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Gariballa S, Nemmar A, Elzaki O, Zaaba NE, Yasin J. Urinary Oxidative Damage Markers and Their Association with Obesity-Related Metabolic Risk Factors. Antioxidants (Basel) 2022; 11:antiox11050844. [PMID: 35624709 PMCID: PMC9138160 DOI: 10.3390/antiox11050844] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/22/2022] [Accepted: 04/22/2022] [Indexed: 02/05/2023] Open
Abstract
Oxidative damage and inflammation are possible mechanisms linking obesity to diabetes and related complications. This study investigates the levels of oxidative damage markers in the urine of community free-living subjects with increased prevalence of obesity. Methods: Participants were assessed regarding clinical, anthropometric, and physical activity data at baseline and at 6 months. Blood and urine samples were taken for the measurements of oxidative markers in urine ((glutathione (GSH), thiobarbituric acid reactive substances (TBARS), pteridine, 8-isoprostane and 8-hydroxy-2′-deoxyguanosine (8-OH-dG)), metabolic and inflammatory markers, and related biochemical variables in the blood. Univariate and multiple regression analyses were used to assess the association between oxidative markers and other clinical prognostic indicators. Results: Overall, 168 participants with a complete 6-month follow-up with a mean (±SD) age of 41 ± 12 (119 (71%) females) were included in the study. In multiple regression analysis, log-transformed urinary pteridine levels were significantly correlated with log-transformed urinary GSH, 8-isoprostane, and TBARS after adjusting for urinary creatinine at both baseline and follow-up. Significant correlations were also found between oxidative damage markers and cardiovascular disease risk factors, including systolic blood pressure, HbA1c, plasma glucose, us-C-reactive proteins, total cholesterol, and HDL. Higher TBARS levels were found in males and diabetic subjects, with lower GSH in diabetic hypertensive and obese subjects, but the latter result did not reach statistical significance. We found nonsignificantly higher TBARS, 8-isoprostane, and pteridine levels in smokers compared to those in nonsmokers. All measured urinary oxidative damage markers levels were higher in obese subjects compared with normal-weight subjects, but results did not reach statistical significance. Conclusion: we found significant associations between urinary oxidative damage and metabolic risk factors, and higher levels of urinary oxidative damage markers in diabetic, hypertensive, smoker, and male subjects.
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Affiliation(s)
- Salah Gariballa
- Correspondence: ; Tel.: +97-137-137-659; Fax: +97-137-672-995
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Zhu J, Smith-Warner SA, Yu D, Zhang X, Blot WJ, Xiang YB, Sinha R, Park Y, Tsugane S, White E, Koh WP, Park SK, Sawada N, Kanemura S, Sugawara Y, Tsuji I, Robien K, Tomata Y, Yoo KY, Kim J, Yuan JM, Gao YT, Rothman N, Lazovich D, Abe SK, Rahman MS, Loftfield E, Takata Y, Li X, Lee JE, Saito E, Freedman ND, Inoue M, Lan Q, Willett WC, Zheng W, Shu XO. Associations of coffee and tea consumption with lung cancer risk. Int J Cancer 2021; 148:2457-2470. [PMID: 33326609 PMCID: PMC8460087 DOI: 10.1002/ijc.33445] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 11/17/2020] [Accepted: 11/19/2020] [Indexed: 12/17/2022]
Abstract
Associations of coffee and tea consumption with lung cancer risk have been inconsistent, and most lung cancer cases investigated were smokers. Included in this study were over 1.1 million participants from 17 prospective cohorts. Cox regression analyses were conducted to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Potential effect modifications by sex, smoking, race, cancer subtype and coffee type were assessed. After a median 8.6 years of follow-up, 20 280 incident lung cancer cases were identified. Compared with noncoffee and nontea consumption, HRs (95% CIs) associated with exclusive coffee drinkers (≥2 cups/d) among current, former and never smokers were 1.30 (1.15-1.47), 1.49 (1.27-1.74) and 1.35 (1.15-1.58), respectively. Corresponding HRs for exclusive tea drinkers (≥2 cups/d) were 1.16 (1.02-1.32), 1.10 (0.92-1.32) and 1.37 (1.17-1.61). In general, the coffee and tea associations did not differ significantly by sex, race or histologic subtype. Our findings suggest that higher consumption of coffee or tea is associated with increased lung cancer risk. However, these findings should not be assumed to be causal because of the likelihood of residual confounding by smoking, including passive smoking, and change of coffee and tea consumption after study enrolment.
