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González-Gil EM, Peruchet-Noray L, Sedlmeier AM, Christakoudi S, Biessy C, Navionis AS, Mahamat-Saleh Y, Jaafar RF, Baurecht H, Guevara M, Etxezarreta PA, Verschuren WMM, Boer JMA, Olsen A, Tjønneland A, Simeon V, Castro-Espin C, Aune D, Heath AK, Gunter M, Colorado-Yohar SM, Zilhão NR, Dahm CC, Llanaj E, Schulze MB, Petrova D, Sieri S, Ricceri F, Masala G, Key T, Viallon V, Rinaldi S, Freisling H, Dossus L. Association of body shape phenotypes and body fat distribution indexes with inflammatory biomarkers in the European Prospective Investigation into Cancer and Nutrition (EPIC) and UK Biobank. BMC Med 2024; 22:334. [PMID: 39148045 PMCID: PMC11328449 DOI: 10.1186/s12916-024-03544-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 07/29/2024] [Indexed: 08/17/2024] Open
Abstract
BACKGROUND The allometric body shape index (ABSI) and hip index (HI), as well as multi-trait body shape phenotypes, have not yet been compared in their associations with inflammatory markers. The aim of this study was to examine the relationship between novel and traditional anthropometric indexes with inflammation using data from the European Prospective Investigation into Cancer and Nutrition (EPIC) and UK Biobank cohorts. METHODS Participants from EPIC (n = 17,943, 69.1% women) and UK Biobank (n = 426,223, 53.2% women) with data on anthropometric indexes and C-reactive protein (CRP) were included in this cross-sectional analysis. A subset of women in EPIC also had at least one measurement for interleukins, tumour necrosis factor alpha, interferon gamma, leptin, and adiponectin. Four distinct body shape phenotypes were derived by a principal component (PC) analysis on height, weight, body mass index (BMI), waist (WC) and hip circumferences (HC), and waist-to-hip ratio (WHR). PC1 described overall adiposity, PC2 tall with low WHR, PC3 tall and centrally obese, and PC4 high BMI and weight with low WC and HC, suggesting an athletic phenotype. ABSI, HI, waist-to-height ratio and waist-to-hip index (WHI) were also calculated. Linear regression models were carried out separately in EPIC and UK Biobank stratified by sex and adjusted for age, smoking status, education, and physical activity. Results were additionally combined in a random-effects meta-analysis. RESULTS Traditional anthropometric indexes, particularly BMI, WC, and weight were positively associated with CRP levels, in men and women. Body shape phenotypes also showed distinct associations with CRP. Specifically, PC2 showed inverse associations with CRP in EPIC and UK Biobank in both sexes, similarly to height. PC3 was inversely associated with CRP among women, whereas positive associations were observed among men. CONCLUSIONS Specific indexes of body size and body fat distribution showed differential associations with inflammation in adults. Notably, our results suggest that in women, height may mitigate the impact of a higher WC and HC on inflammation. This suggests that subtypes of adiposity exhibit substantial variation in their inflammatory potential, which may have implications for inflammation-related chronic diseases.
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Affiliation(s)
- Esther M González-Gil
- Nutrition and Metabolism Branch, International Agency for Research On Cancer, World Health Organization, 69372, Lyon, CEDEX 08, France
| | - Laia Peruchet-Noray
- Nutrition and Metabolism Branch, International Agency for Research On Cancer, World Health Organization, 69372, Lyon, CEDEX 08, France
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Anja M Sedlmeier
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
- Center for Translational Oncology, University Hospital Regensburg, Regensburg, Germany
- Bavarian Cancer Research Center (BZKF), Regensburg, Germany
| | - Sofia Christakoudi
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Carine Biessy
- Nutrition and Metabolism Branch, International Agency for Research On Cancer, World Health Organization, 69372, Lyon, CEDEX 08, France
| | - Anne-Sophie Navionis
- Nutrition and Metabolism Branch, International Agency for Research On Cancer, World Health Organization, 69372, Lyon, CEDEX 08, France
| | - Yahya Mahamat-Saleh
- Nutrition and Metabolism Branch, International Agency for Research On Cancer, World Health Organization, 69372, Lyon, CEDEX 08, France
| | - Rola F Jaafar
- Nutrition and Metabolism Branch, International Agency for Research On Cancer, World Health Organization, 69372, Lyon, CEDEX 08, France
| | - Hansjörg Baurecht
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Marcela Guevara
- Instituto de Salud Pública y Laboral de Navarra, 31003, Pamplona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- Navarra Institute for Health Research (IdiSNA), 31008, Pamplona, Spain
| | - Pilar Amiano Etxezarreta
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- Sub Directorate for Public Health and Addictions of Gipuzkoa, Ministry of Health of the Basque Government, San Sebastian, Spain
- Epidemiology of Chronic and Communicable Diseases Group, BioGipuzkoa (BioDonostia) Health Research Institute, San Sebastián, Spain
| | - W M Monique Verschuren
- Centre forPrevention, Lifestyle and Health, National Institute for Public Health and the Environment, PO Box 1, 3720 BA, Bilthoven, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jolanda M A Boer
- Centre forPrevention, Lifestyle and Health, National Institute for Public Health and the Environment, PO Box 1, 3720 BA, Bilthoven, the Netherlands
| | - Anja Olsen
- Danish Cancer Institute, Danish Cancer Society, Diet, Cancer and Health Strandboulevarden 49 2100, Copenhagen, Denmark
- Department of Public Health, University of Aarhus, Copenhagen, Denmark
| | - Anne Tjønneland
- Danish Cancer Institute, Danish Cancer Society, Diet, Cancer and Health Strandboulevarden 49 2100, Copenhagen, Denmark
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Vittorio Simeon
- Dipartimento Di Salute Mentale E Fisica E Medicina Preventiva, Università Degli Studi Della Campania 'Luigi Vanvitelli', Napoli, Italy
| | - Carlota Castro-Espin
- Unit of Nutrition and Cancer, Catalan Institute of Oncology-ICO, L'Hospitalet de Llobregat, Barcelona, Spain
- Nutrition and Cancer Group, Epidemiology, Public Health, Cancer Prevention and Palliative Care Program, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Dagfinn Aune
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Research, The Cancer Registry of Norway, Oslo, Norway
- Department of Nutrition, Oslo New University College, Oslo, Norway
| | - Alicia K Heath
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Marc Gunter
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Sandra M Colorado-Yohar
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- Department of Epidemiology, Murcia Regional Health Council-IMIB, Murcia, Spain
- Research Group On Demography and Health, National Faculty of Public Health, University of Antioquia, Medellín, Colombia
| | - Nuno R Zilhão
- Department of Public Health, Aarhus University, Aarhus, Denmark
| | | | - Erand Llanaj
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Munchen-Neuherberg, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Dafina Petrova
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- Escuela Andaluza de Salud Pública (EASP), 18011, Granada, Spain
- Instituto de Investigación Biosanitaria Ibs, GRANADA, 18012, Granada, Spain
| | - Sabina Sieri
