Citation: Ting Zeng, Yanshan Liang, Qingyuan Dai, Jinglin Tian, Jinyao Chen, Bo Lei, Zhu Yang, Zongwei Cai. Application of machine learning algorithms to screen potential biomarkers under cadmium exposure based on human urine metabolic profiles[J]. Chinese Chemical Letters, ;2022, 33(12): 5184-5188. doi: 10.1016/j.cclet.2022.03.020 shu

Application of machine learning algorithms to screen potential biomarkers under cadmium exposure based on human urine metabolic profiles

Ting Zeng ^{a, b} ,
Yanshan Liang ^{a, b} ,
Qingyuan Dai ^{a, b} ,
Jinglin Tian ^{a, b} ,
Jinyao Chen ^c ,
Bo Lei ^a ,
Zhu Yang ^b ,
Zongwei Cai ^{b, *,}

a.
Food Science and Technology Program, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai 519087, China
b.
State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
c.
Department of Nutrition, Food Safety and Toxicology, West China School of Public Health, Sichuan University, Chengdu 610041, China

zwcai@hkbu.edu.hk

Received Date: 27 December 2021
Revised Date: 23 January 2022
Accepted Date: 4 March 2022
Available Online: 7 March 2022

Figures(3)

Exposure to environmental cadmium increases the health risk of residents. Early urine metabolic detection using high-resolution mass spectrometry and machine learning algorithms would be advantageous to predict the adverse health effects. Here, we conducted machine learning approaches to screen potential biomarkers under cadmium exposure in 403 urine samples. In positive and negative ionization mode, 4207 and 3558 features were extracted, respectively. We compared seven machine learning algorithms and found that the extreme gradient boosting (XGBoost) and random forest (RF) classifiers showed better accuracy and predictive performance than others. Following 5-fold cross-validation, the value of area under curve (AUC) was both 0.93 for positive and negative ionization modes in XGBoost classifier. In the RF classifier, AUC were 0.80 and 0.84 for positive and negative ionization modes, respectively. We then identified a biomarker panel based on XGBoost and RF classifiers. The incorporation of machine learning models into urine analysis using high-resolution mass spectrometry could allow a convenient assessment of cadmium exposure.
- Human urine,
- High-resolution mass spectrometry,
- Cadmium exposure,
- Metabolic profiles,
- Machine learning
1. [1]
  S. Himeno, K. Aoshima, Cadmium Toxicity: New Aspects in Human Disease, Rice Contamination, and Cytotoxicity, Singapore, 2019.
2. [2]
  S. Satarug, Toxics. 6 (2018) 15. doi: 10.3390/toxics6010015
3. [3]
  E.D. Evans, C. Duvallet, N.D. Chu, et al., Sci. Rep. 10 (2020) 1–13. doi: 10.1038/s41598-019-56847-4
4. [4]
  A.S. Martinez-Vernon, J.A. Covington, R.P. Arasaradnam, et al., PLoS ONE 13 (2018) e0204425. doi: 10.1371/journal.pone.0204425
5. [5]
  Y. Gao, Y. Lu, S. Huang, et al., Environ. Sci. Technol. 48 (2014) 6409–6418. doi: 10.1021/es500750w
6. [6]
  H. Li, K. Huang, S. Jin, et al., Int. J. Hyg. Environ. Heal. 222 (2019) 556–562. doi: 10.1016/j.ijheh.2019.02.007
7. [7]
  T. Zeng, Y.S. Liang, J.Y. Chen, et al., Environ. Int. 154 (2021) 106646. doi: 10.1016/j.envint.2021.106646
8. [8]
  E.H. Wilkes, G. Rumsby, G.M. Woodward, Clin. Chem. 64 (2018) 1586–1595. doi: 10.1373/clinchem.2018.292201
9. [9]
  H.E. Anderson, T. Liden, B.K. Berger, et al., Anal. Chim. Acta 1172 (2021) 338668. doi: 10.1016/j.aca.2021.338668
10. [10]
  F. Roux-Dalvai, C. Gotti, M. Leclercq, et al., Mol. Cell Proteom. 18 (2019) 2492–2505. doi: 10.1074/mcp.tir119.001559
11. [11]
  G.L. Streun, A.E. Steuer, L.C. Ebert, A. Dobay, T. Kraemer, Clin. Chem. Lab. Med. 59 (2021) 1392–1399. doi: 10.1515/cclm-2021-0010
12. [12]
