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Li-Gao R, Bot M, Kurilshikov A, Willemsen G, van Greevenbroek MMJ, Schram MMT, Stehouwer CDA, Fu J, Zhernakova A, Penninx BWJH, De Geus EJC, Boomsma DI, Kupper N. Metabolomics profiling of Type D personality traits. J Psychosom Res 2025; 188:111994. [PMID: 39577138 DOI: 10.1016/j.jpsychores.2024.111994] [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: 01/22/2024] [Revised: 08/30/2024] [Accepted: 11/17/2024] [Indexed: 11/24/2024]
Abstract
OBJECTIVE Type D (Distressed) personality combines negative affectivity (NA) and social inhibition (SI) and is associated with an increased risk of cardiometabolic diseases. Here, we examined the association of Type D traits with 230 (predominantly) lipid metabolites and metabolite ratios. METHODS Four Dutch cohorts were included, comprising 10,834 individuals. Type D personality traits were measured by self-report questionnaires. A proton nuclear magnetic resonance (NMR) metabolomics platform provided 149 absolute measures (98 belonging to lipoprotein subclasses) and 81 derived ratios. For all, linear regression analyses were performed within each cohort, followed by random-effects meta-analyses. A per-measure FDR q-value<0.05 was set as a study-wise significant association. RESULTS SI was significantly associated with a lower omega-3 fatty acids to total fatty acids (FAw3.FA%) ratio, and a lower free cholesterol to total lipids ratio in very small VLDL (XS.VLDL.FC%). FAw3.FA% was also associated to NA (no study-wise significance though). NA showed a suggestive replication (p-value<.05) of the previous reported associations with depression for 5 out of 18 metabolites from the same metabolomics platform: triglycerides in HDL, serum total triglycerides, VLDL cholesterol, mean diameter for VLDL particles and VLDL triglycerides. CONCLUSIONS In this large meta-analysis, SI was associated with omega-3 fatty acids to total fatty acids ratio, which is suggestive of lower omega-3 fatty acid intake. Only some metabolite biomarkers showed tentative links to Type D and NA. In sum, it seems that there are no major alterations in lipid metabolism associated with Type D traits.
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Affiliation(s)
- Ruifang Li-Gao
- CoRPS Center of Research on Psychology in Somatic Diseases, Tilburg University, Tilburg, the Netherlands; Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands; Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Mariska Bot
- Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health research institute and Amsterdam Neuroscience, the Netherlands
| | - Alexander Kurilshikov
- Department of Genetics, University Medical Center Groningen, Groningen, the Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Marleen M J van Greevenbroek
- School for Cardiovascular Diseases CARIM, Maastricht University, Maastricht, the Netherlands; Internal Medicine, MUMC+, Maastricht, the Netherlands
| | - Miranda M T Schram
- School for Cardiovascular Diseases CARIM, Maastricht University, Maastricht, the Netherlands; Internal Medicine, MUMC+, Maastricht, the Netherlands; MHeNs School of Mental Health and Neuroscience, Maastricht University Medical Center+, Maastricht, the Netherlands; Heart and Vascular Center, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Coen D A Stehouwer
- School for Cardiovascular Diseases CARIM, Maastricht University, Maastricht, the Netherlands; Internal Medicine, MUMC+, Maastricht, the Netherlands
| | - Jingyuan Fu
- Department of Genetics, University Medical Center Groningen, Groningen, the Netherlands; Department of Pediatrics, University Medical Center Groningen, Groningen, the Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, Groningen, the Netherlands
| | - Brenda W J H Penninx
- Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health research institute and Amsterdam Neuroscience, the Netherlands
| | - Eco J C De Geus
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands; Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, the Netherlands
| | - Nina Kupper
- CoRPS Center of Research on Psychology in Somatic Diseases, Tilburg University, Tilburg, the Netherlands
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Curro KR, van Nispen RMA, den Braber A, van de Giessen EM, van de Kreeke JA, Tan HS, Visser PJ, Bouwman FH, Verbraak FD. Longitudinal Assessment of Retinal Microvasculature in Preclinical Alzheimer's Disease. Invest Ophthalmol Vis Sci 2024; 65:2. [PMID: 39361291 PMCID: PMC11451830 DOI: 10.1167/iovs.65.12.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 09/03/2024] [Indexed: 10/05/2024] Open
Abstract
Purpose To investigate if changes in vessel density (VD) and the foveal avascular zone (FAZ) occur in the preclinical phase of Alzheimer's disease (pAD) over time. Methods Optical coherence tomography angiography (OCTA) was used to image VD and FAZ at baseline and for a follow-up period of 2 years. Positron emission tomography (PET) was used to determine the amyloid beta (Aβ) status of participants. Results The VD and FAZ of 148 participants (54% female) were analyzed at baseline and follow-up (mean time between measurements, 2.24 ± 0.35 years). The mean age of the participants was 68.3 ± 6.0 years at baseline and 70.3 ± 5.9 years at follow-up. Participants were divided into three groups: control group, participants who had negative Aβ status at both measurements (Aβ-, n = 116); converter group, participants who transitioned from negative to positive between baseline and follow-up (Aβ-+, n = 18); and participants who were consistently positive at both visits (Aβ++, n = 14). The VD of both Aβ+ groups demonstrated non-significant increases over time in both macula and optic nerve head (ONH) regions. The Aβ- group was found to be significantly higher in both ONH and macular regions. The VD of the Aβ++ group was significantly higher in the macula inner and outer rings compared to the Aβ-+ and Aβ- groups. No significant change was found in FAZ values over time. Conclusions Alterations in VD seem to manifest already in pAD, exhibiting distinct variations between the ONH and macula. Further longitudinal studies with a longer follow-up design and known amyloid pathology should be undertaken to validate these observations.
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Affiliation(s)
- Katie R. Curro
- Department of Ophthalmology, Amsterdam UMC, Amsterdam, The Netherlands
- Quality of Care, Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ruth M. A. van Nispen
- Department of Ophthalmology, Amsterdam UMC, Amsterdam, The Netherlands
- Quality of Care, Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, The Netherlands
| | - Anouk den Braber
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Amsterdam, The Netherlands
| | | | | | - H. Stevie Tan
- Department of Ophthalmology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Pieter-Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Femke H. Bouwman
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Frank D. Verbraak
- Department of Ophthalmology, Amsterdam UMC, Amsterdam, The Netherlands
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Panyard DJ, Reus LM, Ali M, Liu J, Deming YK, Lu Q, Kollmorgen G, Carboni M, Wild N, Visser PJ, Bertram L, Zetterberg H, Blennow K, Gobom J, Western D, Sung YJ, Carlsson CM, Johnson SC, Asthana S, Cruchaga C, Tijms BM, Engelman CD, Snyder MP. Post-GWAS multiomic functional investigation of the TNIP1 locus in Alzheimer's disease highlights a potential role for GPX3. Alzheimers Dement 2024; 20:5044-5053. [PMID: 38809917 PMCID: PMC11247664 DOI: 10.1002/alz.13848] [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: 10/10/2023] [Revised: 03/07/2024] [Accepted: 03/27/2024] [Indexed: 05/31/2024]
Abstract
INTRODUCTION Recent genome-wide association studies (GWAS) have reported a genetic association with Alzheimer's disease (AD) at the TNIP1/GPX3 locus, but the mechanism is unclear. METHODS We used cerebrospinal fluid (CSF) proteomics data to test (n = 137) and replicate (n = 446) the association of glutathione peroxidase 3 (GPX3) with CSF biomarkers (including amyloid and tau) and the GWAS-implicated variants (rs34294852 and rs871269). RESULTS CSF GPX3 levels decreased with amyloid and tau positivity (analysis of variance P = 1.5 × 10-5) and higher CSF phosphorylated tau (p-tau) levels (P = 9.28 × 10-7). The rs34294852 minor allele was associated with decreased GPX3 (P = 0.041). The replication cohort found associations of GPX3 with amyloid and tau positivity (P = 2.56 × 10-6) and CSF p-tau levels (P = 4.38 × 10-9). DISCUSSION These results suggest variants in the TNIP1 locus may affect the oxidative stress response in AD via altered GPX3 levels. HIGHLIGHTS Cerebrospinal fluid (CSF) glutathione peroxidase 3 (GPX3) levels decreased with amyloid and tau positivity and higher CSF phosphorylated tau. The minor allele of rs34294852 was associated with lower CSF GPX3. levels when also controlling for amyloid and tau category. GPX3 transcript levels in the prefrontal cortex were lower in Alzheimer's disease than controls. rs34294852 is an expression quantitative trait locus for GPX3 in blood, neutrophils, and microglia.
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Affiliation(s)
- Daniel J. Panyard
- Department of GeneticsStanford University School of MedicineStanford UniversityStanfordCaliforniaUSA
- Department of Population Health SciencesUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Lianne M. Reus
- Alzheimer Center Amsterdam, NeurologyVrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamThe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamThe Netherlands
- Center for Neurobehavioral GeneticsUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Muhammad Ali
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
- NeuroGenomics and Informatics CenterWashington University School of MedicineSt. LouisMissouriUSA
- Hope Center for Neurological DisordersWashington University School of MedicineSt. LouisMissouriUSA
| | - Jihua Liu
- Department of Biostatistics and Medical InformaticsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of StatisticsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Yuetiva K. Deming
- Department of Population Health SciencesUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Wisconsin Alzheimer's Disease Research CenterUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Qiongshi Lu
- Department of Biostatistics and Medical InformaticsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of StatisticsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | | | | | | | - Pieter J. Visser
- Alzheimer Center Amsterdam, NeurologyVrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamThe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamThe Netherlands
- Department of PsychiatryMaastricht UniversityMaastrichtThe Netherlands
- Department of Neurobiology, Care Sciences and Society, Division of NeurogeriatricsKarolinska InstitutetStockholmSweden
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome AnalyticsInstitutes of Neurogenetics and CardiogeneticsUniversity of LübeckLübeckGermany
- Department of PsychologyUniversity of OsloOsloNorway
| | - Henrik Zetterberg
- Institute of Neuroscience and PhysiologyThe Sahlgrenska Academy at the University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
- Department of Neurodegenerative DiseaseUCL Institute of NeurologyLondonUK
- UK Dementia Research Institute at UCLLondonUK
- Hong Kong Center for Neurodegenerative DiseasesHong KongChina
| | - Kaj Blennow
- Institute of Neuroscience and PhysiologyThe Sahlgrenska Academy at the University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
| | - Johan Gobom
- Institute of Neuroscience and PhysiologyThe Sahlgrenska Academy at the University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
| | - Dan Western
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
- NeuroGenomics and Informatics CenterWashington University School of MedicineSt. LouisMissouriUSA
- Hope Center for Neurological DisordersWashington University School of MedicineSt. LouisMissouriUSA
| | - Yun Ju Sung
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
- NeuroGenomics and Informatics CenterWashington University School of MedicineSt. LouisMissouriUSA
- Hope Center for Neurological DisordersWashington University School of MedicineSt. LouisMissouriUSA
| | - Cynthia M. Carlsson
- Wisconsin Alzheimer's Disease Research CenterUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- William S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
| | - Sterling C. Johnson
- Wisconsin Alzheimer's Disease Research CenterUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- William S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
| | - Sanjay Asthana
- Wisconsin Alzheimer's Disease Research CenterUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- William S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
| | - Carlos Cruchaga
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
- NeuroGenomics and Informatics CenterWashington University School of MedicineSt. LouisMissouriUSA
- Hope Center for Neurological DisordersWashington University School of MedicineSt. LouisMissouriUSA
| | - Betty M. Tijms
- Alzheimer Center Amsterdam, NeurologyVrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamThe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamThe Netherlands
| | - Corinne D. Engelman
- Department of Population Health SciencesUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Michael P. Snyder
- Department of GeneticsStanford University School of MedicineStanford UniversityStanfordCaliforniaUSA
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Koenders EA, Wesseldijk LW, Boomsma DI, Larsen JK, Vink JM. Heritability of adult picky eating in the Netherlands. Appetite 2024; 195:107230. [PMID: 38278443 DOI: 10.1016/j.appet.2024.107230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/21/2023] [Accepted: 01/18/2024] [Indexed: 01/28/2024]
Abstract
Adult picky eating (APE), the rejection of familiar and unfamiliar foods leading to a diet with limited variety, is an understudied phenomenon which can have both physical and psychological negative consequences. The aetiology of individual differences in APE is understudied, although there is reason to believe that it is partly heritable. Therefore, we aimed to estimate the heritability of APE with data from the Netherlands Twin Register (n = 8016) with classical genetic structural equation modelling. In order to use these data, we firstly investigated whether a Food Preference Questionnaire (FPQ) could measure APE with a pre-registered prestudy. Adult participants (n = 414) filled in online questionnaires, including a FPQ and measures related to APE. Spearman's rho correlation quantified the relationship between different elements of the Dutch FPQ and different scores on measures of APE. Results of the prestudy showed that the mean liking score on the FPQ could be used to measure APE (ρ > .50). This measure was then used in the main study to estimate the heritability of APE. Results showed that broad-sense heritability for APE is 49 % (additive genetic effects 14 % (95 % CI [00, 38]) + dominance genetic effects 35 % (95 % CI [11, 52]), while the remaining variance is explained by unique environmental factors. Future studies may focus on uncovering the specific genetic and unique environmental factors that play a role in APE.
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Affiliation(s)
- Emma A Koenders
- Behavioural Science Institute, Radboud University, Nijmegen, Netherlands
| | - Laura W Wesseldijk
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Netherlands; Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Junilla K Larsen
- Behavioural Science Institute, Radboud University, Nijmegen, Netherlands
| | - Jacqueline M Vink
- Behavioural Science Institute, Radboud University, Nijmegen, Netherlands.
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5
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Moonen JEF, Haan R, Bos I, Teunissen C, van de Giessen E, Tomassen J, den Braber A, van der Landen SM, de Geus EJC, Legdeur N, van Harten AC, Trieu C, de Boer C, Kroeze L, Barkhof F, Visser PJ, van der Flier WM. Contributions of amyloid beta and cerebral small vessel disease in clinical decline. Alzheimers Dement 2024; 20:1868-1880. [PMID: 38146222 PMCID: PMC10984432 DOI: 10.1002/alz.13607] [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: 06/09/2023] [Revised: 11/21/2023] [Accepted: 11/22/2023] [Indexed: 12/27/2023]
Abstract
INTRODUCTION We assessed whether co-morbid small vessel disease (SVD) has clinical predictive value in preclinical or prodromal Alzheimer's disease. METHODS In 1090 non-demented participants (65.4 ± 10.7 years) SVD was assessed with magnetic resonance imaging and amyloid beta (Aβ) with lumbar puncture and/or positron emission tomography scan (mean follow-up for cognitive function 3.1 ± 2.4 years). RESULTS Thirty-nine percent had neither Aβ nor SVD (A-V-), 21% had SVD only (A-V+), 23% Aβ only (A+V-), and 17% had both (A+V+). Pooled cohort linear mixed model analyses demonstrated that compared to A-V- (reference), A+V- had a faster rate of cognitive decline. Co-morbid SVD (A+V+) did not further increase rate of decline. Cox regression showed that dementia risk was modestly increased in A-V+ (hazard ratio [95% confidence interval: 1.8 [1.0-3.2]) and most strongly in A+ groups. Also, mortality risk was increased in A+ groups. DISCUSSION In non-demented persons Aβ was predictive of cognitive decline, dementia, and mortality. SVD modestly predicts dementia in A-, but did not increase deleterious effects in A+. HIGHLIGHTS Amyloid beta (Aβ; A) was predictive for cognitive decline, dementia, and mortality. Small vessel disease (SVD) had no additional deleterious effects in A+. SVD modestly predicted dementia in A-. Aβ should be assessed even when magnetic resonance imaging indicates vascular cognitive impairment.
