Evidence map›Paper›PMID 40289139›Full record

ArticleScientific reports2025

The biosensor using the target urine volatile organic compounds for detecting diabetic kidney disease.

Chatchai Kreepala, Vichayut Suthat Na Ayutaya, Rungrueang Phatthanakun, Kittipong Thabsuwan, Jirayupa Paewponsong

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Chatchai KreepalaSchool of Internal Medicine, Institute of Medicine, Suranaree University of Technology, Nakhon Ratchasima, Thailand. Chatchaikree@gmail.com.
Vichayut Suthat Na AyutayaSchool of Internal Medicine, Institute of Medicine, Suranaree University of Technology, Nakhon Ratchasima, Thailand.
Rungrueang PhatthanakunSynchrotron Light Research Institute, Nakhon Ratchasima, Thailand.
Kittipong ThabsuwanSchool of Ophthalmology, Institute of Medicine, Suranaree University of Technology, Nakhon Ratchasima, Thailand.
Jirayupa PaewponsongSchool of Internal Medicine, Institute of Medicine, Suranaree University of Technology, Nakhon Ratchasima, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Volatile organic compounds (VOCs) are metabolic byproducts reflecting pathophysiologic processes and have been widely utilized as non-invasive biomarkers for disease differentiation. While VOCs have been extensively studied in oncology, their potential for diagnosing other prevalent public health conditions remains underexplored. This study serves as a proof-of-concept investigation, applying VOC-based detection to diabetic kidney disease (DKD), a leading cause of chronic kidney disease, to distinguish it from other nephrotic syndromes (NS). Utilizing metal oxide semiconductors, we analyzed urinary VOC profiles in 135 participants with biopsy-confirmed diagnoses. All gas sensors in the sensor chamber were of the n-type. Our results demonstrated that hydrogen and ethanol VOCs, measured at a minimum electrical resistance threshold of 4500 mV, effectively distinguished DKD from NS and other control groups. The mean differences in electrical resistance among DKD versus normal, DM without DKD, and NS were 868.3, 145.5, and 881.2 ohms, respectively (p < 0.05). Receiver operating characteristic (ROC) analysis yielded AUC values of 1.0, 0.64, and 0.99, respectively (p < 0.05), underscoring the method's diagnostic potential. VOCs primarily composed of hydrogen and ethanol in DKD patients exhibited distinct electron-release properties when interacting with metal oxide semiconductors compared to healthy individuals and NS patients. These findings suggest that urine-based VOC analysis has potential as a non-invasive alternative for distinguishing DKD from other glomerular diseases. This study highlights the feasibility of extending VOC-based biosensor technology beyond oncology, offering a novel diagnostic framework for broader clinical applications in public health and metabolic disease monitoring.

Indexed as

Biosensing TechniquesDiabetic NephropathiesVolatile Organic CompoundsAdultAgedBiomarkersFemaleHumansMaleMiddle AgedROC CurveSemiconductorsBiomarkersVolatile Organic CompoundsBiosensorDiabetic kidney disease (DKD)Diabetic nephropathy (DN)Volatile organic compounds (VOCs)

Identifiers

PMID40289139
PMCPMC12034762

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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.