Njira Lugogo1, Cynthia L Green2, Noah Agada3, Siyi Zhang2, Susanne Meghdadpour4, Run Zhou2, Siyun Yang2, Kevin J Anstrom2, Elliot Israel5, Richard Martin6, Robert F Lemanske7, Homer Boushey8, Stephen C Lazarus8, Stephen I Wasserman9, Mario Castro10, William Calhoun11, Stephen P Peters12, Emily DiMango13, Vernon Chinchilli14, Susan Kunselman14, Tonya S King14, Nikolina Icitovic15, Monica Kraft16. 1. Department of Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Mich. Electronic address: nlugogo@umich.edu. 2. Department of Biostatistics and BioInformatics, Duke University, Durham, NC. 3. Division of Pediatric Allergy and Immunology, Department of Pediatric Pulmonology, Riley Children's Hospital, and Eli Lilly and Company, Indianapolis, Ind. 4. Department of Pediatrics, Division of Allergy, Immunology, Pulmonary and Sleep Medicine, Duke University, Durham, NC. 5. Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass. 6. Department of Medicine, National Jewish Health and University of Colorado Denver, Denver, Colo. 7. Department of Pediatrics and Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wis. 8. Department of Medicine, Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, University of California, San Francisco, Calif. 9. Department of Medicine, Division of Rheumatology, Allergy and Immunology, University of California, San Diego, Calif. 10. Department of Medicine, Division of Pulmonary and Critical Care Medicine, Washington University, Saint Louis, Mo. 11. Division of Pulmonary, Critical Care, and Sleep and Division of Allergy and Clinical Immunology, Department of Internal Medicine, University of Texas Medical Branch, Galveston, Tex. 12. Department of Internal Medicine, Section on Pulmonary, Critical Care, Allergy and Immunologic Diseases, Wake Forest University, Winston-Salem, NC. 13. Department of Medicine, Division of Pulmonary Allergy and Critical Care Medicine, Columbia University, New York, NY. 14. Department of Public Health Services, Penn State College of Medicine, Division of Biostatistics, Hershey, Pa. 15. Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY. 16. Department of Medicine, University of Arizona, Tucson, Ariz.
Abstract
BACKGROUND: The use of inflammatory biomarkers to delineate the type of lung inflammation present in asthmatic subjects is increasingly common. However, the effect of obesity on these markers is unknown. OBJECTIVES: We aimed to determine the effect of obesity on conventional markers of inflammation in asthmatic subjects. METHODS: We performed secondary analysis of data from 652 subjects previously enrolled in 2 Asthma Clinical Research Network trials. We performed linear correlations between biomarkers and logistic regression analysis to determine the predictive value of IgE levels, blood eosinophil counts, and fraction of exhaled nitric oxide values in relationship to sputum eosinophil counts (>2%), as well as to determine whether cut points existed that would maximize the sensitivity and specificity for predicting sputum eosinophilia in the 3 weight groups. RESULTS: Overall, statistically significant but relatively weak correlations were observed among all 4 markers of inflammation. Within obese subjects, the only significant correlation found was between IgE levels and blood eosinophil counts (r = 0.33, P < .001); furthermore, all other correlations between inflammatory markers were approximately 0, including correlations with sputum eosinophil counts. In addition, the predictive value of each biomarker alone or in combination was poor in obese subjects. In fact, in obese subjects none of the biomarkers of inflammation significantly predicted the presence of high sputum eosinophil counts. Obese asthmatic subjects have lower cut points for IgE levels (268 IU), fraction of exhaled nitric oxide values (14.5 ppb), and blood eosinophil counts (96 cells/μL) than all other groups. CONCLUSIONS: In obese asthmatic subjects conventional biomarkers of inflammation are poorly predictive of eosinophilic airway inflammation. As such, biomarkers currently used to delineate eosinophilic inflammation in asthmatic subjects should be approached with caution in these subjects.
BACKGROUND: The use of inflammatory biomarkers to delineate the type of lung inflammation present in asthmatic subjects is increasingly common. However, the effect of obesity on these markers is unknown. OBJECTIVES: We aimed to determine the effect of obesity on conventional markers of inflammation in asthmatic subjects. METHODS: We performed secondary analysis of data from 652 subjects previously enrolled in 2 Asthma Clinical Research Network trials. We performed linear correlations between biomarkers and logistic regression analysis to determine the predictive value of IgE levels, blood eosinophil counts, and fraction of exhaled nitric oxide values in relationship to sputum eosinophil counts (>2%), as well as to determine whether cut points existed that would maximize the sensitivity and specificity for predicting sputum eosinophilia in the 3 weight groups. RESULTS: Overall, statistically significant but relatively weak correlations were observed among all 4 markers of inflammation. Within obese subjects, the only significant correlation found was between IgE levels and blood eosinophil counts (r = 0.33, P < .001); furthermore, all other correlations between inflammatory markers were approximately 0, including correlations with sputum eosinophil counts. In addition, the predictive value of each biomarker alone or in combination was poor in obese subjects. In fact, in obese subjects none of the biomarkers of inflammation significantly predicted the presence of high sputum eosinophil counts. Obese asthmatic subjects have lower cut points for IgE levels (268 IU), fraction of exhaled nitric oxide values (14.5 ppb), and blood eosinophil counts (96 cells/μL) than all other groups. CONCLUSIONS: In obese asthmatic subjects conventional biomarkers of inflammation are poorly predictive of eosinophilic airway inflammation. As such, biomarkers currently used to delineate eosinophilic inflammation in asthmatic subjects should be approached with caution in these subjects.
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