasmus University Medical Centre, PO Box 2040, 3000, CA, Rotterdam, The Netherlands.,Department of Neurology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Centre, PO Box 2040, 3000, CA, Rotterdam, The Netherlands.
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40
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Huizinga W, Poot DHJ, Vinke EJ, Wenzel F, Bron EE, Toussaint N, Ledig C, Vrooman H, Ikram MA, Niessen WJ, Vernooij MW, Klein S. Differences Between MR Brain Region Segmentation Methods: Impact on Single-Subject Analysis. Front Big Data 2021; 4:577164. [PMID: 34723175 PMCID: PMC8552517 DOI: 10.3389/fdata.2021.577164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 05/21/2021] [Indexed: 12/03/2022] Open
Abstract
For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully automated methods have been proposed and compared to reference segmentations obtained manually. However, systematic differences might exist between the resulting segmentations, depending on the segmentation method and underlying brain atlas. This potentially results in sensitivity differences to disease and can further complicate the comparison of individual patients to normative data. In this study, we aim to answer two research questions: 1) to what extent are methods interchangeable, as long as the same method is being used for computing normative volume distributions and patient-specific volumes? and 2) can different methods be used for computing normative volume distributions and assessing patient-specific volumes? To answer these questions, we compared volumes of six brain regions calculated by five state-of-the-art segmentation methods: Erasmus MC (EMC), FreeSurfer (FS), geodesic information flows (GIF), multi-atlas label propagation with expectation–maximization (MALP-EM), and model-based brain segmentation (MBS). We applied the methods on 988 non-demented (ND) subjects and computed the correlation (PCC-v) and absolute agreement (ICC-v) on the volumes. For most regions, the PCC-v was good (>0.75), indicating that volume differences between methods in ND subjects are mainly due to systematic differences. The ICC-v was generally lower, especially for the smaller regions, indicating that it is essential that the same method is used to generate normative and patient data. To evaluate the impact on single-subject analysis, we also applied the methods to 42 patients with Alzheimer’s disease (AD). In the case where the normative distributions and the patient-specific volumes were calculated by the same method, the patient’s distance to the normative distribution was assessed with the z-score. We determined the diagnostic value of this z-score, which showed to be consistent across methods. The absolute agreement on the AD patients’ z-scores was high for regions of thalamus and putamen. This is encouraging as it indicates that the studied methods are interchangeable for these regions. For regions such as the hippocampus, amygdala, caudate nucleus and accumbens, and globus pallidus, not all method combinations showed a high ICC-z. Whether two methods are indeed interchangeable should be confirmed for the specific application and dataset of interest.
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Affiliation(s)
- W Huizinga
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Medical Informatics, Erasmus MC, Rotterdam, Netherlands
| | - D H J Poot
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Medical Informatics, Erasmus MC, Rotterdam, Netherlands
| | - E J Vinke
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, Netherlands
| | - F Wenzel
- Philips Research Hamburg, Hamburg, Germany
| | - E E Bron
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Medical Informatics, Erasmus MC, Rotterdam, Netherlands
| | - N Toussaint
- School of Biomedical Engineering, King's College London, London, United Kingdom
| | - C Ledig
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - H Vrooman
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Medical Informatics, Erasmus MC, Rotterdam, Netherlands
| | - M A Ikram
- Department of Epidemiology, Erasmus MC, Rotterdam, Netherlands
| | - W J Niessen
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Medical Informatics, Erasmus MC, Rotterdam, Netherlands.,Quantitative Imaging Group, Department of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, Netherlands
| | - M W Vernooij
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, Netherlands
| | - S Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Medical Informatics, Erasmus MC, Rotterdam, Netherlands
