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Zisis V, Papadopoulos P, Kyriakou N, Charisi C, Poulopoulos A. The Occurrence of Etoricoxib-Induced Stevens-Johnson Syndrome With Oral Manifestations in a Female Patient: A Case Study. Cureus 2024; 16:e63353. [PMID: 39077250 PMCID: PMC11283930 DOI: 10.7759/cureus.63353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2024] [Indexed: 07/31/2024] Open
Abstract
Stevens-Johnson Syndrome (SJS) constitutes a rather uncommon, and rarely fatal hypersensitivity reaction that primarily impacts the skin and mucous membranes and in certain cases may be attributed to drug administration. The aim of this article is to present a case of etoricoxib-induced SJS in a 46-year-old, female patient. The patient presented herself, as a medical emergency, to the Department of Oral Medicine/Pathology, School of Dentistry, Aristotle University of Thessaloniki, Greece, reporting pain, especially acute pain while eating certain foods, discomfort, dysphagia, and a wound in the left half of the hard palate. The clinical examination revealed a broad ulcer, in the left half of the hard palate as well as multiple ulcerations and erosions in the upper and lower lip. Her medical history was clear; however, the patient mentioned to have received etoricoxib, due to severe back pain, one day prior to our clinical examination. The patient received methylprednisolone 16 mg, twice per day, for two days, followed by methylprednisolone 8 mg, twice per day, for two more days. Her symptoms resigned and since the connection between etoricoxib and SJS was established, the patient was advised to avoid etoricoxib and be wary of adverse effects, when taking drugs especially non-steroidal anti-inflammatory medication. This is one of the first case reports in the literature, linking etoricoxib administration with the emergence of SJS, highlighting the importance of pharmacovigilance. The up-to-date registration of drug-induced adverse effects is of immense importance to protect future patients. SJS does not have a defined treatment strategy. Therefore, most patients are given supportive care and symptomatic treatment, which most commonly involves corticosteroids and antivirals such as acyclovir.
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Affiliation(s)
- Vasileios Zisis
- Oral Medicine/Pathology, Aristotle University of Thessaloniki, Thessaloniki, GRC
| | - Petros Papadopoulos
- Oral Medicine/Pathology, Aristotle University of Thessaloniki, Thessaloniki, GRC
| | - Nikolaos Kyriakou
- Hospital Dentistry and Oral Medicine/Pathology, Aristotle University of Thessaloniki, Thessaloniki, GRC
| | - Christina Charisi
- Pediatric Dentistry and Oral Medicine/Pathology, Aristotle University of Thessaloniki, Thessaloniki, GRC
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Wu P, Rybin D, Bielak LF, Feitosa MF, Franceschini N, Li Y, Lu Y, Marten J, Musani SK, Noordam R, Raghavan S, Rose LM, Schwander K, Smith AV, Tajuddin SM, Vojinovic D, Amin N, Arnett DK, Bottinger EP, Demirkan A, Florez JC, Ghanbari M, Harris TB, Launer LJ, Liu J, Liu J, Mook-Kanamori DO, Murray AD, Nalls MA, Peyser PA, Uitterlinden AG, Voortman T, Bouchard C, Chasman D, Correa A, de Mutsert R, Evans MK, Gudnason V, Hayward C, Kao L, Kardia SLR, Kooperberg C, Loos RJF, Province MM, Rankinen T, Redline S, Ridker PM, Rotter JI, Siscovick D, Smith BH, van Duijn C, Zonderman AB, Rao DC, Wilson JG, Dupuis J, Meigs JB, Liu CT, Vassy JL. Smoking-by-genotype interaction in type 2 diabetes risk and fasting glucose. PLoS One 2020; 15:e0230815. [PMID: 32379818 PMCID: PMC7205201 DOI: 10.1371/journal.pone.0230815] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 03/09/2020] [Indexed: 02/07/2023] Open
Abstract
Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p<1x10-7 (adjusted for multiple hypothesis-testing with the IBC array). Two SNPs had significant joint effects in the overall model and significant main effects only in one smoking stratum: rs140637 (FBN1) in AA individuals had a significant main effect only among smokers, and rs1444261 (closest gene C2orf63) in EA individuals had a significant main effect only among nonsmokers. Three additional SNPs were identified as having potential interaction by exhibiting a significant main effects only in smokers: rs1801232 (CUBN) in AA individuals, rs12243326 (TCF7L2) in EA individuals, and rs4132670 (TCF7L2) in EA individuals. No SNP met significance for potential interaction with smoking on baseline FG. The identification of these loci provides evidence for genetic interactions with smoking exposure that may explain some of the heterogeneity in the association between smoking and T2D.
