Skeletal Muscle Proteome Modifications following Antibiotic-Induced Microbial Disturbances in Cancer Cachexia.

Cancer cachexia is an involuntary loss of body weight, mostly of skeletal muscle. Previous research favors the existence of a microbiota-muscle crosstalk, so the aim of the study was to evaluate the impact of microbiota alterations induced by antibiotics on skeletal muscle proteins expression. Skeletal muscle proteome changes were investigated in control (CT) or C26 cachectic mice (C26) with or without antibiotic treatment (CT-ATB or C26-ATB, n = 8 per group). Muscle protein extracts were divided into a sarcoplasmic and myofibrillar fraction and then underwent label-free liquid chromatography separation, mass spectrometry analysis, Mascot protein identification, and METASCAPE platform data analysis. In C26 mice, the atrogen mafbx expression was 353% higher than CT mice and 42.3% higher than C26-ATB mice. No effect on the muscle protein synthesis was observed. Proteomic analyses revealed a strong effect of antibiotics on skeletal muscle proteome outside of cachexia, with adaptative processes involved in protein folding, growth, energy metabolism, and muscle contraction. In C26-ATB mice, proteome adaptations observed in CT-ATB mice were blunted. Differentially expressed proteins were involved in other processes like glucose metabolism, oxidative stress response, and proteolysis. This study confirms the existence of a microbiota-muscle axis, with a muscle response after antibiotics that varies depending on whether cachexia is present.

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.

Prediabetes in a French overseas territory: Clinical Characteristics, Risk Factors, and Implications for type 2 diabetes prevention.

Background: People with prediabetes are at high risk of developing type 2 diabetes (T2D). This study evaluates clinical, sociodemographic characteristics, and Finnish Diabetes Risk Score (FINDRISC) of individuals with prediabetes recruited in primary care by their general practitioner (GP) for PREDIABRUN study. Methods: PREDIABRUN, a prospective cohort study in primary care on Reunion Island, aimed to identify risk factors for developing T2D in 500 adults with prediabetes (18-70 years) between July 2019 and December 2022. Sociodemographic, anthropometric, health, and lifestyle data were collected. Participants were categorized as having known prediabetes if their GP was aware of glucose abnormalities before the study, otherwise as newly diagnosed. Results: A total of 469 subjects were included, with a median age of 55 years; 58.4 % were women. Employment was more common among men (53.3 %) than women (36.1 %). Precariousness affected 35.4 % overall, with higher rates in women (41.6 %) than men (26.7 %, p < 0.001). The major associated health issues were obesity (40.1 %), musculoskeletal disorders (50.5 %), hypertension (46.3 %) and cardiovascular diseases (11.5 %). The median FINDRISC score was 16 [IQR: 12-19], higher in women (17 [14-20]) than men (15 [11-17], p < 0.001). For more than half the population (55.0 %), prediabetes status was already known. However, lifestyle habits were similar for those with newly diagnosed prediabetes and those with prediabetes already known. Conclusion: Screened population in primary care on Reunion Island is relatively young, with a high FINDRISC score and numerous medical conditions. Tailored intervention to improve dietary habits and increase physical activity could help prevent diabetes in this high-risk group.

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.