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Ioannidou S, Kazeli K, Ventouris H, Amanatidou D, Gkinoudis A, Lymperaki E. Correlation of Vitamin 25(OH)D, Liver Enzymes, Potassium, and Oxidative Stress Markers with Lipid Profile and Atheromatic Index: A Pilot Study. J Xenobiot 2023; 13:193-204. [PMID: 37092503 PMCID: PMC10123670 DOI: 10.3390/jox13020015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/17/2023] [Accepted: 03/18/2023] [Indexed: 04/05/2023] Open
Abstract
According to recent literature, there is a limited amount of data about the correlation of vitamin 25(OH)D, potassium (K), oxidative stress parameters, and other biomarkers with dyslipidemia, which is an established risk factor for cardiovascular diseases (CVDs). This study aims to investigate the correlation of lipid profile and atheromatic index TC/HDL with several biomarkers and oxidative stress parameters. A total of 102 volunteers, 67 with atheromatic index TC/HDL > 3.5 (Group A) and 35 with TC/HDL < 3.5 (Group B), aged from 26 to 78 years, participated in this study. Serum levels of triglycerides (TG), total cholesterol (TC), low- and high-density lipoproteins (LDL and HDL), vitamin 25(OH)D [25(OH)D], potassium (K), sodium (Na), lactose dehydrogenase (LDH), liver enzymes including serum glutamic oxaloacetic and glutamic pyruvic transaminases (SGOT and SGPT), gamma-glutamyl transferase (γ-GT), and alkaline phosphatase (ALP) were analyzed using standard photometric methods. Oxidative stress parameters such as reactive oxygen species (ROS) were detected with fluorometric methods, whereas total oxidative (TOS) and antioxidative status (TAS) were measured with spectrophotometric methods. According to the results, negative correlations of HDL (r = −0.593) and 25(OH)D (r = −0.340) and K (r = −0.220) were found, and positive expected correlations of LDL (r = 0.731), TC (r = 0.663), and TG (r = 0.584) with atheromatic index in the total studied sample were found. In conclusion, patients with a dyslipidemic profile should frequently check not only their lipid profile but also other biomarkers such as 25(OH)D, potassium, and oxidative stress markers to predict dyslipidemia and avoid subsequent disorders.
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Affiliation(s)
- Stavroula Ioannidou
- Department of Biomedical Sciences, International Hellenic University, 57400 Thessaloniki, Greece
| | - Konstantina Kazeli
- Department of Biomedical Sciences, International Hellenic University, 57400 Thessaloniki, Greece
- Department of Condensed Matter and Materials Physics, School of Physics, Faculty of Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Hristos Ventouris
- Department of Biomedical Sciences, International Hellenic University, 57400 Thessaloniki, Greece
| | - Dionysia Amanatidou
- Department of Biomedical Sciences, International Hellenic University, 57400 Thessaloniki, Greece
| | - Argyrios Gkinoudis
- School of Veterinary Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Evgenia Lymperaki
- Department of Biomedical Sciences, International Hellenic University, 57400 Thessaloniki, Greece
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Louca P, Tran TQB, Toit CD, Christofidou P, Spector TD, Mangino M, Suhre K, Padmanabhan S, Menni C. Machine learning integration of multimodal data identifies key features of blood pressure regulation. EBioMedicine 2022; 84:104243. [PMID: 36084617 PMCID: PMC9463529 DOI: 10.1016/j.ebiom.2022.104243] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/02/2022] [Accepted: 08/11/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Association studies have identified several biomarkers for blood pressure and hypertension, but a thorough understanding of their mutual dependencies is lacking. By integrating two different high-throughput datasets, biochemical and dietary data, we aim to understand the multifactorial contributors of blood pressure (BP). METHODS We included 4,863 participants from TwinsUK with concurrent BP, metabolomics, genomics, biochemical measures, and dietary data. We used 5-fold cross-validation with the machine learning XGBoost algorithm to identify features of importance in context of one another in TwinsUK (80% training, 20% test). The features tested in TwinsUK were then probed using the same algorithm in an independent dataset of 2,807 individuals from the Qatari Biobank (QBB). FINDINGS Our model explained 39·2% [4·5%, MAE:11·32 mmHg (95%CI, +/- 0·65)] of the variance in systolic BP (SBP) in TwinsUK. Of the top 50 features, the most influential non-demographic variables were dihomo-linolenate, cis-4-decenoyl carnitine, lactate, chloride, urate, and creatinine along with dietary intakes of total, trans and saturated fat. We also highlight the incremental value of each included dimension. Furthermore, we replicated our model in the QBB [SBP variance explained = 45·2% (13·39%)] cohort and 30 of the top 50 features overlapped between cohorts. INTERPRETATION We show that an integrated analysis of omics, biochemical and dietary data improves our understanding of their in-between relationships and expands the range of potential biomarkers for blood pressure. Our results point to potentially key biological pathways to be prioritised for mechanistic studies. FUNDING Chronic Disease Research Foundation, Medical Research Council, Wellcome Trust, Qatar Foundation.