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41
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The Effect of Hearing Aid Use on the Association Between Hearing Loss and Brain Structure in Older Adults. Ear Hear 2021; 43:933-940. [PMID: 34711744 DOI: 10.1097/aud.0000000000001148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Recent studies have shown an association between poorer hearing thresholds and smaller brain tissue volumes in older adults. Several underlying causal mechanisms have been opted, with a sensory deprivation hypothesis as one of the most prominent. If hearing deprivation would lead to less brain volume, hearing aids could be hypothesized to moderate this pathway by restoration of hearing. This study aims to investigate whether such a moderating effect of hearing aids exists. DESIGN The authors conducted a cross-sectional study involving aging participants of the population-based Rotterdam Study. Hearing aid use was assessed by interview and hearing loss was quantified using pure-tone audiometry. Total brain volume, gray matter and white matter volume and white matter integrity [fractional anisotropy (FA) and mean diffusivity] were measured using magnetic resonance imaging. Only participants with a pure tone average at 1, 2, and 4 kHz (PTA1,2,4) of ≥35 dB HL were included. Associations of hearing loss with brain volume and global measures of white matter integrity were analyzed using linear regression, with hearing aid use and interaction between hearing aid use and PTA1,2,4 included as independent variables. Models were adjusted for age, sex, time between audiometry and magnetic resonance imaging, level of education, and cardiovascular risk factors. RESULTS Out of 459 included participants with mean age (range) 70.4 (52 to 92) 41% were female. Distributions of age and sex among hearing aid users (n = 172) did not significantly differ from those without hearing aids. PTA1,2,4 was associated with lower FA, but not with a difference in total brain volume, gray matter volume, white matter volume, or mean diffusivity. Interaction between hearing aid use and PTA1,2,4 was not associated with FA or any of the other outcome measures. Additional analysis revealed that interaction between hearing aid use and age was associated with lower FA. CONCLUSIONS We found no evidence for a moderating effect of hearing aids on the relationship between hearing loss and brain structure in a population of older adults. However, use of hearing aids did appear as an effect modifier in the association between age and white matter integrity. Future longitudinal research is needed to clarify these results.
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42
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Estimating the effect of a scanner upgrade on measures of grey matter structure for longitudinal designs. PLoS One 2021; 16:e0239021. [PMID: 34610020 PMCID: PMC8491918 DOI: 10.1371/journal.pone.0239021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 08/13/2021] [Indexed: 12/28/2022] Open
Abstract
Longitudinal imaging studies are crucial for advancing the understanding of brain development over the lifespan. Thus, more and more studies acquire imaging data at multiple time points or with long follow-up intervals. In these studies changes to magnetic resonance imaging (MRI) scanners often become inevitable which may decrease the reliability of the MRI assessments and introduce biases. We therefore investigated the difference between MRI scanners with subsequent versions (3 Tesla Siemens Verio vs. Skyra) on the cortical and subcortical measures of grey matter in 116 healthy, young adults using the well-established longitudinal FreeSurfer stream for T1-weighted brain images. We found excellent between-scanner reliability for cortical and subcortical measures of grey matter structure (intra-class correlation coefficient > 0.8). Yet, paired t-tests revealed statistically significant differences in at least 67% of the regions, with percent differences around 2 to 4%, depending on the outcome measure. Offline correction for gradient distortions only slightly reduced these biases. Further, T1-imaging based quality measures reflecting gray-white matter contrast systematically differed between scanners. We conclude that scanner upgrades during a longitudinal study introduce bias in measures of cortical and subcortical grey matter structure. Therefore, before upgrading a MRI scanner during an ongoing study, researchers should prepare to implement an appropriate correction method for these effects.
