## 1 Introduction
### Early Diagnosis and Prediction of Diabetes
[27] developed three ML-based algorithms (MLP, XGBoost, and support vector machines) to predict the development of T2 DM, relating the clinical variables of age, sex, BMI, smoking, alcohol consumption, HTN, and gut microbiome profile. Faecal samples were collected from 207 participants (118 patients and 89 controls), and the abundance of 10 bacterial species (_Veillonelaceae_, _Clostridium leptin_, _Roseburia inulinivorans_, _Bacteroides_, _Prevotella_, _Bifidobacterium_, _Lactobacillus_, _Faecalibacterium prasunitzii_, _Enterococcus_, _and _Eut bacterium rectale_) was studied by quantitative PCR (qPCR). The presence of _Bacteroides_, _Eubacterium rectale_, _and Roseburia inulinivorans_ was found to be significantly lower in the T2 DM group compared to controls. On the other hand, the abundance of Enterococcus was significantly higher in the T2 DM group. The model with the best predictive ability was SVM, and the clinical variables with the greatest impact on the prediction of early T2 DM stages were age and abundance of Bifidobacterium and _Roseburia inulinivorans_. As previously mentioned, high-energy, high-fat diets are a risk factor for the development of T2 DM, and nutritional interventions that promote high-quality dietary patterns in these patients could reduce the incidence of T2 DM [28]. In the context of this study, we assessed the quality of the diet followed for 5 years using the NRF9.3 and AHEI-2010 indices at baseline and one year after the intervention and studied whether the diet was associated with the risk of developing T2 DM in recruited patients with coronary heart disease who did not have diabetes criteria at baseline (n = 422). Clinical and metabolic characteristics and lipid profiles of the patients were assessed. Both diets showed similar improvements in nutritional quality (according to the NRF9.3 index), which was associated with a reduced risk of developing T2 DM in these patients, although no significant differences were found between the two nutritional interventions [28]. In the CORDIOPREV study, the effect of three-year follow-up was observed on the expression of the splicing machinery and how this relates to the prevention of T2 DM. Significant increases were found in the expression of spliceosome components and splicing factors (such as SPFQ, RVM45, RNU6, and RNU4 ATAC) and a lower expression of other components, such as RNU2 or SRSF. Also, the expression of some components was observed to vary differently in patients who developed T2 DM compared to those who did not. Although most of the observed changes were not dependent on diet type, increased expression of SNW1, SPFQ, and NOVA1 factors was found with the low-fat diet, suggesting that certain components of the splicing machinery may respond differently to variations in diet composition and thus have implications for how diet affects the development of T2 DM [29]. Also, in the context of the CORDIOPREV study, the relationship between chronodistribution (disruption of circadian rhythms), diet, and cardiometabolic risk was assessed. It was concluded that evening chronotypes (evening-type or nocturnal) are associated with increased cardiometabolic risk independently of dietary interventions (Mediterranean/low-fat). Therefore, in line with ADA guidelines, adjustment of meal, sleep, and physical activity schedules is beneficial to improve the prevention and treatment of cardiometabolic diseases, such as T2 DM [30].