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Affiliation(s)
- Panayiotis Louca
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom
| | - Tran Quoc Bao Tran
- Institute of Cardiovascular & Medical Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Clea du Toit
- Institute of Cardiovascular & Medical Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Paraskevi Christofidou
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom; NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, SE1 9RT, United Kingdom
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Doha, Qatar; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Sandosh Padmanabhan
- Institute of Cardiovascular & Medical Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom.
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, England, SE1 7EH, United Kingdom.
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Lewis HM, Liu Y, Frampas CF, Longman K, Spick M, Stewart A, Sinclair E, Kasar N, Greener D, Whetton AD, Barran PE, Chen T, Dunn-Walters D, Skene DJ, Bailey MJ. Metabolomics Markers of COVID-19 Are Dependent on Collection Wave. Metabolites 2022; 12:713. [PMID: 36005585 PMCID: PMC9415837 DOI: 10.3390/metabo12080713] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 12/15/2022] Open
Abstract
The effect of COVID-19 infection on the human metabolome has been widely reported, but to date all such studies have focused on a single wave of infection. COVID-19 has generated numerous waves of disease with different clinical presentations, and therefore it is pertinent to explore whether metabolic disturbance changes accordingly, to gain a better understanding of its impact on host metabolism and enable better treatments. This work used a targeted metabolomics platform (Biocrates Life Sciences) to analyze the serum of 164 hospitalized patients, 123 with confirmed positive COVID-19 RT-PCR tests and 41 providing negative tests, across two waves of infection. Seven COVID-19-positive patients also provided longitudinal samples 2-7 months after infection. Changes to metabolites and lipids between positive and negative patients were found to be dependent on collection wave. A machine learning model identified six metabolites that were robust in diagnosing positive patients across both waves of infection: TG (22:1_32:5), TG (18:0_36:3), glutamic acid (Glu), glycolithocholic acid (GLCA), aspartic acid (Asp) and methionine sulfoxide (Met-SO), with an accuracy of 91%. Although some metabolites (TG (18:0_36:3) and Asp) returned to normal after infection, glutamic acid was still dysregulated in the longitudinal samples. This work demonstrates, for the first time, that metabolic dysregulation has partially changed over the course of the pandemic, reflecting changes in variants, clinical presentation and treatment regimes. It also shows that some metabolic changes are robust across waves, and these can differentiate COVID-19-positive individuals from controls in a hospital setting. This research also supports the hypothesis that some metabolic pathways are disrupted several months after COVID-19 infection.
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Affiliation(s)
- Holly-May Lewis
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK; (H.-M.L.); (Y.L.); (C.F.F.); (K.L.); (M.S.); (T.C.)
| | - Yufan Liu
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK; (H.-M.L.); (Y.L.); (C.F.F.); (K.L.); (M.S.); (T.C.)
| | - Cecile F. Frampas
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK; (H.-M.L.); (Y.L.); (C.F.F.); (K.L.); (M.S.); (T.C.)
| | - Katie Longman
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK; (H.-M.L.); (Y.L.); (C.F.F.); (K.L.); (M.S.); (T.C.)
| | - Matt Spick
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK; (H.-M.L.); (Y.L.); (C.F.F.); (K.L.); (M.S.); (T.C.)
| | - Alexander Stewart
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK; (A.S.); (E.S.); (N.K.); (A.D.W.); (D.D.-W.); (D.J.S.)
| | - Emma Sinclair
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK; (A.S.); (E.S.); (N.K.); (A.D.W.); (D.D.-W.); (D.J.S.)
| | - Nora Kasar
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK; (A.S.); (E.S.); (N.K.); (A.D.W.); (D.D.-W.); (D.J.S.)
| | - Danni Greener
- Frimley Park Hospital, Frimley Health NHS Trust, Camberley GU16 7UJ, UK;
| | - Anthony D. Whetton
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK; (A.S.); (E.S.); (N.K.); (A.D.W.); (D.D.-W.); (D.J.S.)
| | - Perdita E. Barran
- Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK;
| | - Tao Chen
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK; (H.-M.L.); (Y.L.); (C.F.F.); (K.L.); (M.S.); (T.C.)
| | - Deborah Dunn-Walters
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK; (A.S.); (E.S.); (N.K.); (A.D.W.); (D.D.-W.); (D.J.S.)
| | - Debra J. Skene
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK; (A.S.); (E.S.); (N.K.); (A.D.W.); (D.D.-W.); (D.J.S.)
| | - Melanie J. Bailey
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK; (H.-M.L.); (Y.L.); (C.F.F.); (K.L.); (M.S.); (T.C.)
