Evidence map›Paper›PMID 37104231›Full record

ArticleToxins2023

Microcystins Exposure Associated with Blood Lipid Profiles and Dyslipidemia: A Cross-Sectional Study in Hunan Province, China.

Shuidong Feng, Mengyue Cao, Peng Tang, Shuxiang Deng, Limou Chen, Yan Tang, Lemei Zhu, Xiang Chen, Zhijun Huang, Minxue Shen and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Toxins, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
7.5field-weighted citation impact, top 2% 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

21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 53 citations in OpenAlex.

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  17. Probabilistic human health risk assessment of PMFrontiers in public health · 2025
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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 at 3 institutions in 1 country.

Shuidong FengDepartment of Epidemiology and Health Statistics, The Key Laboratory of Typical Environmental Pollution and Health Hazards of Hunan Province, School of Basic Medicine, School of Public Health, Hengyang Medical School, University of South China, Hengyang 421001, China.
Mengyue CaoDepartment of Epidemiology and Health Statistics, The Key Laboratory of Typical Environmental Pollution and Health Hazards of Hunan Province, School of Basic Medicine, School of Public Health, Hengyang Medical School, University of South China, Hengyang 421001, China.
Peng TangDepartment of Epidemiology and Health Statistics, The Key Laboratory of Typical Environmental Pollution and Health Hazards of Hunan Province, School of Basic Medicine, School of Public Health, Hengyang Medical School, University of South China, Hengyang 421001, China.
Shuxiang DengDepartment of Epidemiology and Health Statistics, The Key Laboratory of Typical Environmental Pollution and Health Hazards of Hunan Province, School of Basic Medicine, School of Public Health, Hengyang Medical School, University of South China, Hengyang 421001, China.
Limou ChenDepartment of Epidemiology and Health Statistics, The Key Laboratory of Typical Environmental Pollution and Health Hazards of Hunan Province, School of Basic Medicine, School of Public Health, Hengyang Medical School, University of South China, Hengyang 421001, China.
Yan TangDepartment of Epidemiology and Health Statistics, The Key Laboratory of Typical Environmental Pollution and Health Hazards of Hunan Province, School of Basic Medicine, School of Public Health, Hengyang Medical School, University of South China, Hengyang 421001, China.
Lemei ZhuSchool of Public Health, Changsha Medical University, Changsha 410219, China.
Xiang ChenDepartment of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Clinical Research Center for Cancer Immunotherapy, Xiangya Hospital, Central South University, Changsha 410008, China.
Zhijun HuangFurong Laboratory, Changsha 410008, China.
Minxue ShenFurong Laboratory, Changsha 410008, China.ORCID 0000-0003-0441-9303
Fei YangDepartment of Epidemiology and Health Statistics, The Key Laboratory of Typical Environmental Pollution and Health Hazards of Hunan Province, School of Basic Medicine, School of Public Health, Hengyang Medical School, University of South China, Hengyang 421001, China.ORCID 0000-0001-5687-0556
University of South China · CNCentral South University · CNChangsha Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Increasing evidence from experimental research suggests that exposure to microcystins (MCs) may induce lipid metabolism disorder. However, population-based epidemiological studies of the association between MCs exposure and the risk of dyslipidemia are lacking. Therefore, we conducted a population-based cross-sectional study involving 720 participants in Hunan Province, China, and evaluated the effects of MCs on blood lipids. After adjusting the lipid related metals, we used binary logistic regression and multiple linear regression models to examine the associations among serum MCs concentration, the risk of dyslipidemia and blood lipids (triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C)). Moreover, the additive model was used to explore the interaction effects on dyslipidemia between MCs and metals. Compared to the lowest quartile of MCs exposure, the risk of dyslipidemia [odds ratios (OR) = 2.27, 95% confidence interval (CI): 1.46, 3.53] and hyperTG (OR = 3.01, 95% CI: 1.79, 5.05) in the highest quartile was significantly increased, and showed dose-response relationships. MCs were positively associated with TG level (percent change, 9.43%; 95% CI: 3.53%, 15.67%) and negatively associated with HDL-C level (percent change, -3.53%; 95% CI: -5.70%, -2.10%). In addition, an additive antagonistic effect of MCs and Zn on dyslipidemia was also reported [relative excess risk due to interaction (RERI) = -1.81 (95% CI: -3.56, -0.05)], and the attributable proportion of the reduced risk of dyslipidemia due to the antagonism of these two exposures was 83% (95% CI: -1.66, -0.005). Our study first indicated that MCs exposure is an independent risk factor for dyslipidemia in a dose-response manner.

Indexed as

DyslipidemiasMicrocystinsChinaCholesterol, HDLCross-Sectional StudiesHumansLipidsCholesterol, HDLLipidsMicrocystinscholesterolcross-sectional studydyslipidemialipid profileslipoproteinmicrocystinstriglyceride

Identifiers

PMID37104231
PMCPMC10143012
OpenAlexW4366287479

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.