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Martinelli F, Heinken A, Henning AK, Ulmer MA, Hensen T, González A, Arnold M, Asthana S, Budde K, Engelman CD, Estaki M, Grabe HJ, Heston MB, Johnson S, Kastenmüller G, Martino C, McDonald D, Rey FE, Kilimann I, Peters O, Wang X, Spruth EJ, Schneider A, Fliessbach K, Wiltfang J, Hansen N, Glanz W, Buerger K, Janowitz D, Laske C, Munk MH, Spottke A, Roy N, Nauck M, Teipel S, Knight R, Kaddurah-Daouk RF, Bendlin BB, Hertel J, Thiele I. Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease. Sci Rep 2024; 14:6095. [PMID: 38480804 PMCID: PMC10937638 DOI: 10.1038/s41598-024-55960-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/29/2024] [Indexed: 03/17/2024] Open
Abstract
In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilised whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalised whole-body metabolic models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.
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Affiliation(s)
- Filippo Martinelli
- School of Medicine, University of Galway, Galway, Ireland
- The Ryan Institute, University of Galway, Galway, Ireland
| | - Almut Heinken
- School of Medicine, University of Galway, Galway, Ireland
- The Ryan Institute, University of Galway, Galway, Ireland
- Inserm UMRS 1256 NGERE, University of Lorraine, Nancy, France
| | - Ann-Kristin Henning
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Maria A Ulmer
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Tim Hensen
- School of Medicine, University of Galway, Galway, Ireland
- The Ryan Institute, University of Galway, Galway, Ireland
| | - Antonio González
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioural Sciences, Duke University, Durham, NC, USA
| | - Sanjay Asthana
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA
| | - Kathrin Budde
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Corinne D Engelman
- Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Mehrbod Estaki
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Hans-Jörgen Grabe
- German Center of Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Germany
| | - Margo B Heston
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA
| | - Sterling Johnson
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Cameron Martino
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Federico E Rey
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Ingo Kilimann
- German Center of Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany
| | - Olive Peters
- German Center of Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Xiao Wang
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Eike Jakob Spruth
- German Center of Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Anja Schneider
- German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Klaus Fliessbach
- German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Jens Wiltfang
- German Center of Neurodegenerative Diseases (DZNE), Goettingen, Germany
- Department of Psychiatry and Psychotherapy, University of Goettingen, Goettingen, Germany
- Neurosciences and Signaling Group, Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Niels Hansen
- Department of Psychiatry and Psychotherapy, University of Goettingen, Goettingen, Germany
| | - Wenzel Glanz
- German Center of Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Katharina Buerger
- German Center of Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Christoph Laske
- German Center of Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research, Tübingen, Germany
- Section for Dementia Research, Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Matthias H Munk
- German Center of Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Annika Spottke
- German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Nina Roy
- German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany
| | - Stefan Teipel
- German Center of Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- Shu Chien-Gene Lay Department of Engineering, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | | | - Barbara B Bendlin
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA
| | - Johannes Hertel
- School of Medicine, University of Galway, Galway, Ireland.
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany.
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland.
- The Ryan Institute, University of Galway, Galway, Ireland.
- School of Microbiology, University of Galway, Galway, Ireland.
- APC Microbiome Ireland, Cork, Ireland.
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2
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Montanari S, Jansen R, Schranner D, Kastenmüller G, Arnold M, Janiri D, Sani G, Bhattacharyya S, Dehkordi SM, Dunlop BW, Rush AJ, Penninx BWHJ, Kaddurah-Daouk R, Milaneschi Y. Acylcarnitines metabolism in depression: association with diagnostic status, depression severity and symptom profile in the NESDA cohort. medRxiv 2024:2024.02.14.24302813. [PMID: 38405847 PMCID: PMC10889013 DOI: 10.1101/2024.02.14.24302813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background Acylcarnitines (ACs) are involved in bioenergetics processes that may play a role in the pathophysiology of depression. Studies linking AC levels to depression are few and provide mixed findings. We examined the association of circulating ACs levels with Major Depressive Disorder (MDD) diagnosis, overall depression severity and specific symptom profiles. Methods The sample from the Netherlands Study of Depression and Anxiety included participants with current (n=1035) or remitted (n=739) MDD and healthy controls (n=800). Plasma levels of four ACs (short-chain: acetylcarnitine C2 and propionylcarnitine C3; medium-chain: octanoylcarnitine C8 and decanoylcarnitine C10) were measured. Overall depression severity as well as atypical/energy-related (AES), anhedonic and melancholic symptom profiles were derived from the Inventory of Depressive Symptomatology. Results As compared to healthy controls, subjects with current or remitted MDD presented similarly lower mean C2 levels (Cohen's d=0.2, p≤1e-4). Higher overall depression severity was significantly associated with higher C3 levels (ß=0.06, SE=0.02, p=1.21e-3). No associations were found for C8 and C10. Focusing on symptom profiles, only higher AES scores were linked to lower C2 (ß=-0.05, SE=0.02, p=1.85e-2) and higher C3 (ß=0.08, SE=0.02, p=3.41e-5) levels. Results were confirmed in analyses pooling data with an additional internal replication sample from the same subjects measured at 6-year follow-up (totaling 4195 observations). Conclusions Small alterations in levels of short-chain acylcarnitine levels were related to the presence and severity of depression, especially for symptoms reflecting altered energy homeostasis. Cellular metabolic dysfunctions may represent a key pathway in depression pathophysiology potentially accessible through AC metabolism.
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Affiliation(s)
- Silvia Montanari
- Department of Neuroscience, Section of Psychiatry, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Rick Jansen
- Department of Psychiatry, Amsterdam UMC,Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Mental Health program, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress program, Amsterdam, The Netherlands
| | - Daniela Schranner
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Delfina Janiri
- Department of Neuroscience, Section of Psychiatry, Università Cattolica del Sacro Cuore, Rome, Italy
- Department of Psychiatry, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Gabriele Sani
- Department of Neuroscience, Section of Psychiatry, Università Cattolica del Sacro Cuore, Rome, Italy
- Department of Psychiatry, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Sudeepa Bhattacharyya
- Arkansas Biosciences Institute, Department of Biological Sciences, Arkansas State University, AR, USA
| | | | - Boadie W Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - A John Rush
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke-National University of Singapore, Singapore
| | - Brenda W H J Penninx
- Department of Psychiatry, Amsterdam UMC,Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Mental Health program, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress program, Amsterdam, The Netherlands
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam UMC,Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Mental Health program, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress program, Amsterdam, The Netherlands
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3
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Arnold M, Buyukozkan M, Doraiswamy PM, Nho K, Wu T, Gudnason V, Launer LJ, Wang-Sattler R, Adamski J, De Jager PL, Ertekin-Taner N, Bennett DA, Saykin AJ, Peters A, Suhre K, Kaddurah-Daouk R, Kastenmüller G, Krumsiek J. Individual bioenergetic capacity as a potential source of resilience to Alzheimer's disease. medRxiv 2024:2024.01.23.23297820. [PMID: 38313266 PMCID: PMC10836119 DOI: 10.1101/2024.01.23.23297820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Impaired glucose uptake in the brain is one of the earliest presymptomatic manifestations of Alzheimer's disease (AD). The absence of symptoms for extended periods of time suggests that compensatory metabolic mechanisms can provide resilience. Here, we introduce the concept of a systemic 'bioenergetic capacity' as the innate ability to maintain energy homeostasis under pathological conditions, potentially serving as such a compensatory mechanism. We argue that fasting blood acylcarnitine profiles provide an approximate peripheral measure for this capacity that mirrors bioenergetic dysregulation in the brain. Using unsupervised subgroup identification, we show that fasting serum acylcarnitine profiles of participants from the AD Neuroimaging Initiative yields bioenergetically distinct subgroups with significant differences in AD biomarker profiles and cognitive function. To assess the potential clinical relevance of this finding, we examined factors that may offer diagnostic and therapeutic opportunities. First, we identified a genotype affecting the bioenergetic capacity which was linked to succinylcarnitine metabolism and significantly modulated the rate of future cognitive decline. Second, a potentially modifiable influence of beta-oxidation efficiency seemed to decelerate bioenergetic aging and disease progression. Our findings, which are supported by data from more than 9,000 individuals, suggest that interventions tailored to enhance energetic health and to slow bioenergetic aging could mitigate the risk of symptomatic AD, especially in individuals with specific mitochondrial genotypes.
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Affiliation(s)
- Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Mustafa Buyukozkan
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - P. Murali Doraiswamy
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences and Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Tong Wu
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Vilmundur Gudnason
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Lenore J. Launer
- Laboratory of Epidemiology and Population Science, National Institute on Aging, Bethesda, Maryland
| | - Rui Wang-Sattler
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | | | | | - Philip L. De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Taub Institute, Columbia University Irving Medical Center, New York Presbyterian Hospital, New York, NY, USA
| | | | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; IBE, Medical Faculty, Ludwig-Maximilians-Universität, Munich, Germany; German Center for Diabetes Research (DZD e.V.), Munich, Germany; German Center for Cardiovascular Disease (DZHK e.V.), Munich Heart Alliance, Munich, Germany
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
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4
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Costeira R, Evangelista L, Wilson R, Yan X, Hellbach F, Sinke L, Christiansen C, Villicaña S, Masachs OM, Tsai PC, Mangino M, Menni C, Berry SE, Beekman M, van Heemst D, Slagboom PE, Heijmans BT, Suhre K, Kastenmüller G, Gieger C, Peters A, Small KS, Linseisen J, Waldenberger M, Bell JT. Metabolomic biomarkers of habitual B vitamin intakes unveil novel differentially methylated positions in the human epigenome. Clin Epigenetics 2023; 15:166. [PMID: 37858220 PMCID: PMC10588110 DOI: 10.1186/s13148-023-01578-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/04/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND B vitamins such as folate (B9), B6, and B12 are key in one carbon metabolism, which generates methyl donors for DNA methylation. Several studies have linked differential methylation to self-reported intakes of folate and B12, but these estimates can be imprecise, while metabolomic biomarkers can offer an objective assessment of dietary intakes. We explored blood metabolomic biomarkers of folate and vitamins B6 and B12, to carry out epigenome-wide analyses across up to three European cohorts. Associations between self-reported habitual daily B vitamin intakes and 756 metabolites (Metabolon Inc.) were assessed in serum samples from 1064 UK participants from the TwinsUK cohort. The identified B vitamin metabolomic biomarkers were then used in epigenome-wide association tests with fasting blood DNA methylation levels at 430,768 sites from the Infinium HumanMethylation450 BeadChip in blood samples from 2182 European participants from the TwinsUK and KORA cohorts. Candidate signals were explored for metabolite associations with gene expression levels in a subset of the TwinsUK sample (n = 297). Metabolomic biomarker epigenetic associations were also compared with epigenetic associations of self-reported habitual B vitamin intakes in samples from 2294 European participants. RESULTS Eighteen metabolites were associated with B vitamin intakes after correction for multiple testing (Bonferroni-adj. p < 0.05), of which 7 metabolites were available in both cohorts and tested for epigenome-wide association. Three metabolites - pipecolate (metabolomic biomarker of B6 and folate intakes), pyridoxate (marker of B6 and folate) and docosahexaenoate (DHA, marker of B6) - were associated with 10, 3 and 1 differentially methylated positions (DMPs), respectively. The strongest association was observed between DHA and DMP cg03440556 in the SCD gene (effect = 0.093 ± 0.016, p = 4.07E-09). Pyridoxate, a catabolic product of vitamin B6, was inversely associated with CpG methylation near the SLC1A5 gene promoter region (cg02711608 and cg22304262) and with SLC7A11 (cg06690548), but not with corresponding changes in gene expression levels. The self-reported intake of folate and vitamin B6 had consistent but non-significant associations with the epigenetic signals. CONCLUSION Metabolomic biomarkers are a valuable approach to investigate the effects of dietary B vitamin intake on the human epigenome.
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Affiliation(s)
- Ricardo Costeira
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.
| | - Laila Evangelista
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Rory Wilson
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Xinyu Yan
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Fabian Hellbach
- Epidemiology, Medical Faculty, University Augsburg, University Hospital Augsburg, 86156, Augsburg, Germany
| | - Lucy Sinke
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Colette Christiansen
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Olatz M Masachs
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Pei-Chien Tsai
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
- Department of Biomedical Sciences, Chang Gung University, Taoyuan City, Taiwan
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Chang Gung Memorial Hospital, Taoyuan City, Taiwan
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Sarah E Berry
- Department of Nutritional Sciences, King's College London, London, SE1 9NH, UK
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, 2300RC, Leiden, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, 80802, Munich, Germany
| | - Annette Peters
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, 80802, Munich, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-Universität München, 81377, Munich, Germany
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - Jakob Linseisen
- Epidemiology, Medical Faculty, University Augsburg, University Hospital Augsburg, 86156, Augsburg, Germany
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-Universität München, 81377, Munich, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), Partner Site Munich Heart Alliance, 80802, Munich, Germany
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK.
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5
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Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, Surendran P, Mahajan A, Robins C, Vasquez-Grinnell SG, Hou L, Kvikstad EM, Burren OS, Davitte J, Ferber KL, Gillies CE, Hedman ÅK, Hu S, Lin T, Mikkilineni R, Pendergrass RK, Pickering C, Prins B, Baird D, Chen CY, Ward LD, Deaton AM, Welsh S, Willis CM, Lehner N, Arnold M, Wörheide MA, Suhre K, Kastenmüller G, Sethi A, Cule M, Raj A, Burkitt-Gray L, Melamud E, Black MH, Fauman EB, Howson JMM, Kang HM, McCarthy MI, Nioi P, Petrovski S, Scott RA, Smith EN, Szalma S, Waterworth DM, Mitnaul LJ, Szustakowski JD, Gibson BW, Miller MR, Whelan CD. Plasma proteomic associations with genetics and health in the UK Biobank. Nature 2023; 622:329-338. [PMID: 37794186 PMCID: PMC10567551 DOI: 10.1038/s41586-023-06592-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/31/2023] [Indexed: 10/06/2023]
Abstract
The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.
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Affiliation(s)
- Benjamin B Sun
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.
| | - Joshua Chiou
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Matthew Traylor
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Tom G Richardson
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
- Genomic Sciences, GlaxoSmithKline, Stevenage, UK
| | | | | | - Chloe Robins
- Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA
| | | | - Liping Hou
- Population Analytics, Janssen Research & Development, Spring House, PA, USA
| | | | - Oliver S Burren
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | | | - Kyle L Ferber
- Biostatistics, Research and Development, Biogen, Cambridge, MA, USA
| | | | - Åsa K Hedman
- External Science and Innovation Target Sciences, Worldwide Research, Development and Medical, Pfizer, Stockholm, Sweden
| | - Sile Hu
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Tinchi Lin
- Analytics and Data Sciences, Biogen, Cambridge, MA, USA
| | - Rajesh Mikkilineni
- Data Science Institute, Takeda Development Center Americas, Cambridge, MA, USA
| | | | | | - Bram Prins
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Denis Baird
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA
| | - Chia-Yen Chen
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA
| | - Lucas D Ward
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Aimee M Deaton
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | | | - Carissa M Willis
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Nick Lehner
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | | | | | - Anil Raj
- Calico Life Sciences, San Francisco, CA, USA
| | | | | | - Mary Helen Black
- Population Analytics, Janssen Research & Development, Spring House, PA, USA
| | - Eric B Fauman
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Joanna M M Howson
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Paul Nioi
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
- Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia
| | | | - Erin N Smith
- Takeda Development Center Americas, San Diego, CA, USA
| | - Sándor Szalma
- Takeda Development Center Americas, San Diego, CA, USA
| | | | | | | | | | - Melissa R Miller
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Christopher D Whelan
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.
- Neuroscience Data Science, Janssen Research & Development, Cambridge, MA, USA.
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6
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Martinelli F, Heinken A, Henning AK, Wörheide MA, Hensen T, González A, Arnold M, Asthana S, Budde K, Engelman CD, Estaki M, Grabe HJ, Heston M, Johnson S, Kastenmüller G, Martino C, McDonald D, Rey F, Kilimann I, Peters O, Wang X, Spruth EJ, Schneider A, Fliessbach K, Wiltfang J, Hansen N, Glanz W, Buerger K, Janowitz D, Laske C, Munk MH, Spottke A, Roy N, Nauck M, Teipel S, Knight R, Kaddurah-Daouk R, Bendlin BB, Hertel J, Thiele I. Whole-body modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease. Res Sq 2023:rs.3.rs-3306891. [PMID: 37720019 PMCID: PMC10503865 DOI: 10.21203/rs.3.rs-3306891/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilized whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalized models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.