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Affiliation(s)
- Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, MA, United States of America
| | - Denis Rybin
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, MA, United States of America
| | - Lawrence F. Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States of America
| | - Mary F. Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Nora Franceschini
- University of North Carolina, Chapel Hill, NC, United States of America
| | - Yize Li
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Yingchang Lu
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Jonathan Marten
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Solomon K. Musani
- Jackson Heart Study, University of Mississippi Medical Center, MS, United States of America
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Sridharan Raghavan
- Section of Hospital Medicine, Veterans Affairs Eastern Colorado Healthcare System, Denver, CO, United States of America
- Division of General Internal Medicine, University of Colorado School of Medicine, Aurora, CO, United States of America
- Colorado Cardiovascular Outcomes Research Consortium, Aurora, CO, United States of America
| | - Lynda M. Rose
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
| | - Karen Schwander
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Albert V. Smith
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Salman M. Tajuddin
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Dina Vojinovic
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Najaf Amin
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Donna K. Arnett
- Dean's Office, University of Kentucky College of Public Health, Lexington, Kentucky, United States of America
| | - Erwin P. Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Ayse Demirkan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Jose C. Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Massachusetts General Hospital, Boston, MA, United States of America
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, United States of America
- Department of Medicine, Harvard Medical School, Boston, MA, United States of America
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Tamara B. Harris
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD, United States of America
| | - Lenore J. Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD, United States of America
| | - Jingmin Liu
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
| | - Jun Liu
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Dennis O. Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Alison D. Murray
- The Institute of Medical Sciences, Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, United Kingdom
| | - Mike A. Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States of America
- Data Tecnica International LLC, Glen Echo, MD, United States of America
| | - Patricia A. Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States of America
| | - André G. Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Trudy Voortman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, United States of America
| | - Daniel Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, United States of America
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Michele K. Evans
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- University of Iceland, Reykjavik, Iceland
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Linda Kao
- Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, United States of America
- Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, MD, United States of America
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States of America
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States of America
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Michael M. Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Tuomo Rankinen
- Human Genomics Laboratory, Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, United States of America
| | - Susan Redline
- Harvard Medical School, Boston, MA, United States of America
- Departments of Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
- Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Paul M. Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - David Siscovick
- The New York Academy of Medicine, New York, NY, United States of America
| | - Blair H. Smith
- Division of Population Health and Genomics, University of Dundee, Dundee, United Kingdom
| | - Cornelia van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Alan B. Zonderman
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - D. C. Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States of America
| | - James G. Wilson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, United States of America
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, MA, United States of America
- The National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA, United States of America
| | - James B. Meigs
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, United States of America
- Division of General Internal Medicine Division, Massachusetts General Hospital, Boston, MA, United States of America
- Department of Medicine, Harvard Medical School, Boston, MA, United States of America
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, MA, United States of America
| | - Jason L. Vassy
- Department of Medicine, Harvard Medical School, Boston, MA, United States of America
- VA Boston Healthcare System, Boston, MA, United States of America
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Huang T, Wang T, Heianza Y, Sun D, Ivey K, Durst R, Schwarzfuchs D, Stampfer MJ, Bray GA, Sacks FM, Shai I, Qi L. HNF1A variant, energy-reduced diets and insulin resistance improvement during weight loss: The POUNDS Lost trial and DIRECT. Diabetes Obes Metab 2018; 20:1445-1452. [PMID: 29424957 DOI: 10.1111/dom.13250] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 01/27/2018] [Accepted: 02/05/2018] [Indexed: 11/30/2022]
Abstract
AIM To determine whether weight-loss diets varying in macronutrients modulate the genetic effect of hepatocyte nuclear factor 1α (HNF1A) rs7957197 on weight loss and improvement of insulin resistance. MATERIALS AND METHODS We analysed the interaction between HNF1A rs7957197 and weight-loss diets with regard to weight loss and insulin resistance improvement among 722 overweight/obese adults from a 2-year randomized weight-loss trial, the POUNDS Lost trial. The findings were replicated in another independent 2-year weight-loss trial, the Dietary Intervention Randomized Controlled Trial (DIRECT), in 280 overweight/obese adults. RESULTS In the POUNDS Lost trial, we found that a high-fat diet significantly modified the genetic effect of HNF1A on weight loss and reduction in waist circumference (P for interaction = .006 and .005, respectively). Borderline significant interactions for fasting insulin and insulin resistance (P for interaction = .07 and .06, respectively) were observed. We replicated the results in DIRECT. Pooled results showed similar significant interactions with weight loss, waist circumference reduction, and improvement in fasting insulin and insulin resistance (P values for interaction = .001, .005, .02 and .03, respectively). Greater decreases in weight, waist circumference, fasting insulin level and insulin resistance were observed in participants with the T allele compared to those without the T allele in the high-fat diet group (P = .04, .03 and .01, respectively). CONCLUSIONS Our replicable findings provide strong evidence that individuals with the HNF1A rs7957197 T allele might obtain more benefits in weight loss and improvement of insulin resistance by choosing a hypocaloric and high-fat diet.