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Affiliation(s)
- Justine E. F. Moonen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Renée Haan
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Isabelle Bos
- Nivel, Research Institute for Better CareUtrechtthe Netherlands
| | - Charlotte Teunissen
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
- Neurochemistry LaboratoryDepartment of Clinical ChemistryAmsterdam Neuroscience, Neurodegeneration, Amsterdam UMC, Vrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Elsmarieke van de Giessen
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
- Department of Radiology & Nuclear MedicineVrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
| | - Jori Tomassen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Anouk den Braber
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Sophie M. van der Landen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Eco J. C. de Geus
- Department of Biological PsychologyVU UniversityAmsterdamthe Netherlands
| | - Nienke Legdeur
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Argonde C. van Harten
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Calvin Trieu
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Casper de Boer
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Lior Kroeze
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear MedicineVrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Institute of Healthcare Engineering and the Institute of Neurology, University College LondonLondonUK
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
- Department of Psychiatry and NeuropsychologySchool for Mental Health and Neuroscience (MHeNS), Maastricht UniversityMaastrichtthe Netherlands
- Department of Neurobiology, Care Sciences and Society, Division of NeurogeriatricsKarolinska InstitutetSolnaSweden
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdamthe Netherlands
- Amsterdam Neuroscience, NeurodegenerationAmsterdamthe Netherlands
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Fang F, Quach B, Lawrence KG, van Dongen J, Marks JA, Lundgren S, Lin M, Odintsova VV, Costeira R, Xu Z, Zhou L, Mandal M, Xia Y, Vink JM, Bierut LJ, Ollikainen M, Taylor JA, Bell JT, Kaprio J, Boomsma DI, Xu K, Sandler DP, Hancock DB, Johnson EO. Trans-ancestry epigenome-wide association meta-analysis of DNA methylation with lifetime cannabis use. Mol Psychiatry 2024; 29:124-133. [PMID: 37935791 PMCID: PMC11078760 DOI: 10.1038/s41380-023-02310-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/09/2023]
Abstract
Cannabis is widely used worldwide, yet its links to health outcomes are not fully understood. DNA methylation can serve as a mediator to link environmental exposures to health outcomes. We conducted an epigenome-wide association study (EWAS) of peripheral blood-based DNA methylation and lifetime cannabis use (ever vs. never) in a meta-analysis including 9436 participants (7795 European and 1641 African ancestry) from seven cohorts. Accounting for effects of cigarette smoking, our trans-ancestry EWAS meta-analysis revealed four CpG sites significantly associated with lifetime cannabis use at a false discovery rate of 0.05 ( p < 5.85 × 10 - 7 ) : cg22572071 near gene ADGRF1, cg15280358 in ADAM12, cg00813162 in ACTN1, and cg01101459 near LINC01132. Additionally, our EWAS analysis in participants who never smoked cigarettes identified another epigenome-wide significant CpG site, cg14237301 annotated to APOBR. We used a leave-one-out approach to evaluate methylation scores constructed as a weighted sum of the significant CpGs. The best model can explain 3.79% of the variance in lifetime cannabis use. These findings unravel the DNA methylation changes associated with lifetime cannabis use that are independent of cigarette smoking and may serve as a starting point for further research on the mechanisms through which cannabis exposure impacts health outcomes.
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Affiliation(s)
- Fang Fang
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA.
| | - Bryan Quach
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Kaitlyn G Lawrence
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jesse A Marks
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Sara Lundgren
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Mingkuan Lin
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
| | - Veronika V Odintsova
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ricardo Costeira
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Zongli Xu
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Linran Zhou
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Meisha Mandal
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Yujing Xia
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Jacqueline M Vink
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Laura J Bierut
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, St. Louis, MO, USA
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Jordana T Bell
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Dana B Hancock
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Eric O Johnson
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
- Fellow Program, RTI International, Research Triangle Park, NC, USA
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7
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Kevenaar ST, van Bergen E, Oldehinkel AJ, Boomsma DI, Dolan CV. The relationship of school performance with self-control and grit is strongly genetic and weakly causal. NPJ SCIENCE OF LEARNING 2023; 8:53. [PMID: 38049407 PMCID: PMC10696063 DOI: 10.1038/s41539-023-00198-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 10/17/2023] [Indexed: 12/06/2023]
Abstract
The non-cognitive skills self-control and grit are often considered predictors of school performance, but whether this relationship is causal remains unclear. We investigated the causality of this association using a twin design. Specifically, we evaluated the direct impact of self-control and grit on school performance, while controlling for genetic or environmental influences common to all three traits (i.e., confounding). Teachers of 4891 Dutch 12-year-old twin pairs (of which 3837 were complete pairs) completed a survey about school performance (school grades), self-control (ASEBA self-control scale), and the perseverance aspect of grit. Our analysis aimed to determine the direct impact of self-control and grit on school performance, while simultaneously controlling for genetic or environmental confounding. Establishing the regression relationship corrected for confounding supports the interpretation of the regression relationship as causal. In all analyses, we corrected for sex, rater bias of the teachers, and parental socioeconomic status. Initially, in the standard regression, self-control, and grit explained 28.4% of the school performance variance. However, allowing for genetic confounding (due to genetic pleiotropy) revealed that most of this association could be attributed to genetic influences that the three traits share. In the presence of genetic pleiotropy, the phenotypic regression of school performance on self-control and grit accounted for only 4.4% (i.e., the effect size association with the causal hypothesis). In conclusion, self-control and grit predict school performance primarily due to genetic pleiotropy, with a much smaller causal effect (R2 = 4.4%). This suggests that interventions targeting self-control and grit alone may yield limited improvements in school performance.
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Affiliation(s)
- Sofieke T Kevenaar
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands
- Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Elsje van Bergen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands.
- Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
| | - Albertine J Oldehinkel
- University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, the Netherlands
| | - Conor V Dolan
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands
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Reus LM, Boltz T, Francia M, Bot M, Ramesh N, Koromina M, Pijnenburg YAL, den Braber A, van der Flier WM, Visser PJ, van der Lee SJ, Tijms BM, Teunissen CE, Loohuis LO, Ophoff RA. Quantitative trait loci mapping of circulating metabolites in cerebrospinal fluid to uncover biological mechanisms involved in brain-related phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.26.559021. [PMID: 37808647 PMCID: PMC10557608 DOI: 10.1101/2023.09.26.559021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Genomic studies of molecular traits have provided mechanistic insights into complex disease, though these lag behind for brain-related traits due to the inaccessibility of brain tissue. We leveraged cerebrospinal fluid (CSF) to study neurobiological mechanisms in vivo , measuring 5,543 CSF metabolites, the largest panel in CSF to date, in 977 individuals of European ancestry. Individuals originated from two separate cohorts including cognitively healthy subjects (n=490) and a well-characterized memory clinic sample, the Amsterdam Dementia Cohort (ADC, n=487). We performed metabolite quantitative trait loci (mQTL) mapping on CSF metabolomics and found 126 significant mQTLs, representing 65 unique CSF metabolites across 51 independent loci. To better understand the role of CSF mQTLs in brain-related disorders, we performed a metabolome-wide association study (MWAS), identifying 40 associations between CSF metabolites and brain traits. Similarly, over 90% of significant mQTLs demonstrated colocalized associations with brain-specific gene expression, unveiling potential neurobiological pathways.
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Kevenaar ST, Dolan CV, Boomsma DI, van Bergen E. Self-control and grit are associated with school performance mainly because of shared genetic effects. JCPP ADVANCES 2023; 3:e12159. [PMID: 37753153 PMCID: PMC10519738 DOI: 10.1002/jcv2.12159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 03/06/2023] [Indexed: 09/28/2023] Open
Abstract
Background By combining the classical twin design with regression analysis, we investigated the role of two non-cognitive factors, self-control and grit, in the prediction of school performance. We did so at the phenotypic, genetic, and environmental level. Methods Teachers filled out a survey on the twins' school performance (school grades for reading, literacy, and math), self-control (ASEBA self-control scale), and grit (the perseverance aspect) for 4891 Dutch 12-years-old twin pairs (3837 pairs with data for both and 1054 pairs with data for one of the twins). We employed regression analyses to first assess the contributions of self-control and grit to school performance at the phenotypic level, and next at the genetic and environmental level, while correcting for rater (teacher) effects, parental SES, and sex. Results Higher SES was associated with better school performance, self-control, and grit. On average, girls had more self-control and grit than boys. Corrected for sex, SES, and teacher rater effects, genetic factors accounted for 74%, 69%, and 58% of the phenotypic variance of school performance, self-control, and grit, respectively. Phenotypically, self-control and grit explained 28.3% of the variance in school performance. We found that this phenotypic result largely reflected genetic influences. Conclusions Children who have better self-control and are grittier tend to do better in school. Individual differences in these three traits are not correlated because of shared environmental influences, but mainly because of shared genetic factors.
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Affiliation(s)
- Sofieke T. Kevenaar
- Department of Biological PsychologyVrije Universiteit AmsterdamAmsterdamThe Netherlands
- Research Institute LEARN!Vrije Universiteit AmsterdamAmsterdamThe Netherlands
- Amsterdam Public Health Research InstituteAmsterdamThe Netherlands
| | - Conor V. Dolan
- Department of Biological PsychologyVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Dorret I. Boomsma
- Department of Biological PsychologyVrije Universiteit AmsterdamAmsterdamThe Netherlands
- Amsterdam Public Health Research InstituteAmsterdamThe Netherlands
- Amsterdam Reproduction and Development Research InstituteAmsterdamThe Netherlands
| | - Elsje van Bergen
- Department of Biological PsychologyVrije Universiteit AmsterdamAmsterdamThe Netherlands
- Research Institute LEARN!Vrije Universiteit AmsterdamAmsterdamThe Netherlands
- Amsterdam Public Health Research InstituteAmsterdamThe Netherlands
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Ni J, Wang P, Yin KJ, Yang XK, Cen H, Sui C, Wu GC, Pan HF. Novel insight into the aetiology of rheumatoid arthritis gained by a cross-tissue transcriptome-wide association study. RMD Open 2022; 8:e002529. [PMID: 37582060 PMCID: PMC9462377 DOI: 10.1136/rmdopen-2022-002529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/23/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Although genome-wide association studies (GWASs) have identified more than 100 loci associated with rheumatoid arthritis (RA) susceptibility, the causal genes and biological mechanisms remain largely unknown. METHODS A cross-tissue transcriptome-wide association study (TWAS) using the unified test for molecular signaturestool was performed to integrate GWAS summary statistics from 58 284 individuals (14 361 RA cases and 43 923 controls) with gene-expression matrix in the Genotype-Tissue Expression project. Subsequently, a single tissue by using FUSION software was conducted to validate the significant associations. We also compared the TWAS with different gene-based methodologies, including Summary Data Based Mendelian Randomization (SMR) and Multimarker Analysis of Genomic Annotation (MAGMA). Further in silico analyses (conditional and joint analysis, differential expression analysis and gene-set enrichment analysis) were used to deepen our understanding of genetic architecture and comorbidity aetiology of RA. RESULTS We identified a total of 47 significant candidate genes for RA in both cross-tissue and single-tissue test after multiple testing correction, of which 40 TWAS-identified genes were verified by SMR or MAGMA. Among them, 13 genes were situated outside of previously reported significant loci by RA GWAS. Both TWAS-based and MAGMA-based enrichment analyses illustrated the shared genetic determinants among autoimmune thyroid disease, asthma, type I diabetes mellitus and RA. CONCLUSION Our study unveils 13 new candidate genes whose predicted expression is associated with risk of RA, providing new insights into the underlying genetic architecture of RA.
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Affiliation(s)
- Jing Ni
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Peng Wang
- Teaching Center for Preventive Medicine, School of Public Health, Anhui Medical University, Hefei, China
| | - Kang-Jia Yin
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Xiao-Ke Yang
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Han Cen
- Department of Preventive Medicine, Ningbo University Medical School, Ningbo, Zhejiang, China
| | - Cong Sui
- Department of Orthopedics Trauma, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Guo-Cui Wu
- Department of Obstetrics and Gynecological Nursing, School of Nursing, Anhui Medical University, Hefei, Anhui, China
| | - Hai-Feng Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
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Feng R, Lu M, Yang Y, Luo P, Liu L, Xu K, Xu P. Genome- and transcriptome-wide association studies show that pulmonary embolism is associated with bone-forming proteins. Expert Rev Hematol 2022; 15:951-958. [PMID: 35848930 DOI: 10.1080/17474086.2022.2103534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BACKGROUND Pulmonary embolism (PE) is a leading cause of death in stroke patients and a severe health burden worldwide. There is a pressing need to understand the mechanisms by which it occurs and to identify at-risk patients efficiently and accurately. OBJECTIVES The aim of this paper was to analyze the genetic correlation between PE and human plasma proteins through genome-wide association study (GWAS) with transcriptome-wide association study (TWAS), in combination with mRNA expression profiling at three levels: DNA, RNA, and protein. METHODS First, based on data from GWAS in European populations, we performed a linkage disequilibrium score regression (LDSC) analysis of plasma proteins and PE in 3,283 individuals and additionally analyzed the genetic association between PE and fracture. Then, we performed a TWAS on PE GWAS data using skeletal muscle and blood for gene expression references. Finally, we validated the genetic correlation between PE and human plasma proteins by co-matching the genes encoding the identified proteins and those identified using TWAS with the differentially expressed genes obtained from mRNA expression profiling of PE (Figure1). RESULTS We identified five plasma proteins associated with PE, including hydroxycarboxylic acid receptor 2, defensin 118, and bone morphogenetic protein (BMP) 7, as well as a relationship between PE and fracture. Comparison of genes encoding these proteins with genes obtained from TWAS and then with differentially expressed genes obtained from PE mRNA expression profiling revealed that PE was highly correlated with the BMP family of genes.
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Affiliation(s)
- Ruoyang Feng
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, Shanxi, 710054, China
| | - Mengnan Lu
- Department of Pediatrics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yanni Yang
- Shaanxi University of Chinese Medicine, Xi'an, Shaanxi, China
| | - Pan Luo
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, Shanxi, 710054, China
| | - Lin Liu
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, Shanxi, 710054, China
| | - Ke Xu
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, Shanxi, 710054, China
| | - Peng Xu
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, Shanxi, 710054, China
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Teeuw J, Klein M, Mota NR, Brouwer RM, van ‘t Ent D, Al-Hassaan Z, Franke B, Boomsma DI, Hulshoff Pol HE. Multivariate Genetic Structure of Externalizing Behavior and Structural Brain Development in a Longitudinal Adolescent Twin Sample. Int J Mol Sci 2022; 23:ijms23063176. [PMID: 35328598 PMCID: PMC8949114 DOI: 10.3390/ijms23063176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/10/2022] [Accepted: 03/10/2022] [Indexed: 12/10/2022] Open
Abstract
Externalizing behavior in its more extreme form is often considered a problem to the individual, their families, teachers, and society as a whole. Several brain structures have been linked to externalizing behavior and such associations may arise if the (co)development of externalizing behavior and brain structures share the same genetic and/or environmental factor(s). We assessed externalizing behavior with the Child Behavior Checklist and Youth Self Report, and the brain volumes and white matter integrity (fractional anisotropy [FA] and mean diffusivity [MD]) with magnetic resonance imaging in the BrainSCALE cohort, which consisted of twins and their older siblings from 112 families measured longitudinally at ages 10, 13, and 18 years for the twins. Genetic covariance modeling based on the classical twin design, extended to also include siblings of twins, showed that genes influence externalizing behavior and changes therein (h2 up to 88%). More pronounced externalizing behavior was associated with higher FA (observed correlation rph up to +0.20) and lower MD (rph up to −0.20), with sizeable genetic correlations (FA ra up to +0.42; MD ra up to −0.33). The cortical gray matter (CGM; rph up to −0.20) and cerebral white matter (CWM; rph up to +0.20) volume were phenotypically but not genetically associated with externalizing behavior. These results suggest a potential mediating role for global brain structures in the display of externalizing behavior during adolescence that are both partially explained by the influence of the same genetic factor.
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Affiliation(s)
- Jalmar Teeuw
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands; (R.M.B.); (Z.A.-H.); (H.E.H.P.)
- Correspondence: ; Tel.: +31-(088)-75-53-387
| | - Marieke Klein
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA;
- Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands; (N.R.M.); (B.F.)
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 XZ Nijmegen, The Netherlands
| | - Nina Roth Mota
- Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands; (N.R.M.); (B.F.)
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 XZ Nijmegen, The Netherlands
| | - Rachel M. Brouwer
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands; (R.M.B.); (Z.A.-H.); (H.E.H.P.)
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Dennis van ‘t Ent
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (D.v.‘t.E.); (D.I.B.)
| | - Zyneb Al-Hassaan
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands; (R.M.B.); (Z.A.-H.); (H.E.H.P.)
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands; (N.R.M.); (B.F.)
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 XZ Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (D.v.‘t.E.); (D.I.B.)
- Amsterdam Public Health (APH) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Hilleke E. Hulshoff Pol
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands; (R.M.B.); (Z.A.-H.); (H.E.H.P.)