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori Di Milano, Milan, Italy
| | - Fulvio Ricceri
- Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH) Department of Clinical and Biological Sciences, University of Turin, Turin, Italy
| | - Giovanna Masala
- Clinical Epidemiology Unit, Institute for Cancer Research, Prevention, and Clinical Network (ISPRO), Florence, Italy
| | - Tim Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research On Cancer, World Health Organization, 69372, Lyon, CEDEX 08, France
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research On Cancer, World Health Organization, 69372, Lyon, CEDEX 08, France
| | - Heinz Freisling
- Nutrition and Metabolism Branch, International Agency for Research On Cancer, World Health Organization, 69372, Lyon, CEDEX 08, France
| | - Laure Dossus
- Nutrition and Metabolism Branch, International Agency for Research On Cancer, World Health Organization, 69372, Lyon, CEDEX 08, France
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Albers FEM, Lou MWC, Dashti SG, Swain CTV, Rinaldi S, Viallon V, Karahalios A, Brown KA, Gunter MJ, Milne RL, English DR, Lynch BM. Sex-steroid hormones and risk of postmenopausal estrogen receptor-positive breast cancer: a case-cohort analysis. Cancer Causes Control 2024; 35:921-933. [PMID: 38363402 PMCID: PMC11130059 DOI: 10.1007/s10552-024-01856-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 01/16/2024] [Indexed: 02/17/2024]
Abstract
PURPOSE Sex-steroid hormones are associated with postmenopausal breast cancer but potential confounding from other biological pathways is rarely considered. We estimated risk ratios for sex-steroid hormone biomarkers in relation to postmenopausal estrogen receptor (ER)-positive breast cancer, while accounting for biomarkers from insulin/insulin-like growth factor-signaling and inflammatory pathways. METHODS This analysis included 1208 women from a case-cohort study of postmenopausal breast cancer within the Melbourne Collaborative Cohort Study. Weighted Poisson regression with a robust variance estimator was used to estimate risk ratios (RRs) and 95% confidence intervals (CIs) of postmenopausal ER-positive breast cancer, per doubling plasma concentration of progesterone, estrogens, androgens, and sex-hormone binding globulin (SHBG). Analyses included sociodemographic and lifestyle confounders, and other biomarkers identified as potential confounders. RESULTS Increased risks of postmenopausal ER-positive breast cancer were observed per doubling plasma concentration of progesterone (RR: 1.22, 95% CI 1.03 to 1.44), androstenedione (RR 1.20, 95% CI 0.99 to 1.45), dehydroepiandrosterone (RR: 1.15, 95% CI 1.00 to 1.34), total testosterone (RR: 1.11, 95% CI 0.96 to 1.29), free testosterone (RR: 1.12, 95% CI 0.98 to 1.28), estrone (RR 1.21, 95% CI 0.99 to 1.48), total estradiol (RR 1.19, 95% CI 1.02 to 1.39) and free estradiol (RR 1.22, 95% CI 1.05 to 1.41). A possible decreased risk was observed for SHBG (RR 0.83, 95% CI 0.66 to 1.05). CONCLUSION Progesterone, estrogens and androgens likely increase postmenopausal ER-positive breast cancer risk, whereas SHBG may decrease risk. These findings strengthen the causal evidence surrounding the sex-hormone-driven nature of postmenopausal breast cancer.
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Affiliation(s)
- Frances E M Albers
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Council Victoria, Level 8, 200 Victoria Parade, East Melbourne, Melbourne, VIC, 3002, Australia
| | - Makayla W C Lou
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Council Victoria, Level 8, 200 Victoria Parade, East Melbourne, Melbourne, VIC, 3002, Australia
| | - S Ghazaleh Dashti
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, Melbourne Medical School, University of Melbourne, Melbourne, Australia
| | - Christopher T V Swain
- Cancer Epidemiology Division, Cancer Council Victoria, Council Victoria, Level 8, 200 Victoria Parade, East Melbourne, Melbourne, VIC, 3002, Australia
- Department of Physiotherapy, Melbourne School of Health Sciences, University of Melbourne, Melbourne, Australia
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Amalia Karahalios
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Kristy A Brown
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, USA
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
- Cancer Epidemiology and Prevention Research Unit, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Council Victoria, Level 8, 200 Victoria Parade, East Melbourne, Melbourne, VIC, 3002, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia
| | - Dallas R English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Council Victoria, Level 8, 200 Victoria Parade, East Melbourne, Melbourne, VIC, 3002, Australia
| | - Brigid M Lynch
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
- Cancer Epidemiology Division, Cancer Council Victoria, Council Victoria, Level 8, 200 Victoria Parade, East Melbourne, Melbourne, VIC, 3002, Australia.
- Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia.
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Klont F, Nijdam FB, Bakker SJL, Keski-Rahkonen P, Hopfgartner G, Investigators T. High-abundance peaks and peak clusters associate with pharmaceutical polymers and excipients in urinary untargeted clinical metabolomics data: exploration of their origin and possible impact on label-free quantification. Analyst 2024; 149:1061-1067. [PMID: 38251754 PMCID: PMC10866140 DOI: 10.1039/d3an01874a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 12/08/2023] [Indexed: 01/23/2024]
Abstract
Pharmaceutical polymers and excipients represent interesting but often overlooked chemical classes in clinical exposure and bioanalytical research. These chemicals may cause hypersensitivity reactions, they can be useful to confirm exposure to pharmaceuticals, and they may pose bioanalytical challenges, including ion suppression in liquid chromatography-mass spectrometry (LC-MS-)based workflows. In this work, we assessed these chemicals in light of a rather surprising finding presented in two previously published studies, namely that usage of cyclosporine A, an immunosuppressive drug which is known to be cleared through excretion in the bile, explained the largest amount of variance in principal component analysis of urinary LC-SWATH/MS small-molecule profiling data. Specifically, we examined the freely-accessible 24-hour urine metabolomics data of 570 kidney transplant recipients included in the TransplantLines Biobank and Cohort Study (NCT03272841). These data unveiled thousands of high-abundance polymer peaks in some samples, which were associated with the use of the macrogol (i.e., polyethylene glycol) 3350 oral laxative agent. In addition, we found multiple clusters of high-abundance peaks which were linked to the exposure to two pharmaceutical excipients, namely short-chain polyethylene glycol (molecular weight <1000 Da) and polyethoxylated castor oil (also known as Kolliphor® EL or Cremophor® EL). Respectively, these excipients are used in temazepam capsules and cyclosporine A capsules, and the latter provides a plausible explanation for the rather surprising finding that instigated our work. Moreover, such explanation and our findings in general put emphasis on taking into consideration these and other pharmaceutical polymers and excipients when exploring, processing, and interpreting clinical small-molecule profiling data.
