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Ozdemir S, Sears CG, Harrington JM, Poulsen AH, Buckley J, Howe CJ, James KA, Tjonneland A, Wellenius GA, Raaschou-Nielsen O, Meliker J. Relationship between Urine Creatinine and Urine Osmolality in Spot Samples among Men and Women in the Danish Diet Cancer and Health Cohort. Toxics 2021; 9:282. [PMID: 34822673 PMCID: PMC8625939 DOI: 10.3390/toxics9110282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 11/16/2022]
Abstract
Assays of urine biomarkers often use urine creatinine to account for urinary dilution, even though creatinine levels are influenced by underlying physiology and muscle catabolism. Urine osmolality-a measure of dissolved particles including ions, glucose, and urea-is thought to provide a more robust marker of urinary dilution but is seldom measured. The relationship between urine osmolality and creatinine is not well understood. We calculated correlation coefficients between urine creatinine and osmolality among 1375 members of a subcohort of the Danish Diet, Cancer, and Health Cohort, and within different subgroups. We used linear regression to relate creatinine with osmolality, and a lasso selection procedure to identify other variables that explain remaining variability in osmolality. Spearman correlation between urine creatinine and osmolality was strong overall (ρ = 0.90; 95% CI: 0.89-0.91) and in most subgroups. Linear regression showed that urine creatinine explained 60% of the variability in urine osmolality, with another 9% explained by urine thallium (Tl), cesium (Cs), and strontium (Sr). Urinary creatinine and osmolality are strongly correlated, although urine Tl, Cs, and Sr might help supplement urine creatinine for purposes of urine dilution adjustment when osmolality is not available.
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Affiliation(s)
- Selinay Ozdemir
- Department of Biology, Stony Brook University, Stony Brook, NY 11794, USA;
| | - Clara G. Sears
- Department of Epidemiology, Brown University School of Public Health, Providence, RI 02903, USA; (C.G.S.); (C.J.H.); (G.A.W.)
| | - James M. Harrington
- Analytical Science Division, RTI International, Research Triangle Park, NC 27709, USA;
| | - Aslak Harbo Poulsen
- Danish Cancer Society Research Center, 2100 Copenhagen, Denmark; (A.H.P.); (A.T.); (O.R.-N.)
| | - Jessie Buckley
- Departments of Environment Health and Engineering & Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA;
| | - Chanelle J. Howe
- Department of Epidemiology, Brown University School of Public Health, Providence, RI 02903, USA; (C.G.S.); (C.J.H.); (G.A.W.)
| | - Katherine A. James
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado-Anschutz Medical Campus, Denver, CO 80217, USA;
| | - Anne Tjonneland
- Danish Cancer Society Research Center, 2100 Copenhagen, Denmark; (A.H.P.); (A.T.); (O.R.-N.)
- Department of Public Health, University of Copenhagen, 1165 Copenhagen, Denmark
| | - Gregory A. Wellenius
- Department of Epidemiology, Brown University School of Public Health, Providence, RI 02903, USA; (C.G.S.); (C.J.H.); (G.A.W.)
- Department of Environmental Health, Boston University, Boston, MA 02215, USA
| | - Ole Raaschou-Nielsen
- Danish Cancer Society Research Center, 2100 Copenhagen, Denmark; (A.H.P.); (A.T.); (O.R.-N.)
- Department of Environmental Science, Aarhus University, 4000 Roskilde, Denmark
| | - Jaymie Meliker
- Program in Public Health, Department of Family, Population, & Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA
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Rotter M, Brandmaier S, Covic M, Burek K, Hertel J, Troll M, Bader E, Adam J, Prehn C, Rathkolb B, Hrabe de Angelis M, Grabe HJ, Daniel H, Kantermann T, Harth V, Illig T, Pallapies D, Behrens T, Brüning T, Adamski J, Lickert H, Rabstein S, Wang-Sattler R. Night Shift Work Affects Urine Metabolite Profiles of Nurses with Early Chronotype. Metabolites 2018; 8:E45. [PMID: 30134533 DOI: 10.3390/metabo8030045] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 08/14/2018] [Accepted: 08/18/2018] [Indexed: 01/22/2023] Open
Abstract
Night shift work can have a serious impact on health. Here, we assess whether and how night shift work influences the metabolite profiles, specifically with respect to different chronotype classes. We have recruited 100 women including 68 nurses working both, day shift and night shifts for up to 5 consecutive days and collected 3640 spontaneous urine samples. About 424 waking-up urine samples were measured using a targeted metabolomics approach. To account for urine dilution, we applied three methods to normalize the metabolite values: creatinine-, osmolality- and regression-based normalization. Based on linear mixed effect models, we found 31 metabolites significantly (false discovery rate <0.05) affected in nurses working in night shifts. One metabolite, acylcarnitine C10:2, was consistently identified with all three normalization methods. We further observed 11 and 4 metabolites significantly associated with night shift in early and late chronotype classes, respectively. Increased levels of medium- and long chain acylcarnitines indicate a strong impairment of the fatty acid oxidation. Our results show that night shift work influences acylcarnitines and BCAAs, particularly in nurses in the early chronotype class. Women with intermediate and late chronotypes appear to be less affected by night shift work.
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