Association of polycyclic aromatic hydrocarbons metabolites and risk of diabetes in coke oven workers

Yang, Liangle; Yan, Kai; Zhang, Dan; Lai, Xuefeng; Chen, Xuguang; Fang, Qin; Guo, Huan; Wu, Tangchun; Zhang, Xiaomin

doi:10.1016/j.envpol.2017.01.027

Cited by 48 publications

(26 citation statements)

References 29 publications

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“…Lifestyle information included smoking, drinking, physical activity, and eating habits. Physical activity was defined as regularly doing at least 20 min per day of physical activity during leisure time over the previous 6 months (yes or no) [ 38 ]. Individuals who had been smoking at least one cigarette per day for at least 6 months were defined as smokers, and those who had been drinking alcohol at least once per week for at least 6 months were considered drinkers [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

Xue

et al. 2020

Journal of Diabetes Research

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Background. An estimated 425 million people globally have diabetes, accounting for 12% of the world’s health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas. Methods. A total of 584,168 adult subjects who have participated in the national physical examination were enrolled in this study. The risk factors for type II diabetes mellitus (T2DM) were identified by p values and odds ratio, using logistic regression (LR) based on variables of physical measurement and a questionnaire. Combined with the risk factors selected by LR, we used a decision tree, a random forest, AdaBoost with a decision tree (AdaBoost), and an extreme gradient boosting decision tree (XGBoost) to identify individuals with T2DM, compared the performance of the four machine learning classifiers, and used the best-performing classifier to output the degree of variables’ importance scores of T2DM. Results. The results indicated that XGBoost had the best performance (accuracy=0.906, precision=0.910, recall=0.902, F‐1=0.906, and AUC=0.968). The degree of variables’ importance scores in XGBoost showed that BMI was the most significant feature, followed by age, waist circumference, systolic pressure, ethnicity, smoking amount, fatty liver, hypertension, physical activity, drinking status, dietary ratio (meat to vegetables), drink amount, smoking status, and diet habit (oil loving). Conclusions. We proposed a classifier based on LR-XGBoost which used fourteen variables of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of T2DM. The classifier can accurately screen the risk of diabetes in the early phrase, and the degree of variables’ importance scores gives a clue to prevent diabetes occurrence.

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Section: Methodsmentioning

confidence: 99%

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

Xue

et al. 2020

Journal of Diabetes Research

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“…Moreover, the exposure to PAHs of coke-oven workers was strongly associated with pathological conditions such as diabetes, especially in subjects who were also smokers, but it was also associated with overweight condition, longer working years and works in coke-oven settings [ 48 ], and impaired heart function. Indeed, the presence of urinary 2-hydroxynaphthalene (2-OH-NAP), following occupational exposure to coke-oven emissions, was associated with a dose-response decrease in heart-rate variability, in relation to working years and exposure levels [ 49 ]; urinary 1-hydroxynaphthalene (1-OH-NAP), 2-hydroxynaphthalene, and total PAH metabolites were also associated with increased risk of atherosclerotic cardiovascular disease [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

“…In general, blood is the preferred matrix for many chemicals, as it is in continuous contact with the whole organism and is in equilibrium with organs and tissues; it is used for the determination of POPs [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 38 , 39 , 41 , 42 , 54 , 55 , 56 , 58 , 59 , 62 , 63 , 64 , 65 , 66 , 67 , 69 , 70 , 71 , 72 ] with the exception of some POPs [ 39 , 40 , 44 ], PAHs [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 53 ], and some pesticides (i.e., pyrethroids) [ ...…”

Section: Discussionmentioning

confidence: 99%

Human Biomonitoring of Environmental and Occupational Exposures by GC-MS and Gas Sensor Systems: A Systematic Review

