Evidence map›Paper›PMID 41961635›Full record

ReviewThe Journal of clinical endocrinology and metabolism2026

Studying endocrine disrupting chemicals from molecular targets to mixture model approach: lessons from the thyroid model.

Francesca Coperchini, Alessia Greco, Elena Franchi, Marco Denegri, Mario Rotondi

Abstract readReview
In one paragraph

Review in The Journal of clinical endocrinology and metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Francesca CoperchiniDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia 27100, Italy.
Alessia GrecoDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia 27100, Italy.
Elena FranchiDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia 27100, Italy.
Marco DenegriUnit of Endocrinology and Metabolism, Laboratory for Endocrine Disruptors, Istituti Clinici Scientifici Maugeri IRCCS, Pavia 27100, Italy.
Mario RotondiDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia 27100, Italy.ORCID 0000-0001-6464-2334

Funding

Ministero della Salute, Italy
6 · The paper itself

Abstract

Endocrine-disrupting chemicals (EDCs) pose a significant global health risk by interfering with hormonal balance. The thyroid is particularly suitable for studying endocrine disruption due to its crucial role in development, metabolism, and cognitive function, alongside established molecular targets and regulatory frameworks. This review summarizes current knowledge on thyroid-disrupting chemicals (TDs), focusing on mechanistic pathways acting at both intrathyroidal and extrathyroidal levels. Particular attention is given to molecular initiating events, such as interference with iodide uptake, thyroperoxidase activity, thyroglobulin processing, and thyroid hormone signaling, and to their integration within the adverse outcome pathway (AOP) framework. In addition, the review discusses methodological strategies for assessing thyroid disruption, spanning in silico, in vitro, in vivo, and human epidemiological approaches. Finally, emerging challenges related to real-world exposure to chemical mixtures are addressed, highlighting the need for AOP-informed, mixture-based strategies to improve risk assessment and regulatory decision-making.

Indexed as

Endocrine DisruptorsModels, BiologicalThyroid GlandAdverse Outcome PathwaysAnimalsHumansRisk AssessmentEndocrine DisruptorsAOPendocrine disruptorsmixturesthyroid

Identifiers

PMID41961635
PMCPMC13271808

What OpenQuestion holds

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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.