- Department of Psychology, Utrecht University, 3584 CS Utrecht, The Netherlands
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Subgroup Identification and Regression Analysis of Clustered and Heterogeneous Interval-Censored Data. MATHEMATICS 2022. [DOI: 10.3390/math10060862] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Clustered and heterogeneous interval-censored data occur in many fields such as medical studies. For example, in a migraine study with the Netherlands Twin Registry, the information including time to diagnosis of migraine and gender was collected for 3975 monozygotic and dizygotic twins. Since each study subject is observed only at discrete and periodic follow-up time points, the failure times of interest (i.e., the time when the individual first had a migraine) are known only to belong to certain intervals and hence are interval-censored. Furthermore, these twins come from different genetic backgrounds and may be associated with differential risks for developing migraines. For simultaneous subgroup identification and regression analysis of such data, we propose a latent Cox model where the number of subgroups is not assumed a priori but rather data-driven estimated. The nonparametric maximum likelihood method and an EM algorithm with monotone ascent property are also developed for estimating the model parameters. Simulation studies are conducted to assess the finite sample performance of the proposed estimation procedure. We further illustrate the proposed methodologies by an empirical analysis of migraine data.
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De T, Zhang H, Alarcon C, Lec B, Avitia J, Smithberger E, Chen C, Horvath M, Kwan S, Young M, Adhikari S, Kwon J, Pacheco J, Jarvik G, Wei WQ, Mentch F, Hakonarson H, Sleiman P, Gordon A, Harley J, Linneman J, Hebbring S, Parisiadou L, Perera MA. Genetic association of primary nonresponse to anti-TNFα therapy in patients with inflammatory bowel disease. Pharmacogenet Genomics 2022; 32:1-9. [PMID: 34380996 PMCID: PMC8578201 DOI: 10.1097/fpc.0000000000000445] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
OBJECTIVES Primary nonresponse (PNR) to antitumor necrosis factor-α (TNFα) biologics is a serious concern in patients with inflammatory bowel disease (IBD). We aimed to identify the genetic variants associated with PNR. PATIENTS AND METHODS Patients were recruited from outpatient GI clinics and PNR was determined using both clinical and endoscopic findings. A case-control genome-wide association study was performed in 589 IBD patients and associations were replicated in an independent cohort of 293 patients. Effect of the associated variant on gene expression and TNFα secretion was assessed by cell-based assays. Pleiotropic effects were investigated by Phenome-wide association study (PheWAS). RESULTS We identified rs34767465 as associated with PNR to anti-TNFα therapy (odds ratio: 2.07, 95% CI, 1.46-2.94, P = 2.43 × 10-7, [replication odds ratio: 1.8, 95% CI, 1.04-3.16, P = 0.03]). rs34767465 is a multiple-tissue expression quantitative trait loci for FAM114A2. Using RNA-sequencing and protein quantification from HapMap lymphoblastoid cell lines (LCLs), we found a significant decrease in FAM114A2 mRNA and protein expression in both heterozygous and homozygous genotypes when compared to wild type LCLs. TNFα secretion was significantly higher in THP-1 cells [differentiated into macrophages] with FAM114A2 knockdown versus controls. Immunoblotting experiments showed that depletion of FAM114A2 impaired autophagy-related pathway genes suggesting autophagy-mediated TNFα secretion as a potential mechanism. PheWAS showed rs34767465 was associated with comorbid conditions found in IBD patients (derangement of joints [P = 3.7 × 10-4], pigmentary iris degeneration [P = 5.9 × 10-4], diverticulum of esophagus [P = 7 × 10-4]). CONCLUSIONS We identified a variant rs34767465 associated with PNR to anti-TNFα biologics, which increases TNFα secretion through mechanism related to autophagy. rs34767465 may also explain the comorbidities associated with IBD.
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Affiliation(s)
- Tanima De
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Honghong Zhang
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Cristina Alarcon
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Bianca Lec
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Juan Avitia
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Erin Smithberger
- University of North Carolina, Chapel Hill, NC
- University of Alabama at Birmingham, AL
| | - Chuyu Chen
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Minnie Horvath
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | | | | | | | | | - Jennifer Pacheco
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Gail Jarvik
- Universtiy of Washington Medical Center, Departments of Medicine (Medical Genetics) and Genome Sciences, Seattle, WA
| | - Wei-Qi Wei
- Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Frank Mentch
- The Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Hakon Hakonarson
- The Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Patrick Sleiman
- The Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Adam Gordon
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - John Harley
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- US Department of Veterans Affairs Medical Center, Cincinnati, OH
| | - Jim Linneman
- Marshfield Clinic Research Institute Marshfield, WI
| | - Scott Hebbring
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI
| | - Loukia Parisiadou
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Minoli A. Perera
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL
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15
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Ke X, Tian X, Yao S, Wu H, Duan YY, Wang NN, Shi W, Yang TL, Dong SS, Huang D, Guo Y. Transcriptome-wide association study identifies multiple genes and pathways associated with thyroid function. Hum Mol Genet 2021; 31:1871-1883. [PMID: 34962261 DOI: 10.1093/hmg/ddab371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/03/2021] [Accepted: 12/20/2021] [Indexed: 11/12/2022] Open
Abstract
Thyroid dysfunction is a common endocrine disease measured by thyroid-stimulating hormone (TSH) level. Although more than 70 genetic loci associated with TSH have been reported through genome-wide association studies (GWASs), the variants can only explain a small fraction of the thyroid function heritability. To identify novel candidate genes for thyroid function, we conducted the first large-scale transcriptome-wide association study (TWAS) for thyroid function using GWAS-summary data for TSH levels in up to 119 715 individuals combined with pre-computed gene expression weights of six panels from four tissue types. The candidate genes identified by TWAS were further validated by TWAS replication and gene expression profiles. We identified 74 conditionally independent genes significantly associated with thyroid function, such as PDE8B (P = 1.67 × 10-282), PDE10A (P = 7.61 × 10-119), NR3C2 (P = 1.50 × 10-92), and CAPZB (P = 3.13 × 10-79). After TWAS replication using UKBB datasets, 26 genes were replicated for significant associations with thyroid-relevant diseases/traits. Among them, 16 gene were causal for their associations to thyroid-relevant diseases/traits and further validated in differential expression analyses, including two novel genes (MFSD6 and RBM47) that did not implicate in previous GWASs. Enrichment analyses detected several pathways associated with thyroid function, such as the cAMP signaling pathway (P = 7.27 × 10-4), hemostasis (P = 3.74 × 10-4), and platelet activation, signaling, and aggregation (P = 9.98 × 10-4). Our study identified multiple candidate genes and pathways associated with thyroid function, providing novel clues for revealing the genetic mechanisms of thyroid function and disease.
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Affiliation(s)
- Xin Ke
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China, 710049
| | - Xin Tian
- Department of Orthopaedics, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China
| | - Shi Yao
- National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China, 710004
| | - Hao Wu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China, 710049
| | - Yuan-Yuan Duan
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China, 710049
| | - Nai-Ning Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China, 710049
| | - Wei Shi
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China, 710049
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China, 710049.,National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China, 710004
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China, 710049.,Research Institute of Xi'an Jiaotong University, Hangzhou, Zhejiang, P. R. China
| | - Dageng Huang
- Department of Orthopaedics, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China, 710049.,Department of Orthopaedics, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China
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16
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van der Spek A, Karamujić-Čomić H, Pool R, Bot M, Beekman M, Garmaeva S, Arp PP, Henkelman S, Liu J, Alves AC, Willemsen G, van Grootheest G, Aubert G, Ikram MA, Jarvelin MR, Lansdorp P, Uitterlinden AG, Zhernakova A, Slagboom PE, Penninx BWJH, Boomsma DI, Amin N, van Duijn CM. Fat metabolism is associated with telomere length in six population-based studies. Hum Mol Genet 2021; 31:1159-1170. [PMID: 34875050 DOI: 10.1093/hmg/ddab281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 08/13/2021] [Accepted: 09/07/2021] [Indexed: 11/13/2022] Open
Abstract
Telomeres are repetitive DNA sequences located at the end of chromosomes, which are associated to biological aging, cardiovascular disease, cancer, and mortality. Lipid and fatty acid metabolism have been associated with telomere shortening. We have conducted an in-depth study investigating the association of metabolic biomarkers with telomere length (LTL). We performed an association analysis of 226 metabolic biomarkers with LTL using data from 11 775 individuals from six independent population-based cohorts (BBMRI-NL consortium). Metabolic biomarkers include lipoprotein lipids and subclasses, fatty acids, amino acids, glycolysis measures and ketone bodies. LTL was measured by quantitative polymerase chain reaction or FlowFISH. Linear regression analysis was performed adjusting for age, sex, lipid-lowering medication and cohort-specific covariates (model 1) and additionally for body mass index (BMI) and smoking (model 2), followed by inverse variance-weighted meta-analyses (significance threshold pmeta = 6.5x10-4). We identified four metabolic biomarkers positively associated with LTL, including two cholesterol to lipid ratios in small VLDL (S-VLDL-C % and S-VLDL-ce %) and two omega-6 fatty acid ratios (FAw6/FA and LA/FA). After additionally adjusting for BMI and smoking, these metabolic biomarkers remained associated with LTL with similar effect estimates. In addition, cholesterol esters in very small VLDL (XS-VLDL-ce) became significantly associated with LTL (p = 3.6x10-4). We replicated the association of FAw6/FA with LTL in an independent dataset of 7845 individuals (p = 1.9x10-4). To conclude, we identified multiple metabolic biomarkers involved in lipid and fatty acid metabolism that may be involved in LTL biology. Longitudinal studies are needed to exclude reversed causation.
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Affiliation(s)
- Ashley van der Spek
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.,SkylineDx B.V., Rotterdam, The Netherlands
| | - Hata Karamujić-Čomić
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit University Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health research institute, Amsterdam University Medical Centers, The Netherlands.,BBMRI-NL: Infrastructure for the Application of Metabolomics Technology in Epidemiology (RP4), The Netherlands
| | - Mariska Bot
- Department of Psychiatry and GGZ in Geest, Amsterdam Public Health research institute and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Sanzhima Garmaeva
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Pascal P Arp
- Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Sandra Henkelman
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jun Liu
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.,Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Alexessander Couto Alves
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.,School of Biosciences and Medicine, University of Surrey, Guildford, UK
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit University Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health research institute, Amsterdam University Medical Centers, The Netherlands
| | - Gerard van Grootheest
- Department of Psychiatry and GGZ in Geest, Amsterdam Public Health research institute and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Geraldine Aubert
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, V5Z 1L3 British Columbia, Canada
| | | | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.,Center for Life Course Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland.,Biocenter Oulu, University of Oulu, Oulu, Finland.,Unit of Primary Care, Oulu University Hospital, Oulu, Finland
| | - Peter Lansdorp
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, V5Z 1L3 British Columbia, Canada.,Departments of Medical Genetics and Hematology, University of British Columbia, Vancouver, V6T 1Z4 British Columbia, Canada
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.,Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry and GGZ in Geest, Amsterdam Public Health research institute and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit University Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health research institute, Amsterdam University Medical Centers, The Netherlands.,BBMRI-NL: Infrastructure for the Application of Metabolomics Technology in Epidemiology (RP4), The Netherlands
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.,Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.,Nuffield Department of Population Health, University of Oxford, Oxford, UK
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17
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van Dongen J, Gordon SD, McRae AF, Odintsova VV, Mbarek H, Breeze CE, Sugden K, Lundgren S, Castillo-Fernandez JE, Hannon E, Moffitt TE, Hagenbeek FA, van Beijsterveldt CEM, Jan Hottenga J, Tsai PC, Min JL, Hemani G, Ehli EA, Paul F, Stern CD, Heijmans BT, Slagboom PE, Daxinger L, van der Maarel SM, de Geus EJC, Willemsen G, Montgomery GW, Reversade B, Ollikainen M, Kaprio J, Spector TD, Bell JT, Mill J, Caspi A, Martin NG, Boomsma DI. Identical twins carry a persistent epigenetic signature of early genome programming. Nat Commun 2021; 12:5618. [PMID: 34584077 PMCID: PMC8479069 DOI: 10.1038/s41467-021-25583-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 07/19/2021] [Indexed: 02/08/2023] Open
Abstract
Monozygotic (MZ) twins and higher-order multiples arise when a zygote splits during pre-implantation stages of development. The mechanisms underpinning this event have remained a mystery. Because MZ twinning rarely runs in families, the leading hypothesis is that it occurs at random. Here, we show that MZ twinning is strongly associated with a stable DNA methylation signature in adult somatic tissues. This signature spans regions near telomeres and centromeres, Polycomb-repressed regions and heterochromatin, genes involved in cell-adhesion, WNT signaling, cell fate, and putative human metastable epialleles. Our study also demonstrates a never-anticipated corollary: because identical twins keep a lifelong molecular signature, we can retrospectively diagnose if a person was conceived as monozygotic twin.
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Affiliation(s)
- Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
| | - Scott D Gordon
- Queensland Institute of Medical Research Berghofer, Brisbane, QLD, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Veronika V Odintsova
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Hamdi Mbarek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | | | - Karen Sugden
- Department of Psychology and Neuroscience and Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Sara Lundgren
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | | | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Terrie E Moffitt
- Department of Psychology and Neuroscience and Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Catharina E M van Beijsterveldt
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Pei-Chien Tsai
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Josine L Min
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Erik A Ehli
- Avera Institute for Human Genetics, Sioux Falls, SD, USA
| | - Franziska Paul
- Institute of Molecular and Cellular Biology, A*STAR, Singapore, Singapore
| | - Claudio D Stern
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Lucia Daxinger
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Grant W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Bruno Reversade
- Institute of Molecular and Cellular Biology, A*STAR, Singapore, Singapore
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
- Medical Genetics Department, KOC University, School of Medicine, Istanbul, Turkey
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Avshalom Caspi
- Department of Psychology and Neuroscience and Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Nicholas G Martin
- Queensland Institute of Medical Research Berghofer, Brisbane, QLD, Australia
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
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18
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van der Laan CM, Morosoli-García JJ, van de Weijer SGA, Colodro-Conde L, Lupton MK, Mitchell BL, McAloney K, Parker R, Burns JM, Hickie IB, Pool R, Hottenga JJ, Martin NG, Medland SE, Nivard MG, Boomsma DI. Continuity of Genetic Risk for Aggressive Behavior Across the Life-Course. Behav Genet 2021; 51:592-606. [PMID: 34390460 PMCID: PMC8390412 DOI: 10.1007/s10519-021-10076-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 06/23/2021] [Indexed: 11/24/2022]
Abstract
We test whether genetic influences that explain individual differences in aggression in early life also explain individual differences across the life-course. In two cohorts from The Netherlands (N = 13,471) and Australia (N = 5628), polygenic scores (PGSs) were computed based on a genome-wide meta-analysis of childhood/adolescence aggression. In a novel analytic approach, we ran a mixed effects model for each age (Netherlands: 12-70 years, Australia: 16-73 years), with observations at the focus age weighted as 1, and decaying weights for ages further away. We call this approach a 'rolling weights' model. In The Netherlands, the estimated effect of the PGS was relatively similar from age 12 to age 41, and decreased from age 41-70. In Australia, there was a peak in the effect of the PGS around age 40 years. These results are a first indication from a molecular genetics perspective that genetic influences on aggressive behavior that are expressed in childhood continue to play a role later in life.
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Affiliation(s)
- Camiel M van der Laan
- Biological Psychology, Vrije Universiteit, Van der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands.
