Evidence map›Paper›PMID 41745824›Full record

ArticleToxics2026

Risk Assessment of Dibutyl Phthalate (DBP) and Bis(2-Ethylhexyl) Phthalate (DEHP) in Hot Pot Bases with a Hybrid Modeling Approach.

Xiangyu Bian, Siyu Huang, Dongya Chen, Depeng Jiang, Daoyuan Yang, Yingzi Zhao, Zhujun Liu, Shiqi Chen, Yan Song, Haixia Sui and 1 more

Abstract read
In one paragraph

Article in Toxics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Xiangyu BianDepartment of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210000, China.ORCID 0009-0000-5121-6862
Siyu HuangKey Laboratory of Condiment Supervision Technology, State Administration for Market Regulation, Chongqing Institute for Food and Drug Control, Chongqing 401121, China.
Dongya ChenJiangsu Provincial Center for Disease Control and Prevention, 172 Jiangsu Road, Gulou District, Nanjing 210009, China.
Depeng JiangDepartment of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210000, China.
Daoyuan YangNHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, No. 37 Guangqu Road, Chaoyang District, Beijing 100022, China.
Yingzi ZhaoDepartment of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210000, China.
Zhujun LiuDepartment of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210000, China.
Shiqi ChenKey Laboratory of Condiment Supervision Technology, State Administration for Market Regulation, Chongqing Institute for Food and Drug Control, Chongqing 401121, China.
Yan SongNHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, No. 37 Guangqu Road, Chaoyang District, Beijing 100022, China.
Haixia SuiNHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, No. 37 Guangqu Road, Chaoyang District, Beijing 100022, China.ORCID 0000-0003-0405-0278
Jinfang SunDepartment of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210000, China.ORCID 0009-0002-2278-016X

Funding

China Food Safety Talent Competency development Initiative CFSA 523 ProgramNational Key Research and Development Program of China 2023YFF1103900
6 · The paper itself

Abstract

(1) Background: Hot pot bases are susceptible to phthalate (PAE) contamination due to their high lipid content. Standard risk models often fail to capture extreme values, leading to biased exposure estimates. This study characterized dibutyl phthalate (DBP) and bis(2-ethylhexyl) phthalate (DEHP) contamination using a hybrid modeling framework to ensure precise risk profiling. (2) Methods: A total of 91 samples were analyzed via GC-MS. Concentration data were fitted using traditional parametric, extreme value mixture (EVMM), and finite mixture models. Probabilistic dietary risks were assessed for Chinese demographic groups using 10,000-iteration Monte Carlo simulations. (3) Results: DEHP (detection rate: 55%) and DBP (32%) were best modeled by a two-component Gamma mixture and a Lognormal-Generalized Pareto distribution, respectively. These advanced models significantly outperformed conventional distributions in capturing upper-tail extremes. Crucially, all hazard quotients (HQs) remained below the safety threshold of 1, indicating acceptable risk, although children aged 7-13 exhibited the highest calculated risk (Max DEHP HQ = 0.68). (4) Conclusions: Although current exposure levels are within safe limits, the heavy-tailed distributions identify potential sporadic high-exposure events that traditional models overlook, specifically highlighting the relative vulnerability of children aged 7-13. This study validates that hybrid statistical approaches offer superior precision for analyzing skewed contamination data. Consequently, these findings provide a critical scientific basis for refining regulatory monitoring and implementing targeted source-tracking measures to mitigate long-tail food safety risks.

Indexed as

DBPDEHPdietary exposureextreme value mixture modelmixture modelparametric probabilistic modelingrisk assessment

Identifiers

PMID41745824
PMCPMC12945265

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.