Evidence map›Paper›PMID 37926847›Full record

ArticleJournal of health, population, and nutrition2023

Blood manganese and non-alcoholic fatty liver disease in a high manganese exposure area in China.

Liping Wu, Yanqi Lan, Ze Yu, Yanhong Wang, Wei Liao, Guoqiang Zhang, Li Wang

Open access · goldAbstract read
In one paragraph

Article in Journal of health, population, and nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 30% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Protective effects ofRedox report : communications in free radical research · 2026
    Article
  2. Article
  3. Article
  4. Machine learning uncovers manganese as a key nutrient associated with reduced risk of steatotic liver disease.Liver international : official journal of the International Association for the Study of the Liver · 2024
    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

7 authors at 3 institutions in 1 country.

Liping Wu *Department of Hepatobiliary Surgery, Zhoushan Hospital, Wenzhou Medical University, Zhoushan, 316021, China.
Yanqi Lan *Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, 5 Dong Dan San Tiao, Beijing, 100730, China.
Ze Yu *Department of Hepatobiliary Surgery, Zhoushan Hospital, Wenzhou Medical University, Zhoushan, 316021, China.
Yanhong WangDepartment of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, 5 Dong Dan San Tiao, Beijing, 100730, China.
Wei LiaoDepartment of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, 5 Dong Dan San Tiao, Beijing, 100730, China.
Guoqiang ZhangDepartment of Hepatobiliary Surgery, Zhoushan Hospital, Wenzhou Medical University, Zhoushan, 316021, China. zhanggqzs@163.com.
Li WangDepartment of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking, Union Medical College, 5 Dong Dan San Tiao, Beijing, 100730, China. liwang@ibms.pumc.edu.cn.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNWenzhou Medical University · CNZhoushan Hospital · CN

Funding

CAMS Innovation Fund for Medical Sciences (CIFMS) 2016-I2M-3-001Zhoushan Science and Technology Project 2018C11042
6 · The paper itself

Abstract

BACKGROUND AND

aimsManganese (Mn) deficiency and intoxication may affect nonalcoholic fatty liver disease (NAFLD) risk differently. We aimed to explore the association between blood Mn and NAFLD in an area with high Mn exposure in drinking water.

methodsWe conducted a case-control study among 1407 patients with NAFLD and 1774 sex- and age-matched healthy controls in a physical examination population in Zhoushan hospital, Zhejiang province in China. We used the restricted cubic splines method to investigate the dose-response relationship. Logistic regression models were applied to determine the risk of NAFLD, and severity of NAFLD.

resultsThe blood Mn concentration was higher in the NAFLD group than in the control group in women (16.1 ± 6.2 μg/L vs. 14.7 ± 6.4 μg/L, P = 0.022) and men (14.5 ± 6.3 μg/L vs. 13.6 ± 6.8 μg/L, P < 0.001). We found an inverted L shape relationship between blood Mn and NAFLD in both women and men. Compared to the lowest quartile, the adjusted odds ratio (OR) and 95% confidence interval (CI) of NAFLD for the highest quartile group was 1.646(1.222,2.217), 1.494(1.082,2.061), and 3.146(1.285,7.701) for the total population, men, and women. The positive relationship was only observed in those with fibrosis-4 score < 1.30 and normal alanine transaminase. Stratified analysis showed an interaction between smoking (P = 0.073), alcohol drinking (P = 0.013), and Mn, with a more prominent effect on the NAFLD in the never-smokers (OR = 2.153, 95% CI 1.408-3.290) and drinkers (OR = 2.596, 95% CI 1.608-4.191).

conclusionHigher blood Mn is associated with an elevated NAFLD risk in the high Mn exposure areas, especially in nonsmokers and drinkers. Further studies are needed to verify this result in the areas with high Mn exposure.

Indexed as

Non-alcoholic Fatty Liver DiseaseCase-Control StudiesChinaFemaleHumansLogistic ModelsMaleManganeseRisk FactorsSmokingManganeseCase–control studyHigh manganese exposure areaManganeseNAFLD

Identifiers

PMID37926847
PMCPMC10626744
OpenAlexW4388398862

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.