- The Netherlands Institute for the Study of Crime and Law Enforcement, Amsterdam, The Netherlands.
| | | | - Steve G A van de Weijer
- The Netherlands Institute for the Study of Crime and Law Enforcement, Amsterdam, The Netherlands
| | | | | | | | - Kerrie McAloney
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Richard Parker
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jane M Burns
- Faculty of Health Sciences, The University of Sydney, Sydney, Australia
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, Australia
| | - René Pool
- Biological Psychology, Vrije Universiteit, Van der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands
| | - Jouke-Jan Hottenga
- Biological Psychology, Vrije Universiteit, Van der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands
| | | | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Michel G Nivard
- Biological Psychology, Vrije Universiteit, Van der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Biological Psychology, Vrije Universiteit, Van der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands
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19
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Azarova I, Klyosova E, Polonikov A. The Link between Type 2 Diabetes Mellitus and the Polymorphisms of Glutathione-Metabolizing Genes Suggests a New Hypothesis Explaining Disease Initiation and Progression. Life (Basel) 2021; 11:life11090886. [PMID: 34575035 PMCID: PMC8466482 DOI: 10.3390/life11090886] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 08/25/2021] [Accepted: 08/27/2021] [Indexed: 01/11/2023] Open
Abstract
The present study investigated whether type 2 diabetes (T2D) is associated with polymorphisms of genes encoding glutathione-metabolizing enzymes such as glutathione synthetase (GSS) and gamma-glutamyl transferase 7 (GGT7). A total of 3198 unrelated Russian subjects including 1572 T2D patients and 1626 healthy subjects were enrolled. Single nucleotide polymorphisms (SNPs) of the GSS and GGT7 genes were genotyped using the MassArray-4 system. We found that the GSS and GGT7 gene polymorphisms alone and in combinations are associated with T2D risk regardless of sex, age, and body mass index, as well as correlated with plasma glutathione, hydrogen peroxide, and fasting blood glucose levels. Polymorphisms of GSS (rs13041792) and GGT7 (rs6119534 and rs11546155) genes were associated with the tissue-specific expression of genes involved in unfolded protein response and the regulation of proteostasis. Transcriptome-wide association analysis has shown that the pancreatic expression of some of these genes such as EDEM2, MYH7B, MAP1LC3A, and CPNE1 is linked to the genetic risk of T2D. A comprehensive analysis of the data allowed proposing a new hypothesis for the etiology of type 2 diabetes that endogenous glutathione deficiency might be a key condition responsible for the impaired folding of proinsulin which triggered an unfolded protein response, ultimately leading to beta-cell apoptosis and disease development.
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Affiliation(s)
- Iuliia Azarova
- Department of Biological Chemistry, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia;
- Laboratory of Biochemical Genetics and Metabolomics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya St., 305041 Kursk, Russia;
| | - Elena Klyosova
- Laboratory of Biochemical Genetics and Metabolomics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya St., 305041 Kursk, Russia;
| | - Alexey Polonikov
- Laboratory of Statistical Genetics and Bioinformatics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya St., 305041 Kursk, Russia
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia
- Correspondence: ; Tel.: +7-471-258-8147
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20
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Kreeke JA, Nguyen HT, Konijnenberg E, Tomassen J, Braber A, Kate M, Yaqub M, Berckel B, Lammertsma AA, Boomsma DI, Tan HS, Visser PJ, Verbraak FD. Longitudinal retinal layer changes in preclinical Alzheimer's disease. Acta Ophthalmol 2021; 99:538-544. [PMID: 33073531 PMCID: PMC8451744 DOI: 10.1111/aos.14640] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 06/21/2020] [Accepted: 09/09/2020] [Indexed: 12/15/2022]
Abstract
Purpose Several studies found reduced retinal thickness on optical coherence tomography (OCT) in Alzheimer’s disease (AD), even in preclinical stages, labelling this technique of interest as biomarker. In this study, we examine retinal thickness changes in preclinical AD, as defined by cognitively normal individuals with amyloid‐beta (Aβ) on positron emission tomography (PET). Methods For this monocentre study, 145 cognitively healthy monozygotic twins aged ≥ 60 were included from the Netherlands Twin Register taking part in the EMIF‐AD PreclinAD study. At baseline, participants underwent [18F] flutemetamol PET that was visually rated for cortical Aβ. Binding potential was calculated as continuous measure for Aβ. Optical coherence tomography (OCT) was performed at baseline and after 22 months to assess changes in total and individual inner retinal layer thickness in the macular region (ETDRS circles) and peripapillary retinal nerve fibre layer thickness. Differences in rate of change between amyloid‐beta positive and negative individuals and associations between binding potential and change in retinal thickness were evaluated. Results Sixteen participants (11%) were positive for Aβ. Change in retinal thickness did not differ in any region between Aβ+ and Aβ− individuals. A positive association between binding potential and change in inner plexiform layer thickness was observed in the inner macular ring (beta = 1.708, CI = 0.575 to 2.841, p = 0.003). Conclusion Aβ+ individuals did not differ in rate of change of any retinal layer compared to controls, but higher binding potential at baseline was associated with less IPL thinning over time. Optical coherence tomography (OCT) as a longitudinal screening tool for preclinical AD seems limited, but IPL changes offer leads for further research.
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Affiliation(s)
- Jacoba A. Kreeke
- Ophthalmology Dept. Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Hoang Ton Nguyen
- Ophthalmology Dept. Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Elles Konijnenberg
- Alzheimer Center Neuroscience Amsterdam Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Jori Tomassen
- Alzheimer Center Neuroscience Amsterdam Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Anouk Braber
- Alzheimer Center Neuroscience Amsterdam Amsterdam UMC, location VUmc Amsterdam The Netherlands
- Dept. of Biological Psychology VU University Amsterdam Amsterdam The Netherlands
| | - Mara Kate
- Alzheimer Center Neuroscience Amsterdam Amsterdam UMC, location VUmc Amsterdam The Netherlands
- Department of Radiology and Nuclear Medicine Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Bart Berckel
- Department of Radiology and Nuclear Medicine Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Adriaan A. Lammertsma
- Department of Radiology and Nuclear Medicine Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Dorret I. Boomsma
- Dept. of Biological Psychology VU University Amsterdam Amsterdam The Netherlands
| | - H. Stevie Tan
- Ophthalmology Dept. Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Neuroscience Amsterdam Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Frank D. Verbraak
- Ophthalmology Dept. Amsterdam UMC, location VUmc Amsterdam The Netherlands
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21
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van Dongen J, Hagenbeek FA, Suderman M, Roetman PJ, Sugden K, Chiocchetti AG, Ismail K, Mulder RH, Hafferty JD, Adams MJ, Walker RM, Morris SW, Lahti J, Küpers LK, Escaramis G, Alemany S, Jan Bonder M, Meijer M, Ip HF, Jansen R, Baselmans BML, Parmar P, Lowry E, Streit F, Sirignano L, Send TS, Frank J, Jylhävä J, Wang Y, Mishra PP, Colins OF, Corcoran DL, Poulton R, Mill J, Hannon E, Arseneault L, Korhonen T, Vuoksimaa E, Felix JF, Bakermans-Kranenburg MJ, Campbell A, Czamara D, Binder E, Corpeleijn E, Gonzalez JR, Grazuleviciene R, Gutzkow KB, Evandt J, Vafeiadi M, Klein M, van der Meer D, Ligthart L, Kluft C, Davies GE, Hakulinen C, Keltikangas-Järvinen L, Franke B, Freitag CM, Konrad K, Hervas A, Fernández-Rivas A, Vetro A, Raitakari O, Lehtimäki T, Vermeiren R, Strandberg T, Räikkönen K, Snieder H, Witt SH, Deuschle M, Pedersen NL, Hägg S, Sunyer J, Franke L, Kaprio J, Ollikainen M, Moffitt TE, Tiemeier H, van IJzendoorn MH, Relton C, Vrijheid M, Sebert S, Jarvelin MR, Caspi A, Evans KL, McIntosh AM, Bartels M, Boomsma DI. DNA methylation signatures of aggression and closely related constructs: A meta-analysis of epigenome-wide studies across the lifespan. Mol Psychiatry 2021; 26:2148-2162. [PMID: 33420481 PMCID: PMC8263810 DOI: 10.1038/s41380-020-00987-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 11/04/2020] [Accepted: 12/04/2020] [Indexed: 01/06/2023]
Abstract
DNA methylation profiles of aggressive behavior may capture lifetime cumulative effects of genetic, stochastic, and environmental influences associated with aggression. Here, we report the first large meta-analysis of epigenome-wide association studies (EWAS) of aggressive behavior (N = 15,324 participants). In peripheral blood samples of 14,434 participants from 18 cohorts with mean ages ranging from 7 to 68 years, 13 methylation sites were significantly associated with aggression (alpha = 1.2 × 10-7; Bonferroni correction). In cord blood samples of 2425 children from five cohorts with aggression assessed at mean ages ranging from 4 to 7 years, 83% of these sites showed the same direction of association with childhood aggression (r = 0.74, p = 0.006) but no epigenome-wide significant sites were found. Top-sites (48 at a false discovery rate of 5% in the peripheral blood meta-analysis or in a combined meta-analysis of peripheral blood and cord blood) have been associated with chemical exposures, smoking, cognition, metabolic traits, and genetic variation (mQTLs). Three genes whose expression levels were associated with top-sites were previously linked to schizophrenia and general risk tolerance. At six CpGs, DNA methylation variation in blood mirrors variation in the brain. On average 44% (range = 3-82%) of the aggression-methylation association was explained by current and former smoking and BMI. These findings point at loci that are sensitive to chemical exposures with potential implications for neuronal functions. We hope these results to be a starting point for studies leading to applications as peripheral biomarkers and to reveal causal relationships with aggression and related traits.
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Affiliation(s)
- Jenny van Dongen
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Matthew Suderman
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Peter J Roetman
- Curium-LUMC, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Oegstgeest, The Netherlands
| | - Karen Sugden
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Andreas G Chiocchetti
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, Goethe-Universität, Frankfurt am Main, Germany
| | - Khadeeja Ismail
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Rosa H Mulder
- Institute of Education and Child Studies, Leiden University, Leiden, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Stewart W Morris
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Jari Lahti
- Turku Institute for Advanced Studies, University of Turku, Turku, Finland
- Department of Psychology and logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Leanne K Küpers
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands
| | - Georgia Escaramis
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
- Department of Biomedical Science, Faculty of Medicine and Health Science, University of Barcelona, Barcelona, Spain
- Research Group on Statistics, Econometrics and Health (GRECS), UdG, Girona, Spain
| | - Silvia Alemany
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Marc Jan Bonder
- Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Mandy Meijer
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Hill F Ip
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Rick Jansen
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bart M L Baselmans
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Priyanka Parmar
- Center for Life Course Health Research, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- Biocenter Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
| | - Estelle Lowry
- Center for Life Course Health Research, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- Queen's University Belfast, Belfast, UK
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Lea Sirignano
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Tabea S Send
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Josef Frank
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Juulia Jylhävä
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Yunzhang Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Pashupati Prasad Mishra
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33520, Finland
| | - Olivier F Colins
- Curium-LUMC, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Oegstgeest, The Netherlands
- Department of Special Needs Education, Ghent University, Ghent, Belgium
| | - David L Corcoran
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Louise Arseneault
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Tellervo Korhonen
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Eero Vuoksimaa
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Janine F Felix
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Darina Czamara
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804, Munich, Germany
| | - Elisabeth Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804, Munich, Germany
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, 12 Executive Park Dr, Atlanta, GA, 30329, USA
| | - Eva Corpeleijn
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands
| | - Juan R Gonzalez
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Regina Grazuleviciene
- Department of Environmental Sciences, Vytautas Magnus University, K. Donelaicio str. 58, 44248, Kaunas, Lithuania
| | - Kristine B Gutzkow
- Department of Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Jorunn Evandt
- Department of Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Marina Vafeiadi
- Department of Social Medicine, University of Crete, Heraklion, Greece
| | - Marieke Klein
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- University Medical Center Utrecht, UMC Utrecht Brain Center, Department of Psychiatry, Utrecht, The Netherlands
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Lannie Ligthart
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - Gareth E Davies
- Avera Institute for Human Genetics, 3720 W. 69th Street, Sioux Falls, SD, 57108, USA
| | - Christian Hakulinen
- Department of Psychology and logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | | | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Christine M Freitag
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, Goethe-Universität, Frankfurt am Main, Germany
| | - Kerstin Konrad
- University Hospital, RWTH Aachen, Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Aachen, Germany
- JARA-Brain Institute II, Molecular Neuroscience and Neuroimaging (INM-11), RWTH Aachen & Research Centre Juelich, Juelich, Germany
| | - Amaia Hervas
- Hospital Universitario Mutua de Terrassa, Child and Adolescent Mental Health Service, Barcelona, Spain
| | | | - Agnes Vetro
- Szeged University, Department of Pediatrics and Pediatrics health center, Child and Adolescent Psychiatry, Szeged, Hungary
| | - Olli Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33520, Finland
| | - Robert Vermeiren
- Curium-LUMC, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Oegstgeest, The Netherlands
- Youz, Parnassia Group, The Hague, The Netherlands
| | - Timo Strandberg
- Helsinki University Central Hospital, Geriatrics, Helsinki, Finland
| | - Katri Räikkönen
- Department of Psychology and logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Harold Snieder
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Michael Deuschle
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jordi Sunyer
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Terrie E Moffitt
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Social and Behavioral Science, Harvard TH Chan School of Public Health, Boston, USA
| | - Marinus H van IJzendoorn
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Department of Clinical, Educational and Health Psychology, UCL, University of London, London, UK
| | - Caroline Relton
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Sylvain Sebert
- Center for Life Course Health Research, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- Biocenter Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- Section of Genomics of Common Disease, Department of Medicine, Imperial College London, Hammersmith Hospital Campus, Burlington Danes Building, Du Cane Road, London, W12 0NN, UK
| | - Marjo-Riitta Jarvelin
- Center for Life Course Health Research, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- Biocenter Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- MRC-PHE Centre for Environment and Health, Imperial College London, Hammersmith Hospital Campus, Burlington Danes Building, Du Cane Road, London, W12 0NN, UK
| | - Avshalom Caspi
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Meike Bartels
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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22
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Konijnenberg E, Tomassen J, den Braber A, Ten Kate M, Yaqub M, Mulder SD, Nivard MG, Vanderstichele H, Lammertsma AA, Teunissen CE, van Berckel BNM, Boomsma DI, Scheltens P, Tijms BM, Visser PJ. Onset of Preclinical Alzheimer Disease in Monozygotic Twins. Ann Neurol 2021; 89:987-1000. [PMID: 33583080 PMCID: PMC8251701 DOI: 10.1002/ana.26048] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 02/08/2021] [Accepted: 02/10/2021] [Indexed: 01/01/2023]
Abstract
Objective The present work was undertaken to study the genetic contribution to the start of Alzheimer's disease (AD) with amyloid and tau biomarkers in cognitively intact older identical twins. Methods We studied in 96 monozygotic twin‐pairs relationships between amyloid‐beta (Aβ) aggregation as measured by the Aβ1–42/1–40 ratio in cerebrospinal fluid (CSF; n = 126) and positron emission tomography (PET, n = 194), and CSF markers for Aβ production (beta‐secretase 1, Aβ1–40, and Aβ1–38) and CSF tau. Associations among markers were tested with generalized estimating equations including a random effect for twin status, adjusted for age, gender, and apolipoprotein E ε4 genotype. We used twin analyses to determine relative contributions of genetic and/or environmental factors to AD pathophysiological processes. Results Twenty‐seven individuals (14%) had an abnormal amyloid PET, and 14 twin‐pairs (15%) showed discordant amyloid PET scans. Within twin‐pairs, Aβ production markers and total‐tau (t‐tau) levels strongly correlated (r range = 0.73–0.86, all p < 0.0001), and Aβ aggregation markers and 181‐phosphorylated‐tau (p‐tau) levels correlated moderately strongly (r range = 0.50–0.64, all p < 0.0001). Cross‐twin cross‐trait analysis showed that Aβ1–38 in one twin correlated with Aβ1–42/1–40 ratios, and t‐tau and p‐tau levels in their cotwins (r range = −0.28 to 0.58, all p < .007). Within‐pair differences in Aβ production markers related to differences in tau levels (r range = 0.49–0.61, all p < 0.0001). Twin discordance analyses suggest that Aβ production and tau levels show coordinated increases in very early AD. Interpretation Our results suggest a substantial genetic/shared environmental background contributes to both Aβ and tau increases, suggesting that modulation of environmental risk factors may aid in delaying the onset of AD pathophysiological processes. ANN NEUROL 2021;89:987–1000
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Affiliation(s)
- Elles Konijnenberg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Jori Tomassen
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Anouk den Braber
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Department of Biological Psychology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Mara Ten Kate
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Sandra D Mulder
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Michel G Nivard
- Department of Biological Psychology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Hugo Vanderstichele
- Biomarkable bv, working for this study on behalf of ADx NeuroSciences, Ghent, Belgium
| | - Adriaan A Lammertsma
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Bart N M van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Maastricht, the Netherlands.,Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Instutet, Stockholm, Sweden
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23
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Pistis G, Milaneschi Y, Vandeleur CL, Lasserre AM, Penninx BW, Lamers F, Boomsma DI, Hottenga JJ, Marques-Vidal P, Vollenweider P, Waeber G, Aubry JM, Preisig M, Kutalik Z. Obesity and atypical depression symptoms: findings from Mendelian randomization in two European cohorts. Transl Psychiatry 2021; 11:96. [PMID: 33542229 PMCID: PMC7862438 DOI: 10.1038/s41398-021-01236-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 01/12/2021] [Accepted: 01/15/2021] [Indexed: 02/08/2023] Open
Abstract
Studies considering the causal role of body mass index (BMI) for the predisposition of major depressive disorder (MDD) based on a Mendelian Randomization (MR) approach have shown contradictory results. These inconsistent findings may be attributable to the heterogeneity of MDD; in fact, several studies have documented associations between BMI and mainly the atypical subtype of MDD. Using a MR approach, we investigated the potential causal role of obesity in both the atypical subtype and its five specific symptoms assessed according to the Statistical Manual of Mental Disorders (DSM), in two large European cohorts, CoLaus|PsyCoLaus (n = 3350, 1461 cases and 1889 controls) and NESDA|NTR (n = 4139, 1182 cases and 2957 controls). We first tested general obesity measured by BMI and then the body fat distribution measured by waist-to-hip ratio (WHR). Results suggested that BMI is potentially causally related to the symptom increase in appetite, for which inverse variance weighted, simple median and weighted median MR regression estimated slopes were 0.68 (SE = 0.23, p = 0.004), 0.77 (SE = 0.37, p = 0.036), and 1.11 (SE = 0.39, p = 0.004). No causal effect of BMI or WHR was found on the risk of the atypical subtype or for any of the other atypical symptoms. Our findings show that higher obesity is likely causal for the specific symptom of increase in appetite in depressed participants and reiterate the need to study depression at the granular level of its symptoms to further elucidate potential causal relationships and gain additional insight into its biological underpinnings.
