Evidence map›Paper›PMID 36474262›Full record

ArticleLipids in health and disease2022

Association between combined exposure to plasma heavy metals and dyslipidemia in a chinese population.

Tingyu Luo, Shiyi Chen, Jiansheng Cai, Qiumei Liu, Ruoyu Gou, Xiaoting Mo, Xu Tang, Kailian He, Song Xiao, Yanfei Wei and 7 more

Open access · goldAbstract read
In one paragraph

Article in Lipids in health and disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

8 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
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

17 authors at 3 institutions in 1 country.

Tingyu Luo *Department of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi, 541199, Guilin, China.
Shiyi Chen *School of Public Health and Management, Guangxi University of Chinese Medicine, Guangxi, 530200, Nanning, China.
Jiansheng CaiDepartment of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi, 541199, Guilin, China.
Qiumei LiuDepartment of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, 530021, Nanning, Guangxi, China.
Ruoyu GouDepartment of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi, 541199, Guilin, China.
Xiaoting MoDepartment of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, 530021, Nanning, Guangxi, China.
Xu TangDepartment of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, 530021, Nanning, Guangxi, China.
Kailian HeDepartment of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi, 541199, Guilin, China.
Song XiaoDepartment of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi, 541199, Guilin, China.
Yanfei WeiDepartment of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, 530021, Nanning, Guangxi, China.
Yinxia LinDepartment of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, 530021, Nanning, Guangxi, China.
Shenxiang HuangDepartment of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, 530021, Nanning, Guangxi, China.
Tingjun LiDepartment of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi, 541199, Guilin, China.
Ziqi ChenDepartment of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi, 541199, Guilin, China.
Ruiying LiDepartment of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi, 541199, Guilin, China.
You LiDepartment of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi, 541199, Guilin, China. liyou121300@163.com.
Zhiyong ZhangDepartment of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, No.1 Zhiyuan Road, Guangxi, 541199, Guilin, China. rpazz@glmc.edu.cn.
Guilin Medical University · CNGuangxi Medical University · CNGuangxi University · CN

Funding

Guangxi Graduate Education Innovation Project No. GYYK2021001Guangxi Postgraduate Innovation Project No. YCSW2022376the National Natural Science Foundation of China No. 81960583
6 · The paper itself

Abstract

backgroundExposure to heavy metals in the environment is widespread, while the relationship between combined exposure to heavy metals and dyslipidemia is unclear.

methodsA cross-sectional study was performed, and 3544 participants aged 30 years or older were included in the analyses. Heavy metal concentrations in plasma were based on inductively coupled plasma‒mass spectrometry. The relationship between heavy metals and dyslipidemia was estimated by logistic regression. BKMR was used to evaluate metal mixtures and their potential interactions.

resultsIn logistic regression analysis, participants in the fourth quartile of Fe and Zn (Fe > 1352.38 µg/L; Zn > 4401.42 µg/L) had a relatively higher risk of dyslipidemia (Fe, OR = 1.13, 95% CI: 0.92,1.38; Zn, OR = 1.30, 95% CI: 1.03,1.64). After sex stratification, females in the third quartile of plasma Zn (1062.05-4401.42 µg/L) had a higher relative risk of dyslipidemia (OR = 1.75, 95% CI: 1.28, 2.38). In BKMR analysis, metal mixtures were negatively associated with dyslipidemia in females when all metal concentrations were above the 50th percentile. In the total population (estimated from 0.030 to 0.031), As was positively associated with dyslipidemia when other metals were controlled at the 25th, 50th, or 75th percentile, respectively, and As was below the 75th percentile. In females (estimated from - 0.037 to -0.031), Zn was negatively associated with dyslipidemia when it was above the 50th percentile.

conclusionThis study indicated that As was positively associated with dyslipidemia and that Zn may be negatively associated with dyslipidemia in females. Combined metal exposure was negatively associated with dyslipidemia in females. Females with low plasma Zn levels are more likely to develop dyslipidemia and should receive more clinical attention in this population.

Indexed as

East Asian PeopleMetals, HeavyCross-Sectional StudiesHumansMetals, HeavyBayesian kernel machine regressionDyslipidemiaHeavy metals

Identifiers

PMID36474262
PMCPMC9724421
OpenAlexW4311576970

What Socratic 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 Socratic graph.