ArticleFrontiers in public health2026
Global prostate cancer risk associated with microplastic exposure: a statistical and machine learning analysis.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Prostate cancer is one of the most commonly diagnosed malignancies among men worldwide, with higher reported incidence in many high-income countries. Environmental factors are receiving increasing attention as potential contributors to cancer development. Microplastics, which are found in air, water, food, and personal care items, are one possible risk factor. Methodology: Data from 22 nations were investigated to examine whether an association exists between exposure to microplastics and the rate of prostate cancer. Data on exposure were combined from several sources, such as stool particles, breathed air, drinking water, seafood intake, and personal care products. Statistical and machine learning methods, such as K-means clustering, principal component analysis, and random forest modeling, were applied to find the most important exposure variables linked to cancer risk. Results: Stool microplastic concentrations and heavy metal burden showed the strongest model-based associations with prostate cancer incidence. Countries with higher external exposure indicators did not consistently show higher reported prostate cancer incidence. This pattern suggests that external exposure metrics alone may be insufficient to explain country-level variation. Internal retention and tissue-response pathways remain plausible hypotheses, but they require direct validation using individual-level and tissue-based data. Discussion: The findings support the need to integrate exposure pathways, biomonitoring indicators, and biological-response markers when studying microplastic-related cancer risk. However, this study was limited by its ecological design, cross-sectional structure, and small sample size of 22 countries. Therefore, the results should be interpreted as exploratory and hypothesis-generating rather than causal. Further longitudinal and individual-level studies are required to validate these associations.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.