Protein, amino acids and obesity treatment.

Dietary proteins have been used for years to treat obesity. Body weight loss is beneficial when it concerns fat mass, but loss of fat free mass - especially muscle might be detrimental. This occurs because protein breakdown predominates over synthesis, thus administering anabolic dietary compounds like proteins might counter fat free mass loss while allowing for fat mass loss.Indeed, varying the quantity of proteins will decrease muscle anabolic response and increase hyperphagia in rodents fed a low protein diet; but it will favor lean mass maintenance and promote satiety, in certain age groups of humans fed a high protein diet. Beyond protein quantity, protein source is an important metabolic regulator: whey protein and plant based diets exercize favorable effects on the risk of developing obesity, body composition, metabolic parameters or fat free mass preservation of obese patients. Specific amino-acids like branched chain amino acids (BCAA), methionine, tryptophan and its metabolites, and glutamate can also positively influence parameters and complications of obesity especially in rodent models, with less studies translating this in humans.Tuning the quality and quantity of proteins or even specific amino-acids can thus be seen as a potential therapeutic intervention on the body composition, metabolic syndrome parameters and appetite regulation of obese patients. Since these effects vary across age groups and much of the data comes from murine models, long-term prospective studies modulating proteins and amino acids in the human diet are needed.

Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment.

(1) Background: As outcome of patients with metastatic melanoma treated with anti-PD1 immunotherapy can vary in success, predictors are needed. We aimed to predict at the patients' levels, overall survival (OS) and progression-free survival (PFS) after one year of immunotherapy, based on their pre-treatment 18F-FDG PET; (2) Methods: Fifty-six metastatic melanoma patients-without prior systemic treatment-were retrospectively included. Forty-five 18F-FDG PET-based radiomic features were computed and the top five features associated with the patient's outcome were selected. The analyzed machine learning classifiers were random forest (RF), neural network, naive Bayes, logistic regression and support vector machine. The receiver operating characteristic curve was used to compare model performances, which were validated by cross-validation; (3) Results: The RF model obtained the best performance after validation to predict OS and PFS and presented AUC, sensitivities and specificities (IC95%) of 0.87 ± 0.1, 0.79 ± 0.11 and 0.95 ± 0.06 for OS and 0.9 ± 0.07, 0.88 ± 0.09 and 0.91 ± 0.08 for PFS, respectively. (4) Conclusion: A RF classifier, based on pretreatment 18F-FDG PET radiomic features may be useful for predicting the survival status for melanoma patients, after one year of a first line systemic treatment by immunotherapy.