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Affiliation(s)
- Giorgio Pistis
- Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
| | - Yuri Milaneschi
- grid.420193.d0000 0004 0546 0540Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Vrije Universiteit Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - Caroline L. Vandeleur
- grid.8515.90000 0001 0423 4662Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Aurélie M. Lasserre
- grid.8515.90000 0001 0423 4662Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Brenda W.J.H. Penninx
- grid.420193.d0000 0004 0546 0540Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Vrije Universiteit Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - Femke Lamers
- grid.420193.d0000 0004 0546 0540Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Vrije Universiteit Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - Dorret I. Boomsma
- grid.12380.380000 0004 1754 9227Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | - Jouke-Jan Hottenga
- grid.12380.380000 0004 1754 9227Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | - Pedro Marques-Vidal
- grid.8515.90000 0001 0423 4662Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Peter Vollenweider
- grid.8515.90000 0001 0423 4662Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Gérard Waeber
- grid.8515.90000 0001 0423 4662Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jean-Michel Aubry
- grid.150338.c0000 0001 0721 9812Department of Psychiatry, University Hospital of Geneva, Geneva, Switzerland
| | - Martin Preisig
- grid.8515.90000 0001 0423 4662Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Zoltán Kutalik
- grid.9851.50000 0001 2165 4204Institute of Primary Care and Public Health (Unisante), University of Lausanne, Lausanne, Switzerland ,grid.419765.80000 0001 2223 3006Swiss Institute of Bioinformatics, Lausanne, Switzerland
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24
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Brouwer RM, Schutte J, Janssen R, Boomsma DI, Hulshoff Pol HE, Schnack HG. The Speed of Development of Adolescent Brain Age Depends on Sex and Is Genetically Determined. Cereb Cortex 2021; 31:1296-1306. [PMID: 33073292 PMCID: PMC8204942 DOI: 10.1093/cercor/bhaa296] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/18/2020] [Accepted: 09/10/2020] [Indexed: 11/20/2022] Open
Abstract
Children and adolescents show high variability in brain development. Brain age-the estimated biological age of an individual brain-can be used to index developmental stage. In a longitudinal sample of adolescents (age 9-23 years), including monozygotic and dizygotic twins and their siblings, structural magnetic resonance imaging scans (N = 673) at 3 time points were acquired. Using brain morphology data of different types and at different spatial scales, brain age predictors were trained and validated. Differences in brain age between males and females were assessed and the heritability of individual variation in brain age gaps was calculated. On average, females were ahead of males by at most 1 year, but similar aging patterns were found for both sexes. The difference between brain age and chronological age was heritable, as was the change in brain age gap over time. In conclusion, females and males show similar developmental ("aging") patterns but, on average, females pass through this development earlier. Reliable brain age predictors may be used to detect (extreme) deviations in developmental state of the brain early, possibly indicating aberrant development as a sign of risk of neurodevelopmental disorders.
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Affiliation(s)
- Rachel M Brouwer
- Department of Psychiatry, University Medical Center Utrecht
Brain Center, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Jelle Schutte
- Department of Psychiatry, University Medical Center Utrecht
Brain Center, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Ronald Janssen
- Department of Psychiatry, University Medical Center Utrecht
Brain Center, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology and Netherlands Twin
Register, VU University Amsterdam, 1081 HV
Amsterdam, the Netherlands
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, University Medical Center Utrecht
Brain Center, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Hugo G Schnack
- Department of Psychiatry, University Medical Center Utrecht
Brain Center, Utrecht University, 3584 CX Utrecht, the Netherlands
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25
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Penke L, Denissen JJA, Miller GF. Evolution, genes, and inter‐disciplinary personality research. EUROPEAN JOURNAL OF PERSONALITY 2020. [DOI: 10.1002/per.657] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Most commentaries welcomed an evolutionary genetic approach to personality, but several raised concerns about our integrative model. In response, we clarify the scientific status of evolutionary genetic theory and explain the plausibility and value of our evolutionary genetic model of personality, despite some shortcomings with the currently available theories and data. We also have a closer look at mate choice for personality traits, point to promising ways to assess evolutionarily relevant environmental factors and defend higher‐order personality domains and the g‐factor as the best units for evolutionary genetic analyses. Finally, we discuss which extensions of and alternatives to our model appear most fruitful, and end with a call for more inter‐disciplinary personality research grounded in evolutionary theory. Copyright © 2007 John Wiley & Sons, Ltd.
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Affiliation(s)
- Lars Penke
- Humboldt University, Berlin, Germany
- International Max Planck Research School LIFE, Berlin, Germany
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26
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Li-Gao R, Boomsma DI, de Geus EJC, Denollet J, Kupper N. The Heritability of Type D Personality by an Extended Twin-Pedigree Analysis in the Netherlands Twin Register. Behav Genet 2020; 51:1-11. [PMID: 33064246 PMCID: PMC7815549 DOI: 10.1007/s10519-020-10023-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 09/21/2020] [Indexed: 01/01/2023]
Abstract
Type D (Distressed) personality combines negative affectivity (NA) and social inhibition (SI) and is associated with an increased risk of cardiovascular disease. We aimed to (1) validate a new proxy based on the Achenbach System of Empirically Based Assessment (ASEBA) for Type D personality and its NA and SI subcomponents and (2) estimate the heritability of the Type D proxy in an extended twin-pedigree design in the Netherlands Twin Register (NTR). Proxies for the dichotomous Type D classification, and continuous NA, SI, and NAxSI (the continuous measure of Type D) scales were created based on 12 ASEBA items for 30,433 NTR participants (16,449 twins and 13,984 relatives from 11,106 pedigrees) and sources of variation were analyzed in the ‘Mendel’ software package. We estimated additive and non-additive genetic variance components, shared household and unique environmental variance components and ran bivariate models to estimate the genetic and non-genetic covariance between NA and SI. The Type D proxy showed good reliability and construct validity. The best fitting genetic model included additive and non-additive genetic effects with broad-sense heritabilities for NA, SI and NAxSI estimated at 49%, 50% and 49%, respectively. Household effects showed small contributions (4–9%) to the total phenotypic variation. The genetic correlation between NA and SI was .66 (reflecting both additive and non-additive genetic components). Thus, Type D personality and its NA and SI subcomponents are heritable, with a shared genetic basis for the two subcomponents.
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Affiliation(s)
- Ruifang Li-Gao
- CoRPS Center of Research On Psychology in Somatic Diseases, Tilburg University, Tilburg, The Netherlands.
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands.
- Dept of Medical and Clinical Psychology, Center of Research On Psychology in Somatic Diseases (CoRPS), Tilburg University, P. O. Box 90153, 5000 LE, Tilburg, The Netherlands.
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | - Johan Denollet
- CoRPS Center of Research On Psychology in Somatic Diseases, Tilburg University, Tilburg, The Netherlands
| | - Nina Kupper
- CoRPS Center of Research On Psychology in Somatic Diseases, Tilburg University, Tilburg, The Netherlands
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27
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Junge C, Valkenburg PM, Deković M, Branje S. The building blocks of social competence: Contributions of the Consortium of Individual Development. Dev Cogn Neurosci 2020; 45:100861. [PMID: 32957027 PMCID: PMC7509192 DOI: 10.1016/j.dcn.2020.100861] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 06/26/2020] [Accepted: 09/01/2020] [Indexed: 02/06/2023] Open
Abstract
Social competence refers to the ability to engage in meaningful interactions with others. It is a crucial skill potentially malleable to interventions. Nevertheless, it remains difficult to select which children, which periods in a child's life, and which underlying skills form optimal targets for interventions. Development of social competence is complex to characterize because (a) it is by nature context- dependent; (b) it is subserved by multiple relevant processes that develop at different times in a child's life; and (c) over the years multiple, possibly conflicting, ways have been coined to index a child's social competence. The current paper elaborates upon a theoretical model of social competence developed by Rose-Krasnor (Rose- Krasnor, 1997; Rose-Krasnor and Denham, 2009), and it makes concrete how underlying skills and the variety of contexts of social interaction are both relevant dimensions of social competence that might change over development. It then illustrates how the cohorts and work packages in the Consortium on Individual Development each provide empirical contributions necessary for testing this model on the development of social competence.
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Affiliation(s)
- Caroline Junge
- Departments of Developmental and Experimental Psychology, Utrecht University, Utrecht, the Netherlands.
| | - Patti M Valkenburg
- Amsterdam School of Communication Research ASCoR, University of Amsterdam, Amsterdam, the Netherlands
| | - Maja Deković
- Department of Clinical Child and Family Studies, Utrecht University, Utrecht, the Netherlands
| | - Susan Branje
- Department of Youth and Family, Utrecht University, Utrecht, the Netherlands
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Legdeur N, Tijms BM, Konijnenberg E, den Braber A, ten Kate M, Sudre CH, Tomassen J, Badissi M, Yaqub M, Barkhof F, van Berckel BN, Boomsma DI, Scheltens P, Holstege H, Maier AB, Visser PJ. Associations of Brain Pathology Cognitive and Physical Markers With Age in Cognitively Normal Individuals Aged 60-102 Years. J Gerontol A Biol Sci Med Sci 2020; 75:1609-1617. [PMID: 31411322 PMCID: PMC7494041 DOI: 10.1093/gerona/glz180] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Indexed: 01/23/2023] Open
Abstract
The prevalence of brain pathologies increases with age and cognitive and physical functions worsen over the lifetime. It is unclear whether these processes show a similar increase with age. We studied the association of markers for brain pathology cognitive and physical functions with age in 288 cognitively normal individuals aged 60-102 years selected from the cross-sectional EMIF-AD PreclinAD and 90+ Study at the Amsterdam UMC. An abnormal score was consistent with a score below the 5th percentile in the 60- to 70-year-old individuals. Prevalence of abnormal scores was estimated using Generalized Estimating Equations (GEE) models. The prevalence of abnormal handgrip strength, the Digit Symbol Substitution Test, and hippocampal volume showed the fastest increase with age and abnormal MMSE score, muscle mass, and amyloid aggregation the lowest. The increase in prevalence of abnormal markers was partly dependent on sex, level of education, and amyloid aggregation. We did not find a consistent pattern in which markers of brain pathology cognitive and physical processes became abnormal with age.
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Affiliation(s)
- Nienke Legdeur
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Elles Konijnenberg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Anouk den Braber
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mara ten Kate
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Carole H Sudre
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Jori Tomassen
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Maryam Badissi
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- European Society of Neuroradiology (ESNR), Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | - Bart N van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Henne Holstege
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Andrea B Maier
- Department of Medicine and Aged Care, @AgeMelbourne, Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia
- Department of Human Movement Sciences, @AgeAmsterdam, Vrije Universiteit Amsterdam, Research Institute Amsterdam Movement Sciences, Amsterdam, The Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
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Abstract
Our current society is characterized by an increased availability of industrially processed foods with high salt, fat and sugar content. How is it that some people prefer these unhealthy foods while others prefer more healthy foods? It is suggested that both genetic and environmental factors play a role. The aim of this study was to (1) identify food preference clusters in the largest twin-family study into food preference to date and (2) determine the relative contribution of genetic and environmental factors to individual differences in food preference in the Netherlands. Principal component analysis was performed to identify the preference clusters by using data on food liking/disliking from 16,541 adult multiples and their family members. To estimate the heritability of food preference, the data of 7833 twins were used in structural equation models. We identified seven food preference clusters (Meat, Fish, Fruits, Vegetables, Savory snacks, Sweet snacks and Spices) and one cluster with Drinks. Broad-sense heritability (additive [A] + dominant [D] genetic factors) for these clusters varied between .36 and .60. Dominant genetic effects were found for the clusters Fruit, Fish (males only) and Spices. Quantitative sex differences were found for Meat, Fish and Savory snacks and Drinks. To conclude, our study convincingly showed that genetic factors play a significant role in food preference. A next important step is to identify these genes because genetic vulnerability for food preference is expected to be linked to actual food consumption and different diet-related disorders.
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Vink JM, Veul L, Abdellaoui A, Hottenga JJ, Boomsma DI, Verweij KJH. Illicit drug use and the genetic overlap with Cannabis use. Drug Alcohol Depend 2020; 213:108102. [PMID: 32585418 DOI: 10.1016/j.drugalcdep.2020.108102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 05/01/2020] [Accepted: 05/26/2020] [Indexed: 11/16/2022]
Abstract
BACKGROUND The use of illicit substances is correlated, meaning that individuals who use one illicit substance are more likely to also use another illicit substance. This association could (partly) be explained by overlapping genetic factors. Genetic overlap may indicate a common underlying genetic predisposition, or can be the result of a causal association. METHODS Polygenic scores for lifetime cannabis use were generated in a sample of Dutch participants (N = 8348). We tested the association of a PGS for cannabis use with ecstasy, stimulants and a broad category of illicit drug use. To explore the nature of the relationship: (1) these analyses were repeated separately in cannabis users and non-users and (2) monozogytic twin pairs discordant for cannabis use were compared on their drug use. RESULTS The lifetime prevalence was 24.8 % for cannabis, 6.2 % for ecstasy, 6.5 % for stimulants and 7.1 % for any illicit drug use. Significant, positive associations were found between PGS for cannabis use with ecstasy use, stimulants and any illicit drug use. These associations seemed to be stronger in cannabis users compared to non-users for both ecstasy and stimulant use, but only in people born after 1968 and not significant after correction for multiple testing. The discordant twin pair analyses suggested that cannabis use could play a causal role in drug use. CONCLUSIONS The genetic liability underlying cannabis use significantly explained variability in ecstasy, stimulant and any illicit drug use. Further research should further explore the underlying mechanism to understand the nature of the association.
