Background: Metabolic syndrome is associated with low-grade systemic inflammation, which is a key driver of premature atherosclerosis. We characterized immune cell behavior in metabolic syndrome, its consequences, and the potential involvement of the CX₃CL1/CX₃CR1 and CCL2/CCR2 chemokine axes. Methods: Whole blood from 18 patients with metabolic syndrome and 21 age-matched controls was analyzed by flow cytometry to determine the leukocyte immunophenotypes, activation, platelet-leukocyte aggregates, and CX₃CR1 expression. ELISA determined the plasma marker levels. Platelet-leukocyte aggregates adhesion to tumor necrosis factor-α (TNFα)-stimulated arterial endothelium and the role of CX₃CL1/CX₃CR1 and CCL2/CCR2 axes was investigated with the parallel-plate flow chamber. Results: When compared with the controls, the metabolic syndrome patients presented greater percentages of eosinophils, CD3⁺ T lymphocytes, Mon2/Mon3 monocytes, platelet-eosinophil and -lymphocyte aggregates, activated platelets, neutrophils, eosinophils, monocytes, and CD8⁺ T cells, but lower percentages of Mon1 monocytes. Patients had increased circulating interleukin-8 (IL-8) and TNFα levels and decreased IL-4. CX₃CR1 up-regulation in platelet-Mon1 monocyte aggregates in metabolic syndrome patients led to increased CX₃CR1/CCR2-dependent platelet-Mon1 monocyte adhesion to dysfunctional arterial endothelium. Conclusion: We provide evidence of generalized immune activation in metabolic syndrome. Additionally, CX₃CL1/CX₃CR1 or CCL2/CCR2 axes are potential candidates for therapeutic intervention in cardiovascular disorders in metabolic syndrome patients, as their blockade impairs the augmented arterial platelet-Mon1 monocyte aggregate adhesiveness, which is a key event in atherogenesis.

Roux-en-Y gastric bypass (RYGB) is a treatment for severe obesity. However, many patients have insufficient total weight loss (TWL) after RYGB. Although multiple factors have been involved, their influence is incompletely known. The aim of this exploratory study was to evaluate the feasibility and reliability of the use of machine learning (ML) techniques to estimate the success in weight loss after RYGP, based on clinical, anthropometric and biochemical data, in order to identify morbidly obese patients with poor weight responses. We retrospectively analyzed 118 patients, who underwent RYGB at the Hospital Clínico Universitario of Valencia (Spain) between 2013 and 2017. We applied a ML approach using local linear embedding (LLE) as a tool for the evaluation and classification of the main parameters in conjunction with evolutionary algorithms for the optimization and adjustment of the parameter model. The variables associated with one-year postoperative %TWL were obstructive sleep apnea, osteoarthritis, insulin treatment, preoperative weight, insulin resistance index, apolipoprotein A, uric acid, complement component 3, and vitamin B12. The model correctly classified 71.4% of subjects with TWL < 30% although 36.4% with TWL ≥ 30% were incorrectly classified as “unsuccessful procedures”. The ML-model processed moderate discriminatory precision in the validation set. Thus, in severe obesity, ML-models can be useful to assist in the selection of patients before bariatric surgery.