Observational studyMolecules (Basel, Switzerland)2024
Breath Analysis: Identification of Potential Volatile Biomarkers for Non-Invasive Diagnosis of Chronic Kidney Disease (CKD).
Observational study in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Advances in biomarkers of benzene exposure and toxicity: From mechanistic insights to occupational health applications (Review).Molecular medicine reports · 2026Review
- Recent advancements and future prospects on AI-integrated sensing techniques for non-invasive chronic kidney disease diagnosis: a review.Frontiers in artificial intelligence · 2026Review
- Artificial intelligence-based diagnosis of diabetic kidney disease using urinary VOC biosensor data.BMC nephrology · 2025Article
- The Role of Breath Analysis in the Non-Invasive Early Diagnosis of Malignant Pleural Mesothelioma (MPM) and the Management of At-Risk Individuals.Molecules (Basel, Switzerland) · 2025Observational
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, volatile organic compound (VOC) determination in exhaled breath has seen growing interest due to its promising potential in early diagnosis of several pathological conditions, including chronic kidney disease (CKD). Therefore, this study aimed to identify the breath VOC pattern providing an accurate, reproducible and fast CKD diagnosis at early stages of disease. A cross-sectional observational study was carried out, enrolling a total of 30 subjects matched for age and gender. More specifically, the breath samples were collected from (a) 10 patients with end-stage kidney disease (ESKD) before undergoing hemodialysis treatment (DIAL); (b) 10 patients with mild-moderate CKD (G) including 3 patients in stage G2 with mild albuminuria, and 7 patients in stage G3 and (c) 10 healthy controls (CTRL). For each volunteer, an end-tidal exhaled breath sample and an ambient air sample (AA) were collected at the same time on two sorbent tubes by an automated sampling system and analyzed by Thermal Desorption-Gas Chromatography-Mass Spectrometry. A total of 110 VOCs were detected in breath samples but only 42 showed significatively different levels with respect to AA. Nonparametric tests, such as Wilcoxon/Kruskal-Wallis tests, allowed us to identify the most weighting variables able to discriminate between AA, DIAL, G and CTRL breath samples. A promising multivariate data mining approach incorporating only selected variables (showing
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