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Affiliation(s)
- Jacqueline M Vink
- Behavioural Science Institute, Radboud University, Montessorilaan 3, 6525 HR, Nijmegen, the Netherlands.
| | - Laura Veul
- Amsterdam UMC, location AMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
| | - Abdel Abdellaoui
- Amsterdam UMC, location AMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
| | - Jouke-Jan Hottenga
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, De Boelelaan 1105, 1081 HV, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, De Boelelaan 1105, 1081 HV, Amsterdam, the Netherlands
| | - Karin J H Verweij
- Amsterdam UMC, location AMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
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Van Keulen BJ, Dolan CV, Andrew R, Walker BR, Hulshoff Pol HE, Boomsma DI, Rotteveel J, Finken MJ. Exploring the Temporal Relation between Body Mass Index and Corticosteroid Metabolite Excretion in Childhood. Nutrients 2020; 12:nu12051525. [PMID: 32456232 PMCID: PMC7284460 DOI: 10.3390/nu12051525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 05/10/2020] [Accepted: 05/20/2020] [Indexed: 11/22/2022] Open
Abstract
Childhood obesity is associated with alterations in hypothalamus–pituitary–adrenal (HPA) axis activity. However, it is unknown whether these alterations are a cause or a consequence of obesity. This study aimed to explore the temporal relationship between cortisol production and metabolism, and body mass index (BMI). This prospective follow-up study included 218 children (of whom 50% were male), born between 1995 and 1996, who were assessed at the ages of 9, 12 and 17 years. Morning urine samples were collected for assessment of cortisol metabolites by gas chromatography-tandem mass spectrometry, enabling the calculation of cortisol metabolite excretion rate and cortisol metabolic pathways. A cross-lagged regression model was used to determine whether BMI at various ages during childhood predicted later cortisol production and metabolism parameters, or vice versa. The cross-lagged regression coefficients showed that BMI positively predicted cortisol metabolite excretion (p = 0.03), and not vice versa (p = 0.33). In addition, BMI predicted the later balance of 11β-hydroxysteroid dehydrogenase (HSD) activities (p = 0.07), and not vice versa (p = 0.55). Finally, cytochrome P450 3A4 activity positively predicted later BMI (p = 0.01). Our study suggests that changes in BMI across the normal range predict alterations in HPA axis activity. Therefore, the alterations in HPA axis activity as observed in earlier studies among children with obesity may be a consequence rather than a cause of increased BMI.
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Affiliation(s)
- Britt J. Van Keulen
- Emma Children’s Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Pediatric Endocrinology, Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (J.R.); (M.J.J.F.)
- Correspondence: ; Tel.: +31-20-4444-444
| | - Conor V. Dolan
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-9, 1081 BT, Amsterdam, The Netherlands; (C.V.D.); (D.I.B.)
| | - Ruth Andrew
- Centre for Cardiovascular Science, University of Edinburgh, Queen’s Medical Research Institute, 47, Little France Crescent, Edinburgh EH16 4TJ, UK; (R.A.); (B.R.W.)
| | - Brian R. Walker
- Centre for Cardiovascular Science, University of Edinburgh, Queen’s Medical Research Institute, 47, Little France Crescent, Edinburgh EH16 4TJ, UK; (R.A.); (B.R.W.)
- Institute of Genetic Medicine, Newcastle University, Central Pkwy, Newcastle upon Tyne NE1 3BZ, UK
| | - Hilleke E. Hulshoff Pol
- Department of Psychiatry, University Medical Center Utrecht, Brain Center, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands;
| | - Dorret I. Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-9, 1081 BT, Amsterdam, The Netherlands; (C.V.D.); (D.I.B.)
| | - Joost Rotteveel
- Emma Children’s Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Pediatric Endocrinology, Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (J.R.); (M.J.J.F.)
| | - Martijn J.J. Finken
- Emma Children’s Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Pediatric Endocrinology, Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (J.R.); (M.J.J.F.)
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Qi X, Yu F, Wen Y, Li P, Cheng B, Ma M, Cheng S, Zhang L, Liang C, Liu L, Zhang F. Integration of transcriptome-wide association study and messenger RNA expression profile to identify genes associated with osteoarthritis. Bone Joint Res 2020; 9:130-138. [PMID: 32435465 PMCID: PMC7229301 DOI: 10.1302/2046-3758.93.bjr-2019-0137.r1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Aims Osteoarthritis (OA) is the most prevalent joint disease. However, the specific and definitive genetic mechanisms of OA are still unclear. Methods Tissue-related transcriptome-wide association studies (TWAS) of hip OA and knee OA were performed utilizing the genome-wide association study (GWAS) data of hip OA and knee OA (including 2,396 hospital-diagnosed hip OA patients versus 9,593 controls, and 4,462 hospital-diagnosed knee OA patients versus 17,885 controls) and gene expression reference to skeletal muscle and blood. The OA-associated genes identified by TWAS were further compared with the differentially expressed genes detected by the messenger RNA (mRNA) expression profiles of hip OA and knee OA. Functional enrichment and annotation analysis of identified genes was performed by the DAVID and FUMAGWAS tools. Results We detected 33 common genes, eight common gene ontology (GO) terms, and one common pathway for hip OA, such as calcium and integrin-binding protein 1 (CIB1) (PTWAS = 0.025, FCmRNA = -1.575 for skeletal muscle), adrenomedullin (ADM) (PTWAS = 0.022, FCmRNA = -4.644 for blood), Golgi apparatus (PTWAS <0.001, PmRNA = 0.012 for blood), and phosphatidylinositol 3' -kinase-protein kinase B (PI3K-Akt) signalling pathway (PTWAS = 0.033, PmRNA = 0.005 for blood). For knee OA, we detected 24 common genes, eight common GO terms, and two common pathways, such as histocompatibility complex, class II, DR beta 1 (HLA-DRB1) (PTWAS = 0.040, FCmRNA = 4.062 for skeletal muscle), Follistatin-like 1 (FSTL1) (PTWAS = 0.048, FCmRNA = 3.000 for blood), cytoplasm (PTWAS < 0.001, PmRNA = 0.005 for blood), and complement and coagulation cascades (PTWAS = 0.017, PmRNA = 0.001 for skeletal muscle). Conclusion We identified a group of OA-associated genes and pathways, providing novel clues for understanding the genetic mechanism of OA. Cite this article:Bone Joint Res. 2020;9(3):130–138.
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Affiliation(s)
- Xin Qi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Fangfang Yu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Ping Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Mei Ma
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Lu Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Chujun Liang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
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van de Kreeke JA, Legdeur N, Badissi M, Nguyen HT, Konijnenberg E, Tomassen J, Ten Kate M, den Braber A, Maier AB, Tan HS, Verbraak FD, Visser PJ. Ocular biomarkers for cognitive impairment in nonagenarians; a prospective cross-sectional study. BMC Geriatr 2020; 20:155. [PMID: 32345233 PMCID: PMC7189586 DOI: 10.1186/s12877-020-01556-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 04/13/2020] [Indexed: 12/03/2022] Open
Abstract
Background Ocular imaging receives much attention as a source of potential biomarkers for dementia. In the present study, we analyze these ocular biomarkers in cognitively impaired and healthy participants in a population aged over 90 years (= nonagenarian), and elucidate the effects of age on these biomarkers. Methods For this prospective cross-sectional study, we included individuals from the EMIF-AD 90+ study, consisting of a cognitively healthy (N = 67) and cognitively impaired group (N = 33), and the EMIF-AD PreclinAD study, consisting of cognitively healthy controls aged ≥60 (N = 198). Participants underwent Optical Coherence Tomography (OCT) and fundus photography of both eyes. OCT was used to asses total and individual inner retinal layer thickness in the macular region (Early Treatment Diabetic Retinopathy Study circles) as well as peripapillary retinal nerve fiber layer thickness, fundus images were analyzed with Singapore I Vessel Assessment to obtain 7 retinal vascular parameters. Values for both eyes were averaged. Differences in ocular biomarkers between the 2 nonagenarian groups were analyzed using linear regression, differences between the individual nonagenarian groups and controls were analyzed using generalized estimating equations. Results Ocular biomarkers did not differ between the healthy and cognitively impaired nonagenarian groups. 19 out of 22 ocular biomarkers assessed in this study differed between either nonagenarian group and the younger controls. Conclusion The ocular biomarkers assessed in this study were not associated with cognitive impairment in nonagenarians, making their use as a screening tool for dementing disorders in this group limited. However, ocular biomarkers were significantly associated with chronological age, which were very similar to those ascribed to occur in Alzheimer’s Disease.
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Affiliation(s)
- Jacoba A van de Kreeke
- Ophthalmology Department, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081HV, Amsterdam, The Netherlands.
| | - Nienke Legdeur
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Maryam Badissi
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - H Ton Nguyen
- Ophthalmology Department, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081HV, Amsterdam, The Netherlands
| | - Elles Konijnenberg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Jori Tomassen
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Mara Ten Kate
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Anouk den Braber
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Andrea B Maier
- Department of Medicine and Aged Care, @AgeMelbourne, Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia.,Department of Human Movement Sciences, @AgeAmsterdam, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - H Stevie Tan
- Ophthalmology Department, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081HV, Amsterdam, The Netherlands
| | - Frank D Verbraak
- Ophthalmology Department, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081HV, Amsterdam, The Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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Abstract
The many devastating mental health outcomes associated with chronic loneliness is the motivation behind research into examining personal and demographic characteristics of the lonely. The present study sought to examine the connection of where people live (degree of urbanization) and what people do (leisure activities) with self-report of loneliness in a large sample (N = 8356) of unrelated Dutch adults. Information regarding where people live and what they do in their leisure time was entered into a regression analysis for self-reported loneliness. The overall regression was significant and accounted for 2.8% of the loneliness scale scores. Significant independent predictors for loneliness were living in heavily urbanized areas and engaging in fewer social activities. People who went sightseeing or to amusement parks/zoos or who participated in clubs reported being less lonely. Spending time using a computer predicted higher self-report loneliness scores. Consistent with previous research, after controlling for other variables, gender was not a significant predictor of loneliness but both a younger age and a curvilinear or U-shaped curve of age predicted loneliness (the younger and the much older). The results suggest that meaningful interpersonal interactions may result in lower feelings of loneliness.
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35
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Allegrini AG, Verweij KJH, Abdellaoui A, Treur JL, Hottenga JJ, Willemsen G, Boomsma DI, Vink JM. Genetic Vulnerability for Smoking and Cannabis Use: Associations With E-Cigarette and Water Pipe Use. Nicotine Tob Res 2020; 21:723-730. [PMID: 30053134 DOI: 10.1093/ntr/nty150] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Accepted: 07/17/2018] [Indexed: 02/03/2023]
Abstract
INTRODUCTION Cigarette smoking and cannabis use are heritable traits and share, at least in part, a common genetic substrate. In recent years, the prevalence of alternative methods of nicotine intakes, such as electronic cigarette (e-cigarette) and water pipe use, has risen substantially. We tested whether the genetic vulnerability underlying cigarettes smoking and cannabis use explained variability in e-cigarette and water pipe use phenotypes, as these vaping methods are alternatives for smoking tobacco cigarettes and joints. METHODS On the basis of the summary statistics of the International Cannabis Consortium and the Tobacco and Genetics Consortium, we generated polygenic risk scores (PRSs) for smoking and cannabis use traits, and used these to predict e-cigarette and water pipe use phenotypes in a sample of 5025 individuals from the Netherlands Twin Register. RESULTS PRSs for cigarettes per day were positively associated with lifetime e-cigarette use and early initiation of water pipe use, but only in ex-smokers (odds ratio = 1.43, R2 = 1.56%, p = .011) and never cigarette smokers (odds ratio = 1.35, R2 = 1.60%, p = .013) respectively. CONCLUSIONS Most associations of PRSs for cigarette smoking and cannabis use with e-cigarette and water pipe use were not significant, potentially due to a lack of power. The significant associations between genetic liability to smoking heaviness with e-cigarette and water pipe phenotypes are in line with studies indicating a common genetic background for substance-use phenotypes. These associations emerged only in nonsmokers, and future studies should investigate the nature of this observation. IMPLICATIONS Our study showed that genetic vulnerability to smoking heaviness is associated with lifetime e-cigarette use and age at initiation of water pipe use. This finding has implications for the current debate on whether alternative smoking methods, such as usage of vaping devices, predispose to smoking initiation and related behaviors.
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Affiliation(s)
- Andrea G Allegrini
- Department of Developmental Psychopathology, Behavioural Science Institute, Faculty of Social Sciences, Radboud University, Nijmegen, The Netherlands
| | - Karin J H Verweij
- Department of Developmental Psychopathology, Behavioural Science Institute, Faculty of Social Sciences, Radboud University, Nijmegen, The Netherlands.,Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Abdel Abdellaoui
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jorien L Treur
- Department of Developmental Psychopathology, Behavioural Science Institute, Faculty of Social Sciences, Radboud University, Nijmegen, The Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - Jacqueline M Vink
- Department of Developmental Psychopathology, Behavioural Science Institute, Faculty of Social Sciences, Radboud University, Nijmegen, The Netherlands
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Hagenbeek FA, Roetman PJ, Pool R, Kluft C, Harms AC, van Dongen J, Colins OF, Talens S, van Beijsterveldt CEM, Vandenbosch MMLJZ, de Zeeuw EL, Déjean S, Fanos V, Ehli EA, Davies GE, Hottenga JJ, Hankemeier T, Bartels M, Vermeiren RRJM, Boomsma DI. Urinary Amine and Organic Acid Metabolites Evaluated as Markers for Childhood Aggression: The ACTION Biomarker Study. Front Psychiatry 2020; 11:165. [PMID: 32296350 PMCID: PMC7138132 DOI: 10.3389/fpsyt.2020.00165] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 02/21/2020] [Indexed: 01/05/2023] Open
Abstract
Biomarkers are of interest as potential diagnostic and predictive instruments in personalized medicine. We present the first urinary metabolomics biomarker study of childhood aggression. We aim to examine the association of urinary metabolites and neurotransmitter ratios involved in key metabolic and neurotransmitter pathways in a large cohort of twins (N = 1,347) and clinic-referred children (N = 183) with an average age of 9.7 years. This study is part of ACTION (Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies), in which we developed a standardized protocol for large-scale collection of urine samples in children. Our analytical design consisted of three phases: a discovery phase in twins scoring low or high on aggression (N = 783); a replication phase in twin pairs discordant for aggression (N = 378); and a validation phase in clinical cases and matched twin controls (N = 367). In the discovery phase, 6 biomarkers were significantly associated with childhood aggression, of which the association of O-phosphoserine (β = 0.36; SE = 0.09; p = 0.004), and gamma-L-glutamyl-L-alanine (β = 0.32; SE = 0.09; p = 0.01) remained significant after multiple testing. Although non-significant, the directions of effect were congruent between the discovery and replication analyses for six biomarkers and two neurotransmitter ratios and the concentrations of 6 amines differed between low and high aggressive twins. In the validation analyses, the top biomarkers and neurotransmitter ratios, with congruent directions of effect, showed no significant associations with childhood aggression. We find suggestive evidence for associations of childhood aggression with metabolic dysregulation of neurotransmission, oxidative stress, and energy metabolism. Although replication is required, our findings provide starting points to investigate causal and pleiotropic effects of these dysregulations on childhood aggression.
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Affiliation(s)
- Fiona A. Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Peter J. Roetman
- Curium-LUMC, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Leiden, Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | | | - Amy C. Harms
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands
- The Netherlands Metabolomics Centre, Leiden, Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Olivier F. Colins
- Curium-LUMC, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Leiden, Netherlands
- Department Special Needs Education, Ghent University, Ghent, Belgium
| | | | | | | | - Eveline L. de Zeeuw
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Sébastien Déjean
- Toulouse Mathematics Institute, University of Toulouse, CNRS, Toulouse, France
| | - Vassilios Fanos
- Department of Surgical Sciences, University of Cagliari and Neonatal Intensive Care Unit, Cagliari, Italy
| | - Erik A. Ehli
- Avera Institute for Human Genetics, Sioux Falls, SD, United States
| | - Gareth E. Davies
- Avera Institute for Human Genetics, Sioux Falls, SD, United States
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Thomas Hankemeier
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands
- The Netherlands Metabolomics Centre, Leiden, Netherlands
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, Netherlands
- Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Robert R. J. M. Vermeiren
- Curium-LUMC, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Leiden, Netherlands
| | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, Netherlands
- Amsterdam Neuroscience, Amsterdam, Netherlands
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37
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Long-Term Stability of Cortisol Production and Metabolism Throughout Adolescence: Longitudinal Twin Study. Twin Res Hum Genet 2020; 23:33-38. [PMID: 32209144 DOI: 10.1017/thg.2020.6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Life-course experiences have been postulated to program hypothalamus-pituitary-adrenal (HPA) axis activity, suggesting that HPA axis activity is, at least partially, stable over time. Yet, there is paucity of data on the long-term stability of cortisol production and metabolism. We performed a prospective follow-up study in twins recruited from a nationwide register to estimate the stability of cortisol production and metabolism over time, and the contribution of genetic and environmental factors to this stability. In total, 218 healthy mono- and dizygotic twins were included. At the ages of 9, 12 and 17 years, morning urine samples were collected for assessment (by gas chromatography-tandem mass spectrometry) of cortisol metabolites, enabling the calculation of cortisol metabolite excretion rate and cortisol metabolism activity. Our results showed a low stability for both cortisol metabolite excretion rate (with correlations <.20) and cortisol metabolism activity indices (with correlations of .25 to .46 between 9 and 12 years, -.02 to .15 between 12 and 17 years and .09 to .28 between 9 and 17 years). Because of the low stability over time, genetic and environmental contributions to this stability were difficult to assess, although it seemed to be mostly determined by genetic factors. The low stability in both cortisol production and metabolism between ages 9 and 17 years reflects the dynamic nature of the HPA axis.