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Rizvi ZA, Dalal R, Sadhu S, Kumar Y, Kumar S, Gupta SK, Tripathy MR, Rathore DK, Awasthi A. High-salt diet mediates interplay between NK cells and gut microbiota to induce potent tumor immunity. SCIENCE ADVANCES 2021; 7:eabg5016. [PMID: 34516769 PMCID: PMC8442882 DOI: 10.1126/sciadv.abg5016] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
High-salt diet (HSD) modulates effector and regulatory T cell functions and promotes tissue inflammation in autoimmune diseases. However, effects of HSD and its association with gut microbiota in tumor immunity remain undefined. Here, we report that HSD induces natural killer (NK) cell–mediated tumor immunity by inhibiting PD-1 expression while enhancing IFNγ and serum hippurate. Salt enhanced tumor immunity when combined with a suboptimal dose of anti-PD1 antibody. While HSD-induced tumor immunity was blunted upon gut microbiota depletion, fecal microbiota transplantation (FMT) from HSD mice restored the tumor immunity associated with NK cell functions. HSD increased the abundance of Bifidobacterium and caused increased gut permeability leading to intratumor localization of Bifidobacterium, which enhanced NK cell functions and tumor regression. Intratumoral injections of Bifidobacterium activated NK cells, which inhibited tumor growth. These results indicate that HSD modulates gut microbiome that induces NK cell–dependent tumor immunity with a potential translational perspective.
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Affiliation(s)
- Zaigham Abbas Rizvi
- Immunbiology Lab, Translational Health Science and Technology Institute, NCR-Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
- Infection and Immunology, Translational Health Science and Technology Institute, NCR-Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
| | - Rajdeep Dalal
- Immunbiology Lab, Translational Health Science and Technology Institute, NCR-Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
- Infection and Immunology, Translational Health Science and Technology Institute, NCR-Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
| | - Srikanth Sadhu
- Immunbiology Lab, Translational Health Science and Technology Institute, NCR-Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
- Infection and Immunology, Translational Health Science and Technology Institute, NCR-Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
| | - Yashwant Kumar
- Noncommunicable Disease Center, Translational Health Science and Technology Institute, NCR-Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
| | - Shakti Kumar
- Infection and Immunology, Translational Health Science and Technology Institute, NCR-Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
| | - Sonu Kumar Gupta
- Noncommunicable Disease Center, Translational Health Science and Technology Institute, NCR-Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
| | - Manas Ranjan Tripathy
- Immunbiology Lab, Translational Health Science and Technology Institute, NCR-Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
- Infection and Immunology, Translational Health Science and Technology Institute, NCR-Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
| | - Deepak Kumar Rathore
- Infection and Immunology, Translational Health Science and Technology Institute, NCR-Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
| | - Amit Awasthi
- Immunbiology Lab, Translational Health Science and Technology Institute, NCR-Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
- Infection and Immunology, Translational Health Science and Technology Institute, NCR-Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
- Corresponding author.
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5
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Bennet D, Khorsandian Y, Pelusi J, Mirabella A, Pirrotte P, Zenhausern F. Molecular and physical technologies for monitoring fluid and electrolyte imbalance: A focus on cancer population. Clin Transl Med 2021; 11:e461. [PMID: 34185420 PMCID: PMC8214861 DOI: 10.1002/ctm2.461] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 05/11/2021] [Accepted: 05/29/2021] [Indexed: 12/23/2022] Open
Abstract
Several clinical examinations have shown the essential impact of monitoring (de)hydration (fluid and electrolyte imbalance) in cancer patients. There are multiple risk factors associated with (de)hydration, including aging, excessive or lack of fluid consumption in sports, alcohol consumption, hot weather, diabetes insipidus, vomiting, diarrhea, cancer, radiation, chemotherapy, and use of diuretics. Fluid and electrolyte imbalance mainly involves alterations in the levels of sodium, potassium, calcium, and magnesium in extracellular fluids. Hyponatremia is a common condition among individuals with cancer (62% of cases), along with hypokalemia (40%), hypophosphatemia (32%), hypomagnesemia (17%), hypocalcemia (12%), and hypernatremia (1-5%). Lack of hydration and monitoring of hydration status can lead to severe complications, such as nausea/vomiting, diarrhea, fatigue, seizures, cell swelling or shrinking, kidney failure, shock, coma, and even death. This article aims to review the current (de)hydration (fluid and electrolyte imbalance) monitoring technologies focusing on cancer. First, we discuss the physiological and pathophysiological implications of fluid and electrolyte imbalance in cancer patients. Second, we explore the different molecular and physical monitoring methods used to measure fluid and electrolyte imbalance and the measurement challenges in diverse populations. Hydration status is assessed in various indices; plasma, sweat, tear, saliva, urine, body mass, interstitial fluid, and skin-integration techniques have been extensively investigated. No unified (de)hydration (fluid and electrolyte imbalance) monitoring technology exists for different populations (including sports, elderly, children, and cancer). Establishing novel methods and technologies to facilitate and unify measurements of hydration status represents an excellent opportunity to develop impactful new approaches for patient care.
