Dana M Carroll1,2, Shannon Cigan3, Joshua Ikuemonisan2, Taylor Hammonds4, Irina Stepanov1,2, Gideon St Helen5, Neal Benowitz5, Dorothy K Hatsukami2,6. 1. Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN. 2. Masonic Cancer Center, University of Minnesota, Minneapolis, MN. 3. Division of Epidemiology and Clinical Research, Department of Pediatrics, University of Minnesota, Minneapolis, MN. 4. Graduate Program in Regulatory Sciences and Interdisciplinary Biomedical Sciences, University of Arkansas for Medical Sciences, Little Rock, AR. 5. Clinical Pharmacology Research Program, Division of Cardiology, Department of Medicine, University of California, San Francisco, CA. 6. Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN.
Abstract
INTRODUCTION: We illustrate the differential impact of common analysis approaches to handling urinary creatinine, a measure for urine dilution, on relationships between race, gender, and biomarkers of exposure measured in spot urine. METHODS: In smokers, spot urine levels of total nicotine equivalents (TNE, sum of total nicotine, total cotinine, and total 3'-hydroxycotinine) and total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) overall and per cigarette were examined. Relationships between race (African Americans [AA] n = 373, Whites n = 758) or gender (males n = 629, females n = 502) and TNE or NNAL were examined using the following approaches to handling creatinine: (1) unadjusted/unstandardized; (2) standardization; (3) adjustment as a covariate. Significance was considered at p < .05. RESULTS: Creatinine was higher in AA versus Whites (1.19 vs. 0.96 mg/mL; p < .0001) and in males versus females (1.21 vs. 0.84 mg/mL; p < .0001). Independent of how creatinine was handled, TNE was lower among AA than Whites (TNE ratios AA vs. Whites: 0.67-0.84; p's < .05). Unadjusted TNE per cigarette was higher among AA versus Whites (ratio 1.12; p = .0411); however, the relationship flipped with standardization (ratio 0.90; p = .0360) and adjustment (ratio 0.95; p = .3165). Regarding gender, unadjusted TNE was higher among males versus females (ratio 1.13; p = .0063), but the relationship flipped with standardization (ratio 0.79; p < .0001) or adjustment (ratio 0.89; p = .0018). Unadjusted TNE per cigarette did not differ across gender (ratio 0.98; p = .6591), but lower levels were found in males versus females with standardization (ratio 0.68; p < .0001) and adjustment (ratio 0.74; p < .0001). NNAL displayed similar patterns. CONCLUSIONS: Relationships between race, gender, and spot urine levels of biomarkers of exposure can vary greatly based on how creatinine is handled in analyses. IMPLICATIONS: Lack of appropriate methods can lead to discrepancies across reports on variability of urinary biomarkers by race and gender. We recommend that for any analyses of biomarkers of exposure measure in spot urine samples across race, gender, or other population subgroups that differ in urinary creatinine levels, sensitivity analyses comparing the different methods for handling urinary creatinine should be conducted. If methods result in discrepant findings, this should be clearly noted and discussed.
INTRODUCTION: We illustrate the differential impact of common analysis approaches to handling urinary creatinine, a measure for urine dilution, on relationships between race, gender, and biomarkers of exposure measured in spot urine. METHODS: In smokers, spot urine levels of total nicotine equivalents (TNE, sum of total nicotine, total cotinine, and total 3'-hydroxycotinine) and total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) overall and per cigarette were examined. Relationships between race (African Americans [AA] n = 373, Whites n = 758) or gender (males n = 629, females n = 502) and TNE or NNAL were examined using the following approaches to handling creatinine: (1) unadjusted/unstandardized; (2) standardization; (3) adjustment as a covariate. Significance was considered at p < .05. RESULTS:Creatinine was higher in AA versus Whites (1.19 vs. 0.96 mg/mL; p < .0001) and in males versus females (1.21 vs. 0.84 mg/mL; p < .0001). Independent of how creatinine was handled, TNE was lower among AA than Whites (TNE ratios AA vs. Whites: 0.67-0.84; p's < .05). Unadjusted TNE per cigarette was higher among AA versus Whites (ratio 1.12; p = .0411); however, the relationship flipped with standardization (ratio 0.90; p = .0360) and adjustment (ratio 0.95; p = .3165). Regarding gender, unadjusted TNE was higher among males versus females (ratio 1.13; p = .0063), but the relationship flipped with standardization (ratio 0.79; p < .0001) or adjustment (ratio 0.89; p = .0018). Unadjusted TNE per cigarette did not differ across gender (ratio 0.98; p = .6591), but lower levels were found in males versus females with standardization (ratio 0.68; p < .0001) and adjustment (ratio 0.74; p < .0001). NNAL displayed similar patterns. CONCLUSIONS: Relationships between race, gender, and spot urine levels of biomarkers of exposure can vary greatly based on how creatinine is handled in analyses. IMPLICATIONS: Lack of appropriate methods can lead to discrepancies across reports on variability of urinary biomarkers by race and gender. We recommend that for any analyses of biomarkers of exposure measure in spot urine samples across race, gender, or other population subgroups that differ in urinary creatinine levels, sensitivity analyses comparing the different methods for handling urinary creatinine should be conducted. If methods result in discrepant findings, this should be clearly noted and discussed.
