Cancer patients display systemic inflammation, which leads to an increase in protein catabolism, thus promoting the release of free amino acids to further support metabolism and remodelling of muscle proteins. Inflammation associated with tumor growth leads to malnutrition, a factor that increases the risk of developing cachexia. With cancer-induced cachexia, nutritional interventions have gained traction as a preventative method to manage this condition. Currently, cancer consensus recommendations suggest a protein intake above 1.0 g/kg.day-1 up to 2.0 g/k.day-1 for cancer patients, although an ideal amount for some amino acids in isolation has yet to be determined. Due to controversy in the literature regarding the benefits of the biochemical mechanisms of various muscle mass supplements, such as L-leucine (including whey protein and BCAA), β-hydroxy-beta-methyl butyrate (HMβ), arginine, glutamine and creatine, several studies have carefully examined their effects. L-leucine and its derivatives appear to regulate protein synthesis by direct or indirect activation of the mTORC1 pool of kinases, further promoting muscle protein balance. Arginine and glutamine may act by reducing inflammation and infection progression, thus promoting improvements in food intake. Creatine exerts anabolic activity, acting as an immediate energy substrate to support muscle contraction further increasing lean mass, mainly due to greater water uptake by the muscle. In this narrative review, we highlighted the main findings regarding protein consumption and amino acids to mitigate cancer-induced skeletal muscle depletion.

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Cancer patients possess metabolic and pathophysiological changes and an inflammatory environment that leads to malnutrition. This study aimed to (i) determine whether there is an association between neutrophil-to-lymphocyte ratio (NLR) and nutritional risk, and (ii) identify the cut-off value of NLR that best predicts malnutrition by screening for nutritional risk (NRS 2002). This cross-sectional study included 119 patients with unselected cancer undergoing chemotherapy and/or surgery. The NRS 2002 was applied within 24 h of hospitalisation to determine the nutritional risk. Systemic inflammation was assessed by blood collection, and data on C-reactive protein (CRP), neutrophils, and lymphocytes were collected for later calculation of NLR. A receiver operating characteristic (ROC) curve was used to identify the best cut-point for NLR value that predicted nutritional risk. Differences between the groups were tested using the Student’s t-, Mann–Whitney U and Chi-Square tests. Logistic regression analyses were performed to assess the association between NLR and nutritional risk. The ROC curve showed the best cut-point for predicting nutritional risk was NLR > 5.0 (sensitivity, 60.9%; specificity, 76.4%). The NLR ≥ 5.0 group had a higher prevalence of nutritional risk than the NLR < 5.0 group (NLR ≥ 5.0: 73.6% vs. NLR < 5.0: 37.9%, p = 0.001). The NLR group ≥ 5.0 showed higher values of CRP and NLR than the NLR < 5.0 group. In addition, patients with NLR ≥ 5.0 also had higher NRS 2002 values when compared to the NLR < 5.0 group (NLR ≥ 5.0: 3.0 ± 1.1 vs. NLR < 5.0: 2.3 ± 1.2, p = 0.0004). Logistic regression revealed an association between NRS and NLR values. In hospitalised unselected cancer patients, systemic inflammation measured by NLR was associated with nutritional risk. Therefore, we highlight the importance of measuring the NLR in clinical practice, with the aim to detect nutritional risk.

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