  Y.C. Huang, H.H. Chung, E.P. Dutkiewicz, et al., Anal. Chem. 92 (2019) 1653–1657. doi: 10.1002/cbic.201800814
13. [13]
  U.W. Liebal, A.N.T. Phan, M. Sudhakar, K. Raman, L.M. Blank, Metabolites 10 (2020) 243. doi: 10.3390/metabo10060243
14. [14]
  E.H. Wilkes, E. Emmett, L. Beltran, G.M. Woodward, R.S. Carling, Clin. Chem. 66 (2020) 1210–1218. doi: 10.1093/clinchem/hvaa134
15. [15]
  J. Huo, Z. Huang, R. Li, et al., PLoS ONE 13 (2018) e0201454. doi: 10.1371/journal.pone.0201454
16. [16]
  J.M. Mato, M.L. Martínez-Chantar, S.C. Lu, Ann. Hepatol. 12 (2015) 183–189.
17. [17]
  N.N. Pavlova, S. Hui, J.M. Ghergurovich, et al., Cell Metab. 27 (2018) 428–438. doi: 10.1016/j.cmet.2017.12.006
18. [18]
  J. Płonka, M. Babiuch, H. Barchanska, Chemosphere 286 (2022) 131592. doi: 10.1016/j.chemosphere.2021.131592
19. [19]
  M. Chen, B. Zhang, S. Cai, et al., J. Proteom. 200 (2019) 134–143. doi: 10.1016/j.jprot.2019.03.015
20. [20]
  J. Neunzig, R. Bernhardt, PLoS ONE 9 (2014) e89727. doi: 10.1371/journal.pone.0089727
21. [21]
  F.K. Abu Salem, M. Jurdi, M. Alkadri, F. Hachem, H.R. Dhaini, Environ. Sci. Pollut. Res. 29 (2022) 8253–8268. doi: 10.1007/s11356-021-15897-w
22. [22]
  X. Li, T. Geng, W. Shen, J. Zhang, Y. Zhou, Ecotoxicol. Environ. Saf. 209 (2021) 111773. doi: 10.1016/j.ecoenv.2020.111773
23. [23]
  K. Nakamura, T. Yasutaka, T. Kuwatani, T. Komai, Chemosphere 186 (2017) 501–509. doi: 10.1016/j.chemosphere.2017.07.131
24. [24]
  Z. Xin, S. Jun, T. Yan, et al., Chemometr. Intell. Lab. Syst. 200 (2020) 103996. doi: 10.1016/j.chemolab.2020.103996
25. [25]
  L. Wei, Y. Huang, Z. Chen, et al., Front. Oncol. 11 (2021) 4148.
26. [26]
  X. Zhu, X. Wang, Y.S. Ok, J. Hazard. Mater. 378 (2019) 120727. doi: 10.1016/j.jhazmat.2019.06.004
27. [27]
  V. Cantoni, R. Green, C. Ricciardi, et al., Comput. Math Methods Med. 16 (2021) 5288844.
28. [28]
  X. Jia, B. Hu, B.P. Marchant, et al., Environ. Pollut. 250 (2019) 601–609. doi: 10.1016/j.envpol.2019.04.047
29. [29]
  M. Hussain, Z. Chen, M. Lv, et al., Chin. Chem. Lett. 31 (2020) 3163–3167. doi: 10.1016/j.cclet.2020.04.038
30. [30]
  Y. Guo, A. Graber, R.N. McBurney, R. Balasubramanian, BMC Bioinform. 11 (2010) 1–19.
31. [31]
  S.K. Park, Z. Zhao, B. Mukherjee, Environ. Health 16 (2017) 1–17. doi: 10.1186/s12940-016-0208-y
32. [32]
  T. Guan, Y. Xin, K. Zheng, et al., BioMetals 34 (2021) 33–48. doi: 10.1007/s10534-020-00260-2
33. [33]
  X. He, Z. Qi, H. Hou, J. Gao, X.X. Zhang, J. Hazard. Mater. 388 (2020) 121791. doi: 10.1016/j.jhazmat.2019.121791
34. [34]
  M.Y. Xu, Y.J. Sun, P. Wang, L. Yang, Y.J. Wu, Ecotoxicol. Environ. Saf. 195 (2020) 110467. doi: 10.1016/j.ecoenv.2020.110467
35. [35]
  L. Zong, J. Xing, L. Shu, et al., Ecotoxicol. Environ. Saf. 147 (2018) 26–33. doi: 10.1016/j.ecoenv.2017.08.028
36. [36]
  G. Xun, C. Yang, Y. Hong, et al., Sci. Total Environ. 676 (2019) 72–86. doi: 10.1016/j.scitotenv.2019.04.208
37. [37]
  L. Xie, Y. Zhao, J. Duan, et al., Chem. Res. Toxicol. 33 (2020) 1897–1906. doi: 10.1021/acs.chemrestox.0c00091
38. [38]
  Y. Liang, T. Zeng, J. Tian, et al., Sci. Total Environ. 798 (2021) 149379. doi: 10.1016/j.scitotenv.2021.149379
39. [39]
  M. Valcke, N. Ouellet, M. Dubé, et al., Toxicol. Lett. 312 (2019) 148–156. doi: 10.1016/j.toxlet.2019.05.014
40. [40]
  M.Y. Xu, P. Wang, Y.J. Sun, Y.J. Wu, Toxicology 384 (2017) 50–58. doi: 10.1016/j.tox.2017.04.008
41. [41]
  T. Zeng, W. Guo, L. Jiang, et al., Sci. Total Environ. 801 (2021) 149803. doi: 10.1016/j.scitotenv.2021.149803
42. [42]
  L. Xiang, J. Nie, L. Wang, et al., Chin. Chem. Lett. 32 (2021) 2197–2202. doi: 10.1016/j.cclet.2020.12.042
43. [43]
  H. Luan, W. Gu, H. Li, et al., J. Transl. Med. 19 (2021) 1–10. doi: 10.1186/s12967-020-02683-4
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