Towards a Global Model for Diabetic Kidney Disease Screening using ATR-FTIR Article Swipe
Víctor Navarro-Esteve
,
Ángel Sánchez‐Illana
,
José Pórtoles
,
María Marqués-Vidas
,
Josep Ventura-Gayete
,
Nuria Estañ Capell
,
Iris Viejo-Boyano
,
Francisco Valero-Mena
,
Antonio Sánchez
,
Bayden R. Wood
,
David Pérez-Guaita
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2025-pksq8
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2025-pksq8
Infrared (IR) spectroscopy of urine extracts coupled with Machine Learning (ML) methods has been proposed as a promising technique for Diabetic Kidney Disease (DKD) screening. However, for clinical translation, predictive models based on machine learning techniques necessitate substantial sets of samples for calibration and testing under various experimental parameters and populations, posing a critical barrier to the development of globally deployable tools. Here, we aim to assess the methodology's ability to establish DKD diagnostic models applicable across diverse populations, instruments and experimental conditions worldwide. Two datasets were compared. The Australian set included 155 DKD and 22 control samples from a 24-hour urine collection, with preconcentrated proteins measured on a Bruker spectrometer. The Spanish set, comprising 35 DKD and 26 control spot urine samples, was analyzed using a Perkin-Elmer spectrometer. Different ML methods were developed to identify DKD and microalbuminuria, aiming to compare their performance in terms of generalization and adaptation to different datasets. Models developed using Australian spectra successfully predicted Spanish samples, achieving AUROC values of 0.87 and 0.98 for DKD and microalbuminuria identification, respectively. Both values improved to 0.99 when a global model was calibrated and independently tested with a combined set integrating samples from both countries. Results evidence that the spectral markers found in the IR spectra, based on signals arising from albumin and other glycoproteins, have proven to be robust, minimizing the effects of population and instrument variability. Results exemplify the potential of developing global big-data spectroscopic datasets to facilitate the deployment of IR-based diagnostic methods in real-world settings.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv-2025-pksq8
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/67c569596dde43c90845febe/original/towards-a-global-model-for-diabetic-kidney-disease-screening-using-atr-ftir.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408215678
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- OpenAlex ID
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https://openalex.org/W4408215678Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26434/chemrxiv-2025-pksq8Digital Object Identifier
- Title
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Towards a Global Model for Diabetic Kidney Disease Screening using ATR-FTIRWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-05Full publication date if available
- Authors
-
Víctor Navarro-Esteve, Ángel Sánchez‐Illana, José Pórtoles, María Marqués-Vidas, Josep Ventura-Gayete, Nuria Estañ Capell, Iris Viejo-Boyano, Francisco Valero-Mena, Antonio Sánchez, Bayden R. Wood, David Pérez-GuaitaList of authors in order
- Landing page
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https://doi.org/10.26434/chemrxiv-2025-pksq8Publisher landing page
- PDF URL
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/67c569596dde43c90845febe/original/towards-a-global-model-for-diabetic-kidney-disease-screening-using-atr-ftir.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/67c569596dde43c90845febe/original/towards-a-global-model-for-diabetic-kidney-disease-screening-using-atr-ftir.pdfDirect OA link when available
- Concepts
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Fourier transform infrared spectroscopy, Kidney disease, Medicine, Diabetes mellitus, Internal medicine, Endocrinology, Chemical engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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