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Affiliation(s)
- Devasier Bennet
- Center for Applied NanoBioscience and MedicineThe University of ArizonaCollege of MedicinePhoenixUSA
| | - Yasaman Khorsandian
- Center for Applied NanoBioscience and MedicineThe University of ArizonaCollege of MedicinePhoenixUSA
| | | | | | - Patrick Pirrotte
- Collaborative Center for Translational Mass SpectrometryTranslational Genomics Research InstitutePhoenixUSA
| | - Frederic Zenhausern
- Center for Applied NanoBioscience and MedicineThe University of ArizonaCollege of MedicinePhoenixUSA
- HonorHealth Research InstituteScottsdaleUSA
- Collaborative Center for Translational Mass SpectrometryTranslational Genomics Research InstitutePhoenixUSA
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Henny J, Nadif R, Got SL, Lemonnier S, Ozguler A, Ruiz F, Beaumont K, Brault D, Sandt E, Goldberg M, Zins M. The CONSTANCES Cohort Biobank: An Open Tool for Research in Epidemiology and Prevention of Diseases. Front Public Health 2020; 8:605133. [PMID: 33363097 PMCID: PMC7758208 DOI: 10.3389/fpubh.2020.605133] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 11/18/2020] [Indexed: 12/12/2022] Open
Abstract
“General-purpose cohorts” in epidemiology and public health are designed to cover a broad scope of determinants and outcomes, in order to answer several research questions, including those not defined at study inception. In this context, the general objective of the CONSTANCES project is to set up a large population-based cohort that will contribute to the development of epidemiological research by hosting ancillary projects on a wide range of scientific domains, and to provide public health information. CONSTANCES was designed as a randomly selected sample of French adults aged 18–69 years at study inception; 202,045 subjects were included over an 8-year period. At inclusion, the selected participants are invited to attend one of the 24 participating Health Prevention Centers (HPCs) for a comprehensive health examination. The follow-up includes a yearly self-administered questionnaire, and a periodic visit to an HPC. Procedures have been developed to use the national healthcare databases to allow identification and validation of diseases over the follow-up. The biological collection (serum, lithium heparinized plasma, EDTA plasma, urine and buffy coat) began gradually in June 2018. At the end of the inclusions, specimens from 83,000 donors will have been collected. Specimens are collected according to a standardized protocol, identical in all recruitment centers. All operations relating to bio-banking have been entrusted by Inserm to the Integrated Biobank of Luxembourg (IBBL). A quality management system has been put in place. Particular attention has been paid to the traceability of all operations. The nature of the biological samples stored has been deliberately limited due to the economic and organizational constraints of the inclusion centers. Some research works may require specific collection conditions, and can be developed on request for a limited number of subjects and in specially trained centers. The biological specimens that are collected will allow for a large spectrum of biomarkers studies and genetic and epigenetic markers through candidate or agnostic approaches. By linking the extensive data on personal, lifestyle, environmental, occupational and social factors with the biomarker data, the CONSTANCES cohort offers the opportunity to study the interplays between these factors using an integrative approach and state-of-the-art methods.
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Affiliation(s)
- J Henny
- Inserm UMS 011, Population-based Epidemiological Cohorts, Villejuif, France
| | - R Nadif
- Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, Équipe d'Épidémiologie respiratoire intégrative, CESP, Villejuif, France
| | - S Le Got
- Inserm UMS 011, Population-based Epidemiological Cohorts, Villejuif, France
| | - S Lemonnier
- Inserm UMS 011, Population-based Epidemiological Cohorts, Villejuif, France
| | - A Ozguler
- Inserm UMS 011, Population-based Epidemiological Cohorts, Villejuif, France
| | - F Ruiz
- ClinSearch, Malakoff, France
| | - K Beaumont
- Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - D Brault
- Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - E Sandt
- Integrated Biobank of Luxembourg (IBBL), Dudelange, Luxembourg
| | - M Goldberg
- Inserm UMS 011, Population-based Epidemiological Cohorts, Villejuif, France.,Faculty of Medicine, University of Paris, Paris, France
| | - M Zins
- Inserm UMS 011, Population-based Epidemiological Cohorts, Villejuif, France.,Faculty of Medicine, University of Paris, Paris, France
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