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Affiliation(s)
| | | | | | - Maria A Wörheide
- Helmholtz Zentrum München - German Research Center for Environmental Health
| | | | | | - Matthias Arnold
- Helmholtz Zentrum München - German Research Center for Environmental Health
| | | | | | | | | | | | | | | | - Gabi Kastenmüller
- Helmholtz Zentrum München - German Research Center for Environmental Health
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Nina Roy
- German Center for Neurodegenerative Diseases
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7
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van der Spek A, Stewart ID, Kühnel B, Pietzner M, Alshehri T, Gauß F, Hysi PG, MahmoudianDehkordi S, Heinken A, Luik AI, Ladwig KH, Kastenmüller G, Menni C, Hertel J, Ikram MA, de Mutsert R, Suhre K, Gieger C, Strauch K, Völzke H, Meitinger T, Mangino M, Flaquer A, Waldenberger M, Peters A, Thiele I, Kaddurah-Daouk R, Dunlop BW, Rosendaal FR, Wareham NJ, Spector TD, Kunze S, Grabe HJ, Mook-Kanamori DO, Langenberg C, van Duijn CM, Amin N. Circulating metabolites modulated by diet are associated with depression. Mol Psychiatry 2023; 28:3874-3887. [PMID: 37495887 PMCID: PMC10730409 DOI: 10.1038/s41380-023-02180-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/03/2023] [Accepted: 07/03/2023] [Indexed: 07/28/2023]
Abstract
Metabolome reflects the interplay of genome and exposome at molecular level and thus can provide deep insights into the pathogenesis of a complex disease like major depression. To identify metabolites associated with depression we performed a metabolome-wide association analysis in 13,596 participants from five European population-based cohorts characterized for depression, and circulating metabolites using ultra high-performance liquid chromatography/tandem accurate mass spectrometry (UHPLC/MS/MS) based Metabolon platform. We tested 806 metabolites covering a wide range of biochemical processes including those involved in lipid, amino-acid, energy, carbohydrate, xenobiotic and vitamin metabolism for their association with depression. In a conservative model adjusting for life style factors and cardiovascular and antidepressant medication use we identified 8 metabolites, including 6 novel, significantly associated with depression. In individuals with depression, increased levels of retinol (vitamin A), 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1) (lecithin) and mannitol/sorbitol and lower levels of hippurate, 4-hydroxycoumarin, 2-aminooctanoate (alpha-aminocaprylic acid), 10-undecenoate (11:1n1) (undecylenic acid), 1-linoleoyl-GPA (18:2) (lysophosphatidic acid; LPA 18:2) are observed. These metabolites are either directly food derived or are products of host and gut microbial metabolism of food-derived products. Our Mendelian randomization analysis suggests that low hippurate levels may be in the causal pathway leading towards depression. Our findings highlight putative actionable targets for depression prevention that are easily modifiable through diet interventions.
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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
| | | | - Brigitte Kühnel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Tahani Alshehri
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Friederike Gauß
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Ferdinand-Sauerbruch-Str, 17475, Greifswald, Germany
| | - Pirro G Hysi
- Department of Twins Research and Genetic Epidemiology, Kings College London, London, UK
| | | | - Almut Heinken
- School of Medicine, University of Galway, University Road, Galway, Ireland
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy, France
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Karl-Heinz Ladwig
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Department of Psychosomatic Medicine and Psychotherapy, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), D-85764, Neuherberg, Germany
| | - Cristina Menni
- Department of Twins Research and Genetic Epidemiology, Kings College London, London, UK
| | - Johannes Hertel
- School of Medicine, University of Galway, University Road, Galway, Ireland
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Ellernholzstrasse 1-2, 17489, Greifswald, Germany
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, PO, 24144, Doha, Qatar
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), D-85764, Neuherberg, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU, Munich, Germany
| | - Henry Völzke
- Institute of Community Medicine, University Medicine Greifswald, Walter-Rathenau Str. 48, 17475, Greifswald, Germany
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Massimo Mangino
- Department of Twins Research and Genetic Epidemiology, Kings College London, London, UK
| | - Antonia Flaquer
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU, Munich, Germany
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Annette Peters
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Ludwig-Maximilians-Universität München, IBE-Chair of Epidemiology, Munich, Germany
| | - Ines Thiele
- School of Medicine, University of Galway, University Road, Galway, Ireland
- Division of Microbiology, University of Galway, Galway, Ireland
- APC Microbiome, Ireland, Ireland
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Boadie W Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, US
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Tim D Spector
- Department of Twins Research and Genetic Epidemiology, Kings College London, London, UK
| | - Sonja Kunze
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764, Neuherberg, Germany
| | - Hans Jörgen Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Ellernholzstrasse 1-2, 17489, Greifswald, Germany
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Nuffield Department of Population Health, University of Oxford, OX3 7LF, Oxford, UK
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
- Nuffield Department of Population Health, University of Oxford, OX3 7LF, Oxford, UK.
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8
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Weinisch P, Raffler J, Römisch-Margl W, Arnold M, Mohney RP, Rist MJ, Prehn C, Skurk T, Hauner H, Daniel H, Suhre K, Kastenmüller G. The HuMet Repository: Watching human metabolism at work. bioRxiv 2023:2023.08.08.550079. [PMID: 37609175 PMCID: PMC10441358 DOI: 10.1101/2023.08.08.550079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
The human metabolism constantly responds to stimuli such as food intake, fasting, exercise, and stress, triggering adaptive biochemical processes across multiple metabolic pathways. To understand the role of these processes and disruptions thereof in health and disease, detailed documentation of healthy metabolic responses is needed but still scarce on a time-resolved metabolome-wide level. Here, we present the HuMet Repository, a web-based resource for exploring dynamic metabolic responses to six physiological challenges (exercise, 36 h fasting, oral glucose and lipid loads, mixed meal, cold stress) in healthy subjects. For building this resource, we integrated existing and newly derived metabolomics data measured in blood, urine, and breath samples of 15 young healthy men at up to 56 time points during the six highly standardized challenge tests conducted over four days. The data comprise 1.1 million data points acquired on multiple platforms with temporal profiles of 2,656 metabolites from a broad range of biochemical pathways. By embedding the dataset into an interactive web application, we enable users to easily access, search, filter, analyze, and visualize the time-resolved metabolomic readouts and derived results. Users can put metabolites into their larger context by identifying metabolites with similar trajectories or by visualizing metabolites within holistic metabolic networks to pinpoint pathways of interest. In three showcases, we outline the value of the repository for gaining biological insights and generating hypotheses by analyzing the wash-out of dietary markers, the complementarity of metabolomics platforms in dynamic versus cross-sectional data, and similarities and differences in systemic metabolic responses across challenges. With its comprehensive collection of time-resolved metabolomics data, the HuMet Repository, freely accessible at https://humet.org/, is a reference for normal, healthy responses to metabolic challenges in young males. It will enable researchers with and without computational expertise, to flexibly query the data for their own research into the dynamics of human metabolism.
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Affiliation(s)
- Patrick Weinisch
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Digital Medicine, University Hospital of Augsburg, Augsburg, Germany
| | - Werner Römisch-Margl
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | | | - Manuela J. Rist
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Skurk
- ZIEL Institute for Food and Health, Core Facility Human Studies, Technical University of Munich, Freising, Germany
- Else Kröner Fresenius Center of Nutritional Medicine, Department of Food and Nutrition, Technical University of Munich, Freising, Germany
| | - Hans Hauner
- Else Kröner Fresenius Center of Nutritional Medicine, Department of Food and Nutrition, Technical University of Munich, Freising, Germany
- Institute for Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Hannelore Daniel
- Department of Food and Nutrition, Technical University of Munich, Freising, Germany
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
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9
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Batra R, Krumsiek J, Wang X, Allen M, Blach C, Kastenmüller G, Arnold M, Ertekin-Taner N, Kaddurah-Daouk RF. Comparative brain metabolomics reveals shared and distinct metabolic alterations in Alzheimer's disease and progressive supranuclear palsy. medRxiv 2023:2023.07.25.23293055. [PMID: 37546878 PMCID: PMC10402214 DOI: 10.1101/2023.07.25.23293055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Metabolic dysregulation is a hallmark of neurodegenerative diseases, including Alzheimer's disease (AD) and progressive supranuclear palsy (PSP). While metabolic dysregulation is a common link between these two tauopathies, a comprehensive brain metabolic comparison of the diseases has not yet been performed. We analyzed 342 postmortem brain samples from the Mayo Clinic Brain Bank and examined 658 metabolites in the cerebellar cortex and the temporal cortex between the two tauopathies. Our findings indicate that both diseases display oxidative stress associated with lipid metabolism, mitochondrial dysfunction linked to lysine metabolism, and an indication of tau-induced polyamine stress response. However, specific to AD, we detected glutathione-related neuroinflammation, deregulations of enzymes tied to purines, and cognitive deficits associated with vitamin B. Taken together, our findings underscore vast alterations in the brain's metabolome, illuminating shared neurodegenerative pathways and disease-specific traits in AD and PSP.
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Affiliation(s)
- Richa Batra
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Xue Wang
- Department of Quantitative Health Sciences, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Colette Blach
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Rima F Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke Institute for Brain Sciences and Department of Medicine, Duke University, Durham, NC, USA
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10
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Kim JP, Nho K, Wang T, Huynh K, Arnold M, Risacher SL, Bice PJ, Han X, Kristal BS, Blach C, Baillie R, Kastenmüller G, Meikle PJ, Saykin AJ, Kaddurah-Daouk R. Circulating lipid profiles are associated with cross-sectional and longitudinal changes of central biomarkers for Alzheimer's disease. medRxiv 2023:2023.06.12.23291054. [PMID: 37398438 PMCID: PMC10312871 DOI: 10.1101/2023.06.12.23291054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Investigating the association of lipidome profiles with central Alzheimer's disease (AD) biomarkers, including amyloid/tau/neurodegeneration (A/T/N), can provide a holistic view between the lipidome and AD. We performed cross-sectional and longitudinal association analysis of serum lipidome profiles with AD biomarkers in the Alzheimer's Disease Neuroimaging Initiative cohort (N=1,395). We identified lipid species, classes, and network modules that were significantly associated with cross-sectional and longitudinal changes of A/T/N biomarkers for AD. Notably, we identified the lysoalkylphosphatidylcholine (LPC(O)) as associated with "A/N" biomarkers at baseline at lipid species, class, and module levels. Also, GM3 ganglioside showed significant association with baseline levels and longitudinal changes of the "N" biomarkers at species and class levels. Our study of circulating lipids and central AD biomarkers enabled identification of lipids that play potential roles in the cascade of AD pathogenesis. Our results suggest dysregulation of lipid metabolic pathways as precursors to AD development and progression.
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Affiliation(s)
- Jun Pyo Kim
- Center for Neuroimaging, Radiology and Imaging Sciences, and the Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Neurology, Samsung Medical Center, Seoul, Korea
| | - Kwangsik Nho
- Center for Neuroimaging, Radiology and Imaging Sciences, and the Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Victoria, Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Victoria, Australia
- Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Shannon L Risacher
- Center for Neuroimaging, Radiology and Imaging Sciences, and the Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paula J Bice
- Center for Neuroimaging, Radiology and Imaging Sciences, and the Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Xianlin Han
- University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Bruce S Kristal
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, and Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Colette Blach
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
| | | | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Victoria, Australia
- Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
| | - Andrew J Saykin
- Center for Neuroimaging, Radiology and Imaging Sciences, and the Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
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11
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Singh P, Gollapalli K, Mangiola S, Schranner D, Yusuf MA, Chamoli M, Shi SL, Bastos BL, Nair T, Riermeier A, Vayndorf EM, Wu JZ, Nilakhe A, Nguyen CQ, Muir M, Kiflezghi MG, Foulger A, Junker A, Devine J, Sharan K, Chinta SJ, Rajput S, Rane A, Baumert P, Schönfelder M, Iavarone F, Lorenzo GD, Kumari S, Gupta A, Sarkar R, Khyriem C, Chawla AS, Sharma A, Sarper N, Chattopadhyay N, Biswal BK, Settembre C, Nagarajan P, Targoff KL, Picard M, Gupta S, Velagapudi V, Papenfuss AT, Kaya A, Ferreira MG, Kennedy BK, Andersen JK, Lithgow GJ, Ali AM, Mukhopadhyay A, Palotie A, Kastenmüller G, Kaeberlein M, Wackerhage H, Pal B, Yadav VK. Taurine deficiency as a driver of aging. Science 2023; 380:eabn9257. [PMID: 37289866 PMCID: PMC10630957 DOI: 10.1126/science.abn9257] [Citation(s) in RCA: 45] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Accepted: 04/14/2023] [Indexed: 06/10/2023]
Abstract
Aging is associated with changes in circulating levels of various molecules, some of which remain undefined. We find that concentrations of circulating taurine decline with aging in mice, monkeys, and humans. A reversal of this decline through taurine supplementation increased the health span (the period of healthy living) and life span in mice and health span in monkeys. Mechanistically, taurine reduced cellular senescence, protected against telomerase deficiency, suppressed mitochondrial dysfunction, decreased DNA damage, and attenuated inflammaging. In humans, lower taurine concentrations correlated with several age-related diseases and taurine concentrations increased after acute endurance exercise. Thus, taurine deficiency may be a driver of aging because its reversal increases health span in worms, rodents, and primates and life span in worms and rodents. Clinical trials in humans seem warranted to test whether taurine deficiency might drive aging in humans.
