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Harewood R, Rothwell JA, Bešević J, Viallon V, Achaintre D, Gicquiau A, Rinaldi S, Wedekind R, Prehn C, Adamski J, Schmidt JA, Jacobs I, Tjønneland A, Olsen A, Severi G, Kaaks R, Katzke V, Schulze MB, Prada M, Masala G, Agnoli C, Panico S, Sacerdote C, Jakszyn PG, Sánchez MJ, Castilla J, Chirlaque MD, Atxega AA, van Guelpen B, Heath AK, Papier K, Tong TYN, Summers SA, Playdon M, Cross AJ, Keski-Rahkonen P, Chajès V, Murphy N, Gunter MJ. Association between pre-diagnostic circulating lipid metabolites and colorectal cancer risk: a nested case-control study in the European Prospective Investigation into Cancer and Nutrition (EPIC). EBioMedicine 2024; 101:105024. [PMID: 38412638 PMCID: PMC10907191 DOI: 10.1016/j.ebiom.2024.105024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/26/2024] [Accepted: 02/05/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Altered lipid metabolism is a hallmark of cancer development. However, the role of specific lipid metabolites in colorectal cancer development is uncertain. METHODS In a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC), we examined associations between pre-diagnostic circulating concentrations of 97 lipid metabolites (acylcarnitines, glycerophospholipids and sphingolipids) and colorectal cancer risk. Circulating lipids were measured using targeted mass spectrometry in 1591 incident colorectal cancer cases (55% women) and 1591 matched controls. Multivariable conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between concentrations of individual lipid metabolites and metabolite patterns with colorectal cancer risk. FINDINGS Of the 97 assayed lipids, 24 were inversely associated (nominally p < 0.05) with colorectal cancer risk. Hydroxysphingomyelin (SM (OH)) C22:2 (ORper doubling 0.60, 95% CI 0.47-0.77) and acylakyl-phosphatidylcholine (PC ae) C34:3 (ORper doubling 0.71, 95% CI 0.59-0.87) remained associated after multiple comparisons correction. These associations were unaltered after excluding the first 5 years of follow-up after blood collection and were consistent according to sex, age at diagnosis, BMI, and colorectal subsite. Two lipid patterns, one including 26 phosphatidylcholines and all sphingolipids, and another 30 phosphatidylcholines, were weakly inversely associated with colorectal cancer. INTERPRETATION Elevated pre-diagnostic circulating levels of SM (OH) C22:2 and PC ae C34:3 and lipid patterns including phosphatidylcholines and sphingolipids were associated with lower colorectal cancer risk. This study may provide insight into potential links between specific lipids and colorectal cancer development. Additional prospective studies are needed to validate the observed associations. FUNDING World Cancer Research Fund (reference: 2013/1002); European Commission (FP7: BBMRI-LPC; reference: 313010).
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Affiliation(s)
- Rhea Harewood
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France.
