Deep Learning Model Approach to Predict Diabetes Type 2 Based on Clinical, Biochemical, and Gut Microbiota Profiles
Open accessAbstract
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%.
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Inadequate adherence to T2DM health behaviors substantially increases risk of long-term microvascular and macrovascular complications, reduces quality of life, and contributes to economic burden.
“Inadequate adherence to these behaviors substantially increases the risk of long-term microvascular and macrovascular complications, reduces quality of life, and contributes to the growing economic burden of diabetes-related care [4].”
From Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review (2025) · cites this paper as [4]
The Introduction (page unavailable) links unhealthy diets and insufficient physical activity to vascular complications and says care can prevent or delay complications and improve quality of life. The Conclusions (page unavailable) link T2DM complications to substantial healthcare and indirect costs. Neither section specifically establishes that inadequate adherence to health behaviors substantially increases all these risks or directly causes the stated quality-of-life and economic effects. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCross-sectional data and short-term interventions are insufficient for evaluating the long-term impact of behavioral strategies on glycemic control or health outcomes.
“Most studies relied on cross-sectional data or short-term interventions [28,33], which are insufficient for evaluating the long-term impact of behavioral strategies on glycemic control or health outcomes [4,12].”
From Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review (2025) · cites this paper as [4]
The Discussion (page unavailable) says longitudinal follow-up would improve the model’s ability to study how changes in patient factors relate to the development of type 2 diabetes. That supports the need for follow-up, but the paper does not specifically say that cross-sectional data or short-term behavioral interventions are insufficient to evaluate long-term glycemic control or health outcomes. (LLM, conf=0.82)
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Research works printed but not linked: 1
- American Diabetes Association Professional Practice Committee. 4. Comprehensive Medical Evaluation and Assessment of Comorbidities: Standards of Care in Diabetes-2024. Diabetes Care 2024, 47 (Suppl. S1), S52-S76. [CrossRef] · DOI
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Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review
Khandoker, F., & Grigsby, T. J. (2025) · Applied Sciences · doi:10.3390/app151910567 · Cited by 2 · see citing works
- Authors
- 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.
- Affiliations
- Instituto Maimónides de Investigación Biomédica de Córdoba (ES)
- Maimónides Biomedical Research Institute of Córdoba (IMIBIC), 14004 Córdoba, Spain
- Clinical Analyses Service, Reina Sofía University Hospital, 14004 Córdoba, Spain
- Instituto de Salud Carlos III (ES)
- Corresponding author
- Camargo, Antonio
- ORCID
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2025-02-19
- Volume
- 15
- Issue
- 4
- Pages
- 2228
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4407743143
- Cited by
- 7
- References
- 34
- Citations by year
- 2026: 6 · 2025: 1
- FWCI
- 5.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- Universidad de Córdoba Insitutional Repository (Universidad de Córdoba) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Fundación Patrimonio Comunal Olivarero (CORDIOPREV-CEAS, 1/2016, AGL2012/39615, PIE14/00005, PIE14/00031, AGL2015-67896-P, PID2019-104362RB-I00, PID2023-1468910B-I00, CP14/00114, PI19/00299, DTS19/00007, PI22/00925, PI-0055-2021, PY20_00256)
- CEAS (Centro de Excelencia en Investigación sobre Aceite de Oliva) (CORDIOPREV-CEAS, 1/2016, AGL2012/39615, PIE14/00005, PIE14/00031, AGL2015-67896-P, PID2019-104362RB-I00, PID2023-1468910B-I00, CP14/00114, PI19/00299, DTS19/00007, PI22/00925, PI-0055-2021, PY20_00256)
- Junta de Andalucía (Consejería de Salud, Consejería de Agricultura y Pesca, Consejería de innovación, Ciencia y Empresa) (CORDIOPREV-CEAS, 1/2016, AGL2012/39615, PIE14/00005, PIE14/00031, AGL2015-67896-P, PID2019-104362RB-I00, PID2023-1468910B-I00, CP14/00114, PI19/00299, DTS19/00007, PI22/00925, PI-0055-2021, PY20_00256)
- Diputaciones de Jaén y Córdoba (CORDIOPREV-CEAS, 1/2016, AGL2012/39615, PIE14/00005, PIE14/00031, AGL2015-67896-P, PID2019-104362RB-I00, PID2023-1468910B-I00, CP14/00114, PI19/00299, DTS19/00007, PI22/00925, PI-0055-2021, PY20_00256)
- Ministerio de Medio Ambiente, Medio Rural y Marino (CORDIOPREV-CEAS, 1/2016, AGL2012/39615, PIE14/00005, PIE14/00031, AGL2015-67896-P, PID2019-104362RB-I00, PID2023-1468910B-I00, CP14/00114, PI19/00299, DTS19/00007, PI22/00925, PI-0055-2021, PY20_00256)
- Spanish Government (CORDIOPREV-CEAS, 1/2016, AGL2012/39615, PIE14/00005, PIE14/00031, AGL2015-67896-P, PID2019-104362RB-I00, PID2023-1468910B-I00, CP14/00114, PI19/00299, DTS19/00007, PI22/00925, PI-0055-2021, PY20_00256)
- Ministerio de Ciencia e Innovación, Spain (CORDIOPREV-CEAS, 1/2016, AGL2012/39615, PIE14/00005, PIE14/00031, AGL2015-67896-P, PID2019-104362RB-I00, PID2023-1468910B-I00, CP14/00114, PI19/00299, DTS19/00007, PI22/00925, PI-0055-2021, PY20_00256)
- MCIN/AEI/10.13039/501100011033 (CORDIOPREV-CEAS, 1/2016, AGL2012/39615, PIE14/00005, PIE14/00031, AGL2015-67896-P, PID2019-104362RB-I00, PID2023-1468910B-I00, CP14/00114, PI19/00299, DTS19/00007, PI22/00925, PI-0055-2021, PY20_00256)
- Instituto de Salud Carlos III (CORDIOPREV-CEAS, 1/2016, AGL2012/39615, PIE14/00005, PIE14/00031, AGL2015-67896-P, PID2019-104362RB-I00, PID2023-1468910B-I00, CP14/00114, PI19/00299, DTS19/00007, PI22/00925, PI-0055-2021, PY20_00256)
- Consejería de Salud y Familias, Junta de Andalucía (CORDIOPREV-CEAS, 1/2016, AGL2012/39615, PIE14/00005, PIE14/00031, AGL2015-67896-P, PID2019-104362RB-I00, PID2023-1468910B-I00, CP14/00114, PI19/00299, DTS19/00007, PI22/00925, PI-0055-2021, PY20_00256)
- Junta de Andalucía (CORDIOPREV-CEAS, 1/2016, AGL2012/39615, PIE14/00005, PIE14/00031, AGL2015-67896-P, PID2019-104362RB-I00, PID2023-1468910B-I00, CP14/00114, PI19/00299, DTS19/00007, PI22/00925, PI-0055-2021, PY20_00256)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Keywords
- type 2 diabetes, diabetes risk prediction, gut microbiota, Bayesian hyperparameter optimization, data augmentation, time-to-event prediction, early diagnosis
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