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Affiliation(s)
- Parminder Singh
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
| | - Kishore Gollapalli
- Vagelos College of Physicians and Surgeons, Columbia University; New York, USA
| | - Stefano Mangiola
- Department of Medical Biology, University of Melbourne; Melbourne, Australia
- School of Cancer Medicine, La Trobe University; Bundoora, Australia
- Olivia Newton-John Cancer Research Institute; Heidelberg, Australia
| | - Daniela Schranner
- Exercise Biology Group, Technical University of Munich; Munich, Germany
- Institute of Computational Biology, Helmholtz Zentrum München; Neuherberg, Germany
| | - Mohd Aslam Yusuf
- Department of Bioengineering, Integral University; Lucknow, India
| | - Manish Chamoli
- Buck Institute of Age Research, 8001 Redwood Blvd; California, USA
| | - Sting L. Shi
- Vagelos College of Physicians and Surgeons, Columbia University; New York, USA
| | - Bruno Lopes Bastos
- Institute for Research on Cancer and Aging of Nice (IRCAN); Nice, France
| | - Tripti Nair
- Molecular Aging Laboratory, National Institute of Immunology; New Delhi, India
| | - Annett Riermeier
- Exercise Biology Group, Technical University of Munich; Munich, Germany
| | - Elena M. Vayndorf
- Department of Laboratory Medicine and Pathology, University of Washington; WA, USA
| | - Judy Z. Wu
- Department of Laboratory Medicine and Pathology, University of Washington; WA, USA
| | - Aishwarya Nilakhe
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
| | - Christina Q. Nguyen
- Department of Laboratory Medicine and Pathology, University of Washington; WA, USA
| | - Michael Muir
- Department of Laboratory Medicine and Pathology, University of Washington; WA, USA
| | - Michael G. Kiflezghi
- Department of Laboratory Medicine and Pathology, University of Washington; WA, USA
| | - Anna Foulger
- Buck Institute of Age Research, 8001 Redwood Blvd; California, USA
| | - Alex Junker
- Department of Neurology, Columbia University; New York, USA
| | - Jack Devine
- Department of Neurology, Columbia University; New York, USA
| | - Kunal Sharan
- Mouse Genetics Project, Wellcome Sanger Institute; Cambridge, UK
| | | | - Swati Rajput
- Division of Endocrinology, CSIR-Central Drug Research Institute; Lucknow, India
| | - Anand Rane
- Buck Institute of Age Research, 8001 Redwood Blvd; California, USA
| | - Philipp Baumert
- Exercise Biology Group, Technical University of Munich; Munich, Germany
| | | | | | | | - Swati Kumari
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
| | - Alka Gupta
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
| | - Rajesh Sarkar
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
| | - Costerwell Khyriem
- Harry Perkins Institute of Medical Research; Perth, Australia
- Curtin Medical School, Curtin University; Perth, Australia
| | - Amanpreet S. Chawla
- Immunobiology Laboratory, National Institute of Immunology; New Delhi, India
- MRC-Protein Phosphorylation and Ubiquitination Unit, University of Dundee; Dundee, UK
| | - Ankur Sharma
- Harry Perkins Institute of Medical Research; Perth, Australia
- Curtin Medical School, Curtin University; Perth, Australia
| | - Nazan Sarper
- Pediatrics and Pediatric Hematology, Kocaeli University Hospital; Kocaeli, Turkey
| | | | - Bichitra K. Biswal
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
| | - Carmine Settembre
- Telethon Institute of Genetics and Medicine (TIGEM); Pozzuoli, Italy
- Department of Clinical Medicine and Surgery, Federico II University; Naples, Italy
| | - Perumal Nagarajan
- Primate Research Facility, National Institute of Immunology; New Delhi, India
- Small Animal Research Facility, National Institute of Immunology; New Delhi, India
| | - Kimara L. Targoff
- Division of Cardiology, Department of Pediatrics, Columbia University; New York, USA
| | - Martin Picard
- Department of Neurology, Columbia University; New York, USA
| | - Sarika Gupta
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
| | - Vidya Velagapudi
- Institute for Molecular Medicine Finland FIMM, University of Helsinki; Helsinki, Finland
| | | | - Alaattin Kaya
- Department of Biology, Virginia Commonwealth University; Virginia, USA
| | | | - Brian K. Kennedy
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore; Singapore, Singapore
- Centre for Healthy Longevity, National University Health System; Singapore, Singapore
- Departments of Biochemistry and Physiology, Yong Loo Lin School of Medicine, National University of Singapore; Singapore, Singapore
| | | | | | - Abdullah Mahmood Ali
- Department of Medicine, Columbia University Irving Medical Center; New York, USA
| | - Arnab Mukhopadhyay
- Molecular Aging Laboratory, National Institute of Immunology; New Delhi, India
| | - Aarno Palotie
- Institute for Molecular Medicine Finland FIMM, University of Helsinki; Helsinki, Finland
- Broad Institute of Harvard and MIT; Cambridge, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital; Boston, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München; Neuherberg, Germany
| | - Matt Kaeberlein
- Department of Laboratory Medicine and Pathology, University of Washington; WA, USA
| | | | - Bhupinder Pal
- Department of Medical Biology, University of Melbourne; Melbourne, Australia
- School of Cancer Medicine, La Trobe University; Bundoora, Australia
| | - Vijay K. Yadav
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
- Vagelos College of Physicians and Surgeons, Columbia University; New York, USA
- Mouse Genetics Project, Wellcome Sanger Institute; Cambridge, UK
- Department of Genetics and Development, Columbia University; New York, USA
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12
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Amin N, Liu J, Bonnechere B, MahmoudianDehkordi S, Arnold M, Batra R, Chiou YJ, Fernandes M, Ikram MA, Kraaij R, Krumsiek J, Newby D, Nho K, Radjabzadeh D, Saykin AJ, Shi L, Sproviero W, Winchester L, Yang Y, Nevado-Holgado AJ, Kastenmüller G, Kaddurah-Daouk R, van Duijn CM. Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals. JAMA Psychiatry 2023; 80:597-609. [PMID: 37074710 PMCID: PMC10116384 DOI: 10.1001/jamapsychiatry.2023.0685] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 02/07/2023] [Indexed: 04/20/2023]
Abstract
Importance Metabolomics reflect the net effect of genetic and environmental influences and thus provide a comprehensive approach to evaluating the pathogenesis of complex diseases, such as depression. Objective To identify the metabolic signatures of major depressive disorder (MDD), elucidate the direction of associations using mendelian randomization, and evaluate the interplay of the human gut microbiome and metabolome in the development of MDD. Design, Setting and Participants This cohort study used data from participants in the UK Biobank cohort (n = 500 000; aged 37 to 73 years; recruited from 2006 to 2010) whose blood was profiled for metabolomics. Replication was sought in the PREDICT and BBMRI-NL studies. Publicly available summary statistics from a 2019 genome-wide association study of depression were used for the mendelian randomization (individuals with MDD = 59 851; control individuals = 113 154). Summary statistics for the metabolites were obtained from OpenGWAS in MRbase (n = 118 000). To evaluate the interplay of the metabolome and the gut microbiome in the pathogenesis of depression, metabolic signatures of the gut microbiome were obtained from a 2019 study performed in Dutch cohorts. Data were analyzed from March to December 2021. Main Outcomes and Measures Outcomes were lifetime and recurrent MDD, with 249 metabolites profiled with nuclear magnetic resonance spectroscopy with the Nightingale platform. Results The study included 6811 individuals with lifetime MDD compared with 51 446 control individuals and 4370 individuals with recurrent MDD compared with 62 508 control individuals. Individuals with lifetime MDD were younger (median [IQR] age, 56 [49-62] years vs 58 [51-64] years) and more often female (4447 [65%] vs 2364 [35%]) than control individuals. Metabolic signatures of MDD consisted of 124 metabolites spanning the energy and lipid metabolism pathways. Novel findings included 49 metabolites, including those involved in the tricarboxylic acid cycle (ie, citrate and pyruvate). Citrate was significantly decreased (β [SE], -0.07 [0.02]; FDR = 4 × 10-04) and pyruvate was significantly increased (β [SE], 0.04 [0.02]; FDR = 0.02) in individuals with MDD. Changes observed in these metabolites, particularly lipoproteins, were consistent with the differential composition of gut microbiota belonging to the order Clostridiales and the phyla Proteobacteria/Pseudomonadota and Bacteroidetes/Bacteroidota. Mendelian randomization suggested that fatty acids and intermediate and very large density lipoproteins changed in association with the disease process but high-density lipoproteins and the metabolites in the tricarboxylic acid cycle did not. Conclusions and Relevance The study findings showed that energy metabolism was disturbed in individuals with MDD and that the interplay of the gut microbiome and blood metabolome may play a role in lipid metabolism in individuals with MDD.
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Affiliation(s)
- Najaf Amin
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Jun Liu
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Bruno Bonnechere
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, Hasselt, Belgium
- Technology-Supported and Data-Driven Rehabilitation, Data Sciences Institute, Hasselt University, Hasselt, Belgium
| | | | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Richa Batra
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Yu-Jie Chiou
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Marco Fernandes
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Robert Kraaij
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Danielle Newby
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Kwangsik Nho
- Center for Neuroimaging, Department of Radiology and Imaging Sciences and Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis
| | - Djawad Radjabzadeh
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Andrew J. Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences and Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis
| | - Liu Shi
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - William Sproviero
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Laura Winchester
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Yang Yang
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | | | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
| | - Cornelia M. van Duijn
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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13
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Batra R, Krumsiek J, Wang X, Allen M, Wörheide MA, Blach C, Bennett DA, Kastenmüller G, Arnold M, Ertekin‐Taner N, Kaddurah‐Daouk R. Brain region‐specific metabolic signatures of Alzheimer’s disease. Alzheimers Dement 2022. [DOI: 10.1002/alz.067879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Richa Batra
- Weill Cornell Medicine New York NY USA
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine New York NY USA
| | - Jan Krumsiek
- Weill Cornell Medicine New York NY USA
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine New York NY USA
| | | | | | - Maria A. Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health Neuherberg Germany
| | - Colette Blach
- Duke Molecular Physiology Institute, Duke University Durham NC USA
| | - David A Bennett
- Rush Alzheimer’s Disease Center and Department of Neurological Sciences, Rush University Medical Center Chicago IL USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health Neuherberg Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health Neuherberg Germany
- Department of Psychiatry and Behavioral Sciences, Duke University Durham NC USA
| | | | - Rima Kaddurah‐Daouk
- Duke Institute for Brain Sciences and Department of Medicine Durham NC USA
- Duke University Medical Center Durham NC USA
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14
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van Duijn CM, Amin N, Liu J, Bonnechere B, MahmoudianDehkordi S, Arnold M, Batra R, Chiou Y, Fernandes M, Ikram MA, Kraaij R, Krumsiek J, Newby D, Nho K, Radjabzadeh D, Saykin AJ, Shi L, Sproviero W, Winchester LM, Yang Y, Nevado‐Holgado AJ, Kastenmüller G, Kaddurah‐Daouk R. Interplay of the human exposome, metabolome and gut microbiome in dementia and major depression. Alzheimers Dement 2022. [DOI: 10.1002/alz.067261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Cornelia M van Duijn
- Nuffield Department of Population Health, University of Oxford Oxford United Kingdom
| | - Najaf Amin
- University of Oxford Oxford United Kingdom
| | - Jun Liu
- University of Oxford Oxford United Kingdom
| | | | - Siamak MahmoudianDehkordi
- Bioinformatics Research Center, Department of Statistics, North Carolina State University Raleigh NC USA
| | | | - Richa Batra
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine New York NY USA
| | - Yu‐Jie Chiou
- Nuffield Department of Population Health, Oxford University Oxford United Kingdom
| | | | - M. Arfan Ikram
- Department of Epidemiology, Erasmus MC Rotterdam Netherlands
| | - Robert Kraaij
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam Netherlands
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine New York NY USA
| | | | - Kwangsik Nho
- Indiana Alzheimer’s Disease Research Center Indianapolis IN USA
| | - Djawad Radjabzadeh
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam Netherlands
| | - Andrew J. Saykin
- Department of Radiology and Imaging Services, Indiana University School of Medicine Indianapolis IN USA
| | - Liu Shi
- Department of Psychiatry, University of Oxford Oxford United Kingdom
| | | | | | - Yang Yang
- Department of Computer Science and Engineering Shanghai China
| | | | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health Neuherberg Germany
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15
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Wang T, Huynh K, Giles C, Mellett NA, Duong T, Nguyen A, Lim WLF, Smith AAT, Olshansky G, Cadby G, Hung J, Hui J, Beilby J, Watts GF, Chatterjee P, Martins I, Laws SM, Bush AI, Rowe CC, Villemagne VL, Ames D, Masters CL, Taddei K, Doré V, Fripp J, Arnold M, Kastenmüller G, Nho K, Saykin AJ, Baillie R, Han X, Martins RN, Moses EK, Kaddurah‐Daouk R, Meikle PJ. APOE ε2 resilience for Alzheimer's disease is mediated by plasma lipid species: Analysis of three independent cohort studies. Alzheimers Dement 2022; 18:2151-2166. [PMID: 35077012 PMCID: PMC9787288 DOI: 10.1002/alz.12538] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 10/19/2021] [Accepted: 10/25/2021] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The apolipoprotein E (APOE) genotype is the strongest genetic risk factor for late-onset Alzheimer's disease. However, its effect on lipid metabolic pathways, and their mediating effect on disease risk, is poorly understood. METHODS We performed lipidomic analysis on three independent cohorts (the Australian Imaging, Biomarkers and Lifestyle [AIBL] flagship study, n = 1087; the Alzheimer's Disease Neuroimaging Initiative [ADNI] 1 study, n = 819; and the Busselton Health Study [BHS], n = 4384), and we defined associations between APOE ε2 and ε4 and 569 plasma/serum lipid species. Mediation analysis defined the proportion of the treatment effect of the APOE genotype mediated by plasma/serum lipid species. RESULTS A total of 237 and 104 lipid species were associated with APOE ε2 and ε4, respectively. Of these 68 (ε2) and 24 (ε4) were associated with prevalent Alzheimer's disease. Individual lipid species or lipidomic models of APOE genotypes mediated up to 30% and 10% of APOE ε2 and ε4 treatment effect, respectively. DISCUSSION Plasma lipid species mediate the treatment effect of APOE genotypes on Alzheimer's disease and as such represent a potential therapeutic target.
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Surendran P, Stewart ID, Au Yeung VPW, Pietzner M, Raffler J, Wörheide MA, Li C, Smith RF, Wittemans LBL, Bomba L, Menni C, Zierer J, Rossi N, Sheridan PA, Watkins NA, Mangino M, Hysi PG, Di Angelantonio E, Falchi M, Spector TD, Soranzo N, Michelotti GA, Arlt W, Lotta LA, Denaxas S, Hemingway H, Gamazon ER, Howson JMM, Wood AM, Danesh J, Wareham NJ, Kastenmüller G, Fauman EB, Suhre K, Butterworth AS, Langenberg C. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat Med 2022; 28:2321-2332. [PMID: 36357675 PMCID: PMC9671801 DOI: 10.1038/s41591-022-02046-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 09/16/2022] [Indexed: 11/12/2022]
Abstract
Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10-11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.
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Affiliation(s)
- Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | | | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Digital Medicine, University Hospital of Augsburg, Augsburg, Germany
| | - Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Chen Li
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Rebecca F Smith
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Laura B L Wittemans
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Big Data Institute, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | - Lorenzo Bomba
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
| | - Cristina Menni
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Jonas Zierer
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Niccolò Rossi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | | | | | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Pirro G Hysi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Health Data Science Research Centre, Human Technopole, Milan, Italy
| | - Mario Falchi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Nicole Soranzo
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | | | - Wiebke Arlt
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Clare Hall & MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Joanna M M Howson
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Angela M Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK.
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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Weinisch P, Fiamoncini J, Schranner D, Raffler J, Skurk T, Rist MJ, Römisch-Margl W, Prehn C, Adamski J, Hauner H, Daniel H, Suhre K, Kastenmüller G. Dynamic patterns of postprandial metabolic responses to three dietary challenges. Front Nutr 2022; 9:933526. [PMID: 36211489 PMCID: PMC9540193 DOI: 10.3389/fnut.2022.933526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Food intake triggers extensive changes in the blood metabolome. The kinetics of these changes depend on meal composition and on intrinsic, health-related characteristics of each individual, making the assessment of changes in the postprandial metabolome an opportunity to assess someone's metabolic status. To enable the usage of dietary challenges as diagnostic tools, profound knowledge about changes that occur in the postprandial period in healthy individuals is needed. In this study, we characterize the time-resolved changes in plasma levels of 634 metabolites in response to an oral glucose tolerance test (OGTT), an oral lipid tolerance test (OLTT), and a mixed meal (SLD) in healthy young males (n = 15). Metabolite levels for samples taken at different time points (20 per individual) during the challenges were available from targeted (132 metabolites) and non-targeted (502 metabolites) metabolomics. Almost half of the profiled metabolites (n = 308) showed a significant change in at least one challenge, thereof 111 metabolites responded exclusively to one particular challenge. Examples include azelate, which is linked to ω-oxidation and increased only in OLTT, and a fibrinogen cleavage peptide that has been linked to a higher risk of cardiovascular events in diabetes patients and increased only in OGTT, making its postprandial dynamics a potential target for risk management. A pool of 89 metabolites changed their plasma levels during all three challenges and represents the core postprandial response to food intake regardless of macronutrient composition. We used fuzzy c-means clustering to group these metabolites into eight clusters based on commonalities of their dynamic response patterns, with each cluster following one of four primary response patterns: (i) “decrease-increase” (valley-like) with fatty acids and acylcarnitines indicating the suppression of lipolysis, (ii) “increase-decrease” (mountain-like) including a cluster of conjugated bile acids and the glucose/insulin cluster, (iii) “steady decrease” with metabolites reflecting a carryover from meals prior to the study, and (iv) “mixed” decreasing after the glucose challenge and increasing otherwise. Despite the small number of subjects, the diversity of the challenges and the wealth of metabolomic data make this study an important step toward the characterization of postprandial responses and the identification of markers of metabolic processes regulated by food intake.
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Affiliation(s)
- Patrick Weinisch
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jarlei Fiamoncini
- Food Research Center – FoRC, Department of Food Science and Experimental Nutrition, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - Daniela Schranner
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Digital Medicine, University Hospital of Augsburg, Augsburg, Germany
| | - Thomas Skurk
- Core Facility Human Studies, ZIEL Institute for Food and Health, Technical University of Munich, Freising, Germany
- Else Kröner Fresenius Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Manuela J. Rist
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Werner Römisch-Margl
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Hans Hauner
- Else Kröner Fresenius Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Hannelore Daniel
- Department of Food and Nutrition, Technical University of Munich, Freising, Germany
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine—Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- *Correspondence: Gabi Kastenmüller
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18
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Suls J, Salive ME, Koroukian SM, Alemi F, Silber JH, Kastenmüller G, Klabunde CN. Emerging approaches to multiple chronic condition assessment. J Am Geriatr Soc 2022; 70:2498-2507. [PMID: 35699153 PMCID: PMC9489607 DOI: 10.1111/jgs.17914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 04/25/2022] [Accepted: 05/07/2022] [Indexed: 01/01/2023]
Abstract
Older adults experience a higher prevalence of multiple chronic conditions (MCCs). Establishing the presence and pattern of MCCs in individuals or populations is important for healthcare delivery, research, and policy. This report describes four emerging approaches and discusses their potential applications for enhancing assessment, treatment, and policy for the aging population. The National Institutes of Health convened a 2-day panel workshop of experts in 2018. Four emerging models were identified by the panel, including classification and regression tree (CART), qualifying comorbidity sets (QCS), the multimorbidity index (MMI), and the application of omics to network medicine. Future research into models of multiple chronic condition assessment may improve understanding of the epidemiology, diagnosis, and treatment of older persons.