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Affiliation(s)
- Jingjing Zhu
- Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN
| | | | - Danxia Yu
- Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN
| | - Xuehong Zhang
- Departments of Nutrition and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - William J. Blot
- Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN
| | - Yong-Bing Xiang
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200032, China
| | - Rashmi Sinha
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
| | - Yikyung Park
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO
| | - Shoichiro Tsugane
- Division of Prevention Center for Public Health Sciences National Cancer Center, Tokyo, Japan
| | - Emily White
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Woon-Puay Koh
- Health Services and Systems Research, Duke-NUS Medical School, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Sue K. Park
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Norie Sawada
- Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Seiki Kanemura
- Division of Epidemiology, Department of Health Informatics and Public Health, Tohoku University School of Public Health, Graduate School of Medicine, Sendai, Japan
| | - Yumi Sugawara
- Division of Epidemiology, Department of Health Informatics and Public Health, Tohoku University School of Public Health, Graduate School of Medicine, Sendai, Japan
| | - Ichiro Tsuji
- Division of Epidemiology, Department of Health Informatics and Public Health, Tohoku University School of Public Health, Graduate School of Medicine, Sendai, Japan
| | - Kim Robien
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, Washington, DC
| | - Yasutake Tomata
- Division of Epidemiology, Department of Health Informatics and Public Health, Tohoku University School of Public Health, Graduate School of Medicine, Sendai, Japan
| | - Keun-Young Yoo
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Jeongseon Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center of Korea, Goyang, Korea
| | - Jian-Min Yuan
- Division of Cancer Control and Population Sciences, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center, Pittsburgh, PA
- Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA
| | - Yu-Tang Gao
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200032, China
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
| | - DeAnn Lazovich
- Division of Epidemiology & Community Health, School of Public Health, Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA
| | - Sarah K. Abe
- Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Md Shafiur Rahman
- Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Division of Prevention Center for Public Health Sciences National Cancer Center, Tokyo, Japan
| | - Erikka Loftfield
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
| | - Yumie Takata
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR
| | - Xin Li
- Richard M. Fairbanks School of Public Health, Indiana University
| | - Jung Eun Lee
- Department of Food and Nutrition, College of Human Ecology, Seoul National University, Seoul
| | - Eiko Saito
- Division of Cancer Statistics Integration Center for Cancer Control & Information Services National Cancer Center, Tokyo, Japan
| | - Neal D. Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
| | - Manami Inoue
- Department of Global Health Policy, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
| | - Walter C. Willett
- Departments of Nutrition and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
- The Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School
| | - Wei Zheng
- Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN
| | - Xiao-Ou Shu
- Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN
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Cox LA, Ketelslegers HB, Lewis RJ. The shape of low-concentration dose-response functions for benzene: implications for human health risk assessment. Crit Rev Toxicol 2021; 51:95-116. [PMID: 33853483 DOI: 10.1080/10408444.2020.1860903] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Are dose-response relationships for benzene and health effects such as myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) supra-linear, with disproportionately high risks at low concentrations, e.g. below 1 ppm? To investigate this hypothesis, we apply recent mode of action (MoA) and mechanistic information and modern data science techniques to quantify air benzene-urinary metabolite relationships in a previously studied data set for Tianjin, China factory workers. We find that physiologically based pharmacokinetics (PBPK) models and data for Tianjin workers show approximately linear production of benzene metabolites for air benzene (AB) concentrations below about 15 ppm, with modest sublinearity at low concentrations (e.g. below 5 ppm). Analysis of the Tianjin worker data using partial dependence plots reveals that production of metabolites increases disproportionately with increases in air benzene (AB) concentrations above 10 ppm, exhibiting steep sublinearity (J shape) before becoming saturated. As a consequence, estimated cumulative exposure is not an adequate basis for predicting risk. Risk assessments must consider the variability of exposure concentrations around estimated exposure concentrations to avoid over-estimating risks at low concentrations. The same average concentration for a specified duration is disproportionately risky if it has higher variance. Conversely, if chronic inflammation via activation of inflammasomes is a critical event for induction of MDS and other health effects, then sufficiently low concentrations of benzene are predicted not to cause increased risks of inflammasome-mediated diseases, no matter how long the duration of exposure. Thus, we find no evidence that the dose-response relationship is supra-linear at low doses; instead sublinear or zero excess risk at low concentrations is more consistent with the data. A combination of physiologically based pharmacokinetic (PBPK) modeling, Bayesian network (BN) analysis and inference, and partial dependence plots appears a promising and practical approach for applying current data science methods to advance benzene risk assessment.
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Affiliation(s)
- Louis A Cox
- Cox Associates LLC, Denver, CO, USA.,Department of Business Analytics, University of Colorado, Denver, CO, USA
| | - Hans B Ketelslegers
- Concawe Division, European Petroleum Refiners Association, Brussels, Belgium
| | - R Jeffrey Lewis
- Concawe Division, European Petroleum Refiners Association, Brussels, Belgium.,ExxonMobil Biomedical Sciences, Inc, Clinton, NJ, USA
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