Evidence map›Paper›PMID 40559873›Full record

ReviewToxins2025

From Physicochemical Classification to Multidimensional Insights: A Comprehensive Review of Uremic Toxin Research.

Mario Cozzolino, Lorenza Magagnoli, Paola Ciceri

Abstract readReview
In one paragraph

Review in Toxins, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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

3 authors.

Mario CozzolinoDepartment of Health Sciences, University of Milan, 20122 Milan, Italy.ORCID 0000-0002-8494-6252
Lorenza MagagnoliDepartment of Health Sciences, University of Milan, 20122 Milan, Italy.ORCID 0000-0003-2853-1332
Paola CiceriDepartment of Health Sciences, University of Milan, 20122 Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic kidney disease (CKD) is a global health burden, with uremic toxins (UTs) playing a central role in its pathophysiology. In this review, we systematically examined the evolution of UT classification from the 2003 European Uremic Toxin Work Group (EUTox) system based on molecular weight and protein-binding properties to the 2023 multidimensional framework integrating clinical outcomes, clearance technologies, and artificial intelligence. We highlighted the toxicity mechanisms of UTs across the cardiovascular, immune, and nervous systems and evaluated traditional (e.g., low-/high-flux hemodialysis) and advanced (e.g., high-cutoff dialysis and hemoadsorption) clearance strategies. Despite progress, challenges persist in toxin detection, clearance efficiency, and personalized therapy. Future directions include multi-omics-based biomarker discovery, optimized dialysis membranes, advanced adsorption technology, and AI-driven treatment personalization. This synthesis aims to bridge translational gaps and guide precision medicine in nephrology.

Indexed as

Renal Insufficiency, ChronicUremic ToxinsAnimalsArtificial IntelligenceBiomarkersHumansPrecision MedicineRenal DialysisUremiaBiomarkersUremic Toxinschronic kidney diseasehemoadsorptionhemodialysisprecision medicinetoxicity mechanismsuremic toxins

Identifiers

PMID40559873
PMCPMC12197540

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.