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Affiliation(s)
- Jerry Suls
- Feinstein Institutes for Medical Research/Northwell Health (previously National Cancer Institute)New York CityNew YorkUSA
| | | | | | | | | | - Gabi Kastenmüller
- Helmholtz Zentrum MünchenInstitute for Computational BiologyOberschleißheimGermany
| | - Carrie N. Klabunde
- Office of Disease PreventionNational Institutes of HealthBethesdaMarylandUSA
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19
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Fiamoncini J, Rist MJ, Frommherz L, Giesbertz P, Pfrang B, Kremer W, Huber F, Kastenmüller G, Skurk T, Hauner H, Suhre K, Daniel H, Kulling SE. Dynamics and determinants of human plasma bile acid profiles during dietary challenges. Front Nutr 2022; 9:932937. [PMID: 35967802 PMCID: PMC9366195 DOI: 10.3389/fnut.2022.932937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 07/01/2022] [Indexed: 11/30/2022] Open
Abstract
In recent years, bile acids (BA) have received great interest due to their pleiotropic biological activity and the presence of plasma membrane-bound and nuclear receptors. Moreover, BA in blood have been identified by metabolite screening approaches as biomarkers that are associated with various diseases and even with a human longevity phenotype. With the growing interest in the microbiota contribution to the health-disease trajectory, BA that undergo deconjugation and other modifications by bacteria in the large intestine have become a prime target as a microbiome diversity modifier. We here profiled BA by a quantitative and a semiquantitative approach in 15 healthy and phenotypically very similar young individuals for over a 36-h fasting period, an oral glucose tolerance test (OGTT), and an oral lipid tolerance test (OLTT). We demonstrate a remarkable heterogeneity of the responses and describe the different dynamics of the plasma changes that likely originate from different routes by which BA enters the peripheral blood, and that may represent a direct secretion from the liver into the blood and a route that reaches the blood as a spill-over after passing from the gallbladder through the intestine and the portal system. We discuss the finding that an individual transport process involved in the passage of BA could be a critical determinant in the kinetics of plasma appearance and the overall phenotypic variability found.
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Affiliation(s)
- Jarlei Fiamoncini
- Department of Food Science and Experimental Nutrition, School of Pharmaceutical Sciences, Food Research Center - FoRC, University of São Paulo, São Paulo, Brazil
| | - Manuela J Rist
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Karlsruhe, Germany
| | - Lara Frommherz
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Karlsruhe, Germany
| | - Pieter Giesbertz
- Department of Nutritional Physiology, Technische Universität München, Freising-Weihenstephan, Germany
| | - Birgit Pfrang
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Karlsruhe, Germany
| | - Werner Kremer
- Biophysics I, Regensburg Center for Biochemistry, Universität Regensburg, Regensburg, Germany
| | - Fritz Huber
- Department of Nutritional Physiology, Technische Universität München, Freising-Weihenstephan, Germany
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Skurk
- Chair of Nutritional Medicine, Else Kroener-Fresenius-Centre for Nutritional Medicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Hans Hauner
- Chair of Nutritional Medicine, Else Kroener-Fresenius-Centre for Nutritional Medicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Karsten Suhre
- Bioinformatics Core, Research Department, Weill Cornell Medicine in Qatar, Doha, Qatar
| | - Hannelore Daniel
- Department of Nutritional Physiology, Technische Universität München, Freising-Weihenstephan, Germany
| | - Sabine E Kulling
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Karlsruhe, Germany
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20
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Batra R, Arnold M, Wörheide MA, Allen M, Wang X, Blach C, Levey AI, Seyfried NT, Ertekin-Taner N, Bennett DA, Kastenmüller G, Kaddurah-Daouk RF, Krumsiek J. The landscape of metabolic brain alterations in Alzheimer's disease. Alzheimers Dement 2022; 19:10.1002/alz.12714. [PMID: 35829654 PMCID: PMC9837312 DOI: 10.1002/alz.12714] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 05/12/2022] [Accepted: 05/18/2022] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Alzheimer's disease (AD) is accompanied by metabolic alterations both in the periphery and the central nervous system. However, so far, a global view of AD-associated metabolic changes in the brain has been missing. METHODS We metabolically profiled 500 samples from the dorsolateral prefrontal cortex. Metabolite levels were correlated with eight clinical parameters, covering both late-life cognitive performance and AD neuropathology measures. RESULTS We observed widespread metabolic dysregulation associated with AD, spanning 298 metabolites from various AD-relevant pathways. These included alterations to bioenergetics, cholesterol metabolism, neuroinflammation, and metabolic consequences of neurotransmitter ratio imbalances. Our findings further suggest impaired osmoregulation as a potential pathomechanism in AD. Finally, inspecting the interplay of proteinopathies provided evidence that metabolic associations were largely driven by tau pathology rather than amyloid beta pathology. DISCUSSION This work provides a comprehensive reference map of metabolic brain changes in AD that lays the foundation for future mechanistic follow-up studies.
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Affiliation(s)
- Richa Batra
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Maria A. Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Xue Wang
- Department of Health Sciences Research, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Colette Blach
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Allan I. Levey
- Goizueta Alzheimer’s Disease Research Center, Emory University, Atlanta, GA, USA
| | | | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, USA
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Rima F. Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke Institute for Brain Sciences and Department of Medicine, Duke University, Durham, NC, 27708, USA
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
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21
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Milaneschi Y, Arnold M, Kastenmüller G, Dehkordi SM, Krishnan RR, Dunlop BW, Rush AJ, Penninx BWJH, Kaddurah-Daouk R. Genomics-based identification of a potential causal role for acylcarnitine metabolism in depression. J Affect Disord 2022; 307:254-263. [PMID: 35381295 DOI: 10.1016/j.jad.2022.03.070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 03/11/2022] [Accepted: 03/29/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND Altered metabolism of acylcarnitines - transporting fatty acids to mitochondria - may link cellular energy dysfunction to depression. We examined the potential causal role of acylcarnitine metabolism in depression by leveraging genomics and Mendelian randomization. METHODS Summary statistics were obtained from large GWAS: the Fenland Study (N = 9363), and the Psychiatric Genomics Consortium (246,363 depression cases and 561,190 controls). Two-sample Mendelian randomization analyses tested the potential causal link of 15 endogenous acylcarnitines with depression. RESULTS In univariable analyses, genetically-predicted lower levels of short-chain acylcarnitines C2 (odds ratio [OR] 0.97, 95% confidence intervals [CIs] 0.95-1.00) and C3 (OR 0.97, 95%CIs 0.96-0.99) and higher levels of medium-chain acylcarnitines C8 (OR 1.04, 95%CIs 1.01-1.06) and C10 (OR 1.04, 95%CIs 1.02-1.06) were associated with increased depression risk. No reverse potential causal role of depression genetic liability on acylcarnitines levels was found. Multivariable analyses showed that the association with depression was driven by the medium-chain acylcarnitines C8 (OR 1.04, 95%CIs 1.02-1.06) and C10 (OR 1.04, 95%CIs 1.02-1.06), suggesting a potential causal role in the risk of depression. Causal estimates for C8 (OR = 1.05, 95%CIs = 1.02-1.07) and C10 (OR = 1.05, 95%CIs = 1.02-1.08) were confirmed in follow-up analyses using genetic instruments derived from a GWAS meta-analysis including up to 16,841 samples. DISCUSSION Accumulation of medium-chain acylcarnitines is a signature of inborn errors of fatty acid metabolism and age-related metabolic conditions. Our findings point to a link between altered mitochondrial energy production and depression pathogenesis. Acylcarnitine metabolism represents a promising access point for the development of novel therapeutic approaches for depression.
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Affiliation(s)
- Yuri Milaneschi
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Public Health, Mental Health program, Amsterdam, The Netherlands; Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress program, Amsterdam, The Netherlands; Amsterdam Neuroscience, Complex Trait Genetics, Amsterdam, The Netherlands.
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany
| | | | - Ranga R Krishnan
- Department of Psychiatry, Rush Medical College, Chicago, IL, USA
| | - Boadie W Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - A John Rush
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA; Duke-National University of Singapore, Singapore; Department of Psychiatry, Texas Tech University, Health Sciences Center, Permian Basin, TX, USA
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Public Health, Mental Health program, Amsterdam, The Netherlands
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC, USA; Department of Medicine, Duke University, Durham, NC, USA
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22
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Cadby G, Giles C, Melton PE, Huynh K, Mellett NA, Duong T, Nguyen A, Cinel M, Smith A, Olshansky G, Wang T, Brozynska M, Inouye M, McCarthy NS, Ariff A, Hung J, Hui J, Beilby J, Dubé MP, Watts GF, Shah S, Wray NR, Lim WLF, Chatterjee P, Martins I, Laws SM, Porter T, Vacher M, Bush AI, Rowe CC, Villemagne VL, Ames D, Masters CL, Taddei K, Arnold M, Kastenmüller G, Nho K, Saykin AJ, Han X, Kaddurah-Daouk R, Martins RN, Blangero J, Meikle PJ, Moses EK. Comprehensive genetic analysis of the human lipidome identifies loci associated with lipid homeostasis with links to coronary artery disease. Nat Commun 2022; 13:3124. [PMID: 35668104 PMCID: PMC9170690 DOI: 10.1038/s41467-022-30875-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 05/17/2022] [Indexed: 12/26/2022] Open
Abstract
We integrated lipidomics and genomics to unravel the genetic architecture of lipid metabolism and identify genetic variants associated with lipid species putatively in the mechanistic pathway for coronary artery disease (CAD). We quantified 596 lipid species in serum from 4,492 individuals from the Busselton Health Study. The discovery GWAS identified 3,361 independent lipid-loci associations, involving 667 genomic regions (479 previously unreported), with validation in two independent cohorts. A meta-analysis revealed an additional 70 independent genomic regions associated with lipid species. We identified 134 lipid endophenotypes for CAD associated with 186 genomic loci. Associations between independent lipid-loci with coronary atherosclerosis were assessed in ∼456,000 individuals from the UK Biobank. Of the 53 lipid-loci that showed evidence of association (P < 1 × 10-3), 43 loci were associated with at least one lipid endophenotype. These findings illustrate the value of integrative biology to investigate the aetiology of atherosclerosis and CAD, with implications for other complex diseases.
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Affiliation(s)
- Gemma Cadby
- School of Population and Global Health, University of Western Australia, Crawley, WA, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia
| | - Phillip E Melton
- School of Population and Global Health, University of Western Australia, Crawley, WA, Australia
- Menzies Research Institute, University of Tasmania, Hobart, TAS, Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia
| | | | - Thy Duong
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Anh Nguyen
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Michelle Cinel
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Alex Smith
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Gavriel Olshansky
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia
| | - Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia
| | - Marta Brozynska
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Mike Inouye
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Nina S McCarthy
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia
| | - Amir Ariff
- School of Women's and Children's Health, University of New South Wales, Sydney, NSW, Australia
| | - Joseph Hung
- School of Medicine, The University of Western Australia, Crawley, WA, Australia
- Department of Cardiovascular Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
- Busselton Population Medical Research Institute Inc., Perth, WA, Australia
| | - Jennie Hui
- Busselton Population Medical Research Institute Inc., Perth, WA, Australia
- PathWest Laboratory Medicine WA, Perth, WA, Australia
| | - John Beilby
- Busselton Population Medical Research Institute Inc., Perth, WA, Australia
- PathWest Laboratory Medicine WA, Perth, WA, Australia
| | - Marie-Pierre Dubé
- Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal Heart Institute, Montreal, QC, Canada
| | - Gerald F Watts
- School of Medicine, The University of Western Australia, Crawley, WA, Australia
- Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, WA, Australia
| | - Sonia Shah
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Wei Ling Florence Lim
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Cooperative research Centre (CRC) for Mental Health, Joondalup, WA, Australia
| | - Pratishtha Chatterjee
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, Australia
- KaRa Institute of Neurological Disease, Sydney, Macquarie Park, NSW, Australia
| | - Ian Martins
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Simon M Laws
- Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia
- Collaborative Genomics Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia
| | - Tenielle Porter
- Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia
- Collaborative Genomics Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia
| | - Michael Vacher
- Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia
- Collaborative Genomics Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- The Australian e-Health Research Centre, Health and Biosecurity, CSIRO, Floreat, WA, Australia
| | - Ashley I Bush
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Christopher C Rowe
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
- Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, VIC, Australia
| | - Victor L Villemagne
- Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, VIC, Australia
- Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, VIC, Australia
| | - David Ames
- National Ageing Research Institute, Parkville, VIC, Australia
- University of Melbourne Academic Unit for Psychiatry of Old Age, St George's Hospital, Kew, VIC, Australia
| | - Colin L Masters
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Kevin Taddei
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Xianlin Han
- Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Ralph N Martins
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- Cooperative research Centre (CRC) for Mental Health, Joondalup, WA, Australia
- Department of Biomedical Sciences, Macquarie University, North Ryde, NSW, Australia
- KaRa Institute of Neurological Disease, Sydney, Macquarie Park, NSW, Australia
| | - John Blangero
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, VIC, Australia.
- Monash University, Melbourne, VIC, Australia.
| | - Eric K Moses
- Menzies Research Institute, University of Tasmania, Hobart, TAS, Australia.
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia.
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23
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Horgusluoglu E, Neff R, Song W, Wang M, Wang Q, Arnold M, Krumsiek J, Galindo‐Prieto B, Ming C, Nho K, Kastenmüller G, Han X, Baillie R, Zeng Q, Andrews S, Cheng H, Hao K, Goate A, Bennett DA, Saykin AJ, Kaddurah‐Daouk R, Zhang B. Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease. Alzheimers Dement 2022; 18:1260-1278. [PMID: 34757660 PMCID: PMC9085975 DOI: 10.1002/alz.12468] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 04/14/2021] [Accepted: 04/17/2021] [Indexed: 12/29/2022]
Abstract
Metabolites, the biochemical products of the cellular process, can be used to measure alterations in biochemical pathways related to the pathogenesis of Alzheimer's disease (AD). However, the relationships between systemic abnormalities in metabolism and the pathogenesis of AD are poorly understood. In this study, we aim to identify AD-specific metabolomic changes and their potential upstream genetic and transcriptional regulators through an integrative systems biology framework for analyzing genetic, transcriptomic, metabolomic, and proteomic data in AD. Metabolite co-expression network analysis of the blood metabolomic data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) shows short-chain acylcarnitines/amino acids and medium/long-chain acylcarnitines are most associated with AD clinical outcomes, including episodic memory scores and disease severity. Integration of the gene expression data in both the blood from the ADNI and the brain from the Accelerating Medicines Partnership Alzheimer's Disease (AMP-AD) program reveals ABCA1 and CPT1A are involved in the regulation of acylcarnitines and amino acids in AD. Gene co-expression network analysis of the AMP-AD brain RNA-seq data suggests the CPT1A- and ABCA1-centered subnetworks are associated with neuronal system and immune response, respectively. Increased ABCA1 gene expression and adiponectin protein, a regulator of ABCA1, correspond to decreased short-chain acylcarnitines and amines in AD in the ADNI. In summary, our integrated analysis of large-scale multiomics data in AD systematically identifies novel metabolites and their potential regulators in AD and the findings pave a way for not only developing sensitive and specific diagnostic biomarkers for AD but also identifying novel molecular mechanisms of AD pathogenesis.