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43
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Lamballais S, Zijlmans JL, Vernooij MW, Ikram MK, Luik AI, Ikram MA. The Risk of Dementia in Relation to Cognitive and Brain Reserve. J Alzheimers Dis 2021; 77:607-618. [PMID: 32741820 PMCID: PMC7592692 DOI: 10.3233/jad-200264] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background: Individual differences in the risk to develop dementia remain poorly understood. These differences may partly be explained through reserve, which is the ability to buffer cognitive decline due to neuropathology and age. Objective: To determine how much early and late–life cognitive reserve (CR) and brain reserve (BR) contribute to the risk of dementia. Methods: 4,112 dementia-free participants (mean age = 66.3 years) from the Rotterdam Study were followed up for on average 6.0 years. Early-life CR and BR were defined as attained education and intracranial volume, respectively. Late-life CR was derived through variance decomposition based on cognition. Late-life BR was set as the total non-lesioned brain volume divided by intracranial volume. Results: Higher early-life CR (hazard ratio = 0.48, 95% CI = [0.21; 1.06]) but not early-life BR associated with a lower risk of incident dementia. Higher late-life CR (hazard ratio = 0.57, 95% CI = [0.48; 0.68]) and late-life BR (hazard ratio = 0.54, 95% CI = [0.43; 0.68]) also showed lower levels of dementia. Combining all proxies into one model attenuated the association between early-life CR and dementia (hazard ratio = 0.56, 95% CI = [0.25; 1.25]) whereas the other associations were unaffected. These findings were stable upon stratification for sex, age, and APOEɛ4. Finally, high levels of late-life CR and BR provided additive protection against dementia. Conclusion: The findings illustrate the importance of late-life over early-life reserve in understanding the risk of dementia, and show the need to study CR and BR conjointly.
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Affiliation(s)
- Sander Lamballais
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, the Netherlands
| | - Jendé L Zijlmans
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, the Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, the Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, the Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, the Netherlands
| | - M Kamran Ikram
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, the Netherlands.,Department of Neurology, Erasmus MC University Medical Center Rotterdam, the Netherlands
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, the Netherlands.,Department of Child and Adolescent Psychiatry and Psychology, Erasmus MC University Medical Center Rotterdam, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, the Netherlands
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44
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Pemberton HG, Zaki LAM, Goodkin O, Das RK, Steketee RME, Barkhof F, Vernooij MW. Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review. Neuroradiology 2021; 63:1773-1789. [PMID: 34476511 PMCID: PMC8528755 DOI: 10.1007/s00234-021-02746-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 06/02/2021] [Indexed: 12/22/2022]
Abstract
Developments in neuroradiological MRI analysis offer promise in enhancing objectivity and consistency in dementia diagnosis through the use of quantitative volumetric reporting tools (QReports). Translation into clinical settings should follow a structured framework of development, including technical and clinical validation steps. However, published technical and clinical validation of the available commercial/proprietary tools is not always easy to find and pathways for successful integration into the clinical workflow are varied. The quantitative neuroradiology initiative (QNI) framework highlights six necessary steps for the development, validation and integration of quantitative tools in the clinic. In this paper, we reviewed the published evidence regarding regulatory-approved QReports for use in the memory clinic and to what extent this evidence fulfils the steps of the QNI framework. We summarize unbiased technical details of available products in order to increase the transparency of evidence and present the range of reporting tools on the market. Our intention is to assist neuroradiologists in making informed decisions regarding the adoption of these methods in the clinic. For the 17 products identified, 11 companies have published some form of technical validation on their methods, but only 4 have published clinical validation of their QReports in a dementia population. Upon systematically reviewing the published evidence for regulatory-approved QReports in dementia, we concluded that there is a significant evidence gap in the literature regarding clinical validation, workflow integration and in-use evaluation of these tools in dementia MRI diagnosis.