| | - Joseph A Rothwell
- Centre for Epidemiology and Population Health (U1018), Exposome and Heredity Team, Faculté de Médecine, Université Paris-Saclay, UVSQ, INSERM, Gustave Roussy, F-94805, Villejuif, France
| | - Jelena Bešević
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Vivian Viallon
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - David Achaintre
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France; School of Plant Sciences and Food Security, Faculty of Biology, Tel-Aviv University, Tel Aviv-Yafo, Israel
| | - Audrey Gicquiau
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Sabina Rinaldi
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Roland Wedekind
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, 85764, Neuherberg, Germany
| | - Jerzy Adamski
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597; Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany; Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia
| | - Julie A Schmidt
- Department of Clinical Medicine, Department of Clinical Epidemiology, Aarhus University and Aarhus University Hospital, Olof Palmes Allé 43-45, 8200 Aarhus N, Denmark
| | - Inarie Jacobs
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Diet, Cancer and Health, Strandboulevarden 49, DK-2100, Copenhagen, Denmark; Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Anja Olsen
- Danish Cancer Society Research Center, Diet, Cancer and Health, Strandboulevarden 49, DK-2100, Copenhagen, Denmark; The Department of Public Health, University of Aarhus, Aarhus, Denmark
| | - Gianluca Severi
- Centre for Epidemiology and Population Health (U1018), Exposome and Heredity Team, Faculté de Médecine, Université Paris-Saclay, UVSQ, INSERM, Gustave Roussy, F-94805, Villejuif, France; Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Florence, Italy
| | - Rudolf Kaaks
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany
| | - Verena Katzke
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558, Nuthetal, Germany; Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Marcela Prada
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558, Nuthetal, Germany
| | - Giovanna Masala
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy
| | - Claudia Agnoli
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian, 1, 20133, Milan, Italy
| | - Salvatore Panico
- Dipartimento Di Medicina Clinica E Chirurgia Federico Ii University, Naples, Italy
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO), Via Santena 7, 10126, Turin, Italy
| | - Paula Gabriela 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
| | - 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
| | - Jesús Castilla
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain; Instituto de Salud Pública de Navarra - IdiSNA, Pamplona, 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, Murcia, Spain
| | - Amaia Aizpurua Atxega
- 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
| | - Bethany van Guelpen
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden; Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Alicia K Heath
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Keren Papier
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Tammy Y N Tong
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Scott A Summers
- Department of Nutrition and Integrative Physiology and the Diabetes and Metabolism Research Center, University of Utah, Salt Lake City, Utah, USA
| | - Mary Playdon
- Department of Nutrition and Integrative Physiology and the Diabetes and Metabolism Research Center, University of Utah, Salt Lake City, Utah, USA; Cancer Control and Population Sciences, Huntsman Cancer Institute, Salt Lake City, Utah, USA
| | - Amanda J Cross
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Pekka Keski-Rahkonen
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Véronique Chajès
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Neil Murphy
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Marc J Gunter
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
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Chen YL, Su CC, Chang CH, James K, Chen MY. High Prevalence of Cardiometabolic Risks and Health Needs Among Patients With Hepatocellular Carcinoma After Treatment: A Cross-sectional Study. Cancer Nurs 2024:00002820-990000000-00217. [PMID: 38417128 DOI: 10.1097/ncc.0000000000001326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
BACKGROUND Many studies have indicated that inadequate health-related behavior is associated with the progression of cancer and cardiometabolic disorders. Because patients with hepatocellular carcinoma may adopt inadequate health behaviors, they are at risk for cancer recurrence, mortality, cardiometabolic disease progression, and worse quality of life. OBJECTIVE To explore the prevalence of cardiometabolic risks and health-promoting behaviors associated with quality of life among patients with hepatocellular carcinoma after treatment. METHODS A cross-sectional study was conducted from October 2021 to August 2022. Data were collected through face-to-face interviews using a structured questionnaire, and cardiometabolic information was recorded from medical charts. RESULTS In total, 115 patients with liver cancer after treatment were enrolled; 73% were male. The mean age was 67.7 years, and high prevalence rates of central obesity (72.2%) and metabolic syndrome (47%) were noted. Inadequate exercise, oral hygiene, and cigarette smoking were common. The health-promoting score was significantly associated with exercise, the number of remaining teeth, and metabolic syndrome, which correlated with quality of life, including positive functional and negative symptoms dimensions. CONCLUSIONS The findings demonstrated a high prevalence of cardiometabolic risks and inadequate health-related behaviors among patients with liver cancer after treatment. This study highlights the need for healthcare providers to help patients increase health literacy for preventing cardiometabolic risks at outpatient clinics. IMPLICATIONS FOR PRACTICE Findings from this study can be used to advise healthcare providers to educate patients with liver cancer during and after treatment on improving their health-promoting behaviors.