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Affiliation(s)
- Emrin Horgusluoglu
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Ryan Neff
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Won‐Min Song
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Minghui Wang
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Qian Wang
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Matthias Arnold
- Institute of Computational BiologyHelmholtz Zentrum MünchenGerman Research Center for Environmental HealthNeuherbergGermany
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
| | - Jan Krumsiek
- Department of Physiology and BiophysicsWeill Cornell MedicineInstitute for Computational BiomedicineEnglander Institute for Precision MedicineNew YorkNew YorkUSA
| | - Beatriz Galindo‐Prieto
- Department of Physiology and BiophysicsWeill Cornell MedicineInstitute for Computational BiomedicineEnglander Institute for Precision MedicineNew YorkNew YorkUSA
- Helen and Robert Appel Alzheimer's Disease Research InstituteBrain and Mind Research InstituteWeill Cornell MedicineNew YorkNew YorkUSA
| | - Chen Ming
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences; Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - Gabi Kastenmüller
- Institute of Computational BiologyHelmholtz Zentrum MünchenGerman Research Center for Environmental HealthNeuherbergGermany
| | - Xianlin Han
- Barshop Institute for Longevity and Aging StudiesUniversity of Texas Health Science Center at San AntonioSan AntonioTexasUSA
| | | | - Qi Zeng
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Shea Andrews
- Department of NeuroscienceRonald M. Loeb Center for Alzheimer's DiseaseIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Haoxiang Cheng
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Ke Hao
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
| | - Alison Goate
- Department of NeuroscienceRonald M. Loeb Center for Alzheimer's DiseaseIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - David A. Bennett
- Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences; Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rima Kaddurah‐Daouk
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
- Duke Institute of Brain SciencesDuke UniversityDurhamNorth CarolinaUSA
- Department of MedicineDuke UniversityDurhamNorth CarolinaUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesMount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiIcahn Institute of Genomics and Multiscale BiologyNew YorkNew YorkUSA
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24
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Brydges CR, Bhattacharyya S, Dehkordi SM, Milaneschi Y, Penninx B, Jansen R, Kristal BS, Han X, Arnold M, Kastenmüller G, Bekhbat M, Mayberg HS, Craighead WE, Rush AJ, Fiehn O, Dunlop BW, Kaddurah-Daouk R. Metabolomic and inflammatory signatures of symptom dimensions in major depression. Brain Behav Immun 2022; 102:42-52. [PMID: 35131442 PMCID: PMC9241382 DOI: 10.1016/j.bbi.2022.02.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 01/25/2022] [Accepted: 02/01/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions. METHODS Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism. RESULTS The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids. CONCLUSION The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.
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Affiliation(s)
| | - Sudeepa Bhattacharyya
- Arkansas Biosciences Institute, Department of Biological Sciences, Arkansas State University, AR, USA
| | | | - Yuri Milaneschi
- Amsterdam UMC / GGZ inGeest Research & Innovation, Amsterdam, Netherlands
| | - Brenda Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands
| | - Rick Jansen
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Department of Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Bruce S Kristal
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Xianlin Han
- University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Mandakh Bekhbat
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Helen S Mayberg
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - W Edward Craighead
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA; Department of Psychology, Emory University, Atlanta, GA, USA
| | - A John Rush
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Department of Psychiatry, Health Sciences Center, Texas Tech University, Permian Basin, TX, USA; Duke-National University of Singapore, Singapore
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California, Davis, USA
| | - Boadie W Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Department of Medicine, Duke University, Durham, NC, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC, USA.
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25
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Baloni P, Arnold M, Moreno H, Nho K, Kastenmüller G, Suhre K, Buitrago L, Louie G, Kueider‐Paisley A, Saykin AJ, Ekroos K, Meikle PJ, Huynh K, Funk CC, Hood L, Price ND, Baillie R, Han X, Kaddurah‐Daouk RF. Transcriptomics, metabolomics, lipidomics, metabolic flux and mGWAS analyses of sphingolipid pathway highlights novel drugs for Alzheimer’s disease. Alzheimers Dement 2021. [DOI: 10.1002/alz.056152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
| | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University Durham NC USA
- Helmholtz Zentrum München, German Research Center for Environmental Health Neuherberg Germany
| | | | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine Indianapolis IN USA
| | - Gabi Kastenmüller
- Helmholtz Zentrum München, German Research Center for Environmental Health Neuherberg Germany
- German Center for Diabetes Research (DZD) Neuherberg Germany
| | | | | | - Gregory Louie
- Department of Psychiatry and Behavioral Sciences, Duke University Durham NC USA
| | | | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine Indianapolis IN USA
| | | | | | - Kevin Huynh
- Baker Heart and Diabetes Institute Melbourne VIC Australia
| | | | - Leroy Hood
- Institute for Systems Biology Seattle WA USA
- Providence St. Joseph Health Renton WA USA
| | | | | | - Xianlin Han
- University of Texas Health Sciences Center, San Antonio San Antonio TX USA
| | - Rima F. Kaddurah‐Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University Durham NC USA
- Duke Institute for Brain Sciences, Duke University Durham NC USA
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26
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Batra R, Heinken A, Karu N, Arnold M, Kastenmüller G, Hankemeier T, Bennett DA, Knight R, Krumsiek J, Thiele I, Kaddurah‐Daouk RF. Mapping the human brain metabolome and influences of gut microbiome. Alzheimers Dement 2021. [DOI: 10.1002/alz.056270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | - Almut Heinken
- National University of Ireland Galway Galway Ireland
| | - Naama Karu
- Leiden Academic Centre for Drug Research Leiden Netherlands
| | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University Durham NC USA
- German Center for Diabetes Research (DZD) Neuherberg Germany
- Duke University Durham NC USA
| | | | | | | | - Rob Knight
- University of California at San Diego San Diego CA USA
| | | | - Ines Thiele
- National University of Ireland Galway Ireland
| | - Rima F. Kaddurah‐Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University Durham NC USA
- Duke Institute for Brain Sciences, Duke University Durham NC USA
- Department of Medicine, Duke University Durham NC USA
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27
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Wang T, Huynh K, Giles C, Lim WLF, Duong T, Mellett NA, Smith A, Olshansky G, Drew BG, Cadby G, Melton PE, Hung J, Beilby J, Watts GF, Chatterjee P, Martins I, Laws SM, Bush AI, Rowe CC, Villemagne VL, Ames D, Masters CL, Arnold M, Kastenmüller G, Nho K, Saykin AJ, Baillie R, Han X, Martins RN, Moses E, Kaddurah‐Daouk RF, Meikle PJ. Lipidomic signatures for APOE genotypes provides new insights about mechanisms of resilience in Alzheimer’s disease. Alzheimers Dement 2021. [DOI: 10.1002/alz.056703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Tingting Wang
- Baker Heart and Diabetes Institute Melbourne VIC Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute Melbourne VIC Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute Melbourne VIC Australia
| | | | - Thy Duong
- Baker Heart and Diabetes Institute Melbourne VIC Australia
| | | | | | | | - Brian G. Drew
- Baker Heart and Diabetes Institute Melbourne VIC Australia
| | - Gemma Cadby
- School of Medicine Faculty of Health and Medical Sciences University of Western Australia Perth WA Australia
| | - Phillip E. Melton
- School of Medicine Faculty of Health and Medical Sciences University of Western Australia Perth WA Australia
| | - Joseph Hung
- Lipid Disorders Clinic Department of Cardiology Royal Perth Hospital Perth WA Australia
| | - John Beilby
- PathWest Laboratory Medicine of Western Australia Nedlands WA Australia
| | - Gerald F. Watts
- School of Medicine Faculty of Health and Medical Sciences University of Western Australia Perth WA Australia
| | | | - Ian Martins
- Co‐operative research Centre (CRC) for Mental Health Carlton South VIC Australia
| | | | - Ashley I. Bush
- The Florey Department of Neuroscience and Mental Health The University of Melbourne Melbourne VIC Australia
| | - Christopher C. Rowe
- The Florey Department of Neuroscience and Mental Health The University of Melbourne Melbourne VIC Australia
| | - Victor L. Villemagne
- Department of Nuclear Medicine and Centre for PET Austin Health Heidelberg, Vic, Heidelberg, QLD 3084 Australia Australia
| | - David Ames
- National Ageing Research Institute Parkville, VIC Australia
| | - Colin L. Masters
- The Florey Department of Neuroscience and Mental Health The University of Melbourne Melbourne VIC Australia
| | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences Duke University Durham NC USA
- Institute of Computational Biology Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany
| | - Gabi Kastenmüller
- Institute of Computational Biology Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis IN USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis IN USA
| | | | - Xianlin Han
- Barshop Institute for Longevity and Aging Studies University of Texas Health Science Center at San Antonio San Antonio TX USA
| | - Ralph N. Martins
- Co‐operative research Centre (CRC) for Mental Health Carlton South VIC Australia
| | - Eric Moses
- School of Medicine Faculty of Health and Medical Sciences University of Western Australia Perth WA Australia
| | - Rima F. Kaddurah‐Daouk
- Department of Psychiatry and Behavioral Sciences Duke University Durham NC USA
- Duke Institute for Brain Sciences Duke University Durham NC USA
- Department of Medicine Duke University Durham NC USA
| | - Peter J Meikle
- Baker Heart and Diabetes Institute Melbourne VIC Australia
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28
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Ahmad S, Arnold M, Hankemeier T, Ghanbari M, Uitterlinden AG, Kraaij R, Van Duijn CM, Kaddurah‐Daouk RF, Kastenmüller G, Ikram MA. Gut microbiome‐related metabolites in plasma are associated with general cognition. Alzheimers Dement 2021. [DOI: 10.1002/alz.056142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Shahzad Ahmad
- Department of Epidemiology, Erasmus Medical Center Rotterdam Netherlands
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research Leiden Netherlands
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health Neuherberg Germany
- Department of Psychiatry and Behavioral Sciences, Duke University Durham NC USA
| | - Thomas Hankemeier
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research Leiden Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus Medical Centre Rotterdam Netherlands
| | - André G. Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam Netherlands
| | - Robert Kraaij
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam Netherlands
| | - Cornelia M. Van Duijn
- Department of Epidemiology, Erasmus Medical Center Rotterdam Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford United Kingdom
| | - Rima F. Kaddurah‐Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University Durham NC USA
- Duke Institute for Brain Sciences, Duke University Durham NC USA
- Department of Medicine, Duke University Durham NC USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health Neuherberg Germany
- German Center for Diabetes Research (DZD) Neuherberg Germany
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus Medical Center Rotterdam Netherlands
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29
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Liu J, Amin N, Arnold M, Nho K, Saykin AJ, Newby D, Winchester LM, Nevado‐Holgado AJ, Kastenmüller G, Kaddurah‐Daouk RF, Van Duijn CM. Profiling the metabolome of patients with dementia in the UK Biobank. Alzheimers Dement 2021. [DOI: 10.1002/alz.056147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jun Liu
- University of Oxford Oxford United Kingdom
| | - Najaf Amin
- University of Oxford Oxford United Kingdom
| | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University Durham NC USA
- Duke University Durham NC USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine Indianapolis IN USA
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30
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Pietzner M, Wheeler E, Carrasco-Zanini J, Cortes A, Koprulu M, Wörheide MA, Oerton E, Cook J, Stewart ID, Kerrison ND, Luan J, Raffler J, Arnold M, Arlt W, O’Rahilly S, Kastenmüller G, Gamazon ER, Hingorani AD, Scott RA, Wareham NJ, Langenberg C. Mapping the proteo-genomic convergence of human diseases. Science 2021; 374:eabj1541. [PMID: 34648354 PMCID: PMC9904207 DOI: 10.1126/science.abj1541] [Citation(s) in RCA: 134] [Impact Index Per Article: 44.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Characterization of the genetic regulation of proteins is essential for understanding disease etiology and developing therapies. We identified 10,674 genetic associations for 3892 plasma proteins to create a cis-anchored gene-protein-disease map of 1859 connections that highlights strong cross-disease biological convergence. This proteo-genomic map provides a framework to connect etiologically related diseases, to provide biological context for new or emerging disorders, and to integrate different biological domains to establish mechanisms for known gene-disease links. Our results identify proteo-genomic connections within and between diseases and establish the value of cis-protein variants for annotation of likely causal disease genes at loci identified in genome-wide association studies, thereby addressing a major barrier to experimental validation and clinical translation of genetic discoveries.
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Affiliation(s)
- Maik Pietzner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK,Computational Medicine, Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Julia Carrasco-Zanini
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | | | - Mine Koprulu
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Maria A. Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Erin Oerton
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - James Cook
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Isobel D. Stewart
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Nicola D. Kerrison
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Jian’an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany,Institut für Digitale Medizin, Universitätsklinikum Augsburg, 86156 Augsburg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany,Department of Psychiatry and Behavioural Sciences, Duke University, Durham, NC 27710, USA
| | - Wiebke Arlt
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Stephen O’Rahilly
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany,German Centre for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Eric R. Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37203, USA,Clare Hall, University of Cambridge, Cambridge CB3 9AL, United Kingdom
| | - Aroon D. Hingorani
- UCL British Heart Foundation Research Accelerator, Institute of Cardiovascular Science, University College London, WC1E 6BT, UK.,Health Data Research UK, Gibbs Building, 215 Euston Road, London NW1 2BE, UK,Institute of Health Informatics, University College London, 222 Euston Road, London NW1 2DA, UK
| | | | - Nicholas J. Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK,Health Data Research UK, Gibbs Building, 215 Euston Road, London NW1 2BE, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK,Computational Medicine, Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin, 10117 Berlin, Germany,Health Data Research UK, Gibbs Building, 215 Euston Road, London NW1 2BE, UK,Correspondence to Dr. Claudia Langenberg ()
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31
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Aboulmaouahib B, Kastenmüller G, Suhre K, Zöllner S, Weissensteiner H, Gieger C, Wang-Sattler R, Lichtner P, Strauch K, Flaquer A. First mitochondrial genome wide association study with metabolomics. Hum Mol Genet 2021; 31:3367-3376. [PMID: 34718574 PMCID: PMC9523559 DOI: 10.1093/hmg/ddab312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 09/14/2021] [Accepted: 10/11/2021] [Indexed: 12/01/2022] Open
Abstract
In the era of personalized medicine with more and more patient-specific targeted therapies being used, we need reliable, dynamic, faster and sensitive biomarkers both to track the causes of disease and to develop and evolve therapies during the course of treatment. Metabolomics recently has shown substantial evidence to support its emerging role in disease diagnosis and prognosis. Aside from biomarkers and development of therapies, it is also an important goal to understand the involvement of mitochondrial DNA (mtDNA) in metabolic regulation, aging and disease development. Somatic mutations of the mitochondrial genome are also heavily implicated in age-related disease and aging. The general hypothesis is that an alteration in the concentration of metabolite profiles (possibly conveyed by lifestyle and environmental factors) influences the increase of mutation rate in the mtDNA and thereby contributes to a range of pathophysiological alterations observed in complex diseases. We performed an inverted mitochondrial genome-wide association analysis between mitochondrial nucleotide variants (mtSNVs) and concentration of metabolites. We used 151 metabolites and the whole sequenced mitochondrial genome from 2718 individuals to identify the genetic variants associated with metabolite profiles. Because of the high coverage, next-generation sequencing-based analysis of the mitochondrial genome allows for an accurate detection of mitochondrial heteroplasmy and for the identification of variants associated with the metabolome. The strongest association was found for mt715G > A located in the MT-12SrRNA with the metabolite ratio of C2/C10:1 (P-value = 6.82*10−09, β = 0.909). The second most significant mtSNV was found for mt3714A > G located in the MT-ND1 with the metabolite ratio of phosphatidylcholine (PC) ae C42:5/PC ae C44:5 (P-value = 1.02*10−08, β = 3.631). A large number of significant metabolite ratios were observed involving PC aa C36:6 and the variant mt10689G > A, located in the MT-ND4L gene. These results show an important interconnection between mitochondria and metabolite concentrations. Considering that some of the significant metabolites found in this study have been previously related to complex diseases, such as neurological disorders and metabolic conditions, these associations found here might play a crucial role for further investigations of such complex diseases. Understanding the mechanisms that control human health and disease, in particular, the role of genetic predispositions and their interaction with environmental factors is a prerequisite for the development of safe and efficient therapies for complex disorders.
