Evidence map›Paper›PMID 38486191›Full record

ArticleBMC public health2024

Association of urinary chlorpyrifos, paraquat, and cyproconazole levels with the severity of fatty liver based on MRI.

Peiqi Ma, Hongliang Gao, Ning Shen, Lei Zhang, Yang Zhang, Kai Zheng, Boqun Xu, Jian Qin, Jian He, Tao Xu and 4 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 3 institutions in 1 country.

Peiqi Ma *Medical imaging center, Fuyang People's Hospital, 236000, Fuyang, China.
Hongliang Gao *Core Laboratory, Department of Clinical Laboratory Sir Run Run Hospital, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211166, Nanjing, China.
Ning ShenChina Exposomics Institute (CEI) Precision Medicine Co. Ltd, 200120, Shanghai, China.
Lei ZhangMedical imaging center, Fuyang People's Hospital, 236000, Fuyang, China.
Yang ZhangMedical imaging center, Fuyang People's Hospital, 236000, Fuyang, China.
Kai ZhengJiangsu Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, 210029, Nanjing, China.
Boqun XuDepartment of Obstetrics and Gynecology, the Second Affiliated Hospital of Nanjing Medical University, 210011, Nanjing, China.
Jian QinDepartment of Orthopaedics, Sir Run Run Hospital, Nanjing Medical University, 211100, Nanjing, China.
Jian HeDepartment of Nuclear Medicine, Affiliated Hospital of Medical School, Nanjing Drum Tower Hospital, Nanjing University, 210029, Nanjing, China.
Tao XuCore Laboratory, Department of Clinical Laboratory Sir Run Run Hospital, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211166, Nanjing, China.
Yan LiCore Laboratory, Department of Clinical Laboratory Sir Run Run Hospital, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211166, Nanjing, China. yanli@njmu.edu.cn.
Jing WuCore Laboratory, Department of Clinical Laboratory Sir Run Run Hospital, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211166, Nanjing, China. wujingshanx@163.com.
Yushan YuanMedical imaging center, Fuyang People's Hospital, 236000, Fuyang, China. fysyyyys@163.com.
Bin XueDepartment of General Surgery, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Nanjing Medical University, 213003, Changzhou, China. xuebin@njmu.edu.cn.
Nanjing Medical University · CNNanjing Drum Tower Hospital · CNSecond Affiliated Hospital of Nanjing Medical University · CN

Funding

Collaborative Innovation Center for Cancer Personalized Medicine - Clinical Research Fund of Hengrui Medicine JZ21449020210617Jiangsu Educational Development, Young and middle-aged academic leader of Jiangsu Qinglan Project KY520R202025Natural Science Foundation of Xinjiang Uygur Autonomous Region 2023D01D05Science and Technology Benefit People Program of Jiangning District 2023003SScience and Technology Bureau self-funded project of Fuyang FK202081049the Basic Research Project of Changzhou Medical Center, Nanjing Medical University CMCB202305The Chinese National Science Foundation 8170071The National Natural Science Foundation of China 32071142, 32271187Top innovative talent program of Nanjing Medical University NJMUTY20230093
6 · The paper itself

Abstract

backgroundThe objective of this study was to detect the urinary levels of chlorpyrifos, paraquat, and cyproconazole in residents living in Fuyang City and to analyze the correlation between these urinary pesticides levels and the severity of fatty liver disease (FLD).

methodsAll participants' fat fraction (FF) values were recorded by MRI (Magnetic resonance imaging). First-morning urine samples were collected from 53 participants from Fuyang Peoples'Hospital. The levels of three urinary pesticides were measured using β-glucuronidase hydrolysis followed by a. The results were analyzed by using Pearson correlation analysis and binary logistic regression analysis to reveal the correlation between three urinary pesticides and the severity of fatty liver.

results53 individuals were divided into 3 groups based on the results from MRI, with 20 cases in the normal control group, 16 cases in the mild fatty liver group, and 17 cases in the moderate and severe fatty liver group. Urinary chlorpyrifos level was increased along with the increase of the severity of fatty liver. Urinary paraquat level was significantly higher both in the low-grade fatty liver group and moderate & serve grade fatty liver group compared with the control group. No significant differences in urinary cyproconazole levels were observed among the three groups. Furthermore, urinary chlorpyrifos and paraquat levels were positively correlated with FF value. And chlorpyrifos was the risk factor that may be involved in the development of FLD and Receiver Operating Characteristic curve (ROC curve) analysis showed that chlorpyrifos and paraquat may serve as potential predictors of FLD.

conclusionThe present findings indicate urinary chlorpyrifos and paraquat were positively correlated with the severity of fatty liver. Moreover, urinary chlorpyrifos and paraquat have the potential to be considered as the predictors for development of FLD. Thus, this study may provide a new perspective from the environmental factors for the diagnosis, prevention, and treatment of FLD.

Indexed as

ChlorpyrifosNon-alcoholic Fatty Liver DiseasePesticidesTriazolesHumansMagnetic Resonance ImagingParaquatChlorpyrifoscyproconazoleParaquatPesticidesTriazolesChlorpyrifosCyproconazoleExposureFatty liver diseaseParaquat

Identifiers

PMID38486191
PMCPMC10941454
OpenAlexW4392800166

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.