Authors: Cindy M Chang; Selvin H Edwards; Aarthi Arab; Arseima Y Del Valle-Pinero; Ling Yang; Dorothy K Hatsukami Journal: Cancer Epidemiol Biomarkers Prev Date: 2016-11-09 Impact factor: 4.254
Authors: Dorothy K Hatsukami; Xianghua Luo; Joni A Jensen; Mustafa al'Absi; Sharon S Allen; Steven G Carmella; Menglan Chen; Paul M Cinciripini; Rachel Denlinger-Apte; David J Drobes; Joseph S Koopmeiners; Tonya Lane; Chap T Le; Scott Leischow; Kai Luo; F Joseph McClernon; Sharon E Murphy; Viviana Paiano; Jason D Robinson; Herbert Severson; Christopher Sipe; Andrew A Strasser; Lori G Strayer; Mei Kuen Tang; Ryan Vandrey; Stephen S Hecht; Neal L Benowitz; Eric C Donny Journal: JAMA Date: 2018-09-04 Impact factor: 56.272
Authors: Steven G Carmella; Xun Ming; Natalie Olvera; Claire Brookmeyer; Andrea Yoder; Stephen S Hecht Journal: Chem Res Toxicol Date: 2013-07-24 Impact factor: 3.739
Authors: Sungshim L Park; Steven G Carmella; Xun Ming; Elizabeth Vielguth; Daniel O Stram; Loic Le Marchand; Stephen S Hecht Journal: Cancer Epidemiol Biomarkers Prev Date: 2014-12-26 Impact factor: 4.254
Authors: Carolyn J Brooks; Steven L Gortmaker; Michael W Long; Angie L Cradock; Erica L Kenney Journal: Am J Public Health Date: 2017-07-20 Impact factor: 9.308
Authors: Sharon E Murphy; Peter Villalta; Sing-Wei Ho; Linda B von Weymarn Journal: J Chromatogr B Analyt Technol Biomed Life Sci Date: 2007-06-29 Impact factor: 3.205
Authors: Dana B Barr; Lynn C Wilder; Samuel P Caudill; Amanda J Gonzalez; Lance L Needham; James L Pirkle Journal: Environ Health Perspect Date: 2005-02 Impact factor: 9.031
Authors: Maciej L Goniewicz; Danielle M Smith; Kathryn C Edwards; Benjamin C Blount; Kathleen L Caldwell; Jun Feng; Lanqing Wang; Carol Christensen; Bridget Ambrose; Nicolette Borek; Dana van Bemmel; Karen Konkel; Gladys Erives; Cassandra A Stanton; Elizabeth Lambert; Heather L Kimmel; Dorothy Hatsukami; Stephen S Hecht; Raymond S Niaura; Mark Travers; Charles Lawrence; Andrew J Hyland Journal: JAMA Netw Open Date: 2018-12-07
Authors: Andrew W Bergen; Christopher S McMahan; Stephen McGee; Carolyn M Ervin; Hilary A Tindle; Loïc Le Marchand; Sharon E Murphy; Daniel O Stram; Yesha M Patel; Sungshim L Park; James W Baurley Journal: Nicotine Tob Res Date: 2021-11-05 Impact factor: 4.244
Authors: Gal Cohen; Nicholas I Goldenson; Patrick C Bailey; Stephanie Chan; Saul Shiffman Journal: Nicotine Tob Res Date: 2021-11-05 Impact factor: 4.244