Evidence map›Paper›PMID 40737036›Full record

ArticleEuropean thyroid journal2025

Long-term exposure to PM1 and thyroid diseases in China.

Jing Wang, Di Teng, Kaijie Yang, Siying Liu, Cihang Lu, Zhongyan Shan, Weiping Teng, Yongze Li

Abstract read
In one paragraph

Article in European thyroid journal, 2025. 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

8 authors.

Jing Wang
Di Teng
Kaijie Yang
Siying Liu
Cihang Lu
Zhongyan Shan

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Globally prevalent, thyroid diseases are linked to environmental factors such as air pollution. This study examines the link between particulate matter (PM)1 exposure and thyroid disease rates in China. Methods: We analyzed data from 73,900 adults across 31 Chinese provinces, using a high-resolution spatial-temporal extremity tree model to estimate PM1 and PM2.5 levels, and thyroid function tests to assess disease prevalence. Multivariate-adjusted ORs evaluated PM1's link to thyroid disease. This cross-sectional study is adept at identifying associations but cannot establish causality due to its single-time data collection limitation. Results: Higher PM1 level exposure was significantly linked to an increased prevalence of thyroid diseases, including overt hypothyroidism, autoimmune thyroiditis (AIT), and TgAb positivity. A linear dose-response relationship was observed between PM1 concentration and both AIT and TgAb positivity. The study also revealed a significant association between PM1 exposure and autoimmune overt hypothyroidism, suggesting that thyroid dysfunction may be primarily mediated through autoimmune mechanisms. In addition, iodine status significantly influenced PM1's effects, with lower levels enhancing susceptibility to thyroid issues. Furthermore, every 1% increase in the PM1/PM2.5 ratio was related to the prevalence of overt hypothyroidism (OR = 1.03; 95% CI: 1.03-1.04) and subclinical hypothyroidism (OR = 1.03; 95% CI: 1.03-1.04). Conclusions: PM1 exposure is associated with thyroid diseases, particularly AIT and TgAb positivity, with iodine status playing a modifying role. PM1 may be a key factor in PM2.5-related thyroid disease risk. Further prospective cohort studies are warranted to validate these findings.

Indexed as

Air PollutantsAir PollutionEnvironmental ExposureParticulate MatterThyroid DiseasesAdultAgedChinaCross-Sectional StudiesFemaleHumansHypothyroidismMaleMiddle AgedPrevalenceThyroiditis, AutoimmuneAir PollutantsParticulate MatterChinaepidemiologyiodinePM1thyroid disorders

Identifiers

PMID40737036
PMCPMC12344400

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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.