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Affiliation(s)
- Frank Klont
- Unit of PharmacoTherapy, -Epidemiology & -Economics, Groningen Research Institute of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands.
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, Quai Ernest Ansermet 24, 1211 Geneva, Switzerland
| | - Fleur B Nijdam
- Unit of PharmacoTherapy, -Epidemiology & -Economics, Groningen Research Institute of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands.
| | - Stephan J L Bakker
- Department of Internal Medicine, Division of Nephrology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Avenue Tony Garnier 25, 69007 Lyon, France
| | - Gérard Hopfgartner
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, Quai Ernest Ansermet 24, 1211 Geneva, Switzerland
| | - TransplantLines Investigators
- Group of Authors on Behalf of the Transplant Lines Biobank and Cohort Study, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
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Albers FE, Lou MW, Dashti SG, Swain CT, Rinaldi S, Viallon V, Karahalios A, Brown KA, Gunter MJ, Milne RL, English DR, Lynch BM. Sex-steroid hormones and risk of postmenopausal estrogen receptor-positive breast cancer: a case-cohort analysis. RESEARCH SQUARE 2023:rs.3.rs-3406466. [PMID: 37886482 PMCID: PMC10602098 DOI: 10.21203/rs.3.rs-3406466/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Purpose Sex-steroid hormones are associated with postmenopausal breast cancer but potential confounding from other biological pathways is rarely considered. We estimated risk ratios for sex-steroid hormone biomarkers in relation to postmenopausal estrogen receptor (ER)-positive breast cancer, while accounting for biomarkers from insulin/insulin-like growth factor-signaling and inflammatory pathways. Methods This analysis included 1,208 women from a case-cohort study of postmenopausal breast cancer within the Melbourne Collaborative Cohort Study. Weighted Poisson regression with a robust variance estimator was used to estimate risk ratios (RRs) and 95% confidence intervals (CIs) of postmenopausal ER-positive breast cancer, per doubling plasma concentration of progesterone, estrogens, androgens, and sex hormone binding globulin (SHBG). Analyses included sociodemographic and lifestyle confounders, and other biomarkers identified as potential confounders. Results Increased risks of postmenopausal ER-positive breast cancer were observed per doubling plasma concentration of progesterone (RR: 1.22, 95% CI: 1.03 to 1.44), androstenedione (RR: 1.20, 95% CI: 0.99 to 1.45), dehydroepiandrosterone (RR: 1.15, 95% CI: 1.00 to 1.34), total testosterone (RR: 1.11, 95% CI: 0.96 to 1.29), free testosterone (RR: 1.12, 95% CI: 0.98 to 1.28), estrone (RR: 1.21, 95% CI: 0.99 to 1.48), total estradiol (RR: 1.19, 95% CI: 1.02 to 1.39) and free estradiol (RR: 1.22, 95% CI: 1.05 to 1.41). A possible decreased risk was observed for SHBG (RR: 0.83, 95% CI: 0.66 to 1.05). Conclusion Progesterone, estrogens and androgens likely increase postmenopausal ER-positive breast cancer risk, whereas SHBG may decrease risk. These findings strengthen the causal evidence surrounding the sex hormone-driven nature of postmenopausal breast cancer.
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Yu CT, Farhat Z, Livinski AA, Loftfield E, Zanetti KA. Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review. Cancer Epidemiol Biomarkers Prev 2023; 32:1130-1145. [PMID: 37410086 PMCID: PMC10472112 DOI: 10.1158/1055-9965.epi-23-0045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 04/26/2023] [Accepted: 06/28/2023] [Indexed: 07/07/2023] Open
Abstract
An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.
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Affiliation(s)
- Catherine T Yu
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, Maryland
| | - Zeinab Farhat
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - Alicia A Livinski
- National Institutes of Health Library, Office of Research Services, Office of the Director, National Institutes of Health, Bethesda, Maryland
| | - Erikka Loftfield
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - Krista A Zanetti
- Office of Nutrition Research, Division of Program Coordination, Planning, and Strategic Initiatives, Office of the Director, National Institutes of Health, Bethesda, Maryland
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Lécuyer L, Laouali N, Viallon V, Artaud F, Hébert JR, Shivappa N, Agudo A, Tjønneland A, Mellemkjær L, Kaaks R, Katzke VA, Schulze MB, Frenoy P, Mancini FR, De Magistris MS, Macciotta A, Masala G, Agnoli C, Tumino R, Boer JMA, Verschuren WMM, Enget Jensen TM, Olsen KS, Skeie G, Chirlaque MD, Petrova D, Castro-Espin C, Quirós JR, Guevara M, Amiano P, Borné Y, Sandström M, Nilsson LM, Heath AK, Mayen AL, Huybrechts I, Weiderpass E, Boutron-Ruault MC, Dossus L, Rinaldi S, Truong T. Associations between dietary inflammatory scores and biomarkers of inflammation in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Clin Nutr 2023; 42:1115-1125. [PMID: 37271707 DOI: 10.1016/j.clnu.2023.05.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 04/14/2023] [Accepted: 05/12/2023] [Indexed: 06/06/2023]
Abstract
BACKGROUND Since the first version of the dietary inflammatory index (DII®) developed in the past decade, several other versions have been developed. However, to date no study has attempted to compare these versions with respect to their associations with biomarkers of inflammation. OBJECTIVE We aimed to investigate the relationship between four dietary inflammatory scores [DII, two energy-adjusted derivatives (E-DII and E-DIIr), and the Inflammatory Score of the Diet (ISD)], and circulating levels of several inflammatory markers and adipokines. METHODS This study included 17 637 participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort with at least one marker of inflammation measured in blood. Associations between the four scores and C-reactive protein (CRP), interleukin (IL)6, IL10, IL1RA, tumor necrosis factor-α (TNFα), soluble tumor necrosis factor receptor-1 (sTNFR1), sTNFR2, leptin, soluble leptin receptor (sLeptin R), adiponectin, and High Molecular Weight (HMW) adiponectin were evaluated using multivariable linear regressions adjusted for potential confounders. RESULTS Positive associations were observed between the four dietary inflammatory scores and levels of CRP, IL6, sTNFR1, sTNFR2 and leptin. However, only the DII and the ISD were positively associated with IL1RA levels and only the DII and the E-DIIr were positively associated with TNFα levels. The proportion of variance of each biomarker explained by the scores was lower than 2%, which was equivalent to the variance explained by smoking status but much lower than that explained by body mass index. CONCLUSIONS Our results suggest that the four dietary inflammatory scores were associated with some biomarkers of inflammation and could be used to assess the inflammatory potential of diet in European adults but are not sufficient to capture the inflammatory status of an individual. These findings can help to better understand the inflammatory potential of diet, but they need to be replicated in studies with repeated dietary measurements.