Longo

Forleo

Giampetruzzi

et al. 2021

IJERPH

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Environmental chemicals and contaminants coming from multiple external sources enter the human body, determining a potential risk for human health. Human biomonitoring (HBM), measuring the concentrations of biomarkers in human specimens, has become an emerging approach for assessing population-wide exposure to hazardous chemicals and health risk through large-scale studies in many countries. However, systematic mapping of HBM studies, including their characteristics, targeted hazardous pollutants, analytical techniques, and sample population (general population and occupationally exposed workers), has not been done so far. We conducted a systematic review of the literature related to airborne hazardous pollutants in biofluids to answer the following questions: Which main chemicals have been included in the literature, which bodily fluids have been used, and what are the main findings? Following PRISMA protocol, we summarized the publications published up to 4 February 2021 of studies based on two methods: gas-chromatography/mass spectrometry (GC/MS) and electronic noses (e-noses). We screened 2606 records and 117 publications were included in the analysis, the most based on GC/MS analysis. The selected HBM studies include measurements of biomarkers in different bodily fluids, such as blood, urine, breast milk, and human semen as well as exhaled air. The papers cover numerous airborne hazardous pollutants that we grouped in chemical classes; a lot of hazardous and noxious compounds, mainly persistent organic pollutants (POPs) and volatile organic compounds (VOCs), have been detected in biological fluids at alarming levels. The scenario that emerged from this survey demonstrates the importance of HBM in human exposure to hazardous pollutants and the need to use it as valid tool in health surveillance. This systematic review represents a starting point for researchers who focus on the world of pollutant biomonitoring in the human body and gives them important insights into how to improve the methods based on GC/MS. Moreover, it makes a first overview of the use of gas sensor array and e-noses in HBM studies.

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“…Elevated 4-OHPh is dose-responsive and was associated with an increased risk of diabetes in coke oven workers. The risk assessment of diabetes related to occupational PAH exposure should take working years and BMI into consideration [21]. The results indicated that urinary copper and zinc levels were positively associated with the risk of diabetes and hyperglycemia in coke oven workers.…”

Section: Organ Dysfunctions and Diseases Of Coke Oven Workersmentioning

confidence: 98%

Environmental Monitoring of PAHs Exposure, Biomarkers and Vital Status in Coke Oven Workers

Vimercati

Bisceglia

Cavone

et al. 2020

IJERPH

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A follow-up study of a cohort of workers from a coke plant compared with a control group from the same industrial area was conducted in 2019. The recruitment and environmental and biomarker measurements were performed during 1993/1994. The environmental concentrations of polycyclic aromatic hydrocarbons (PAH), B(a)P, pyrene and nitro-PAH were measured. Personal data were collected via an individual semi-structured questionnaire by a trained physician. All biomarkers were measured after a specific blood drawing for every test. Significant risks (ORs) were observed for nitro-PAH (≥0.12 µg/m3) [OR = 7.96 (1.01–62.82)], urinary 1-hydroxypyrene (1-OHpy) (≥0.99 µmoles/moles of creatinine) [OR = 11.71 (1.47–92.90)], PAH DNA adducts (P32) (≥2.69 adducts/108 nucleotides) [OR = 5.46 (1.17–25.58)], total nitro-PAH hemoglobin adducts (≥161.68 fg/µg of Hb) [OR = 5.92 (1.26–27.86)], sister chromatid exchange (SCE) with TCR (≥377.84 SCE/cell chromosomes) [OR = 13.06 (3.95–93.10)], sister chromatid exchange with T (≥394.72 total SCE) [OR = 13.06 (3.95–93.10)], and sister chromatid exchange with X (≥8.19 mean SCE) [OR = 13.06 (3.95–93.10)]. Significant risk of death for all causes and chromosomal aberrations (48 h) (OR = 7.19 [1.19–43.44]) or micronuclei in culture at 48 h (OR = 3.86 [1.04–14.38]) were also found.

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Association of polycyclic aromatic hydrocarbons metabolites and risk of diabetes in coke oven workers

Cited by 48 publications

References 29 publications

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

Human Biomonitoring of Environmental and Occupational Exposures by GC-MS and Gas Sensor Systems: A Systematic Review

Environmental Monitoring of PAHs Exposure, Biomarkers and Vital Status in Coke Oven Workers

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