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Affiliation(s)
- Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisana
| | - Tiange Wang
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisana
- Shanghai Institute of Endocrine and Metabolic Diseases, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yoriko Heianza
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisana
| | - Dianjianyi Sun
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisana
| | - Kerry Ivey
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Ronen Durst
- Cardiology Department, Hadassah Hebrew University Medical Center, Jerusalem, Israel
- Center for Research Prevention and Treatment of Atherosclerosis, Internal Medicine Department, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | | | - Meir J Stampfer
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - George A Bray
- Pennington Biomedical Research Center of the Louisiana State University System, Baton Rouge, Lousiana
| | - Frank M Sacks
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Iris Shai
- Department of Public Health, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Lu Qi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisana
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
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Agarwal G, Jiang Y, Rogers Van Katwyk S, Lemieux C, Orpana H, Mao Y, Hanley B, Davis K, Leuschen L, Morrison H. Effectiveness of the CANRISK tool in the identification of dysglycemia in First Nations and Métis in Canada. Health Promot Chronic Dis Prev Can 2018; 38:55-63. [PMID: 29443485 PMCID: PMC5833636 DOI: 10.24095/hpcdp.38.2.02] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
INTRODUCTION First Nations/Métis populations develop diabetes earlier and at higher rates than other Canadians. The Canadian diabetes risk questionnaire (CANRISK) was developed as a diabetes screening tool for Canadians aged 40 years or over. The primary aim of this paper is to assess the effectiveness of the existing CANRISK tool and risk scores in detecting dysglycemia in First Nations/Métis participants, including among those under the age of 40. A secondary aim was to determine whether alternative waist circumference (WC) and body mass index (BMI) cut-off points improved the predictive ability of logistic regression models using CANRISK variables to predict dysglycemia. METHODS Information from a self-administered CANRISK questionnaire, anthropometric measurements, and results of a standard oral glucose tolerance test (OGTT) were collected from First Nations and Métis participants (n = 1479). Sensitivity and specificity of CANRISK scores using published risk score cut-off points were calculated. Logistic regression was conducted with alternative ethnicity-specific BMI and WC cut-off points to predict dysglycemia using CANRISK variables. RESULTS Compared with OGTT results, using a CANRISK score cut-off point of 33, the sensitivity and specificity of CANRISK was 68% and 63% among individuals aged 40 or over; it was 27% and 87%, respectively among those under 40. Using a lower cut-off point of 21, the sensitivity for individuals under 40 improved to 77% with a specificity of 44%. Though specificity at this threshold was low, the higher level of sensitivity reflects the importance of the identification of high risk individuals in this population. Despite altered cut-off points of BMI and WC, logistic regression models demonstrated similar predictive ability. CONCLUSION CANRISK functioned well as a preliminary step for diabetes screening in a broad age range of First Nations and Métis in Canada, with an adjusted CANRISK cutoff point for individuals under 40, and with no incremental improvement from using alternative BMI/WC cut-off points.
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Affiliation(s)
- Gina Agarwal
- Departments of Family Medicine and Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
| | - Ying Jiang
- Public Health Agency of Canada, Ottawa, Ontario, Canada
| | | | - Chantal Lemieux
- Public Health Agency of Canada, Ottawa, Ontario, Canada
- School of Psychology, University of Ottawa, Ottawa, Ontario, Canada
| | - Heather Orpana
- Public Health Agency of Canada, Ottawa, Ontario, Canada
- School of Psychology, University of Ottawa, Ottawa, Ontario, Canada
| | - Yang Mao
- Public Health Agency of Canada, Ottawa, Ontario, Canada
| | - Brandan Hanley
- Yukon Health and Social Services, Whitehorse, Yukon, Canada
| | - Karen Davis
- Saskatoon Health Region, Saskatoon, Saskatchewan, Canada
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