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Affiliation(s)
- Lucie Lécuyer
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team "Exposome and Heredity", 94807, Villejuif, France
| | - Nasser Laouali
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team "Exposome and Heredity", 94807, Villejuif, France; Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA
| | - Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Fanny Artaud
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team "Exposome and Heredity", 94807, Villejuif, France
| | - James R Hébert
- Cancer Prevention and Control Program and Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA; Department of Nutrition, Connecting Health Innovations LLC, Columbia, SC, USA
| | - Nitin Shivappa
- Cancer Prevention and Control Program and Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA; Department of Nutrition, Connecting Health Innovations LLC, Columbia, SC, USA
| | - Antonio Agudo
- Unit of Nutrition and Cancer, Catalan Institute of Oncology - ICO, L'Hospitalet de Llobregat, Spain; Nutrition and Cancer Group, Epidemiology, Public Health, Cancer Prevention and Palliative Care Program, Bellvitge Biomedical Research Institute - IDIBELL, L'Hospitalet de Llobregat, Spain
| | - Anne Tjønneland
- Danish Cancer Society Research Center, "Diet, Cancer and Health", Copenhagen, Denmark; University of Copenhagen, Department of Public Health, Copenhagen, Denmark
| | - Lene Mellemkjær
- Danish Cancer Society Research Center, "Diet, Cancer and Health", Copenhagen, Denmark
| | - Rudolf Kaaks
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Verena A Katzke
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Matthias B Schulze
- German Institute of Human Nutrition Potsdam-Rehbruecke, Dept. of Molecular Epidemiology, Nuthetal, Germany; Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Pauline Frenoy
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team "Exposome and Heredity", 94807, Villejuif, France
| | - Francesca Romana Mancini
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team "Exposome and Heredity", 94807, Villejuif, France
| | | | - Alessandra Macciotta
- Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH), Department of Clinical and Biological Sciences, University of Turin, Turin, Italy
| | - Giovanna Masala
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy
| | - Claudia Agnoli
- Epidemiology and Prevention Unit, Department of Epidemiology and Data Science, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Rosario Tumino
- Hyblean Association for Epidemiological Research, AIRE-ONLUS, Ragusa, Italy
| | - Jolanda M A Boer
- National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - W M Monique Verschuren
- National Institute for Public Health and the Environment, Bilthoven, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Torill M Enget Jensen
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
| | - Karina Standahl Olsen
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
| | - Guri Skeie
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
| | - María-Dolores Chirlaque
- Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, Murcia, Spain; CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Dafina Petrova
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain; Escuela Andaluza de Salud Pública (EASP), 18011, Granada, Spain; Instituto de Investigación Biosanitaria ibs.GRANADA, 18012, Granada, Spain
| | - Carlota Castro-Espin
- Unit of Nutrition and Cancer, Catalan Institute of Oncology - ICO, L'Hospitalet de Llobregat, Spain; Nutrition and Cancer Group, Epidemiology, Public Health, Cancer Prevention and Palliative Care Program, Bellvitge Biomedical Research Institute - IDIBELL, L'Hospitalet de Llobregat, Spain
| | | | - Marcela Guevara
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain; Navarra Public Health Institute, Pamplona, Spain; Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
| | - Pilar Amiano
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain; Ministry of Health of the Basque Government, Sub Directorate for Public Health and Addictions of Gipuzkoa, San Sebastián, Spain; Biodonostia Health Research Institute, Epidemiology of Chronic and Communicable Diseases Group, San Sebastián, Spain
| | - Yan Borné
- Nutritional Epidemiology, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Malmö, Sweden
| | - Maria Sandström
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Lena Maria Nilsson
- Department of Epidemiology and Global Health/ Arcum, Arctic Centre, Umeå University, Umeå, Sweden
| | - Alicia K Heath
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Ana-Lucia Mayen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Inge Huybrechts
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Elisabete Weiderpass
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | | | - Laure Dossus
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Thérèse Truong
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team "Exposome and Heredity", 94807, Villejuif, France.
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7
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Targeted lipidomics data of COVID-19 patients. Data Brief 2023; 48:109089. [PMID: 37006392 PMCID: PMC10050192 DOI: 10.1016/j.dib.2023.109089] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 02/27/2023] [Accepted: 03/20/2023] [Indexed: 03/31/2023] Open
Abstract
The dataset provided with this article describes a targeted lipidomics analysis performed on the serum of COVID-19 patients characterized by different degree of severity. As the ongoing pandemic has posed a challenging threat for humanity, the data here presented belong to one of the first lipidomics studies carried out on COVID-19 patients’ samples collected during the first pandemic waves. Serum samples were obtained from hospitalized patients with a molecular diagnosis of SARS-CoV-2 infection detected after nasal swab, and categorized as mild, moderate, or severe according to pre-established clinical descriptors. The MS-based targeted lipidomic analysis was performed by MRM using a Triple Quad 5500+ mass spectrometer, and the quantitative data were acquired on a panel of 483 lipids. The characterization of this lipidomic dataset has been outlined using multivariate and univariate descriptive statistics and bioinformatics tools.
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8
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Habra H, Kachman M, Padmanabhan V, Burant C, Karnovsky A, Meijer J. Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples. J Proteome Res 2022; 21:2936-2946. [PMID: 36367990 DOI: 10.1021/acs.jproteome.2c00371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.