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Affiliation(s)
- Yi-Lin Chen
- Author Affiliations: Departments of Nursing (Ms Chen) and Internal Medicine (Dr Su), Chiayi Christian Hospital; and Department of Nursing, Chang Gung University of Science and Technology (Drs Chang and Chen), Chiayi, Taiwan; Department of Nursing, University of San Diego, California (Dr James); and Department of Cardiology, Chang Gung Memorial Hospital, Chiayi, Taiwan (Dr Chen); School of Nursing, Chang Gung University, Taoyuan, Taiwan (Dr Chen)
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His M, Gunter MJ, Keski-Rahkonen P, Rinaldi S. Application of Metabolomics to Epidemiologic Studies of Breast Cancer: New Perspectives for Etiology and Prevention. J Clin Oncol 2024; 42:103-115. [PMID: 37944067 DOI: 10.1200/jco.22.02754] [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: 12/08/2022] [Revised: 07/24/2023] [Accepted: 09/11/2023] [Indexed: 11/12/2023] Open
Abstract
PURPOSE To provide an overview on how the application of metabolomics (high-throughput characterization of metabolites from cells, organs, tissues, or biofluids) to population-based studies may inform our understanding of breast cancer etiology. METHODS We evaluated studies that applied metabolomic analyses to prediagnostic blood samples from prospective epidemiologic studies to identify circulating metabolites associated with breast cancer risk, overall and by breast cancer subtype and menopausal status. We provide some important considerations for the application and interpretation of metabolomics approaches in this context. RESULTS Overall, specific lipids and amino acids were indicated as the most common metabolite classes associated with breast cancer development. However, comparison of results across studies is challenging because of heterogeneity in laboratory techniques, analytical methods, sample size, and applied statistical methods. CONCLUSION Metabolomics is being increasingly applied to population-based studies for the identification of new etiologic hypotheses and/or mechanisms related to breast cancer development. Despite its success in applications to epidemiology, studies of larger sample size with detailed information on menopausal status, breast cancer subtypes, and repeated biologic samples collected over time are needed to improve comparison of results between studies and enhance validation of results, allowing potential clinical translation of findings.
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Affiliation(s)
- Mathilde His
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
- Prevention Cancer Environment Department, Centre Léon Bérard, Lyon, France
- Inserm, U1296 Unit, "Radiation: Defense, Health and Environment", Centre Léon Bérard, Lyon, France
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
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Cao B, Yang S, Yan L, Li N. Comprehensive serum lipidomic analyses reveal potential biomarkers for malignant breast cancer: A case-control study. Cancer Biomark 2023; 37:289-297. [PMID: 37302027 DOI: 10.3233/cbm-220462] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
BACKGROUND Breast cancer is the most worldwide commonly found malignancy among women. The evidence for lipidomic studies of breast cancer in the Chinese population is relatively limited. OBJECTIVE Our current study aimed to identify peripheral lipids capable of distinguishing adults with and without malignant breast cancer in a Chinese population and to explore the potential lipid metabolism pathways implicated in breast cancer. METHODS Lipidomics was performed with an Ultimate 3000 UHPLC system coupled with a Q-Exactive HF MS platform by using the serum of 71 female patients with malignant breast cancer and 92 age-matched (± 2 years) healthy women. The data were uploaded to and processed by the specialized online software Metaboanalyst 5.0. Both univariate and multivariate analyses were carried out for potential biomarker screening. Areas under the receiver-operating characteristic (ROC) curves (AUCs) of identified differential lipids were obtained for evaluating their classification capacity. RESULTS A total of 47 significantly different lipids were identified by applying the following criteria: false discovery rate-adjusted P < 0.05, variable importance in projection ⩾ 1.0, and fold change ⩾ 2.0 or ⩽ 0.5. Among them, 13 lipids were identified as diagnostic biomarkers with the area under curve (AUC) greater than 0.7. Multivariate ROC curves indicated that AUCs greater than 0.8 could be achieved with 2-47 lipids. CONCLUSIONS Using an untargeted LC-MS-based metabolic profiling approach, our study provides preliminary evidence that extensive dysregulations of OxPCs, PCs, SMs and TAGs were involved in the pathological processes of breast cancer. We provided clues for furtherly investigating the role of lipid alterations in the pathoetiology of breast cancer.