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38
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Bot M, Milaneschi Y, Al-Shehri T, Amin N, Garmaeva S, Onderwater GLJ, Pool R, Thesing CS, Vijfhuizen LS, Vogelzangs N, Arts ICW, Demirkan A, van Duijn C, van Greevenbroek M, van der Kallen CJH, Köhler S, Ligthart L, van den Maagdenberg AMJM, Mook-Kanamori DO, de Mutsert R, Tiemeier H, Schram MT, Stehouwer CDA, Terwindt GM, Willems van Dijk K, Fu J, Zhernakova A, Beekman M, Slagboom PE, Boomsma DI, Penninx BWJH. Metabolomics Profile in Depression: A Pooled Analysis of 230 Metabolic Markers in 5283 Cases With Depression and 10,145 Controls. Biol Psychiatry 2020; 87:409-418. [PMID: 31635762 DOI: 10.1016/j.biopsych.2019.08.016] [Citation(s) in RCA: 136] [Impact Index Per Article: 27.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 08/19/2019] [Accepted: 08/19/2019] [Indexed: 11/29/2022]
Abstract
BACKGROUND Depression has been associated with metabolic alterations, which adversely impact cardiometabolic health. Here, a comprehensive set of metabolic markers, predominantly lipids, was compared between depressed and nondepressed persons. METHODS Nine Dutch cohorts were included, comprising 10,145 control subjects and 5283 persons with depression, established with diagnostic interviews or questionnaires. A proton nuclear magnetic resonance metabolomics platform provided 230 metabolite measures: 51 lipids, fatty acids, and low-molecular-weight metabolites; 98 lipid composition and particle concentration measures of lipoprotein subclasses; and 81 lipid and fatty acids ratios. For each metabolite measure, logistic regression analyses adjusted for gender, age, smoking, fasting status, and lipid-modifying medication were performed within cohort, followed by random-effects meta-analyses. RESULTS Of the 51 lipids, fatty acids, and low-molecular-weight metabolites, 21 were significantly related to depression (false discovery rate q < .05). Higher levels of apolipoprotein B, very-low-density lipoprotein cholesterol, triglycerides, diglycerides, total and monounsaturated fatty acids, fatty acid chain length, glycoprotein acetyls, tyrosine, and isoleucine and lower levels of high-density lipoprotein cholesterol, acetate, and apolipoprotein A1 were associated with increased odds of depression. Analyses of lipid composition indicators confirmed a shift toward less high-density lipoprotein and more very-low-density lipoprotein and triglyceride particles in depression. Associations appeared generally consistent across gender, age, and body mass index strata and across cohorts with depressive diagnoses versus symptoms. CONCLUSIONS This large-scale meta-analysis indicates a clear distinctive profile of circulating lipid metabolites associated with depression, potentially opening new prevention or treatment avenues for depression and its associated cardiometabolic comorbidity.
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Affiliation(s)
- Mariska Bot
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Tahani Al-Shehri
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Najaf Amin
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Sanzhima Garmaeva
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Rene Pool
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Carisha S Thesing
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Lisanne S Vijfhuizen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Nicole Vogelzangs
- Department of Epidemiology, Maastricht University, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands; Maastricht Center for Systems Biology, Maastricht University, Maastricht, The Netherlands
| | - Ilja C W Arts
- Department of Epidemiology, Maastricht University, Maastricht, The Netherlands; Department of Internal Medicine, Maastricht University, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands; Maastricht Center for Systems Biology, Maastricht University, Maastricht, The Netherlands
| | - Ayse Demirkan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Human Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Cornelia van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Marleen van Greevenbroek
- Department of Internal Medicine, Maastricht University, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Carla J H van der Kallen
- Department of Internal Medicine, Maastricht University, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Sebastian Köhler
- Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands; School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Lannie Ligthart
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Arn M J M van den Maagdenberg
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands; Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Henning Tiemeier
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Miranda T Schram
- Department of Internal Medicine, Maastricht University, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Coen D A Stehouwer
- Department of Internal Medicine, Maastricht University, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Gisela M Terwindt
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands; Department of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jingyuan Fu
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands; Department of Pediatrics, University Medical Center Groningen, Groningen, The Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands
| | - Marian Beekman
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - P Eline Slagboom
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
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39
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van de Kreeke JA, Nguyen HT, Konijnenberg E, Tomassen J, den Braber A, Ten Kate M, Yaqub M, van Berckel B, Lammertsma AA, Boomsma DI, Tan SH, Verbraak F, Visser PJ. Optical coherence tomography angiography in preclinical Alzheimer's disease. Br J Ophthalmol 2020; 104:157-161. [PMID: 31118186 PMCID: PMC7025728 DOI: 10.1136/bjophthalmol-2019-314127] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 03/19/2019] [Accepted: 04/02/2019] [Indexed: 12/31/2022]
Abstract
BACKGROUND/AIMS As a protrusion from the brain, the retina might reflect the status of the brain. Previous studies showed a decrease in vessel density and foveal avascular zone (FAZ) enlargement on optical coherence tomography angiography (OCTA) in individuals suffering from Alzheimer's disease (AD). This study aims to assess whether such changes are already present in preclinical stages of AD, in a population of monozygotic (MZ) twins. METHODS 124 cognitively healthy individuals (MZ twins, ages 60-93 years) underwent [18F]flutemetamol amyloid positron emission tomography (PET) scanning and OCTA. PET scans were visually rated for cortical amyloid-beta (Aβ) positivity. Parametric global cortical non-displaceable binding potential (BPND) was used as a continuous measure for Aβ aggregation. FAZ size and vessel densities for the inner and outer ring of the macular ETDRS grid and in a 3-6 mm ring around the optic nerve head (ONH) were measured.OCTA measures were associated with visual Aβ score, BPND and amyloid load estimated by twin concordance on visual Aβ score. Twin correlations were estimated as a measure of maximum heritability of OCTA measures. RESULTS 13 of 124 participants were Aβ+. Aβ+ individuals had significantly higher vessel density than Aβ- individuals in all regions but did not differ in FAZ size. Twin analyses showed a positive association between and vessel densities in all regions. BPND tended to be associated with higher vessel density in the inner ring. Twin correlations were moderate/high for all OCTA parameters except vessel density around the ONH, which correlated weakly. CONCLUSION Retinal vessel density was higher in individuals with preclinical AD.
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Affiliation(s)
| | - Hoang-Ton Nguyen
- Department of Ophthalmology, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
| | - Elles Konijnenberg
- Alzheimer Center, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
| | - Jori Tomassen
- Alzheimer Center, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
| | - Anouk den Braber
- Alzheimer Center, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mara Ten Kate
- Alzheimer Center, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
| | - Maqsood Yaqub
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
| | - Bart van Berckel
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
| | - Adriaan A Lammertsma
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Stevie H Tan
- Department of Ophthalmology, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
| | - Frank Verbraak
- Department of Ophthalmology, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
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40
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van Keulen BJ, Dolan CV, Andrew R, Walker BR, Hulshoff Pol HE, Boomsma DI, Rotteveel J, Finken MJJ. Heritability of Cortisol Production and Metabolism Throughout Adolescence. J Clin Endocrinol Metab 2020; 105:5586817. [PMID: 31608377 PMCID: PMC7046020 DOI: 10.1210/clinem/dgz016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 09/25/2019] [Indexed: 12/12/2022]
Abstract
CONTEXT Inter-individual differences in cortisol production and metabolism emerge with age and may be explained by genetic factors. OBJECTIVE To estimate the relative contributions of genetic and environmental factors to inter-individual differences in cortisol production and metabolism throughout adolescence. DESIGN Prospective follow-up study of twins. SETTING Nationwide register. PARTICIPANTS 218 mono- and dizygotic twins (N = 109 pairs) born between 1995 amd 1996, recruited from the Netherlands Twin Register. Cortisol metabolites were determined in 213, 169, and 160 urine samples at the ages of 9, 12, and 17, respectively. MAIN OUTCOME MEASURES The total contribution of genetic factors (broad-sense heritability) and shared and unshared environmental influences to inter-individual differences in cortisol production and activities of 5α-reductase, 5β-reductase, and 11β-hydroxysteroid dehydrogenases and cytochrome P450 3A4. RESULTS For cortisol production rate at the ages of 9, 12, and 17, broad-sense heritability was estimated as 42%, 30%, and 0%, respectively, and the remainder of the variance was explained by unshared environmental factors. For cortisol metabolism indices, the following heritability was observed: for the A-ring reductases (5α-and 5β-reductases), broad-sense heritability increased with age (to >50%), while for the other indices (renal 11β-HSD2, global 11β-HSD, and CYP3A4), the contribution of genetic factors was highest (68%, 18%, and 67%, respectively) at age 12. CONCLUSIONS The contribution of genetic factors to inter-individual differences in cortisol production decreased between 12 and 17y, indicative of a predominant role of individual circumstances. For cortisol metabolism, distinct patterns of genetic and environmental influences were observed, with heritability that either increased with age or peaked at age 12y.
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Affiliation(s)
- Britt J van Keulen
- Emma Children’s Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Pediatric Endocrinology, Amsterdam, The Netherlands
- Correspondence and Requests: Britt J van Keulen, MD, Emma Children’s Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Pediatric endocrinology, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands. E-mail:
| | - Conor V Dolan
- Department of Biological Psychology, Vrije Universiteit Amsterdam, The Netherlands
| | - Ruth Andrew
- Centre for Cardiovascular Science, University of Edinburgh, Queen’s Medical Research Institute, Edinburgh, UK
| | - Brian R Walker
- Centre for Cardiovascular Science, University of Edinburgh, Queen’s Medical Research Institute, Edinburgh, UK
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, Brian Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, The Netherlands
| | - Joost Rotteveel
- Emma Children’s Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Pediatric Endocrinology, Amsterdam, The Netherlands
| | - Martijn J J Finken
- Emma Children’s Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Pediatric Endocrinology, Amsterdam, The Netherlands
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41
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Liu J, Lahousse L, Nivard MG, Bot M, Chen L, van Klinken JB, Thesing CS, Beekman M, van den Akker EB, Slieker RC, Waterham E, van der Kallen CJH, de Boer I, Li-Gao R, Vojinovic D, Amin N, Radjabzadeh D, Kraaij R, Alferink LJM, Murad SD, Uitterlinden AG, Willemsen G, Pool R, Milaneschi Y, van Heemst D, Suchiman HED, Rutters F, Elders PJM, Beulens JWJ, van der Heijden AAWA, van Greevenbroek MMJ, Arts ICW, Onderwater GLJ, van den Maagdenberg AMJM, Mook-Kanamori DO, Hankemeier T, Terwindt GM, Stehouwer CDA, Geleijnse JM, 't Hart LM, Slagboom PE, van Dijk KW, Zhernakova A, Fu J, Penninx BWJH, Boomsma DI, Demirkan A, Stricker BHC, van Duijn CM. Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas. Nat Med 2020; 26:110-117. [PMID: 31932804 DOI: 10.1038/s41591-019-0722-x] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 11/27/2019] [Indexed: 12/17/2022]
Abstract
Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/).
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Affiliation(s)
- Jun Liu
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands. .,Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Lies Lahousse
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands.,Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium
| | - Michel G Nivard
- Department of Biological Psychology, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Mariska Bot
- Department of Biological Psychology, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Lianmin Chen
- Department of Genetics, University Medical Center Groningen, Groningen, the Netherlands.,Department of Pediatrics, University Medical Center Groningen, Groningen, the Netherlands
| | - Jan Bert van Klinken
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands.,Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands.,Department of Clinical Chemistry, Laboratory Genetic Metabolic Disease, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Carisha S Thesing
- Department of Biological Psychology, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Marian Beekman
- Department of Biomedical Data Sciences, section of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Erik Ben van den Akker
- Department of Biomedical Data Sciences, section of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.,Department of Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, the Netherlands.,Leiden Computational Biology Center, Leiden University Medical Center, Leiden, the Netherlands
| | - Roderick C Slieker
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.,Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands.,Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Eveline Waterham
- Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands
| | - Carla J H van der Kallen
- Department of Internal Medicine, Maastricht University, Maastricht, the Netherlands.,School for Cardiovascular Diseases, Maastricht University, Maastricht, the Netherlands
| | - Irene de Boer
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Dina Vojinovic
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Djawad Radjabzadeh
- Department of Internal Medicine, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Robert Kraaij
- Department of Internal Medicine, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Louise J M Alferink
- Department of Gastroenterology and Hepatology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Sarwa Darwish Murad
- Department of Gastroenterology and Hepatology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands.,Department of Internal Medicine, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Rene Pool
- Department of Biological Psychology, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Yuri Milaneschi
- Department of Biological Psychology, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - H Eka D Suchiman
- Department of Biomedical Data Sciences, section of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Femke Rutters
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.,Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Petra J M Elders
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.,Department of General Practice and Elderly Care Medicine, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Joline W J Beulens
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.,Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Amber A W A van der Heijden
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.,Department of General Practice and Elderly Care Medicine, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Marleen M J van Greevenbroek
- Department of Internal Medicine, Maastricht University, Maastricht, the Netherlands.,School for Cardiovascular Diseases, Maastricht University, Maastricht, the Netherlands
| | - Ilja C W Arts
- School for Cardiovascular Diseases, Maastricht University, Maastricht, the Netherlands.,Department of Epidemiology, Maastricht University, Maastricht, the Netherlands.,Maastricht Center for Systems Biology, Maastricht University, Maastricht, the Netherlands
| | | | - Arn M J M van den Maagdenberg
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands.,Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.,Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Thomas Hankemeier
- Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands.,Netherlands Metabolomics Center, Leiden, the Netherlands
| | - Gisela M Terwindt
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Coen D A Stehouwer
- Department of Internal Medicine, Maastricht University, Maastricht, the Netherlands.,School for Cardiovascular Diseases, Maastricht University, Maastricht, the Netherlands
| | - Johanna M Geleijnse
- Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands
| | - Leen M 't Hart
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.,Department of Biomedical Data Sciences, section of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.,Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands.,Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - P Eline Slagboom
- Department of Biomedical Data Sciences, section of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands.,Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands.,Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, Groningen, the Netherlands
| | - Jingyuan Fu
- Department of Genetics, University Medical Center Groningen, Groningen, the Netherlands.,Department of Pediatrics, University Medical Center Groningen, Groningen, the Netherlands
| | - Brenda W J H Penninx
- Department of Biological Psychology, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Ayşe Demirkan
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands.,Department of Genetics, University Medical Center Groningen, Groningen, the Netherlands.,Section of Statistical Multi-omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK
| | - Bruno H C Stricker
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands.,Department of Internal Medicine, Erasmus MC, University Medical Center, Rotterdam, the Netherlands.,Inspectorate of Healthcare, The Hague, the Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands. .,Nuffield Department of Population Health, University of Oxford, Oxford, UK. .,Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands.
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42
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Hagenbeek FA, Pool R, van Dongen J, Draisma HHM, Jan Hottenga J, Willemsen G, Abdellaoui A, Fedko IO, den Braber A, Visser PJ, de Geus EJCN, Willems van Dijk K, Verhoeven A, Suchiman HE, Beekman M, Slagboom PE, van Duijn CM, Harms AC, Hankemeier T, Bartels M, Nivard MG, Boomsma DI. Heritability estimates for 361 blood metabolites across 40 genome-wide association studies. Nat Commun 2020; 11:39. [PMID: 31911595 PMCID: PMC6946682 DOI: 10.1038/s41467-019-13770-6] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 11/25/2019] [Indexed: 01/16/2023] Open
Abstract
Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2Metabolite-hits estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes.
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Affiliation(s)
- Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Harmen H M Draisma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Abdel Abdellaoui
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Iryna O Fedko
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anouk den Braber
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, VU Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, VU Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Maastricht, The Netherlands
| | - Eco J C N de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Ko Willems van Dijk
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Aswin Verhoeven
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - H Eka Suchiman
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Marian Beekman
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - P Eline Slagboom
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Amy C Harms
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University and The Netherlands Metabolomics Centre, Leiden, The Netherlands
| | - Thomas Hankemeier
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University and The Netherlands Metabolomics Centre, Leiden, The Netherlands
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Amsterdam, The Netherlands.
