Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats

• Published in: Frontiers in endocrinology (Lausanne) Vol. 15; p. 1384984
• Main Authors: Matboli, Marwa, Al-Amodi, Hiba S, Khaled, Abdelrahman, Khaled, Radwa, Roushdy, Marian M S, Ali, Marwa, Diab, Gouda Ibrahim, Elnagar, Mahmoud Fawzy, Elmansy, Rasha A, TAhmed, Hagir H, Ahmed, Enshrah M E, Elzoghby, Doaa M A, M Kamel, Hala F, Farag, Mohamed F, ELsawi, Hind A, Farid, Laila M, Abouelkhair, Mariam B, Habib, Eman K, Fikry, Heba, Saleh, Lobna A, Aboughaleb, Ibrahim H
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 24.05.2024
• Subjects: Animals; Biomarkers; Caffeic Acids; Diabetes Mellitus, Experimental - drug therapy; Diabetes Mellitus, Experimental - metabolism; Diabetes Mellitus, Type 2 - drug therapy; Diabetes Mellitus, Type 2 - metabolism; drug response; Endocrinology; Hypoglycemic Agents - pharmacology; Hypoglycemic Agents - therapeutic use; Insulin Resistance; Liver - drug effects; Liver - metabolism; Liver - pathology; Machine Learning; Male; Quercetin - pharmacology; Quercetin - therapeutic use; Rats; Rats, Sprague-Dawley; therapeutic targets; type 2 diabetes; drug response; type 2 diabetes; rats; therapeutic targets; machine learning
• ISSN: 1664-2392; 1664-2392
• DOI: 10.3389/fendo.2024.1384984

With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses. In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic acid, and Isorhamnetin), and a probiotics drug (Z-biotic), at different doses. A hundred rats were randomly assigned to ten groups, including a normal group, a streptozotocin-induced diabetic group, and eight treated groups. Serum samples were collected for biochemical analysis, while liver tissues (L) and adipose tissues (A) underwent histopathological examination and molecular biomarker extraction using quantitative PCR. Utilizing five machine learning algorithms, we integrated 32 molecular features and 12 biochemical features to select the most predictive targets for each model and the combined model. Our results indicated that high doses of the selected drugs effectively mitigated liver inflammation, reduced insulin resistance, and improved lipid profiles and renal function biomarkers. The machine learning model identified 13 molecular features, 10 biochemical features, and 20 combined features with an accuracy of 80% and AUC (0.894, 0.93, and 0.896), respectively. This study presents an ML model that accurately identifies effective therapeutic targets implicated in the molecular pathways associated with T2DM pathogenesis.