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Affiliation(s)
- Hugh G Pemberton
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Lara A M Zaki
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Olivia Goodkin
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ravi K Das
- Clinical, Educational and Health Psychology, University College London, London, UK
| | - Rebecca M E Steketee
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
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45
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Moll M, Jackson VE, Yu B, Grove ML, London SJ, Gharib SA, Bartz TM, Sitlani CM, Dupuis J, O'Connor GT, Xu H, Cassano PA, Patchen BK, Kim WJ, Park J, Kim KH, Han B, Barr RG, Manichaikul A, Nguyen JN, Rich SS, Lahousse L, Terzikhan N, Brusselle G, Sakornsakolpat P, Liu J, Benway CJ, Hall IP, Tobin MD, Wain LV, Silverman EK, Cho MH, Hobbs BD. A systematic analysis of protein-altering exonic variants in chronic obstructive pulmonary disease. Am J Physiol Lung Cell Mol Physiol 2021; 321:L130-L143. [PMID: 33909500 PMCID: PMC8321852 DOI: 10.1152/ajplung.00009.2021] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 04/15/2021] [Accepted: 04/27/2021] [Indexed: 12/14/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified regions associated with chronic obstructive pulmonary disease (COPD). GWASs of other diseases have shown an approximately 10-fold overrepresentation of nonsynonymous variants, despite limited exonic coverage on genotyping arrays. We hypothesized that a large-scale analysis of coding variants could discover novel genetic associations with COPD, including rare variants with large effect sizes. We performed a meta-analysis of exome arrays from 218,399 controls and 33,851 moderate-to-severe COPD cases. All exome-wide significant associations were present in regions previously identified by GWAS. We did not identify any novel rare coding variants with large effect sizes. Within GWAS regions on chromosomes 5q, 6p, and 15q, four coding variants were conditionally significant (P < 0.00015) when adjusting for lead GWAS single-nucleotide polymorphisms A common gasdermin B (GSDMB) splice variant (rs11078928) previously associated with a decreased risk for asthma was nominally associated with a decreased risk for COPD [minor allele frequency (MAF) = 0.46, P = 1.8e-4]. Two stop variants in coiled-coil α-helical rod protein 1 (CCHCR1), a gene involved in regulating cell proliferation, were associated with COPD (both P < 0.0001). The SERPINA1 Z allele was associated with a random-effects odds ratio of 1.43 for COPD (95% confidence interval = 1.17-1.74), though with marked heterogeneity across studies. Overall, COPD-associated exonic variants were identified in genes involved in DNA methylation, cell-matrix interactions, cell proliferation, and cell death. In conclusion, we performed the largest exome array meta-analysis of COPD to date and identified potential functional coding variants. Future studies are needed to identify rarer variants and further define the role of coding variants in COPD pathogenesis.
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Affiliation(s)
- Matthew Moll
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Victoria E Jackson
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia
| | - Bing Yu
- School of Public Health, University of Texas Health Science Center, Houston, Texas
| | - Megan L Grove
- School of Public Health, University of Texas Health Science Center, Houston, Texas
| | - Stephanie J London
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services Research, Research Triangle Park, Durham, North Carolina
| | - Sina A Gharib
- Center for Lung Biology, Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Washington, Seattle, Washington
| | - Traci M Bartz
- Department of Biostatistics, University of Washington, Seattle, Washington
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - George T O'Connor
- Division of Pulmonary, Allergy, Sleep, and Critical Care Medicine, Department of Medicine, Pulmonary Center, Boston University School of Medicine, Boston Medical Center, Boston, Massachusetts
| | - Hanfei Xu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Patricia A Cassano
- Division of Nutritional Sciences, Cornell University, Ithaca, New York
- Division of Epidemiology, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
| | | | - Woo Jin Kim
- Department of Internal Medicine, Kangwon National University, Chuncheon, South Korea
| | - Jinkyeong Park
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Goyang-Si, Gyeonggi-do, South Korea
| | - Kun Hee Kim
- Department of Convergence Medicine and Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Buhm Han
- Department of Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - R Graham Barr
- Department of Medicine, Columbia University Medical Center, New York, New York
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
| | - Jennifer N Nguyen
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
| | - Lies Lahousse
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Bioanalysis, Ghent University, Ghent, Belgium
| | - Natalie Terzikhan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Guy Brusselle
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Phuwanat Sakornsakolpat
- Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Jiangyuan Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Christopher J Benway
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Ian P Hall
