Application of mixture analysis methods in association between metals mixture exposure and DNA oxidative damage
Objectives: To study the association between metals mixture exposure and DNA oxidative damage using mixture analysis methods, and to explore the most significant exposure factors that cause DNA oxidative damage. Methods: Workers from steel enterprises were recruited in Shandong Province. Urinary metals were measured by using the inductively coupled plasma mass spectrometry method. The level of urinary 8-hydroxy-2'-deoxyguanosine (8-OHdG) was determined by using the ultra-high performance liquid chromatography-mass spectrometry method. Bayesian kernel machine regression (BKMR), elastic net regression and quantile g-computation regression were used to analyze the association between urinary metals and urinary 8-OHdG. Results: A total of 768 subjects aged (36.15±7.40) years old were included in the study. BKMR, elastic net regression and quantile g-computation all revealed an overall positive association between the mixture concentration and increased urinary 8-OHdG. The quantile g-computation results showed that with a 25% increase in metal mixtures, the urinary 8-OHdG level increased by 77.60%. The elastic net regression showed that with a 25% increase in exposure risk score, the urinary 8-OHdG level increased by 26%. The BKMR summarized the contribution of individual exposures to the response, and selenium, zinc, and nickel were significant contributors to the urinary 8-OHdG elevation. Conclusion: Exposure to mixed metals causes elevated levels of DNA oxidative damage, and selenium, zinc, and nickel are significant exposure factors.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:57 |
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| Enthalten in: |
Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine - 57(2023), 7 vom: 06. Juli, Seite 1026-1031 |
| Sprache: |
Chinesisch |
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| Beteiligte Personen: |
Wang, Y H [VerfasserIn] |
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| Links: |
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| Themen: |
7OV03QG267 |
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| Anmerkungen: |
Date Completed 25.07.2023 Date Revised 25.07.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.3760/cma.j.cn112150-20221031-01046 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM359829805 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Objectives: To study the association between metals mixture exposure and DNA oxidative damage using mixture analysis methods, and to explore the most significant exposure factors that cause DNA oxidative damage. Methods: Workers from steel enterprises were recruited in Shandong Province. Urinary metals were measured by using the inductively coupled plasma mass spectrometry method. The level of urinary 8-hydroxy-2'-deoxyguanosine (8-OHdG) was determined by using the ultra-high performance liquid chromatography-mass spectrometry method. Bayesian kernel machine regression (BKMR), elastic net regression and quantile g-computation regression were used to analyze the association between urinary metals and urinary 8-OHdG. Results: A total of 768 subjects aged (36.15±7.40) years old were included in the study. BKMR, elastic net regression and quantile g-computation all revealed an overall positive association between the mixture concentration and increased urinary 8-OHdG. The quantile g-computation results showed that with a 25% increase in metal mixtures, the urinary 8-OHdG level increased by 77.60%. The elastic net regression showed that with a 25% increase in exposure risk score, the urinary 8-OHdG level increased by 26%. The BKMR summarized the contribution of individual exposures to the response, and selenium, zinc, and nickel were significant contributors to the urinary 8-OHdG elevation. Conclusion: Exposure to mixed metals causes elevated levels of DNA oxidative damage, and selenium, zinc, and nickel are significant exposure factors | ||
| 650 | 4 | |a English Abstract | |
| 650 | 4 | |a Journal Article | |
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| 650 | 7 | |a Selenium |2 NLM | |
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| 700 | 1 | |a Yuan, H G |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, L Y |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Y |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, T |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, H |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, X |e verfasserin |4 aut | |
| 700 | 1 | |a Duan, H W |e verfasserin |4 aut | |
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