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Affiliation(s)
- Hani Habra
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Maureen Kachman
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, Michigan 48105, United States
| | - Vasantha Padmanabhan
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan 48109, United States
- Department of Obstetrics & Gynecology, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Charles Burant
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, Michigan 48105, United States
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Jennifer Meijer
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
- Department of Medicine, Geisel School of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire 03756, United States
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9
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Breeur M, Ferrari P, Dossus L, Jenab M, Johansson M, Rinaldi S, Travis RC, His M, Key TJ, Schmidt JA, Overvad K, Tjønneland A, Kyrø C, Rothwell JA, Laouali N, Severi G, Kaaks R, Katzke V, Schulze MB, Eichelmann F, Palli D, Grioni S, Panico S, Tumino R, Sacerdote C, Bueno-de-Mesquita B, Olsen KS, Sandanger TM, Nøst TH, Quirós JR, Bonet C, Barranco MR, Chirlaque MD, Ardanaz E, Sandsveden M, Manjer J, Vidman L, Rentoft M, Muller D, Tsilidis K, Heath AK, Keun H, Adamski J, Keski-Rahkonen P, Scalbert A, Gunter MJ, Viallon V. Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition. BMC Med 2022; 20:351. [PMID: 36258205 PMCID: PMC9580145 DOI: 10.1186/s12916-022-02553-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 09/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations. METHODS We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty. RESULTS Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk. CONCLUSIONS These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.
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Affiliation(s)
- Marie Breeur
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Pietro Ferrari
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Laure Dossus
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Mazda Jenab
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Mattias Johansson
- Genetics Branch, International Agency for Research on Cancer, 69372 CEDEX 08, Lyon, France
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Mathilde His
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Tim J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Julie A Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
- Department of Clinical Epidemiology, Department of Clinical Medicine, Aarhus University Hospital and Aarhus University, DK-8200, Aarhus N, Denmark
| | - Kim Overvad
- Department of Public Health, Aarhus University, DK-8000, Aarhus C, Denmark
| | - Anne Tjønneland
- Danish Cancer Society Research Center Diet, Genes and Environment Nutrition and Biomarkers, DK-2100, Copenhagen, Denmark
| | - Cecilie Kyrø
- Danish Cancer Society Research Center Diet, Genes and Environment Nutrition and Biomarkers, DK-2100, Copenhagen, Denmark
| | - Joseph A Rothwell
- Université Paris-Saclay, UVSQ, Inserm, CESP U1018, "Exposome and Heredity" team, Gustave Roussy, 94800, Villejuif, France
| | - Nasser Laouali
- Université Paris-Saclay, UVSQ, Inserm, CESP U1018, "Exposome and Heredity" team, Gustave Roussy, 94800, Villejuif, France
| | - Gianluca Severi
- Université Paris-Saclay, UVSQ, Inserm, CESP U1018, "Exposome and Heredity" team, Gustave Roussy, 94800, Villejuif, France
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Verena Katzke
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition, 14558, Nuthetal, Germany
| | - Fabian Eichelmann
- Department of Molecular Epidemiology, German Institute of Human Nutrition, 14558, Nuthetal, Germany
- German Center for Diabetes Research (DZD), 85764, Neuherberg, Germany
| | - Domenico Palli
- Institute of Cancer Research, Prevention and Clinical Network (ISPRO), 50139, Florence, Italy
| | - Sara Grioni
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133, Milan, Italy
| | - Salvatore Panico
- Dipartimento di Medicina Clinica e Chirurgia, Federico II University, 80131, Naples, Italy
| | - Rosario Tumino
- Hyblean Association for Epidemiological Research, AIRE-ONLUS, 97100, Ragusa, Italy
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology Città della Salute e della Scienza University-Hospital, 10126, Turin, Italy
| | - Bas Bueno-de-Mesquita
- Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720, BA, Bilthoven, The Netherlands
| | - Karina Standahl Olsen
- Department of Community Medicine, UiT The Arctic University of Norway, N-9037, Tromsø, Norway
| | | | - Therese Haugdahl Nøst
- Department of Community Medicine, UiT The Arctic University of Norway, N-9037, Tromsø, Norway
| | - J Ramón Quirós
- Public Health Directorate, 33006, Oviedo, Asturias, Spain
| | - Catalina Bonet
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain
| | - Miguel Rodríguez Barranco
- Escuela Andaluza de Salud Pública (EASP), 18011, Granada, Spain
- Instituto de Investigación Biosanitaria ibs. GRANADA, 18012, Granada, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
| | - María-Dolores Chirlaque
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, 30003, Murcia, Spain
| | - Eva Ardanaz
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- Navarra Public Health Institute, 31003, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, 31008, Pamplona, Spain
| | - Malte Sandsveden
- Department of Clinical Sciences Malmö Lund University, SE-214 28, Malmö, Sweden
| | - Jonas Manjer
- Departement of Surgery, Skåne University Hospital Malmö, Lund University, SE-214 28, Malmö, Sweden
| | - Linda Vidman
- Department of Radiation Sciences, Oncology Umeå University, SE-901 87, Umeå, Sweden
| | - Matilda Rentoft
- Department of Radiation Sciences, Oncology Umeå University, SE-901 87, Umeå, Sweden
| | - David Muller
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Kostas Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Alicia K Heath
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Hector Keun
- Department of Surgery and Cancer, Cancer Metabolism and Systems Toxicology Group, Division of Cancer, Imperial College London, London, SW7 2AZ, UK
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, 1000, Ljubljana, Slovenia
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Augustin Scalbert
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France.
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10
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Liu Z, Zhang M, Chen P, Harnly JM, Sun J. Mass Spectrometry-Based Nontargeted and Targeted Analytical Approaches in Fingerprinting and Metabolomics of Food and Agricultural Research. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:11138-11153. [PMID: 35998657 DOI: 10.1021/acs.jafc.2c01878] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Mass spectrometry (MS)-based techniques have been extensively applied in food and agricultural research. This review aims to address the advances and applications of MS-based analytical strategies in nontargeted and targeted analysis and summarizes the recent publications of MS-based techniques, including flow injection MS fingerprinting, chromatography-tandem MS metabolomics, direct analysis using ambient mass spectrometry, as well as development in MS data deconvolution software packages and databases for metabolomic studies. Various nontargeted and targeted approaches are employed in marker compounds identification, material adulteration detection, and the analysis of specific classes of secondary metabolites. In the newly emerged applications, the recent advances in computer tools for the fast deconvolution of MS data in targeted secondary metabolite analysis are highlighted.
