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Article

Deep Learning Model Approach to Predict Diabetes Type 2 Based on Clinical, Biochemical, and Gut Microbiota Profiles

by
Pablo Caballero-María
1,
Javier Caballero-Villarraso
1,2,3,*,
Javier Arenas-Montes
1,4,5,6,
Alberto Díaz-Cáceres
1,4,5,6,
Sofía Castañeda-Nieto
3,
Juan F. Alcalá-Díaz
1,4,5,6,
Javier Delgado-Lista
1,4,5,6,
Fernando Rodríguez-Cantalejo
1,2,3,
Pablo Pérez-Martínez
1,4,5,6,
José López-Miranda
1,4,5,6,† and
Antonio Camargo
1,4,5,6,*,†
1
Maimónides Biomedical Research Institute of Córdoba (IMIBIC), 14004 Córdoba, Spain
2
Department of Biochemistry and Molecular Biology, Universidad of Córdoba, 14004 Córdoba, Spain
3
Clinical Analyses Service, Reina Sofía University Hospital, 14004 Córdoba, Spain
4
Lipids and Atherosclerosis Unit, Department of Internal Medicine, Reina Sofía University Hospital, 14004 Córdoba, Spain
5
Department of Medical and Surgical Sciences, University of Córdoba, 14004 Córdoba, Spain
6
CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2025, 15(4), 2228; https://doi.org/10.3390/app15042228
Submission received: 6 January 2025 / Revised: 7 February 2025 / Accepted: 13 February 2025 / Published: 19 February 2025
(This article belongs to the Section Biomedical Engineering)
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Abstract

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease. Gut microbiota plays a key role in metabolic homeostasis and the development of T2DM and its complications. With the advance of artificial intelligence (AI), it is possible to develop novel models based on machine learning (ML) that can predict the risk of developing certain diseases and facilitate their early diagnosis, or even take preventive measures in advance. This can be the case of T2DM, for example. Our objective was to develop a predictive model of the risk of developing T2DM based on clinical, biochemical, and intestinal microbiota parameters, which estimates the time margin for developing this disease. To this end, a Deep Learning Multilayer Perceptron (MLP) algorithm was developed and trained with data from real patients from a current large population epidemiological study. The data were normalised and augmented to increase their diversity and avoid overfitting. The neural network developed was optimised, and the best hyperparameters were chosen for model building by Bayesian optimisation. We succeeded in getting the model to return a numerical result corresponding to the number of months it will take for a particular individual to develop T2DM with an accuracy of 95.2%.
Keywords: deep learning; neural network; machine learning; artificial intelligence; predictive modelling; diabetes mellitus; cardiovascular risk; gut microbiota deep learning; neural network; machine learning; artificial intelligence; predictive modelling; diabetes mellitus; cardiovascular risk; gut microbiota

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MDPI and ACS Style

Caballero-María, P.; Caballero-Villarraso, J.; Arenas-Montes, J.; Díaz-Cáceres, A.; Castañeda-Nieto, S.; Alcalá-Díaz, J.F.; Delgado-Lista, J.; Rodríguez-Cantalejo, F.; Pérez-Martínez, P.; López-Miranda, J.; et al. Deep Learning Model Approach to Predict Diabetes Type 2 Based on Clinical, Biochemical, and Gut Microbiota Profiles. Appl. Sci. 2025, 15, 2228. https://doi.org/10.3390/app15042228

AMA Style

Caballero-María P, Caballero-Villarraso J, Arenas-Montes J, Díaz-Cáceres A, Castañeda-Nieto S, Alcalá-Díaz JF, Delgado-Lista J, Rodríguez-Cantalejo F, Pérez-Martínez P, López-Miranda J, et al. Deep Learning Model Approach to Predict Diabetes Type 2 Based on Clinical, Biochemical, and Gut Microbiota Profiles. Applied Sciences. 2025; 15(4):2228. https://doi.org/10.3390/app15042228

Chicago/Turabian Style

Caballero-María, Pablo, Javier Caballero-Villarraso, Javier Arenas-Montes, Alberto Díaz-Cáceres, Sofía Castañeda-Nieto, Juan F. Alcalá-Díaz, Javier Delgado-Lista, Fernando Rodríguez-Cantalejo, Pablo Pérez-Martínez, José López-Miranda, and et al. 2025. "Deep Learning Model Approach to Predict Diabetes Type 2 Based on Clinical, Biochemical, and Gut Microbiota Profiles" Applied Sciences 15, no. 4: 2228. https://doi.org/10.3390/app15042228

APA Style

Caballero-María, P., Caballero-Villarraso, J., Arenas-Montes, J., Díaz-Cáceres, A., Castañeda-Nieto, S., Alcalá-Díaz, J. F., Delgado-Lista, J., Rodríguez-Cantalejo, F., Pérez-Martínez, P., López-Miranda, J., & Camargo, A. (2025). Deep Learning Model Approach to Predict Diabetes Type 2 Based on Clinical, Biochemical, and Gut Microbiota Profiles. Applied Sciences, 15(4), 2228. https://doi.org/10.3390/app15042228

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