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Amsterdam, The Netherlands.
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43
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Kreeke JA, Nguyen H, Haan J, Konijnenberg E, Tomassen J, Braber A, Kate M, Collij L, Yaqub M, Berckel B, Lammertsma AA, Boomsma DI, Tan HS, Verbraak FD, Visser PJ. Retinal layer thickness in preclinical Alzheimer's disease. Acta Ophthalmol 2019; 97:798-804. [PMID: 31058465 PMCID: PMC6900176 DOI: 10.1111/aos.14121] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 03/28/2019] [Indexed: 12/26/2022]
Abstract
Purpose There is urgent need for non‐invasive diagnostic biomarkers in the preclinical phase of Alzheimer's Disease (AD). Several studies suggest that retinal thickness is reduced in AD. Here, we aim to test the diagnostic value of retinal thickness in preclinical AD, as defined by cognitively normal individuals with amyloid pathology on PET. Methods One hundred and sixty five cognitively healthy monozygotic twins aged ≥ 60 were included from the Netherlands Twin Register taking part in the European Medical Information Framework for Alzheimer's Disease PreclinAD study. Participants underwent [18F] flutemetamol PET that was visually rated for presence or absence of cortical amyloid beta (Aβ). Binding potential (BPND) was calculated as continuous measure for Aβ. Spectral Domain OCT was used to asses total and individual inner retinal layer thickness in the macular region (ETDRS circles) as well as peripapillary retinal nerve fibre layer (pRNFL) thickness. Differences between Aβ+ and Aβ− individuals and associations between BPND and retinal thickness were analyzed. Results No differences were found in retinal layer thickness in the macula or pRNFL between Aβ+ and Aβ− individuals. A positive associations between BPND and macular total retinal thickness was observed in the inner ring (p = 0.018), but this was not statistically significant after correction for multiple testing (p = 0.144). Brain/eye parameters had moderate to high intra‐twin correlations (p < 0.001) except visual rating score of Aβ, which did not correlate (r = 0.21, p = 0.068). Conclusion Variation in retinal thickness likely reflects genetic differences between individuals, but cannot discriminate between healthy and preclinical AD cases, making its use as biomarker in these early stages limited.
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Affiliation(s)
- Jacoba A. Kreeke
- Ophthalmology Department Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Hoang‐Ton Nguyen
- Ophthalmology Department Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Jurre Haan
- Alzheimer Center Neuroscience Amsterdam Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Elles Konijnenberg
- Alzheimer Center Neuroscience Amsterdam Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Jori Tomassen
- Alzheimer Center Neuroscience Amsterdam Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Anouk Braber
- Alzheimer Center Neuroscience Amsterdam Amsterdam UMC, location VUmc Amsterdam The Netherlands
- Department of Biological Psychology VU University Amsterdam Amsterdam The Netherlands
| | - Mara Kate
- Alzheimer Center Neuroscience Amsterdam Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Lyduine Collij
- Department of Radiology and Nuclear Medicine Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Bart Berckel
- Department of Radiology and Nuclear Medicine Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Adriaan A. Lammertsma
- Department of Radiology and Nuclear Medicine Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Dorret I. Boomsma
- Department of Biological Psychology VU University Amsterdam Amsterdam The Netherlands
| | - Hendra Stevie Tan
- Ophthalmology Department Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Frank D. Verbraak
- Ophthalmology Department Amsterdam UMC, location VUmc Amsterdam The Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Neuroscience Amsterdam Amsterdam UMC, location VUmc Amsterdam The Netherlands
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44
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The Netherlands Twin Register: Longitudinal Research Based on Twin and Twin-Family Designs. Twin Res Hum Genet 2019; 22:623-636. [PMID: 31666148 DOI: 10.1017/thg.2019.93] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The Netherlands Twin Register (NTR) is a national register in which twins, multiples and their parents, siblings, spouses and other family members participate. Here we describe the NTR resources that were created from more than 30 years of data collections; the development and maintenance of the newly developed database systems, and the possibilities these resources create for future research. Since the early 1980s, the NTR has enrolled around 120,000 twins and a roughly equal number of their relatives. The majority of twin families have participated in survey studies, and subsamples took part in biomaterial collection (e.g., DNA) and dedicated projects, for example, for neuropsychological, biomarker and behavioral traits. The recruitment into the NTR is all inclusive without any restrictions on enrollment. These resources - the longitudinal phenotyping, the extended pedigree structures and the multigeneration genotyping - allow for future twin-family research that will contribute to gene discovery, causality modeling, and studies of genetic and cultural inheritance.
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Abstract
The German Twin Family Panel (TwinLife) is a German longitudinal study of monozygotic and dizygotic same-sex twin pairs and their families that was designed to investigate the development of social inequalities over the life course. The study covers an observation period from approximately 2014 to 2023. The target population of the sample are reared-together twins of four different age cohorts that were born in 2009/2010 (cohort 1), in 2003/2004 (cohort 2), in 1997/1998 (cohort 3) and between 1990 and 1993 (cohort 4). In the first wave, the study included data on 4097 twin families. Families were recruited in all parts of Germany so that the sample comprises the whole range of the educational, occupational and income structure. As of 2019, two face-to-face, at-home interviews and two telephone interviews have been conducted. Data from the first home and telephone interviews are already available free of charge as a scientific use-file from the GESIS data archive. This report aims to provide an overview of the study sample and design as well as constructs that are unique in TwinLife in comparison with previous twin studies - such as an assessment of cognitive abilities or information based on the children's medical records and report cards. In addition, major findings based on the data already released are displayed, and future directions of the study are presented and discussed.
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46
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Genetic Similarity Assessment of Twin-Family Populations by Custom-Designed Genotyping Array. Twin Res Hum Genet 2019; 22:210-219. [PMID: 31379313 DOI: 10.1017/thg.2019.41] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Twin registries often take part in large collaborative projects and are major contributors to genome-wide association (GWA) meta-analysis studies. In this article, we describe genotyping of twin-family populations from Australia, the Midwestern USA (Avera Twin Register), the Netherlands (Netherlands Twin Register), as well as a sample of mothers of twins from Nigeria to assess the extent, if any, of genetic differences between them. Genotyping in all cohorts was done using a custom-designed Illumina Global Screening Array (GSA), optimized to improve imputation quality for population-specific GWA studies. We investigated the degree of genetic similarity between the populations using several measures of population variation with genotype data generated from the GSA. Visualization of principal component analysis (PCA) revealed that the Australian, Dutch and Midwestern American populations exhibit negligible interpopulation stratification when compared to each other, to a reference European population and to globally distant populations. Estimations of fixation indices (FST values) between the Australian, Midwestern American and Netherlands populations suggest minimal genetic differentiation compared to the estimates between each population and a genetically distinct cohort (i.e., samples from Nigeria genotyped on GSA). Thus, results from this study demonstrate that genotype data from the Australian, Dutch and Midwestern American twin-family populations can be reasonably combined for joint-genetic analysis.
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47
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Genetic and environmental influences on functional connectivity within and between canonical cortical resting-state networks throughout adolescent development in boys and girls. Neuroimage 2019; 202:116073. [PMID: 31386921 DOI: 10.1016/j.neuroimage.2019.116073] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 06/27/2019] [Accepted: 08/02/2019] [Indexed: 12/11/2022] Open
Abstract
The human brain is active during rest and hierarchically organized into intrinsic functional networks. These functional networks are largely established early in development, with reports of a shift from a local to more distributed organization during childhood and adolescence. It remains unknown to what extent genetic and environmental influences on functional connectivity change throughout adolescent development. We measured functional connectivity within and between eight cortical networks in a longitudinal resting-state fMRI study of adolescent twins and their older siblings on two occasions (mean ages 13 and 18 years). We modelled the reliability for these inherently noisy and head-motion sensitive measurements by analyzing data from split-half sessions. Functional connectivity between resting-state networks decreased with age whereas functional connectivity within resting-state networks generally increased with age, independent of general cognitive functioning. Sex effects were sparse, with stronger functional connectivity in the default mode network for girls compared to boys, and stronger functional connectivity in the salience network for boys compared to girls. Heritability explained up to 53% of the variation in functional connectivity within and between resting-state networks, and common environment explained up to 33%. Genetic influences on functional connectivity remained stable during adolescent development. In conclusion, longitudinal age-related changes in functional connectivity within and between cortical resting-state networks are subtle but wide-spread throughout adolescence. Genes play a considerable role in explaining individual variation in functional connectivity with mostly stable influences throughout adolescence.
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48
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Ma M, Huang DG, Liang X, Zhang L, Cheng S, Cheng B, Qi X, Li P, Du Y, Liu L, Zhao Y, Ding M, Wen Y, Guo X, Zhang F. Integrating transcriptome-wide association study and mRNA expression profiling identifies novel genes associated with bone mineral density. Osteoporos Int 2019; 30:1521-1528. [PMID: 30993394 DOI: 10.1007/s00198-019-04958-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 03/25/2019] [Indexed: 01/16/2023]
Abstract
UNLABELLED To scan novel candidate genes associated with osteoporosis, a two-stage transcriptome-wide association study (TWAS) of bone mineral density (BMD) was conducted. The BMD-associated genes identified by TWAS were then compared with the gene expression profiling of BMD in bone cells, B cells, and mesenchymal stem cells. We identified multiple candidate genes and gene ontology (GO) terms associated with BMD. INTRODUCTION Osteoporosis (OP) is a metabolic bone disease characterized by decrease in BMD. Our objective is to scan novel candidate genes associated with OP. METHODS A transcriptome-wide association study (TWAS) was performed by integrating the genome-wide association study (GWAS) summary of bone mineral density (BMD) with two pre-computed mRNA expression weights of peripheral blood and muscle skeleton. Then, another independent GWAS data of BMD was used to verify the discovery results. The BMD-associated genes identified between discovery and replicate TWAS were further subjected to gene ontology (GO) analysis implemented by DAVID. Finally, the BMD-associated genes and GO terms were further compared with the mRNA expression profiling results of BMD to detect the common genes and GO terms shared by both DNA-level TWAS and mRNA expression profile analysis. RESULTS TWAS identified 95 common genes with permutation P value < 0.05 for peripheral blood and muscle skeleton, such as TMTC4 in muscle skeleton and DDX17 in peripheral blood. Further comparing the genes detected by discovery-replicate TWAS with the differentially expressed genes identified by mRNA expression profiling of OP patients found 18 overlapped genes, such as MUL1 in muscle skeleton and SPTBN1 in peripheral blood. GO analysis of the genes identified by discovery-replicate TWAS detected 12 BMD-associated GO terms, such as negative regulation of cell growth and regulation of glycogen catabolic process. Further comparing the GO results of discovery-replicate TWAS and mRNA expression profiles found 6 overlapped GO terms, such as membrane and cell adhesion. CONCLUSION Our study identified multiple candidate genes and GO terms for BMD, providing novel clues for understanding the genetic mechanism of OP.
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Affiliation(s)
- M Ma
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - D-G Huang
- Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - X Liang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - L Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - S Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - B Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - X Qi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - P Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - Y Du
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - L Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - Y Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - M Ding
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - Y Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - X Guo
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - F Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China.
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Beenakker KGM, Westendorp RGJ, de Craen AJM, Chen S, Raz Y, Ballieux BEPB, Nelissen RGHH, Later AFL, Huizinga TW, Slagboom PE, Boomsma DI, Maier AB. Men Have a Stronger Monocyte-Derived Cytokine Production Response upon Stimulation with the Gram-Negative Stimulus Lipopolysaccharide than Women: A Pooled Analysis Including 15 Study Populations. J Innate Immun 2019; 12:142-153. [PMID: 31230049 PMCID: PMC7098282 DOI: 10.1159/000499840] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Revised: 02/25/2019] [Accepted: 02/25/2019] [Indexed: 01/21/2023] Open
Abstract
The incidence of bacterial infections and sepsis, as well as the mortality risk from sepsis, is sex specific. These clinical findings have been attributed to sex differences in immune responsiveness. The aim of the present study was to investigate sex differences in monocyte-derived cytokine production response upon stimulation with the gram-negative stimulus lipopolysaccharide (LPS) using cytokine data from 15 study populations. Individual data on ex vivo cytokine production response upon stimulation with LPS in whole blood were available for 4,020 subjects originating from these 15 study populations, either from the general population or from patient populations with specific diseases. Men had a stronger cytokine production response than women to LPS for tumour necrosis factor-α, interleukin (IL)-6, IL-12, IL-1β, IL-1RA, and IL-10, but not for interferon-γ. The granulocyte-macrophage colony-stimulating factor production response was lower in men than in women. These sex differences were independent of chronological age. As men had higher monocyte concentrations, we normalized the cytokine production responses for monocyte concentration. After normalization, the sex differences in cytokine production response to LPS disappeared, except for IL-10, for which the production response was lower in men than in women. A sex-based approach to interpreting immune responsiveness is crucial.
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Affiliation(s)
- Karel G M Beenakker
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
- Rivierduinen Mental Health Institute, Leiden, The Netherlands
| | - Rudi G J Westendorp
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health and Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark
| | - Anton J M de Craen
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Sijia Chen
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Experimental Immunology, Academic Medical Center, Amsterdam, The Netherlands
| | - Yotam Raz
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Medical Statistics, Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Bart E P B Ballieux
- Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Rob G H H Nelissen
- Department of Orthopaedics, Leiden University Medical Center, Leiden, The Netherlands
| | - Alexander F L Later
- Department of Cardiothoracic Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - Tom W Huizinga
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Pieternella E Slagboom
- Department of Medical Statistics, Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Andrea B Maier
- Department of Medicine and Aged Care, @AgeMelbourne, Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia,
- Department of Human Movement Sciences, @AgeAmsterdam, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, The Netherlands,
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50
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van de Kreeke JA, Nguyen HT, Konijnenberg E, Tomassen J, den Braber A, Ten Kate M, Sudre CH, Barkhof F, Boomsma DI, Tan HS, Verbraak FD, Visser PJ. Retinal and Cerebral Microvasculopathy: Relationships and Their Genetic Contributions. Invest Ophthalmol Vis Sci 2019; 59:5025-5031. [PMID: 30326071 DOI: 10.1167/iovs.18-25341] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose Retinal microvasculopathy may reflect small vessel disease in the brain. Here we test the relationships between retinal vascular parameters and small vessel disease, the influence of cardiovascular risk factors on these relationships, and their common genetic background in a monozygotic twin cohort. Methods We selected 134 cognitively healthy individuals (67 monozygotic twin pairs) aged ≥60 years from the Netherlands Twin Register for the EMIF-AD PreclinAD study. We measured seven retinal vascular parameters averaged over both eyes using fundus images analyzed with Singapore I Vessel Assessment. Small vessel disease was assessed on MRI by a volumetric measurement of periventricular and deep white matter hyperintensities. We calculated associations between RVPs and WMH, estimated intratwin pair correlations, and performed twin-specific analyses on relationships of interest. Results Deep white matter hyperintensities volume was positively associated with retinal tortuosity in veins (P = 0.004) and fractal dimension in arteries (P = 0.001) and veins (P = 0.032), periventricular white matter hyperintensities volume was positively associated with retinal venous width (P = 0.028). Intratwin pair correlations were moderate to high for all small vessel disease/retinal vascular parameter variables (r = 0.49-0.87, P < 0.001). Cross-twin cross-trait analyses showed that retinal venous tortuosity of twin 1 could predict deep white matter hyperintensities volume of the co-twin (r = 0.23, P = 0.030). Within twin-pair differences for retinal venous tortuosity were associated with within twin-pair differences in deep white matter hyperintensities volume (r = 0.39, P = 0.001). Conclusions Retinal arterial fractal dimension and venous tortuosity have associations with deep white matter hyperintensities volume. Twin-specific analyses suggest that retinal venous tortuosity and deep white matter hyperintensities volume have a common etiology driven by both shared genetic factors and unique environmental factors, supporting the robustness of this relationship.
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Affiliation(s)
- Jacoba A van de Kreeke
- Ophthalmology Department, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - H Ton Nguyen
- Ophthalmology Department, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Elles Konijnenberg
- Alzheimer Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Jori Tomassen
- Alzheimer Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Anouk den Braber
- Alzheimer Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Mara Ten Kate
- Alzheimer Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Carole H Sudre
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom
| | - Frederik Barkhof
- Radiology Department, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Netherlands Twin Register, Amsterdam, Netherlands
| | - H Stevie Tan
- Ophthalmology Department, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Frank D Verbraak
- Ophthalmology Department, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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