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Affiliation(s)
- Brahim Aboulmaouahib
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, LMU, Munich, Germany.,Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, State of Qatar
| | - Sebastian Zöllner
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America.,Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Hansi Weissensteiner
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Rui Wang-Sattler
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Peter Lichtner
- Institute of Human Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Konstantin Strauch
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, LMU, Munich, Germany.,Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Antònia Flaquer
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, LMU, Munich, Germany.,Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
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32
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Andörfer L, Holtfreter B, Weiss S, Matthes R, Pitchika V, Schmidt CO, Samietz S, Kastenmüller G, Nauck M, Völker U, Völzke H, Csonka LN, Suhre K, Pietzner M, Kocher T. Salivary metabolites associated with a 5-year tooth loss identified in a population-based setting. BMC Med 2021; 19:161. [PMID: 34256740 PMCID: PMC8278731 DOI: 10.1186/s12916-021-02035-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 06/11/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Periodontitis is among the most common chronic diseases worldwide, and it is one of the main reasons for tooth loss. Comprehensive profiling of the metabolite content of the saliva can enable the identification of novel pathways associated with periodontitis and highlight non-invasive markers to facilitate time and cost-effective screening efforts for the presence of periodontitis and the prediction of tooth loss. METHODS We first investigated cross-sectional associations of 13 oral health variables with saliva levels of 562 metabolites, measured by untargeted mass spectrometry among a sub-sample (n = 938) of the Study of Health in Pomerania (SHIP-2) using linear regression models adjusting for common confounders. We took forward any candidate metabolite associated with at least two oral variables, to test for an association with a 5-year tooth loss over and above baseline oral health status using negative binomial regression models. RESULTS We identified 84 saliva metabolites that were associated with at least one oral variable cross-sectionally, for a subset of which we observed robust replication in an independent study. Out of 34 metabolites associated with more than two oral variables, baseline saliva levels of nine metabolites were positively associated with a 5-year tooth loss. Across all analyses, the metabolites 2-pyrrolidineacetic acid and butyrylputrescine were the most consistent candidate metabolites, likely reflecting oral dysbiosis. Other candidate metabolites likely reflected tissue destruction and cell proliferation. CONCLUSIONS Untargeted metabolic profiling of saliva replicated metabolic signatures of periodontal status and revealed novel metabolites associated with periodontitis and future tooth loss.
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Affiliation(s)
- Leonie Andörfer
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany
| | - Birte Holtfreter
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Rutger Matthes
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany
| | - Vinay Pitchika
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany
| | - Carsten Oliver Schmidt
- Institute for Community Medicine, SHIP/Clinical Epidemiology Research, University Medicine Greifswald, Greifswald, Germany
| | - Stefanie Samietz
- Department of Prosthetic Dentistry, Gerodontology and Biomaterials, University Medicine Greifswald, Greifswald, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Matthias Nauck
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Henry Völzke
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Institute for Community Medicine, SHIP/Clinical Epidemiology Research, University Medicine Greifswald, Greifswald, Germany
| | - Laszlo N Csonka
- Department of Biological Sciences, Purdue University, West Lafayette, USA
| | - Karsten Suhre
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.,Weill Cornell Medicine-Qatar, Education City, Qatar Foundation, Doha, Qatar
| | - Maik Pietzner
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany.,Computational Medicine, Berlin Institute of Health (BIH), Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Kocher
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Fleischmannstr. 42, 17475, Greifswald, Germany.
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33
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Klaus VS, Schriever SC, Monroy Kuhn JM, Peter A, Irmler M, Tokarz J, Prehn C, Kastenmüller G, Beckers J, Adamski J, Königsrainer A, Müller TD, Heni M, Tschöp MH, Pfluger PT, Lutter D. Correlation guided Network Integration (CoNI) reveals novel genes affecting hepatic metabolism. Mol Metab 2021; 53:101295. [PMID: 34271221 PMCID: PMC8361260 DOI: 10.1016/j.molmet.2021.101295] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 06/24/2021] [Accepted: 07/09/2021] [Indexed: 11/19/2022] Open
Abstract
Objective Technological advances have brought a steady increase in the availability of various types of omics data, from genomics to metabolomics. Integrating these multi-omics data is a chance and challenge for systems biology; yet, tools to fully tap their potential remain scarce. Methods We present here a fully unsupervised and versatile correlation-based method – termed Correlation guided Network Integration (CoNI) – to integrate multi-omics data into a hypergraph structure that allows for the identification of effective modulators of metabolism. Our approach yields single transcripts of potential relevance that map to specific, densely connected, metabolic subgraphs or pathways. Results By applying our method on transcriptomics and metabolomics data from murine livers under standard Chow or high-fat diet, we identified eleven genes with potential regulatory effects on hepatic metabolism. Five candidates, including the hepatokine INHBE, were validated in human liver biopsies to correlate with diabetes-related traits such as overweight, hepatic fat content, and insulin resistance (HOMA-IR). Conclusion Our method's successful application to an independent omics dataset confirmed that the novel CoNI framework is a transferable, entirely data-driven, flexible, and versatile tool for multiple omics data integration and interpretation.
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Affiliation(s)
- Valentina S Klaus
- Computational Discovery Research Unit, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany; TUM School of Medicine, Neurobiology of Diabetes, Technical University Munich, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany
| | - Sonja C Schriever
- German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany; Research Unit Neurobiology of Diabetes, Helmholtz Zentrum München, Neuherberg, Germany
| | - José Manuel Monroy Kuhn
- Computational Discovery Research Unit, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany
| | - Andreas Peter
- German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany; Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Tübingen, Germany
| | - Martin Irmler
- Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Janina Tokarz
- Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Neuherberg, Germany
| | - Cornelia Prehn
- Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Neuherberg, Germany
| | - Gabi Kastenmüller
- German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Johannes Beckers
- German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany; Chair of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, Neuherberg, Germany
| | - Jerzy Adamski
- Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Neuherberg, Germany; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Chair of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, Neuherberg, Germany
| | - Alfred Königsrainer
- Department of General, Visceral and Transplant Surgery, University Hospital Tübingen, Germany
| | - Timo D Müller
- German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany; Department of Pharmacology and Experimental Therapy, Institute of Experimental and Clinical Pharmacology and Toxicology, Eberhard Karls University Hospitals and Clinics, Tübingen, Germany
| | - Martin Heni
- German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany; Department of Internal Medicine, Division of Endocrinology, Diabetology, and Nephrology, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Matthias H Tschöp
- TUM School of Medicine, Neurobiology of Diabetes, Technical University Munich, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany; Division of Metabolic Diseases, Department of Medicine, Technical University Munich, Munich, Germany
| | - Paul T Pfluger
- TUM School of Medicine, Neurobiology of Diabetes, Technical University Munich, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany; Research Unit Neurobiology of Diabetes, Helmholtz Zentrum München, Neuherberg, Germany
| | - Dominik Lutter
- Computational Discovery Research Unit, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany.
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34
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Nag A, Kurushima Y, Bowyer RCE, Wells PM, Weiss S, Pietzner M, Kocher T, Raffler J, Völker U, Mangino M, Spector TD, Milburn MV, Kastenmüller G, Mohney RP, Suhre K, Menni C, Steves CJ. Genome-wide scan identifies novel genetic loci regulating salivary metabolite levels. Hum Mol Genet 2021; 29:864-875. [PMID: 31960908 PMCID: PMC7104674 DOI: 10.1093/hmg/ddz308] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2019] [Revised: 12/05/2019] [Accepted: 12/11/2019] [Indexed: 12/26/2022] Open
Abstract
Saliva, as a biofluid, is inexpensive and non-invasive to obtain, and provides a vital tool to investigate oral health and its interaction with systemic health conditions. There is growing interest in salivary biomarkers for systemic diseases, notably cardiovascular disease. Whereas hundreds of genetic loci have been shown to be involved in the regulation of blood metabolites, leading to significant insights into the pathogenesis of complex human diseases, little is known about the impact of host genetics on salivary metabolites. Here we report the first genome-wide association study exploring 476 salivary metabolites in 1419 subjects from the TwinsUK cohort (discovery phase), followed by replication in the Study of Health in Pomerania (SHIP-2) cohort. A total of 14 distinct locus-metabolite associations were identified in the discovery phase, most of which were replicated in SHIP-2. While only a limited number of the loci that are known to regulate blood metabolites were also associated with salivary metabolites in our study, we identified several novel saliva-specific locus-metabolite associations, including associations for the AGMAT (with the metabolites 4-guanidinobutanoate and beta-guanidinopropanoate), ATP13A5 (with the metabolite creatinine) and DPYS (with the metabolites 3-ureidopropionate and 3-ureidoisobutyrate) loci. Our study suggests that there may be regulatory pathways of particular relevance to the salivary metabolome. In addition, some of our findings may have clinical significance, such as the utility of the pyrimidine (uracil) degradation metabolites in predicting 5-fluorouracil toxicity and the role of the agmatine pathway metabolites as biomarkers of oral health.
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Affiliation(s)
- Abhishek Nag
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK.,Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Yuko Kurushima
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Ruth C E Bowyer
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Philippa M Wells
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Stefan Weiss
- Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald & University of Greifswald, 17489 Greifswald, Germany
| | - Maik Pietzner
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, 17489 Greifswald, Germany
| | - Thomas Kocher
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, 17489 Greifswald, Germany
| | - Johannes Raffler
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764 Neuherberg, Germany
| | - Uwe Völker
- Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald & University of Greifswald, 17489 Greifswald, Germany
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Michael V Milburn
- Discovery and Translational Sciences, Metabolon, Inc., Morrisville, NC 27560, USA
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764 Neuherberg, Germany
| | - Robert P Mohney
- Discovery and Translational Sciences, Metabolon, Inc., Morrisville, NC 27560, USA
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha 24144, Qatar
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
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35
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Nho K, Kueider-Paisley A, Arnold M, MahmoudianDehkordi S, Risacher SL, Louie G, Blach C, Baillie R, Han X, Kastenmüller G, Doraiswamy PM, Kaddurah-Daouk R, Saykin AJ. Serum metabolites associated with brain amyloid beta deposition, cognition and dementia progression. Brain Commun 2021; 3:fcab139. [PMID: 34396103 PMCID: PMC8361396 DOI: 10.1093/braincomms/fcab139] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 06/30/2021] [Indexed: 11/13/2022] Open
Abstract
Metabolomics in the Alzheimer's Disease Neuroimaging Initiative cohort provides a powerful tool for mapping biochemical changes in Alzheimer's disease, and a unique opportunity to learn about the association between circulating blood metabolites and brain amyloid-β deposition in Alzheimer's disease. We examined 140 serum metabolites and their associations with brain amyloid-β deposition, cognition and conversion from mild cognitive impairment to Alzheimer's disease in the Alzheimer's Disease Neuroimaging Initiative. Processed [18F] Florbetapir PET images were used to perform a voxel-wise statistical analysis of the effect of metabolite levels on amyloid-β accumulation across the whole brain. We performed a multivariable regression analysis using age, sex, body mass index, apolipoprotein E ε4 status and study phase as covariates. We identified nine metabolites as significantly associated with amyloid-β deposition after multiple comparison correction. Higher levels of one acylcarnitine (C3; propionylcarnitine) and one biogenic amine (kynurenine) were associated with decreased amyloid-β accumulation and higher memory scores. However, higher levels of seven phosphatidylcholines (lysoPC a C18:2, PC aa C42:0, PC ae C42:3, PC ae C44:3, PC ae C44:4, PC ae C44:5 and PC ae C44:6) were associated with increased brain amyloid-β deposition. In addition, higher levels of PC ae C44:4 were significantly associated with lower memory and executive function scores and conversion from mild cognitive impairment to Alzheimer's disease dementia. Our findings suggest that dysregulation of peripheral phosphatidylcholine metabolism is associated with earlier pathological changes noted in Alzheimer's disease as measured by brain amyloid-β deposition as well as later clinical features including changes in memory and executive functioning. Perturbations in phosphatidylcholine metabolism may point to issues with membrane restructuring leading to the accumulation of amyloid-β in the brain. Additional studies are needed to explore whether these metabolites play a causal role in the pathogenesis of Alzheimer's disease or if they are biomarkers for systemic changes during preclinical phases of the disease.
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Affiliation(s)
- Kwangsik Nho
- Department of Radiology and Imaging Sciences, Center for Computational Biology and Bioinformatics, and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | | | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, USA
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg 85764, Germany
| | | | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Center for Computational Biology and Bioinformatics, and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Gregory Louie
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, USA
| | - Colette Blach
- Duke Molecular Physiology Institute, Duke University, Durham, NC 27710, USA
| | | | - Xianlin Han
- University of Texas Health Science Center at San Antonio, San Antonio, TX 78249, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg 85764, Germany
- German Center for Diabetes Research (DZD), Neuherberg 85764, Germany
| | - P Murali Doraiswamy
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC 27710, USA
- Department of Medicine, Duke University, Durham, NC 27710, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC 27710, USA
- Department of Medicine, Duke University, Durham, NC 27710, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Center for Computational Biology and Bioinformatics, and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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36
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Portero V, Nicol T, Podliesna S, Marchal GA, Baartscheer A, Casini S, Tadros R, Treur JL, Tanck MWT, Cox IJ, Probert F, Hough TA, Falcone S, Beekman L, Müller-Nurasyid M, Kastenmüller G, Gieger C, Peters A, Kääb S, Sinner MF, Blease A, Verkerk AO, Bezzina CR, Potter PK, Remme CA. Chronically elevated branched chain amino acid levels are pro-arrhythmic. Cardiovasc Res 2021; 118:1742-1757. [PMID: 34142125 PMCID: PMC9215196 DOI: 10.1093/cvr/cvab207] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 06/16/2021] [Indexed: 01/03/2023] Open
Abstract
Aims Cardiac arrhythmias comprise a major health and economic burden and are associated with significant morbidity and mortality, including cardiac failure, stroke, and sudden cardiac death (SCD). Development of efficient preventive and therapeutic strategies is hampered by incomplete knowledge of disease mechanisms and pathways. Our aim is to identify novel mechanisms underlying cardiac arrhythmia and SCD using an unbiased approach. Methods and results We employed a phenotype-driven N-ethyl-N-nitrosourea mutagenesis screen and identified a mouse line with a high incidence of sudden death at young age (6–9 weeks) in the absence of prior symptoms. Affected mice were found to be homozygous for the nonsense mutation Bcat2p.Q300*/p.Q300* in the Bcat2 gene encoding branched chain amino acid transaminase 2. At the age of 4–5 weeks, Bcat2p.Q300*/p.Q300* mice displayed drastic increase of plasma levels of branch chain amino acids (BCAAs—leucine, isoleucine, valine) due to the incomplete catabolism of BCAAs, in addition to inducible arrhythmias ex vivo as well as cardiac conduction and repolarization disturbances. In line with these findings, plasma BCAA levels were positively correlated to electrocardiogram indices of conduction and repolarization in the German community-based KORA F4 Study. Isolated cardiomyocytes from Bcat2p.Q300*/p.Q300* mice revealed action potential (AP) prolongation, pro-arrhythmic events (early and late afterdepolarizations, triggered APs), and dysregulated calcium homeostasis. Incubation of human pluripotent stem cell-derived cardiomyocytes with elevated concentration of BCAAs induced similar calcium dysregulation and pro-arrhythmic events which were prevented by rapamycin, demonstrating the crucial involvement of mTOR pathway activation. Conclusions Our findings identify for the first time a causative link between elevated BCAAs and arrhythmia, which has implications for arrhythmogenesis in conditions associated with BCAA metabolism dysregulation such as diabetes, metabolic syndrome, and heart failure.