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Affiliation(s)
- Zhihao Liu
- United States Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, Maryland 20705, United States
- Department of Nutrition and Food Science, University of Maryland, College Park, Maryland 20742, United States
| | - Mengliang Zhang
- Department of Chemistry, Middle Tennessee State University, Murfreesboro, Tennessee 37132, United States
| | - Pei Chen
- United States Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, Maryland 20705, United States
| | - James M Harnly
- United States Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, Maryland 20705, United States
| | - Jianghao Sun
- United States Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, Maryland 20705, United States
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11
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Grenville ZS, Noor U, His M, Viallon V, Rinaldi S, Aglago EK, Amiano P, Brunkwall L, Chirlaque MD, Drake I, Eichelmann F, Freisling H, Grioni S, Heath AK, Kaaks R, Katzke V, Mayén-Chacon AL, Milani L, Moreno-Iribas C, Pala V, Olsen A, Sánchez MJ, Schulze MB, Tjønneland A, Tsilidis KK, Weiderpass E, Winkvist A, Zamora-Ros R, Key TJ, Smith-Byrne K, Travis RC, Schmidt JA. Diet and BMI Correlate with Metabolite Patterns Associated with Aggressive Prostate Cancer. Nutrients 2022; 14:3306. [PMID: 36014812 PMCID: PMC9415102 DOI: 10.3390/nu14163306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 11/16/2022] Open
Abstract
Three metabolite patterns have previously shown prospective inverse associations with the risk of aggressive prostate cancer within the European Prospective Investigation into Cancer and Nutrition (EPIC). Here, we investigated dietary and lifestyle correlates of these three prostate cancer-related metabolite patterns, which included: 64 phosphatidylcholines and three hydroxysphingomyelins (Pattern 1), acylcarnitines C18:1 and C18:2, glutamate, ornithine, and taurine (Pattern 2), and 8 lysophosphatidylcholines (Pattern 3). In a two-stage cross-sectional discovery (n = 2524) and validation (n = 518) design containing 3042 men free of cancer in EPIC, we estimated the associations of 24 dietary and lifestyle variables with each pattern and the contributing individual metabolites. Associations statistically significant after both correction for multiple testing (False Discovery Rate = 0.05) in the discovery set and at p < 0.05 in the validation set were considered robust. Intakes of alcohol, total fish products, and its subsets total fish and lean fish were positively associated with Pattern 1. Body mass index (BMI) was positively associated with Pattern 2, which appeared to be driven by a strong positive BMI-glutamate association. Finally, both BMI and fatty fish were inversely associated with Pattern 3. In conclusion, these results indicate associations of fish and its subtypes, alcohol, and BMI with metabolite patterns that are inversely associated with risk of aggressive prostate cancer.
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Affiliation(s)
- Zoe S. Grenville
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Urwah Noor
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Mathilde His
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, 69008 Lyon, France
| | - Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, 69008 Lyon, France
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, 69008 Lyon, France
| | - Elom K. Aglago
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Pilar Amiano
- Ministry of Health of the Basque Government, Sub Directorate for Public Health and Addictions of Gipuzkoa, 20013 San Sebastian, Spain
- Biodonostia Health Research Institute, Epidemiology of Chronic and Communicable Diseases Group, 20014 San Sebastián, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Louise Brunkwall
- Department of Clinical Sciences, Lund University, 221 84 Malmö, Sweden
| | - María Dolores Chirlaque
- CIBER of Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, 30008 Murcia, Spain
| | - Isabel Drake
- Department of Clinical Sciences, Lund University, 221 84 Malmö, Sweden
- Skåne University Hospital, 214 28 Malmö, Sweden
| | - Fabian Eichelmann
- Department of Molecular Epidemiology, German Institute of Human Nutrition, 14558 Nuthetal, Germany
| | - Heinz Freisling
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, 69008 Lyon, France
| | - Sara Grioni
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Alicia K. Heath
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Rudolf Kaaks
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Verena Katzke
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Ana-Lucia Mayén-Chacon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, 69008 Lyon, France
| | - Lorenzo Milani
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, 10124 Turin, Italy
| | - Conchi Moreno-Iribas
- CIBER of Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Navarra Public Health Institute, 31003 Pamplona, Spain
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain
| | - Valeria Pala
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Anja Olsen
- Danish Cancer Society Research Center, DK-2100 Copenhagen, Denmark
- Department of Public Health, Aarhus University, DK-8000 Aarhus, Denmark
| | - Maria-Jose Sánchez
- Escuela Andaluza de Salud Pública (EASP), 18011 Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Granada, 18071 Granada, Spain
| | - Matthias B. Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition, 14558 Nuthetal, Germany
| | - Anne Tjønneland
- Danish Cancer Society Research Center, DK-2100 Copenhagen, Denmark
- Department of Public Health, University of Copenhagen, DK-1353 Copenhagen, Denmark
| | - Konstantinos K. Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, UK
- Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, 45110 Ioannina, Greece
| | - Elisabete Weiderpass
- International Agency for Research on Cancer, World Health Organization, 69008 Lyon, France
| | - Anna Winkvist
- Sustainable Health, Department of Public Health and Clinical Medicine, Umeå University, 901 87 Umeå, Sweden
- Department of Internal Medicine and Clinical Nutrition, The Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden
| | - Raul Zamora-Ros
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain
| | - Timothy J. Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Karl Smith-Byrne
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Ruth C. Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Julie A. Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- Department of Clinical Epidemiology, Department of Clinical Medicine, University Hospital, Aarhus University and Aarhus, DK-8200 Aarhus N, Denmark
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12
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Wedekind R, Rothwell JA, Viallon V, Keski-Rahkonen P, Schmidt JA, Chajes V, Katzke V, Johnson T, Santucci de Magistris M, Krogh V, Amiano P, Sacerdote C, Redondo-Sánchez D, Huerta JM, Tjønneland A, Pokharel P, Jakszyn P, Tumino R, Ardanaz E, Sandanger TM, Winkvist A, Hultdin J, Schulze MB, Weiderpass E, Gunter MJ, Huybrechts I, Scalbert A. Determinants of blood acylcarnitine concentrations in healthy individuals of the European Prospective Investigation into Cancer and Nutrition. Clin Nutr 2022; 41:1735-1745. [PMID: 35779425 PMCID: PMC9358353 DOI: 10.1016/j.clnu.2022.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 04/07/2022] [Accepted: 05/28/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND & AIMS Circulating levels of acylcarnitines (ACs) have been associated with the risk of various diseases such as cancer and type 2 diabetes. Diet and lifestyle factors have been shown to influence AC concentrations but a better understanding of their biological, lifestyle and metabolic determinants is needed. METHODS Circulating ACs were measured in blood by targeted (15 ACs) and untargeted metabolomics (50 ACs) in 7770 and 395 healthy participants of the European Prospective Investigation into Cancer and Nutrition (EPIC), respectively. Associations with biological and lifestyle characteristics, dietary patterns, self-reported intake of individual foods, estimated intake of carnitine and fatty acids, and fatty acids in plasma phospholipid fraction and amino acids in blood were assessed. RESULTS Age, sex and fasting status were associated with the largest proportion of AC variability (partial-r up to 0.19, 0.18 and 0.16, respectively). Some AC species of medium or long-chain fatty acid moiety were associated with the corresponding fatty acids in plasma (partial-r = 0.24) or with intake of specific foods such as dairy foods containing the same fatty acid. ACs of short-chain fatty acid moiety (propionylcarnitine and valerylcarnitine) were moderately associated with concentrations of branched-chain amino acids (partial-r = 0.5). Intake of most other foods and of carnitine showed little association with AC levels. CONCLUSIONS Our results show that determinants of ACs in blood vary according to their fatty acid moiety, and that their concentrations are related to age, sex, diet, and fasting status. Knowledge on their potential determinants may help interpret associations of ACs with disease risk and inform on potential dietary and lifestyle factors that might be modified for disease prevention.