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, Nottingham, United Kingdom
| | - Martin D Tobin
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Louise V Wain
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Brian D Hobbs
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
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Resistance to developing brain pathology due to vascular risk factors: the role of educational attainment. Neurobiol Aging 2021; 106:197-206. [PMID: 34298318 DOI: 10.1016/j.neurobiolaging.2021.06.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 04/19/2021] [Accepted: 06/10/2021] [Indexed: 11/22/2022]
Abstract
Brain pathology develops at different rates between individuals with similar burden of risk factors, possibly explained by brain resistance. We examined if education contributes to brain resistance by studying its influence on the association between vascular risk factors and brain pathology. In 4111 stroke-free and dementia-free community-dwelling participants (62.9 ± 10.7 years), we explored the association between vascular risk factors (hypertension and the Framingham Stroke Risk Profile [FRSP]) and imaging markers of brain pathology (markers of cerebral small vessel disease and brain volumetry), stratified by educational attainment level. Associations of hypertension and FSRP with markers of brain pathology were not significantly different between levels of educational attainment. Certain associations appeared weaker in those with higher compared to lower educational attainment, particularly for white matter hyperintensities (WMH). Supplementary residual analyses showed significant associations between higher educational attainment and stronger resistance to WMH among others. Our results suggest a role for educational attainment in resistance to vascular brain pathology. Yet, further research is needed to better characterize determinants of brain resistance.
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Venkatraghavan V, Vinke EJ, Bron EE, Niessen WJ, Arfan Ikram M, Klein S, Vernooij MW. Progression along data-driven disease timelines is predictive of Alzheimer's disease in a population-based cohort. Neuroimage 2021; 238:118233. [PMID: 34091030 DOI: 10.1016/j.neuroimage.2021.118233] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 04/11/2021] [Accepted: 06/01/2021] [Indexed: 11/15/2022] Open
Abstract
Data-driven disease progression models have provided important insight into the timeline of brain changes in AD phenotypes. However, their utility in predicting the progression of pre-symptomatic AD in a population-based setting has not yet been investigated. In this study, we investigated if the disease timelines constructed in a case-controlled setting, with subjects stratified according to APOE status, are generalizable to a population-based cohort, and if progression along these disease timelines is predictive of AD. Seven volumetric biomarkers derived from structural MRI were considered. We estimated APOE-specific disease timelines of changes in these biomarkers using a recently proposed method called co-initialized discriminative event-based modeling (co-init DEBM). This method can also estimate a disease stage for new subjects by calculating their position along the disease timelines. The model was trained and cross-validated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and tested on the population-based Rotterdam Study (RS) cohort. We compared the diagnostic and prognostic value of the disease stage in the two cohorts. Furthermore, we investigated if the rate of change of disease stage in RS participants with longitudinal MRI data was predictive of AD. In ADNI, the estimated disease timeslines for ϵ4 non-carriers and carriers were found to be significantly different from one another (p<0.001). The estimate disease stage along the respective timelines distinguished AD subjects from controls with an AUC of 0.83 in both APOEϵ4 non-carriers and carriers. In the RS cohort, we obtained an AUC of 0.83 and 0.85 in ϵ4 non-carriers and carriers, respectively. Progression along the disease timelines as estimated by the rate of change of disease stage showed a significant difference (p<0.005) for subjects with pre-symptomatic AD as compared to the general aging population in RS. It distinguished pre-symptomatic AD subjects with an AUC of 0.81 in APOEϵ4 non-carriers and 0.88 in carriers, which was better than any individual volumetric biomarker, or its rate of change, could achieve. Our results suggest that co-init DEBM trained on case-controlled data is generalizable to a population-based cohort setting and that progression along the disease timelines is predictive of the development of AD in the general population. We expect that this approach can help to identify at-risk individuals from the general population for targeted clinical trials as well as to provide biomarker based objective assessment in such trials.
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Affiliation(s)
- Vikram Venkatraghavan
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands
| | - Elisabeth J Vinke
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, The Netherlands
| | - Esther E Bron
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands
| | - Wiro J Niessen
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands; Quantitative Imaging Group, Dept. of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, The Netherlands
| | - Stefan Klein
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, The Netherlands.