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Affiliation(s)
- Vincent Portero
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Thomas Nicol
- Mammalian genetics Unit, MRC Harwell Institute, Harwell, Oxfordshire, United Kingdom
| | - Svitlana Podliesna
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Gerard A Marchal
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Antonius Baartscheer
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Simona Casini
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Rafik Tadros
- Cardiovascular Genetics Center, Montreal Heart Institute and Faculty of Medicine, Université de Montréal, Montreal, Canada
| | - Jorien L Treur
- Department of Psychiatry, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Michael W T Tanck
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - I Jane Cox
- Institute for Hepatology London, Foundation for Liver Research, London, UK.,Faculty of Life Sciences & Medicine, Kings College, London, UK
| | - Fay Probert
- Department of Chemistry, University of Oxford, Oxford UK
| | - Tertius A Hough
- Mammalian genetics Unit, MRC Harwell Institute, Harwell, Oxfordshire, United Kingdom
| | - Sara Falcone
- Mammalian genetics Unit, MRC Harwell Institute, Harwell, Oxfordshire, United Kingdom
| | - Leander Beekman
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,IBE, Faculty of Medicine, Ludwig Maximilian's University (LMU) Munich, Germany.,Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Christian Gieger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
| | - Stefan Kääb
- Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilian's University (LMU) Munich, Germany.,German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
| | - Moritz F Sinner
- Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilian's University (LMU) Munich, Germany.,German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
| | - Andrew Blease
- Mammalian genetics Unit, MRC Harwell Institute, Harwell, Oxfordshire, United Kingdom
| | - Arie O Verkerk
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Connie R Bezzina
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
| | - Paul K Potter
- Mammalian genetics Unit, MRC Harwell Institute, Harwell, Oxfordshire, United Kingdom.,Department of Biological and Medical Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford, UK
| | - Carol Ann Remme
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, location AMC, Amsterdam, The Netherlands
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37
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Schranner D, Schönfelder M, Römisch‐Margl W, Scherr J, Schlegel J, Zelger O, Riermeier A, Kaps S, Prehn C, Adamski J, Söhnlein Q, Stöcker F, Kreuzpointner F, Halle M, Kastenmüller G, Wackerhage H. Physiological extremes of the human blood metabolome: A metabolomics analysis of highly glycolytic, oxidative, and anabolic athletes. Physiol Rep 2021; 9:e14885. [PMID: 34152092 PMCID: PMC8215680 DOI: 10.14814/phy2.14885] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/01/2021] [Accepted: 05/04/2021] [Indexed: 12/17/2022] Open
Abstract
Human metabolism is highly variable. At one end of the spectrum, defects of enzymes, transporters, and metabolic regulation result in metabolic diseases such as diabetes mellitus or inborn errors of metabolism. At the other end of the spectrum, favorable genetics and years of training combine to result in physiologically extreme forms of metabolism in athletes. Here, we investigated how the highly glycolytic metabolism of sprinters, highly oxidative metabolism of endurance athletes, and highly anabolic metabolism of natural bodybuilders affect their serum metabolome at rest and after a bout of exercise to exhaustion. We used targeted mass spectrometry-based metabolomics to measure the serum concentrations of 151 metabolites and 43 metabolite ratios or sums in 15 competitive male athletes (6 endurance athletes, 5 sprinters, and 4 natural bodybuilders) and 4 untrained control subjects at fasted rest and 5 minutes after a maximum graded bicycle test to exhaustion. The analysis of all 194 metabolite concentrations, ratios and sums revealed that natural bodybuilders and endurance athletes had overall different metabolite profiles, whereas sprinters and untrained controls were more similar. Specifically, natural bodybuilders had 1.5 to 1.8-fold higher concentrations of specific phosphatidylcholines and lower levels of branched chain amino acids than all other subjects. Endurance athletes had 1.4-fold higher levels of a metabolite ratio showing the activity of carnitine-palmitoyl-transferase I and 1.4-fold lower levels of various alkyl-acyl-phosphatidylcholines. When we compared the effect of exercise between groups, endurance athletes showed 1.3-fold higher increases of hexose and of tetradecenoylcarnitine (C14:1). In summary, physiologically extreme metabolic capacities of endurance athletes and natural bodybuilders are associated with unique blood metabolite concentrations, ratios, and sums at rest and after exercise. Our results suggest that long-term specific training, along with genetics and other athlete-specific factors systematically change metabolite concentrations at rest and after exercise.
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Affiliation(s)
- Daniela Schranner
- Exercise BiologyDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
| | - Martin Schönfelder
- Exercise BiologyDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
| | | | - Johannes Scherr
- University Center for Prevention and Sports MedicineUniversity Hospital BalgristUniversität ZürichZurichSwitzerland
| | - Jürgen Schlegel
- Department of NeuropathologyInstitute of PathologyTechnische Universität MünchenMunichGermany
| | - Otto Zelger
- Department of Prevention and Sports MedicineTechnische Universität MünchenMunichGermany
| | - Annett Riermeier
- Exercise BiologyDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
| | - Stephanie Kaps
- Exercise BiologyDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
| | - Cornelia Prehn
- Research Unit Molecular Endocrinology and MetabolismHelmholtz Zentrum MünchenNeuherbergGermany
| | - Jerzy Adamski
- Research Unit Molecular Endocrinology and MetabolismHelmholtz Zentrum MünchenNeuherbergGermany
- German Center for Diabetes ResearchNeuherbergGermany
- Chair of Experimental GeneticsTechnische Universität MünchenFreising‐WeihenstephanGermany
- Department of BiochemistryYong Loo Lin School of MedicineNational University of SingaporeSingapore
| | - Quirin Söhnlein
- Exercise BiologyDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
| | - Fabian Stöcker
- Teaching and Educational LabDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
| | - Florian Kreuzpointner
- Prevention CenterDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
| | - Martin Halle
- Department of Prevention and Sports MedicineTechnische Universität MünchenMunichGermany
| | - Gabi Kastenmüller
- Institute of Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
- German Center for Diabetes ResearchNeuherbergGermany
| | - Henning Wackerhage
- Exercise BiologyDepartment of Sport and Health SciencesTechnische Universität MünchenMunichGermany
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38
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Huang J, Covic M, Huth C, Rommel M, Adam J, Zukunft S, Prehn C, Wang L, Nano J, Scheerer MF, Neschen S, Kastenmüller G, Gieger C, Laxy M, Schliess F, Adamski J, Suhre K, de Angelis MH, Peters A, Wang-Sattler R. Validation of Candidate Phospholipid Biomarkers of Chronic Kidney Disease in Hyperglycemic Individuals and Their Organ-Specific Exploration in Leptin Receptor-Deficient db/db Mouse. Metabolites 2021; 11:metabo11020089. [PMID: 33546276 PMCID: PMC7913334 DOI: 10.3390/metabo11020089] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/27/2021] [Accepted: 01/29/2021] [Indexed: 12/03/2022] Open
Abstract
Biological exploration of early biomarkers for chronic kidney disease (CKD) in (pre)diabetic individuals is crucial for personalized management of diabetes. Here, we evaluated two candidate biomarkers of incident CKD (sphingomyelin (SM) C18:1 and phosphatidylcholine diacyl (PC aa) C38:0) concerning kidney function in hyperglycemic participants of the Cooperative Health Research in the Region of Augsburg (KORA) cohort, and in two biofluids and six organs of leptin receptor-deficient (db/db) mice and wild type controls. Higher serum concentrations of SM C18:1 and PC aa C38:0 in hyperglycemic individuals were found to be associated with lower estimated glomerular filtration rate (eGFR) and higher odds of CKD. In db/db mice, both metabolites had a significantly lower concentration in urine and adipose tissue, but higher in the lungs. Additionally, db/db mice had significantly higher SM C18:1 levels in plasma and liver, and PC aa C38:0 in adrenal glands. This cross-sectional human study confirms that SM C18:1 and PC aa C38:0 associate with kidney dysfunction in pre(diabetic) individuals, and the animal study suggests a potential implication of liver, lungs, adrenal glands, and visceral fat in their systemic regulation. Our results support further validation of the two phospholipids as early biomarkers of renal disease in patients with (pre)diabetes.
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Affiliation(s)
- Jialing Huang
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (J.H.); (M.C.); (M.R.); (J.A.); (L.W.); (C.G.)
- Institute of Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (C.H.); (J.N.); (A.P.)
- German Center for Diabetes Research (DZD), 85764 München-Neuherberg, Germany;
| | - Marcela Covic
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (J.H.); (M.C.); (M.R.); (J.A.); (L.W.); (C.G.)
- Institute of Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (C.H.); (J.N.); (A.P.)
- German Center for Diabetes Research (DZD), 85764 München-Neuherberg, Germany;
| | - Cornelia Huth
- Institute of Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (C.H.); (J.N.); (A.P.)
| | - Martina Rommel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (J.H.); (M.C.); (M.R.); (J.A.); (L.W.); (C.G.)
- Institute of Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (C.H.); (J.N.); (A.P.)
| | - Jonathan Adam
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (J.H.); (M.C.); (M.R.); (J.A.); (L.W.); (C.G.)
- Institute of Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (C.H.); (J.N.); (A.P.)
| | - Sven Zukunft
- Research Unit of Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (S.Z.); (J.A.)
- Centre for Molecular Medicine, Institute for Vascular Signaling, Goethe University, 60323 Frankfurt am Main, Germany
| | - Cornelia Prehn
- Metabolomics and Proteomics Core Facility, Helmholtz Zentrum München, 85764 Neuherberg, Germany;
| | - Li Wang
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (J.H.); (M.C.); (M.R.); (J.A.); (L.W.); (C.G.)
- Institute of Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (C.H.); (J.N.); (A.P.)
- Liaocheng People’s Hospital—Department of Scientific Research, Shandong University Postdoctoral Work Station, Liaocheng 252000, China
| | - Jana Nano
- Institute of Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (C.H.); (J.N.); (A.P.)
- German Center for Diabetes Research (DZD), 85764 München-Neuherberg, Germany;
| | - Markus F. Scheerer
- Institute of Experimental Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (M.F.S.); (S.N.)
- Bayer AG, Medical Affairs & Pharmacovigilance, 13353 Berlin, Germany
| | - Susanne Neschen
- Institute of Experimental Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (M.F.S.); (S.N.)
- Sanofi Aventis Deutschland GmbH, Industriepark Hoechst, 65929 Frankfurt am Main, Germany
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany;
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (J.H.); (M.C.); (M.R.); (J.A.); (L.W.); (C.G.)
- Institute of Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (C.H.); (J.N.); (A.P.)
- German Center for Diabetes Research (DZD), 85764 München-Neuherberg, Germany;
| | - Michael Laxy
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, 85764 Neuherberg, Germany;
| | | | - Jerzy Adamski
- Research Unit of Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (S.Z.); (J.A.)
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
- Chair of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, 85353 Freising, Germany
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar (WCMC-Q), Education City, Qatar Foundation, Doha P.O. Box 24144, Qatar;
| | - Martin Hrabe de Angelis
- German Center for Diabetes Research (DZD), 85764 München-Neuherberg, Germany;
- Institute of Experimental Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (M.F.S.); (S.N.)
- Chair of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, 85353 Freising, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (C.H.); (J.N.); (A.P.)
- German Center for Diabetes Research (DZD), 85764 München-Neuherberg, Germany;
| | - Rui Wang-Sattler
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (J.H.); (M.C.); (M.R.); (J.A.); (L.W.); (C.G.)
- Institute of Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (C.H.); (J.N.); (A.P.)
- German Center for Diabetes Research (DZD), 85764 München-Neuherberg, Germany;
- Correspondence: ; Tel.: +49-89-3187-3978; Fax: + 49-89-3187-2428
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39
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Pietzner M, Wheeler E, Carrasco-Zanini J, Raffler J, Kerrison ND, Oerton E, Auyeung VPW, Luan J, Finan C, Casas JP, Ostroff R, Williams SA, Kastenmüller G, Ralser M, Gamazon ER, Wareham NJ, Hingorani AD, Langenberg C. Author Correction: Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nat Commun 2021; 12:845. [PMID: 33531486 PMCID: PMC7854714 DOI: 10.1038/s41467-021-21370-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-21370-6
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Affiliation(s)
- Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Erin Oerton
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, WC1E 6BT, UK.,UCL BHF Research Accelerator centre, London, UK
| | - Juan P Casas
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | | | | | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Markus Ralser
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.,Department of Biochemistry, Charité University Medicine, Berlin, Germany
| | - Eric R Gamazon
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.,Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, WC1E 6BT, UK. .,UCL BHF Research Accelerator centre, London, UK. .,Health Data Research UK, Institute of Health Informatics, University College London, London, UK.
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK. .,The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK. .,Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Cambridge, UK. .,Computational Medicine, Berlin Institute of Health (BIH), Charité University Medicine, Berlin, Germany.
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40
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Wörheide MA, Krumsiek J, Kastenmüller G, Arnold M. Multi-omics integration in biomedical research - A metabolomics-centric review. Anal Chim Acta 2021; 1141:144-162. [PMID: 33248648 PMCID: PMC7701361 DOI: 10.1016/j.aca.2020.10.038] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/09/2020] [Accepted: 10/19/2020] [Indexed: 02/07/2023]
Abstract
Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
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Affiliation(s)
- Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
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41
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Lotta LA, Pietzner M, Stewart ID, Wittemans LB, Li C, Bonelli R, Raffler J, Biggs EK, Oliver-Williams C, Auyeung VP, Luan J, Wheeler E, Paige E, Surendran P, Michelotti GA, Scott RA, Burgess S, Zuber V, Sanderson E, Koulman A, Imamura F, Forouhi NG, Khaw KT, Griffin JL, Wood AM, Kastenmüller G, Danesh J, Butterworth AS, Gribble FM, Reimann F, Bahlo M, Fauman E, Wareham NJ, Langenberg C. A cross-platform approach identifies genetic regulators of human metabolism and health. Nat Genet 2021; 53:54-64. [PMID: 33414548 PMCID: PMC7612925 DOI: 10.1038/s41588-020-00751-5] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 11/20/2020] [Indexed: 02/02/2023]
Abstract
In cross-platform analyses of 174 metabolites, we identify 499 associations (P < 4.9 × 10-10) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for nonsynonymous variation. We identify a signal at GLP2R (p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights.
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Affiliation(s)
- Luca A. Lotta
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Laura B.L. Wittemans
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK,The Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford
| | - Chen Li
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Roberto Bonelli
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia,Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | - Emma K. Biggs
- Metabolic Research Laboratories, University of Cambridge, Cambridge, United Kingdom
| | - Clare Oliver-Williams
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK,Homerton College, University of Cambridge, Cambridge, UK
| | | | - Jian’an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Ellie Paige
- National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australia
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK,British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK,Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK,Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, UK
| | | | - Robert A. Scott
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom,Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Verena Zuber
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom,Department of Epidemiology and Biostatistics, Imperial College London, UK
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, UK
| | - Albert Koulman
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK,Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany,NIHR BRC Nutritional Biomarker Laboratory, University of Cambridge, UK
| | - Fumiaki Imamura
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Nita G. Forouhi
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | | | - Julian L. Griffin
- Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, UK
| | - Angela M. Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK,British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK,Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK,National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK,The Alan Turing Institute, London, UK
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK,British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK,Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK,National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK,National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK,Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Adam S. Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK,British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK,Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK,National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK,National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK,Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Fiona M. Gribble
- Metabolic Research Laboratories, University of Cambridge, Cambridge, United Kingdom
| | - Frank Reimann
- Metabolic Research Laboratories, University of Cambridge, Cambridge, United Kingdom
| | - Melanie Bahlo
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia,Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Eric Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Cambridge, MA 02142, USA
| | | | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK. .,Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK. .,Computational Medicine, Berlin Institute of Health (BIH), Charité University Medicine, Berlin, Germany.
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42
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Ott R, Pawlow X, Weiß A, Hofelich A, Herbst M, Hummel N, Prehn C, Adamski J, Römisch-Margl W, Kastenmüller G, Ziegler AG, Hummel S. Intergenerational Metabolomic Analysis of Mothers with a History of Gestational Diabetes Mellitus and Their Offspring. Int J Mol Sci 2020; 21:E9647. [PMID: 33348910 PMCID: PMC7766614 DOI: 10.3390/ijms21249647] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 12/10/2020] [Accepted: 12/16/2020] [Indexed: 12/05/2022] Open
Abstract
Shared metabolomic patterns at delivery have been suggested to underlie the mother-to-child transmission of adverse metabolic health. This study aimed to investigate whether mothers with gestational diabetes mellitus (GDM) and their offspring show similar metabolomic patterns several years postpartum. Targeted metabolomics (including 137 metabolites) was performed in plasma samples obtained during an oral glucose tolerance test from 48 mothers with GDM and their offspring at a cross-sectional study visit 8 years after delivery. Partial Pearson's correlations between the area under the curve (AUC) of maternal and offspring metabolites were calculated, yielding so-called Gaussian graphical models. Spearman's correlations were applied to investigate correlations of body mass index (BMI), Matsuda insulin sensitivity index (ISI-M), dietary intake, and physical activity between generations, and correlations of metabolite AUCs with lifestyle variables. This study revealed that BMI, ISI-M, and the AUC of six metabolites (carnitine, taurine, proline, SM(-OH) C14:1, creatinine, and PC ae C34:3) were significantly correlated between mothers and offspring several years postpartum. Intergenerational metabolite correlations were independent of shared BMI, ISI-M, age, sex, and all other metabolites. Furthermore, creatinine was correlated with physical activity in mothers. This study suggests that there is long-term metabolic programming in the offspring of mothers with GDM and informs us about targets that could be addressed by future intervention studies.
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Affiliation(s)
- Raffael Ott
- Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, 85764 Neuherberg, Germany; (R.O.); (X.P.); (A.W.); (A.H.); (M.H.); (N.H.); (A.-G.Z.)
- Forschergruppe Diabetes e.V., 85764 Neuherberg, Germany
| | - Xenia Pawlow
- Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, 85764 Neuherberg, Germany; (R.O.); (X.P.); (A.W.); (A.H.); (M.H.); (N.H.); (A.-G.Z.)
- Forschergruppe Diabetes e.V., 85764 Neuherberg, Germany
| | - Andreas Weiß
- Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, 85764 Neuherberg, Germany; (R.O.); (X.P.); (A.W.); (A.H.); (M.H.); (N.H.); (A.-G.Z.)
- Forschergruppe Diabetes e.V., 85764 Neuherberg, Germany
| | - Anna Hofelich
- Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, 85764 Neuherberg, Germany; (R.O.); (X.P.); (A.W.); (A.H.); (M.H.); (N.H.); (A.-G.Z.)
