Evidence map›Paper›PMID 38705256›Full record

ArticleJournal of advanced research2025

Associations between per- and polyfluoroalkyl chemicals and abdominal aortic calcification in middle-aged and older adults.

Jijingru Yang, Tian Wang, Kai Li, Yán Wāng

Abstract read
In one paragraph

Article in Journal of advanced research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026
    Review
  3. Review
  4. Article
  5. Article
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  7. Observational
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

4 authors.

Jijingru YangResearch Center for Translational Medicine, the Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China; The Second School of Clinical Medicine, Anhui Medical University, Hefei, 230032, China.
Tian WangKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China.
Kai LiSchool of Public Health, Shanxi Medical University, Taiyuan 030001, China.
Yán WāngResearch Center for Translational Medicine, the Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China; Department of Toxicology, School of Public Health, Anhui Medical University, Hefei 230032, China. Electronic address: yanwang829@ahmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPer- and polyfluoroalkyl substances (PFAS) have infiltrated countless everyday products, raising concerns about potential effects on human health, specifically on the cardiovascular system and the development of abdominal aortic calcification (AAC). However, our understanding of this relationship is still limited.

objectivesThis study aims to investigate the effects of PFAS on AAC using machine learning algorithms.

methodsLeveraging the power of machine learning technique, extreme gradient boosting (XGBoost), we assessed the relationship between PFAS exposure and AAC risk. We focused on three PFAS compounds, perfluorodecanoic acid (PFDeA), perfluorohexane sulfonic acid (PFHxS), and perfluorononanoic acid (PFNA) through multiple logistic regression, restricted cubic spline (RCS), and quantile g-computation (QGC) models. To get more insight into the underlying mechanisms, mediation analyses are used to investigate the potential mediating role of fatty acids and blood cell fractions in AAC.

resultsOur findings indicate that elevated serum levels of PFHxS and PFDeA are associated with the increased risk of AAC. The QGC analyses underscore the overall positive association between the PFAS mixture and AAC risk, with PFHxS carrying the greatest weight, followed by PFDeA. The RCS analyses reveal a dose-dependent increase between serum PFHxS concentration and AAC risk in an inverted V-shape way. Moreover, age and PFHxS exposure are identified as the primary factors contributing to abdominal aortic calcification risk in SHapley Additive exPlanation (SHAP) summary plot combined with XGBoost technique. Although PFAS significantly change the profile of fatty acids, we do not find any mediating roles of them in AAC. Despite strong associations between PFAS exposure and hematological indicators, our analysis does not find evidence that these indicators mediate the development of AAC.

conclusionsIn summary, our study highlights the detrimental impact of PFAS on abdominal aortic health and emphasizes the need for further research to understand the underlying mechanisms involved.

Indexed as

Aorta, AbdominalEnvironmental ExposureFluorocarbonsVascular CalcificationAgedDecanoic AcidsFemaleHumansMachine LearningMaleMiddle AgedRisk FactorsDecanoic AcidsFluorocarbonsperfluorodecanoic acidAbdominal aortic calcification (AAC)Blood cellsFatty acidsMachine learningPerfluoroalkyl and polyfluoroalkyl substances (PFAS)

Identifiers

PMID38705256
PMCPMC11976567

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Registered trials

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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.