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Association of Speech Recognition Thresholds With Brain Volumes and White Matter Microstructure: The Rotterdam Study. Otol Neurotol 2021; 41:1202-1209. [PMID: 32925839 DOI: 10.1097/mao.0000000000002739] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVES Brain volumetric declines may underlie the association between hearing loss and dementia. While much is known about the peripheral auditory function and brain volumetric declines, poorer central auditory speech processing may also be associated with decreases in brain volumes. METHODS Central auditory speech processing, measured by the speech recognition threshold (SRT) from the Digits-in-Noise task, and neuroimaging assessments (structural magnetic resonance imaging [MRI] and fractional anisotropy and mean diffusivity from diffusion tensor imaging), were assessed cross-sectionally in 2,368 Rotterdam Study participants aged 51.8 to 97.8 years. SRTs were defined continuously and categorically by degrees of auditory performance (normal, insufficient, and poor). Brain volumes from structural MRI were assessed on a global and lobar level, as well as for specific dementia-related structures (hippocampus, entorhinal cortex, parahippocampal gyrus). Multivariable linear regression models adjusted by age, age-squared, sex, educational level, alcohol consumption, intracranial volume (MRI only), cardiovascular risk factors (hypertension, diabetes, obesity, current smoking), and pure-tone average were used to determine associations between SRT and brain structure. RESULTS Poorer central auditory speech processing was associated with larger parietal lobe volume (difference in mL per dB increase= 0.24, 95% CI: 0.05, 0.42), but not with diffusion tensor imaging measures. Degrees of auditory performance were not associated with brain volumes and white matter microstructure. CONCLUSIONS Central auditory speech processing in the presence of both vascular burden and pure-tone average may not be related to brain volumes and white matter microstructure. Longitudinal follow-up is needed to explore these relationships thoroughly.
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The interaction of cognitive and brain reserve with frailty in the association with mortality: an observational cohort study. THE LANCET HEALTHY LONGEVITY 2021; 2:e194-e201. [DOI: 10.1016/s2666-7568(21)00028-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 02/08/2021] [Accepted: 02/10/2021] [Indexed: 11/24/2022]
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Tinnitus and Its Central Correlates: A Neuroimaging Study in a Large Aging Population. Ear Hear 2021; 42:1428-1435. [PMID: 33974782 DOI: 10.1097/aud.0000000000001042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES To elucidate the association between tinnitus and brain tissue volumes and white matter microstructural integrity. DESIGN Two thousand six hundred sixteen participants (mean age, 65.7 years [SD: 7.5 years]; 53.9% female) of the population-based Rotterdam Study underwent tinnitus assessment (2011 to 2014) and magnetic resonance imaging of the brain (2011 to 2014). Associations between tinnitus (present versus absent) and total, gray, and white matter volume and global white matter microstructure were assessed using multivariable linear regression models adjusting for demographic factors, cardiovascular risk factors, depressive symptoms, Mini-Mental State Examination score, and hearing loss. Finally, potential regional gray matter density and white matter microstructural volume differences were assessed on a voxel-based level again using multivariable linear regression. RESULTS Participants with tinnitus (21.8%) had significantly larger brain tissue volumes (difference in SD, 0.09; 95% confidence interval, 0.06 to 0.13), driven by larger white matter volumes (difference, 0.12; 95% confidence interval, 0.04 to 0.21) independent of hearing loss. There was no association between tinnitus and gray matter volumes nor with global white matter microstructure. On a lobar level, tinnitus was associated with larger white matter volumes in each lobe, not with gray matter volume. Voxel-based results did not show regional specificity. CONCLUSIONS We found that tinnitus in older adults was associated with larger brain tissue volumes, driven by larger white matter volumes, independent of age, and hearing loss. Based on these results, it may be hypothesized that tinnitus potentially has a neurodevelopmental origin in earlier life independent of aging processes.
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