- Forschergruppe Diabetes e.V., 85764 Neuherberg, Germany
| | - Melanie Herbst
- Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, 85764 Neuherberg, Germany; (R.O.); (X.P.); (A.W.); (A.H.); (M.H.); (N.H.); (A.-G.Z.)
- Forschergruppe Diabetes e.V., 85764 Neuherberg, Germany
| | - Nadine Hummel
- Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, 85764 Neuherberg, Germany; (R.O.); (X.P.); (A.W.); (A.H.); (M.H.); (N.H.); (A.-G.Z.)
| | - Cornelia Prehn
- Research Unit Molecular Endocrinology and Metabolism, Genome Analysis Center, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (C.P.); (J.A.)
| | - Jerzy Adamski
- Research Unit Molecular Endocrinology and Metabolism, Genome Analysis Center, Helmholtz Zentrum München, 85764 Neuherberg, Germany; (C.P.); (J.A.)
- Chair for Experimental Genetics, Technical University of Munich, 85354 Freising-Weihenstephan, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
- German Center for Diabetes Research (DZD), München-Neuherberg, 85764 Neuherberg, Germany; (W.R.-M.); (G.K.)
| | - Werner Römisch-Margl
- German Center for Diabetes Research (DZD), München-Neuherberg, 85764 Neuherberg, Germany; (W.R.-M.); (G.K.)
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Gabi Kastenmüller
- German Center for Diabetes Research (DZD), München-Neuherberg, 85764 Neuherberg, Germany; (W.R.-M.); (G.K.)
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Anette-G. Ziegler
- Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, 85764 Neuherberg, Germany; (R.O.); (X.P.); (A.W.); (A.H.); (M.H.); (N.H.); (A.-G.Z.)
- Forschergruppe Diabetes e.V., 85764 Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, 85764 Neuherberg, Germany; (W.R.-M.); (G.K.)
| | - Sandra Hummel
- Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, 85764 Neuherberg, Germany; (R.O.); (X.P.); (A.W.); (A.H.); (M.H.); (N.H.); (A.-G.Z.)
- Forschergruppe Diabetes e.V., 85764 Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, 85764 Neuherberg, Germany; (W.R.-M.); (G.K.)
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Pietzner M, Wheeler E, Carrasco-Zanini J, Raffler J, Kerrison ND, Oerton E, Auyeung VPW, Luan J, Finan C, Casas JP, Ostroff R, Williams SA, Kastenmüller G, Ralser M, Gamazon ER, Wareham NJ, Hingorani AD, Langenberg C. Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nat Commun 2020; 11:6397. [PMID: 33328453 PMCID: PMC7744536 DOI: 10.1038/s41467-020-19996-z] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022] Open
Abstract
Understanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).
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Affiliation(s)
- Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Erin Oerton
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, WC1E 6BT, UK
- UCL BHF Research Accelerator centre, London, UK
| | - Juan P Casas
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | | | | | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Markus Ralser
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Department of Biochemistry, Charité University Medicine, Berlin, Germany
| | - Eric R Gamazon
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, WC1E 6BT, UK.
- UCL BHF Research Accelerator centre, London, UK.
- Health Data Research UK, Institute of Health Informatics, University College London, London, UK.
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health (BIH), Charité University Medicine, Berlin, Germany.
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Huang J, Huth C, Covic M, Troll M, Adam J, Zukunft S, Prehn C, Wang L, Nano J, Scheerer MF, Neschen S, Kastenmüller G, Suhre K, Laxy M, Schliess F, Gieger C, Adamski J, Hrabe de Angelis M, Peters A, Wang-Sattler R. Machine Learning Approaches Reveal Metabolic Signatures of Incident Chronic Kidney Disease in Individuals With Prediabetes and Type 2 Diabetes. Diabetes 2020; 69:2756-2765. [PMID: 33024004 DOI: 10.2337/db20-0586] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 09/29/2020] [Indexed: 11/13/2022]
Abstract
Early and precise identification of individuals with prediabetes and type 2 diabetes (T2D) at risk for progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolite biomarkers and the best set of predictors of CKD in the longitudinal, population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort by targeted metabolomics and machine learning approaches. Out of 125 targeted metabolites, sphingomyelin C18:1 and phosphatidylcholine diacyl C38:0 were identified as candidate metabolite biomarkers of incident CKD specifically in hyperglycemic individuals followed during 6.5 years. Sets of predictors for incident CKD developed from 125 metabolites and 14 clinical variables showed highly stable performances in all three machine learning approaches and outperformed the currently established clinical algorithm for CKD. The two metabolites in combination with five clinical variables were identified as the best set of predictors, and their predictive performance yielded a mean area value under the receiver operating characteristic curve of 0.857. The inclusion of metabolite variables in the clinical prediction of future CKD may thus improve the risk prediction in people with prediabetes and T2D. The metabolite link with hyperglycemia-related early kidney dysfunction warrants further investigation.
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Affiliation(s)
- Jialing Huang
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Cornelia Huth
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Marcela Covic
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Martina Troll
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Jonathan Adam
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Sven Zukunft
- Research Unit of Molecular Endocrinology and Metabolism, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Cornelia Prehn
- Research Unit of Molecular Endocrinology and Metabolism, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Li Wang
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Scientific Research and Shandong University Postdoctoral Work Station, Liaocheng People's Hospital, Shandong, P. R. China
| | - Jana Nano
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Markus F Scheerer
- Institute of Experimental Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Susanne Neschen
- Institute of Experimental Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Michael Laxy
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Jerzy Adamski
- Research Unit of Molecular Endocrinology and Metabolism, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Chair of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, Freising, Germany
| | - Martin Hrabe de Angelis
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute of Experimental Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, Freising, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Rui Wang-Sattler
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
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45
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Meikle PJ, Giles C, Cadby G, Huynh K, Mellett NA, Olshansky G, Smith A, Nguyen A, Chatterjee P, Martins I, Laws SM, Bush AI, Rowe CC, Villemagne VL, Ames D, Masters CL, Arnold M, Kastenmüller G, Nho K, Saykin AJ, Baillie R, Han X, Inouye M, Martins RN, Kaddurah‐Daouk RF, Moses E. Genome‐wide study of the human lipidome and links to Alzheimer’s disease risk. Alzheimers Dement 2020. [DOI: 10.1002/alz.045600] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
| | - Corey Giles
- Baker Heart and Diabetes Institute Melbourne Australia
| | - Gemma Cadby
- The University of Western Australia Perth Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute Melbourne Australia
| | | | | | | | - Anh Nguyen
- Baker Heart and Diabetes Institute Melbourne Australia
| | | | | | | | - Ashley I. Bush
- The Florey Institute of Neuroscience and Mental Health Melbourne Australia
| | | | | | - David Ames
- The University of Melbourne Parkville Australia
| | - Colin L. Masters
- The Florey Institute of Neuroscience and Mental Health Melbourne Australia
| | - Matthias Arnold
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health Neuherberg Germany
| | | | - Kwangsik Nho
- Indiana University School of Medicine Indianapolis IN USA
| | | | | | - Xianlin Han
- Sanford‐Burnham Medical Research Institute Orlando FL USA
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46
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Wörheide M, Krumsiek J, Nho K, Huynh K, Meikle PJ, Saykin AJ, Kaddurah‐Daouk RF, Kastenmüller G, Arnold M. A network‐based, multi‐omics atlas for target identification and prioritization in Alzheimer’s disease. Alzheimers Dement 2020. [DOI: 10.1002/alz.045594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | - Jan Krumsiek
- Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA
| | - Kwangsik Nho
- Indiana University School of Medicine Indianapolis IN USA
| | - Kevin Huynh
- Baker Heart and Diabetes Institute Melbourne Australia
| | | | | | | | | | - Matthias Arnold
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health Neuherberg Germany
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47
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Nho K, Kueider‐Paisley A, Arnold M, Dehkordi SM, Risacher SL, Louie G, Blach C, Baillie R, Han X, Kastenmüller G, Doraiswamy PM, Kaddurah‐Daouk RF, Saykin AJ. Serum metabolome informs neuroimaging biomarkers for Alzheimer’s disease. Alzheimers Dement 2020. [DOI: 10.1002/alz.045596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Kwangsik Nho
- Indiana University School of Medicine Indianapolis IN USA
| | | | - Matthias Arnold
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health Neuherberg Germany
| | | | | | - Gregory Louie
- Department of Psychiatry and Behavioral Sciences Duke University Durham NC USA
| | - Colette Blach
- Duke Molecular Physiology Institute Duke University Durham NC USA
| | | | - Xianlin Han
- Sanford‐Burnham Medical Research Institute Orlando FL USA
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48
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Horgusluoglu‐Moloch E, Wang M, Arnold M, Krumsiek J, Galindo‐Prieto B, Kastenmüller G, Han X, Baillie R, Nho K, Saykin AJ, Kaddurah‐Daouk RF, Zhang B. Integrative metabolomics‐genomics approach reveals that pathways related to the metabolism of acylcarnitines and amines are new potential targets of Alzheimer’s disease. Alzheimers Dement 2020. [DOI: 10.1002/alz.045610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Emrin Horgusluoglu‐Moloch
- Icahn Institute of Genomics and Multiscale Biology New York NY USA
- Icahn School of Medicine at Mount Sinai New York NY USA
| | - Minghui Wang
- Icahn School of Medicine at Mount Sinai New York NY USA
| | - Matthias Arnold
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health Neuherberg Germany
| | - Jan Krumsiek
- Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA
| | | | | | - Xianlin Han
- Sanford‐Burnham Medical Research Institute Orlando FL USA
| | | | - Kwangsik Nho
- Indiana University School of Medicine Indianapolis IN USA
| | | | | | - Bin Zhang
- Icahn Institute for Data Science and Genomic Technology New York NY USA
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49
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Faquih T, van Smeden M, Luo J, le Cessie S, Kastenmüller G, Krumsiek J, Noordam R, van Heemst D, Rosendaal FR, van Hylckama Vlieg A, Willems van Dijk K, Mook-Kanamori DO. A Workflow for Missing Values Imputation of Untargeted Metabolomics Data. Metabolites 2020; 10:metabo10120486. [PMID: 33256233 PMCID: PMC7761057 DOI: 10.3390/metabo10120486] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 11/12/2020] [Accepted: 11/25/2020] [Indexed: 01/09/2023] Open
Abstract
Metabolomics studies have seen a steady growth due to the development and implementation of affordable and high-quality metabolomics platforms. In large metabolite panels, measurement values are frequently missing and, if neglected or sub-optimally imputed, can cause biased study results. We provided a publicly available, user-friendly R script to streamline the imputation of missing endogenous, unannotated, and xenobiotic metabolites. We evaluated the multivariate imputation by chained equations (MICE) and k-nearest neighbors (kNN) analyses implemented in our script by simulations using measured metabolites data from the Netherlands Epidemiology of Obesity (NEO) study (n = 599). We simulated missing values in four unique metabolites from different pathways with different correlation structures in three sample sizes (599, 150, 50) with three missing percentages (15%, 30%, 60%), and using two missing mechanisms (completely at random and not at random). Based on the simulations, we found that for MICE, larger sample size was the primary factor decreasing bias and error. For kNN, the primary factor reducing bias and error was the metabolite correlation with its predictor metabolites. MICE provided consistently higher performance measures particularly for larger datasets (n > 50). In conclusion, we presented an imputation workflow in a publicly available R script to impute untargeted metabolomics data. Our simulations provided insight into the effects of sample size, percentage missing, and correlation structure on the accuracy of the two imputation methods.
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Affiliation(s)
- Tariq Faquih
- Department of Clinical Epidemiology, Leiden University Medical Center, Postal Zone C7-P, PO Box 9600, 2300 RC Leiden, The Netherlands; (T.F.); (J.L.); (S.l.C.); (F.R.R.); (A.v.H.V.)
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, 8, 3584 Utrecht, The Netherlands;
| | - Jiao Luo
- Department of Clinical Epidemiology, Leiden University Medical Center, Postal Zone C7-P, PO Box 9600, 2300 RC Leiden, The Netherlands; (T.F.); (J.L.); (S.l.C.); (F.R.R.); (A.v.H.V.)
| | - Saskia le Cessie
- Department of Clinical Epidemiology, Leiden University Medical Center, Postal Zone C7-P, PO Box 9600, 2300 RC Leiden, The Netherlands; (T.F.); (J.L.); (S.l.C.); (F.R.R.); (A.v.H.V.)
- Department of Biomedical Data Sciences, Section Medical Statistics and Bioinformatics, Leiden University Medical Center, 2, 2333 Leiden, The Netherlands
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz-Zentrum München, 85764 Neuherberg, Germany;
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Jan Krumsiek
- Department of Physiology, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10065, USA;
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, 2333ZA Leiden, The Netherlands; (R.N.); (D.v.H.)
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, 2333ZA Leiden, The Netherlands; (R.N.); (D.v.H.)
| | - Frits R. Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Postal Zone C7-P, PO Box 9600, 2300 RC Leiden, The Netherlands; (T.F.); (J.L.); (S.l.C.); (F.R.R.); (A.v.H.V.)
| | - Astrid van Hylckama Vlieg
- Department of Clinical Epidemiology, Leiden University Medical Center, Postal Zone C7-P, PO Box 9600, 2300 RC Leiden, The Netherlands; (T.F.); (J.L.); (S.l.C.); (F.R.R.); (A.v.H.V.)
| | - Ko Willems van Dijk
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, 2, 2333 Leiden, The Netherlands;
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, 2, 2333 Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, 2, 2333 Leiden, The Netherlands
| | - Dennis O. Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Postal Zone C7-P, PO Box 9600, 2300 RC Leiden, The Netherlands; (T.F.); (J.L.); (S.l.C.); (F.R.R.); (A.v.H.V.)
- Department of Public Health and Primary Care, Leiden University Medical Center, 2, 233 Leiden, The Netherlands
- Metabolon Inc., Morrisville, NC 27560, USA
- Correspondence:
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Baloni P, Funk CC, Yan J, Yurkovich JT, Kueider-Paisley A, Nho K, Heinken A, Jia W, Mahmoudiandehkordi S, Louie G, Saykin AJ, Arnold M, Kastenmüller G, Griffiths WJ, Thiele I, Kaddurah-Daouk R, Price ND. Metabolic Network Analysis Reveals Altered Bile Acid Synthesis and Metabolism in Alzheimer's Disease. Cell Rep Med 2020; 1:100138. [PMID: 33294859 PMCID: PMC7691449 DOI: 10.1016/j.xcrm.2020.100138] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 06/26/2020] [Accepted: 10/19/2020] [Indexed: 12/12/2022]
Abstract
Increasing evidence suggests Alzheimer's disease (AD) pathophysiology is influenced by primary and secondary bile acids, the end product of cholesterol metabolism. We analyze 2,114 post-mortem brain transcriptomes and identify genes in the alternative bile acid synthesis pathway to be expressed in the brain. A targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals supports these results. Our metabolic network analysis suggests that taurine transport, bile acid synthesis, and cholesterol metabolism differ in AD and cognitively normal individuals. We also identify putative transcription factors regulating metabolic genes and influencing altered metabolism in AD. Intriguingly, some bile acids measured in brain tissue cannot be explained by the presence of enzymes responsible for their synthesis, suggesting that they may originate from the gut microbiome and are transported to the brain. These findings motivate further research into bile acid metabolism in AD to elucidate their possible connection to cognitive decline.
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Affiliation(s)
| | - Cory C Funk
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Jingwen Yan
- Indiana Alzheimer Disease Center and Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Alexandra Kueider-Paisley
- Department of Psychiatry and Behavioral Medicine, Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, USA
| | - Kwangsik Nho
- Indiana Alzheimer Disease Center and Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Almut Heinken
- School of Medicine, National University of Ireland, Galway, Ireland
| | - Wei Jia
- Cancer Biology Program, The University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Siamak Mahmoudiandehkordi
- Department of Psychiatry and Behavioral Medicine, Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, USA
| | - Gregory Louie
- Department of Psychiatry and Behavioral Medicine, Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, USA
| | - Andrew J Saykin
- Indiana Alzheimer Disease Center and Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Matthias Arnold
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - William J Griffiths
- Swansea University Medical School, ILS1 Building, Singleton Park, Swansea SA2 8PP, UK
| | - Ines Thiele
- School of Medicine, National University of Ireland, Galway, Ireland.,Discipline of Microbiology, School of Natural Sciences, National University of Ireland, Galway, Ireland
| | | | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Medicine, Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, USA
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