Title: Analysis of MiRNAs as Biomarkers for Nutritional Therapy in Type 2 Diabetes Remission
Abstract:
Background: Type 2 diabetes mellitus (T2DM) is a significant global health issue. Achieving remission from this disease is possible; however, effective biomarkers for selecting the most suitable diet are limited. This study aims to evaluate the role of microRNAs (miRNAs) in determining whether patients with coronary heart disease benefit more from the Mediterranean or low-fat diet for T2DM remission.
Methods: Participants were drawn from the CORDIOPREV study, which included a total of 1002 patients. Among these, 190 patients newly diagnosed with T2DM were analyzed. After adhering to either a low-fat or Mediterranean diet for 60 months, 73 participants achieved remission (Responders), while 110 did not (Non-responders). Plasma levels of 56 miRNAs were measured using RT-PCR, followed by statistical analyses including generalized linear models and ROC curves.
Results: Findings indicated that patients with lower levels of miR-let7b-3p had a higher likelihood of achieving T2DM remission on a low-fat diet. Conversely, those with elevated levels of miR-141-5p, miR-182, and miR-192 were more likely to achieve remission when following a Mediterranean diet. Additionally, combining miRNA data with clinical factors showed that a higher score for adhering to these diets was linked with greater chances of remission.
Conclusion: MiRNAs may serve as effective biomarkers for selecting the optimal dietary intervention (Mediterranean or low-fat diet) to promote T2DM remission in patients with coronary heart disease.
Practical Healthcare Solutions:
1. Identify and Address Inefficiencies: Use trial data to pinpoint areas requiring improvement, potentially enhancing patient outcomes and resource utilization.
2. Define Clear Goals: Establish specific, measurable outcomes for clinics, such as speeding up diagnoses, minimizing costs, and improving patient care.
3. Select Appropriate AI Tools: Implement AI solutions that are tailored to specific clinical tasks, including predictive analytics, documentation automation, and patient monitoring.
4. Implement Gradually: Launch pilot projects to test AI solutions, monitor their effectiveness, and expand based on performance and real-world impact.
For inquiries about AI solutions in medical management, please contact us via:
Telegram: https://t.me/itinai
X: https://x.com/vlruso
LinkedIn: https://www.linkedin.com/company/itinai/