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Affiliation(s)
- Roland Wedekind
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France.
| | - Joseph A Rothwell
- (CESP), Faculté de Medicine, Université Paris-Saclay, Inserm, Villejuif, France; Institut Gustave Roussy, Villejuif, France
| | - Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France
| | - Julie A Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, UK
| | - Veronique Chajes
- International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France
| | - Vna Katzke
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Theron Johnson
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Vittorio Krogh
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori di Milano, Milan, Italy
| | - Pilar Amiano
- Ministry of Health of the Basque Government, Sub Directorate for Public Health and Addictions of Gipuzkoa, San Sebastian, Spain; Biodonostia Health Research Institute, Epidemiology of Chronic and Communicable Diseases Group, San Sebastián, Spain; Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Città Della Salute e Della Scienza University-Hospital, Via Santena 7, 10126 Turin, Italy
| | - Daniel Redondo-Sánchez
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; Escuela Andaluza de Salud Pública (EASP), 18011 Granada, Spain; Instituto de Investigación Biosanitaria Ibs.GRANADA, 18012 Granada, Spain
| | - José María Huerta
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Copenhagen, Denmark; Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Pratik Pokharel
- Danish Cancer Society Research Center, Copenhagen, Denmark; Institute for Nutrition Research, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Paula Jakszyn
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology (ICO-IDIBELL), Barcelona, Spain; Blanquerna School of Health Sciences, Ramon Llull University, Barcelona, Spain
| | - Rosario Tumino
- Hyblean Association for Epidemiological Research AIRE - ONLUS, Ragusa, Italy
| | - Eva Ardanaz
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; Navarra Public Health Institute, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Torkjel M Sandanger
- Department of Community Medicine, UiT - the Arctic University of Norway, Langnes, Tromsø, Norway
| | - Anna Winkvist
- Sustainable Health, Dept Epidemiology and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Johan Hultdin
- Medical Biosciences, Clinical Chemistry, Umeå University, Umeå, Sweden
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; University of Potsdam, Institute of Nutritional Science, Potsdam, Germany
| | - Elisabete Weiderpass
- International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France
| | - Inge Huybrechts
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France
| | - Augustin Scalbert
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France
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13
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A Two-Step Data Normalization Approach for Improving Classification Accuracy in the Medical Diagnosis Domain. MATHEMATICS 2022. [DOI: 10.3390/math10111942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Data normalization is a data preprocessing task and one of the first to be performed during intellectual analysis, particularly in the case of tabular data. The importance of its implementation is determined by the need to reduce the sensitivity of the artificial intelligence model to the values of the features in the dataset to increase the studied model’s adequacy. This paper focuses on the problem of effectively preprocessing data to improve the accuracy of intellectual analysis in the case of performing medical diagnostic tasks. We developed a new two-step method for data normalization of numerical medical datasets. It is based on the possibility of considering both the interdependencies between the features of each observation from the dataset and their absolute values to improve the accuracy when performing medical data mining tasks. We describe and substantiate each step of the algorithmic implementation of the method. We also visualize the results of the proposed method. The proposed method was modeled using six different machine learning methods based on decision trees when performing binary and multiclass classification tasks. We used six real-world, freely available medical datasets with different numbers of vectors, attributes, and classes to conduct experiments. A comparison between the effectiveness of the developed method and that of five existing data normalization methods was carried out. It was experimentally established that the developed method increases the accuracy of the Decision Tree and Extra Trees Classifier by 1–5% in the case of performing the binary classification task and the accuracy of the Bagging, Decision Tree, and Extra Trees Classifier by 1–6% in the case of performing the multiclass classification task. Increasing the accuracy of these classifiers only by using the new data normalization method satisfies all the prerequisites for its application in practice when performing various medical data mining tasks.
