Heavy metals and probabilistic risk assessment via <i>Prunella vulgaris</i> (food and medicine homology) consumption in Guangdong Province, China

<i>Prunella vulgaris</i> is widely used as the main ingredient of herb tea in Southeast Asia, as well as a traditional Chinese medicine. However, the heavy metal contaminations such as arsenic, cadmium, mercury and lead in <i>P. vulgaris</i> may be a cause for concern due to the environment pollution around, plantation and processing contamination. Thus, this study intented to assess both non-carcinogenic risks and carcinogenic risks attributed to cumulative exposure to the four heavy metals in <i>P. vulgaris.</i> The contaminations levels of heavy metals were determined in 90 batches of <i>P. vulgaris.</i> And the consumption level was obtained through a questionnaire survey among a total of 6,235 adult participants in Guangdong province. This study estimated the probabilistic health risks using Monte Carlo simulation, and found that the estimated mean and the 95th percentile values for cumulative noncarcinogenic risk (HI value) and carcinogenic risk (TCR value) of <i>P. vulgaris</i> were all within the acceptable risk. And the assessment results indicated that arsenic was the primary contributors to both noncarcinogenic and carcinogenic risks through <i>P. vulgaris</i> consumption. These findings and continuing the surveillance of heavy metals in <i>P. vulgaris</i> will be particularly relevant to both consumers and policy makers.

Authors

Institutions

Publication Details

Journal
Toxicology Research
Published
2024-09-02
DOI
https://doi.org/10.1093/toxres/tfae142
Citations
2
Primary Topic
Heavy metals in environment
Type
article
Field-Weighted Citation Impact
0.27

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Heavy metals and probabilistic risk assessment via Prunella vulgaris (food and medicine homology) consumption in Guangdong Province, China

XU Xiu-min, Rui Huang, Ping Wang, Jiewen Peng et al.
2 citations
Toxicology Research
Heavy metals in environment
0.27
article

Heavy metals and probabilistic risk assessment via Prunella vulgaris (food and medicine homology) consumption in Guangdong Province, China

XU Xiu-min, Rui Huang, Ping Wang, Jiewen Peng, S S Chen, Zihui Chen, Pan Zhu
article en
2 citations

Abstract

<i>Prunella vulgaris</i> is widely used as the main ingredient of herb tea in Southeast Asia, as well as a traditional Chinese medicine. However, the heavy metal contaminations such as arsenic, cadmium, mercury and lead in <i>P. vulgaris</i> may be a cause for concern due to the environment pollution around, plantation and processing contamination. Thus, this study intented to assess both non-carcinogenic risks and carcinogenic risks attributed to cumulative exposure to the four heavy metals in <i>P. vulgaris.</i> The contaminations levels of heavy metals were determined in 90 batches of <i>P. vulgaris.</i> And the consumption level was obtained through a questionnaire survey among a total of 6,235 adult participants in Guangdong province. This study estimated the probabilistic health risks using Monte Carlo simulation, and found that the estimated mean and the 95th percentile values for cumulative noncarcinogenic risk (HI value) and carcinogenic risk (TCR value) of <i>P. vulgaris</i> were all within the acceptable risk. And the assessment results indicated that arsenic was the primary contributors to both noncarcinogenic and carcinogenic risks through <i>P. vulgaris</i> consumption. These findings and continuing the surveillance of heavy metals in <i>P. vulgaris</i> will be particularly relevant to both consumers and policy makers.

Toxicology ResearchVol. 13(5)
Guangdong Provincial Center for Disease Control and Prevention (CN)
Guangdong Medical Research Foundation
Life in Land
Openalex Percentile: Top 53%
Heavy metals in environment
0.27
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.