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Affiliation(s)
- Bing Cao
- Key Laboratory of Cognition and Personality (SWU), Faculty of Psychology, Ministry of Education, Southwest University, Chongqing, China
| | - Siyu Yang
- Tianjin Centers for Disease Control and Prevention, Tianjin, China
| | - Lailai Yan
- Department of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, China
- Vaccine Research Center, School of Public Health, Peking University, Beijing, China
| | - Nan Li
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
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McKenzie ND, Ahmad S. Impact of Lifestyle Interventions on Gynecologic Cancers: Beyond Diet and Exercise. Am J Lifestyle Med 2022. [DOI: 10.1177/15598276221123764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
A Lifestyle Medicine approach to compliment cancer care is less commonly researched or implemented for women with gynecologic cancers as compared to better funded malignancies such as breast, prostate, and colorectal. Yet, several gynecologic malignancies are linked to obesity, estrogen/metabolic signaling pathways, and altered tumor microenvironment which could benefit greatly from a lifestyle medicine program. Lifestyle medicine, an evidenced-based branch of science, has expanded to the prevention and treatment of disorders caused by lifestyle factors (including cancer). Modifiable lifestyle factors such as obesity, lack of physical activity/nutrient density, microbial dysbiosis, sleep disturbance, and chronic stressors contribute greatly to cancer morbidity and mortality worldwide. This overarching area of research is evolving with some subtopics in their infancy requiring further investigation. Modern tools have allowed for better understanding of mechanisms by which adiposity and inactivity affect tumor promoting signaling pathways as well as the local tumor environment. Through the evolving use of these sophisticated techniques, novel prognostic biomarkers have emerged to explore efficacy of pharmacologic and lifestyle interventions in cancer. This state-of-the-art review article appraises recent evidence for a lifestyle medicine approach, beyond diet and exercise, to optimize survivorship and quality of life for patients with gynecologic cancers and introduces the 8-week web-based comprehensive HEAL-GYN program.
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Affiliation(s)
| | - Sarfraz Ahmad
- Gynecologic Oncology Program, AdventHealth Cancer Institute, Orlando, FL, USA
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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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Lu C, Liu C, Mei D, Yu M, Bai J, Bao X, Wang M, Fu K, Yi X, Ge W, Shen J, Peng Y, Xu W. Comprehensive metabolomic characterization of atrial fibrillation. Front Cardiovasc Med 2022; 9:911845. [PMID: 36003904 PMCID: PMC9393302 DOI: 10.3389/fcvm.2022.911845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 07/12/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundUsing human humoral metabolomic profiling, we can discover the diagnostic biomarkers and pathogenesis of disease. The specific characterization of atrial fibrillation (AF) subtypes with metabolomics may facilitate effective and targeted treatment, especially in early stages.ObjectivesBy investigating disturbed metabolic pathways, we could evaluate the diagnostic value of biomarkers based on metabolomics for different types of AF.MethodsA cohort of 363 patients was enrolled and divided into a discovery and validation set. Patients underwent an electrocardiogram (ECG) for suspected AF. Groups were divided as follows: healthy individuals (Control), suspected AF (Sus-AF), first diagnosed AF (Fir-AF), paroxysmal AF (Par-AF), persistent AF (Per-AF), and AF causing a cardiogenic ischemic stroke (Car-AF). Serum metabolomic profiles were determined by gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Metabolomic variables were analyzed with clinical information to identify relevant diagnostic biomarkers.ResultsThe metabolic disorders were characterized by 16 cross-comparisons. We focused on comparing all of the types of AF (All-AFs) plus Car-AF vs. Control, All-AFs vs. Car-AF, Par-AF vs. Control, and Par-AF vs. Per-AF. Then, 117 and 94 metabolites were identified by GC/MS and LC-QTOF-MS, respectively. The essential altered metabolic pathways during AF progression included D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism, etc. For differential diagnosis, the area under the curve (AUC) of specific metabolomic biomarkers ranged from 0.8237 to 0.9890 during the discovery phase, and the predictive values in the validation cohort were 78.8–90.2%.ConclusionsSerum metabolomics is a powerful way to identify metabolic disturbances. Differences in small–molecule metabolites may serve as biomarkers for AF onset, progression, and differential diagnosis.
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