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14
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Hughes DA, Taylor K, McBride N, Lee MA, Mason D, Lawlor DA, Timpson NJ, Corbin LJ. metaboprep: an R package for preanalysis data description and processing. Bioinformatics 2022; 38:1980-1987. [PMID: 35134881 PMCID: PMC8963298 DOI: 10.1093/bioinformatics/btac059] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 12/10/2021] [Accepted: 01/28/2022] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION Metabolomics is an increasingly common part of health research and there is need for preanalytical data processing. Researchers typically need to characterize the data and to exclude errors within the context of the intended analysis. Whilst some preprocessing steps are common, there is currently a lack of standardization and reporting transparency for these procedures. RESULTS Here, we introduce metaboprep, a standardized data processing workflow to extract and characterize high quality metabolomics datasets. The package extracts data from preformed worksheets, provides summary statistics and enables the user to select samples and metabolites for their analysis based on a set of quality metrics. A report summarizing quality metrics and the influence of available batch variables on the data are generated for the purpose of open disclosure. Where possible, we provide users flexibility in defining their own selection thresholds. AVAILABILITY AND IMPLEMENTATION metaboprep is an open-source R package available at https://github.com/MRCIEU/metaboprep. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David A Hughes
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
| | - Kurt Taylor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
| | - Nancy McBride
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol BS8 1TH, UK
| | - Matthew A Lee
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford BD9 6RJ, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol BS8 1TH, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
| | - Laura J Corbin
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
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15
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His M, Viallon V, Dossus L, Schmidt JA, Travis RC, Gunter MJ, Overvad K, Kyrø C, Tjønneland A, Lécuyer L, Rothwell JA, Severi G, Johnson T, Katzke V, Schulze MB, Masala G, Sieri S, Panico S, Tumino R, Macciotta A, Boer JMA, Monninkhof EM, Olsen KS, Nøst TH, Sandanger TM, Agudo A, Sánchez MJ, Amiano P, Colorado-Yohar SM, Ardanaz E, Vidman L, Winkvist A, Heath AK, Weiderpass E, Huybrechts I, Rinaldi S. Lifestyle correlates of eight breast cancer-related metabolites: a cross-sectional study within the EPIC cohort. BMC Med 2021; 19:312. [PMID: 34886862 PMCID: PMC8662901 DOI: 10.1186/s12916-021-02183-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 11/09/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Metabolomics is a promising molecular tool for identifying novel etiological pathways leading to cancer. In an earlier prospective study among pre- and postmenopausal women not using exogenous hormones, we observed a higher risk of breast cancer associated with higher blood concentrations of one metabolite (acetylcarnitine) and a lower risk associated with higher blood concentrations of seven others (arginine, asparagine, phosphatidylcholines (PCs) aa C36:3, ae C34:2, ae C36:2, ae C36:3, and ae C38:2). METHODS To identify determinants of these breast cancer-related metabolites, we conducted a cross-sectional analysis to identify their lifestyle and anthropometric correlates in 2358 women, who were previously included as controls in case-control studies nested within the European Prospective Investigation into Cancer and Nutrition cohort and not using exogenous hormones at blood collection. Associations of each metabolite concentration with 42 variables were assessed using linear regression models in a discovery set of 1572 participants. Significant associations were evaluated in a validation set (n = 786). RESULTS For the metabolites previously associated with a lower risk of breast cancer, concentrations of PCs ae C34:2, C36:2, C36:3, and C38:2 were negatively associated with adiposity and positively associated with total and saturated fat intakes. PC ae C36:2 was also negatively associated with alcohol consumption and positively associated with two scores reflecting adherence to a healthy lifestyle. Asparagine concentration was negatively associated with adiposity. Arginine and PC aa C36:3 concentrations were not associated to any of the factors examined. For the metabolite previously associated with a higher risk of breast cancer, acetylcarnitine, a positive association with age was observed. CONCLUSIONS These associations may indicate possible mechanisms underlying associations between lifestyle and anthropometric factors, and risk of breast cancer. Further research is needed to identify potential non-lifestyle correlates of the metabolites investigated.
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Affiliation(s)
- Mathilde His
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 150 cours Albert Thomas, 69372, CEDEX 08, Lyon, France
| | - Vivian Viallon
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 150 cours Albert Thomas, 69372, CEDEX 08, Lyon, France
| | - Laure Dossus
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 150 cours Albert Thomas, 69372, CEDEX 08, Lyon, France
| | - Julie A Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Marc J Gunter
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 150 cours Albert Thomas, 69372, CEDEX 08, Lyon, France
| | - Kim Overvad
- Department of Public Health, Aarhus University, Aarhus, Denmark
| | - Cecilie Kyrø
- Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Copenhagen, Denmark
- Department of Public Health, Section of Environmental Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lucie Lécuyer
- Université Paris-Saclay, UVSQ, Inserm, CESP U1018, "Exposome and Heredity" team, Gustave Roussy, Villejuif, France
| | - Joseph A Rothwell
- Université Paris-Saclay, UVSQ, Inserm, CESP U1018, "Exposome and Heredity" team, Gustave Roussy, Villejuif, France
| | - Gianluca Severi
- Université Paris-Saclay, UVSQ, Inserm, CESP U1018, "Exposome and Heredity" team, Gustave Roussy, Villejuif, France
- Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Florence, Italy
| | - Theron Johnson
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Verena Katzke
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition, Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Potsdam, Germany
| | - Giovanna Masala
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy
| | - Sabina Sieri
- Epidemiology and Prevention Unit, Fondazione IRCCS Instituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Salvatore Panico
- Dipartimento Di Medicina Clinica E Chirurgia, Federico Ii University, Naples, Italy
| | - Rosario Tumino
- Cancer Registry and Histopathology Department, Provincial Health Authority (ASP 7) Ragusa, Ragusa, Italy
| | - Alessandra Macciotta
- Department of Clinical and Biological Sciences, University of Turin, Turin, Italy
| | - Jolanda M A Boer
- Center for Nutrition, Prevention, and Health Services, National Institute for Public Health and the Environment (RIVM), Bilthoven, 3720, BA, the Netherlands
| | - Evelyn M Monninkhof
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Karina Standahl Olsen
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, NO-9037, Tromsø, Norway
| | - Therese H Nøst
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, NO-9037, Tromsø, Norway
| | - Torkjel M Sandanger
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, NO-9037, Tromsø, Norway
| | - Antonio Agudo
- Unit of Nutrition and Cancer, Catalan Institute of Oncology - ICO, L'Hospitalet de Llobregat, Spain
- Nutrition and Cancer Group; Epidemiology, Public Health, Cancer Prevention and Palliative Care Program; Bellvitge Biomedical Research Institute - IDIBELL, L'Hospitalet de Llobregat, Spain
| | - Maria-Jose Sánchez
- Escuela Andaluza de Salud Pública (EASP), Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain
| | - Pilar Amiano
- Ministry of Health of the Basque Government, Sub-Directorate for Public Health and Addictions of Gipuzkoa, San Sebastián, Spain
- Biodonostia Health Research Institute, Group of Epidemiology of Chronic and Communicable Diseases, San Sebastián, Spain
- CIBER Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Sandra M Colorado-Yohar
- CIBER Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain
- Research Group on Demography and Health, National Faculty of Public Health, University of Antioquia, Medellín, Colombia
| | - Eva Ardanaz
- CIBER Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Navarra Public Health Institute, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Linda Vidman
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Anna Winkvist
- Sustainable Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Alicia K Heath
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Elisabete Weiderpass
- International Agency for Research on Cancer (IARC/WHO), Office of the Director, Lyon, France
| | - Inge Huybrechts
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 150 cours Albert Thomas, 69372, CEDEX 08, Lyon, France
| | - Sabina Rinaldi
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 150 cours Albert Thomas, 69372, CEDEX 08, Lyon, France.
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