Muscle atrophy in cancer: a role for nutrition and exercise

2009 ◽  
Vol 34 (5) ◽  
pp. 950-956 ◽  
Author(s):  
Marina Mourtzakis ◽  
Megan Bedbrook
Keyword(s):  
Cancer Research ◽  
Body Composition ◽  
Cancer Patients ◽  
Muscle Mass ◽  
Muscle Atrophy ◽  
Exercise Interventions ◽  
Cancer Trajectory ◽  
Potential Factors ◽  
Primary Focus

Developing successful antineoplastic therapies has been a primary focus of cancer research, whereas less attention has been directed at body composition and metabolism in cancer patients. Here, we examine the metabolic implications of muscle atrophy in cancer as well as the potential factors that contribute to muscle atrophy, including energy imbalance, hormone perturbations, and inflammation. The role of nutrition and exercise interventions in maintaining muscle mass during the cancer trajectory is examined.

scholarly journals Expression Levels of Long Non-Coding RNAs Change in Models of Altered Muscle Activity and Muscle Mass

2020 ◽  
Vol 21 (5) ◽  
pp. 1628 ◽  
Author(s):  
Keisuke Hitachi ◽  
Masashi Nakatani ◽  
Shiori Funasaki ◽  
Ikumi Hijikata ◽  
Mizuki Maekawa ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Muscle Mass ◽  
Muscle Atrophy ◽  
Skeletal Muscle Mass ◽  
Muscle Size ◽  
Skeletal Muscle Atrophy ◽  
Myostatin Gene ◽  
Expression Levels ◽  
Non Coding Rnas

Skeletal muscle is a highly plastic organ that is necessary for homeostasis and health of the human body. The size of skeletal muscle changes in response to intrinsic and extrinsic stimuli. Although protein-coding RNAs including myostatin, NF-κβ, and insulin-like growth factor-1 (IGF-1), have pivotal roles in determining the skeletal muscle mass, the role of long non-coding RNAs (lncRNAs) in the regulation of skeletal muscle mass remains to be elucidated. Here, we performed expression profiling of nine skeletal muscle differentiation-related lncRNAs (DRR, DUM1, linc-MD1, linc-YY1, LncMyod, Neat1, Myoparr, Malat1, and SRA) and three genomic imprinting-related lncRNAs (Gtl2, H19, and IG-DMR) in mouse skeletal muscle. The expression levels of these lncRNAs were examined by quantitative RT-PCR in six skeletal muscle atrophy models (denervation, casting, tail suspension, dexamethasone-administration, cancer cachexia, and fasting) and two skeletal muscle hypertrophy models (mechanical overload and deficiency of the myostatin gene). Cluster analyses of these lncRNA expression levels were successfully used to categorize the muscle atrophy models into two sub-groups. In addition, the expression of Gtl2, IG-DMR, and DUM1 was altered along with changes in the skeletal muscle size. The overview of the expression levels of lncRNAs in multiple muscle atrophy and hypertrophy models provides a novel insight into the role of lncRNAs in determining the skeletal muscle mass.

scholarly journals A Machine Learning Approach to Predict Body Composition in Advanced Cancer Patients.

2021 ◽  
Author(s):  
Pablo Cresta Morgado ◽  
Alfredo Navigante ◽  
Adriana Pérez
Keyword(s):  
Machine Learning ◽  
Skeletal Muscle ◽  
Body Composition ◽  
Variable Selection ◽  
Cancer Patients ◽  
Muscle Mass ◽  
Fat Mass ◽  
Skeletal Muscle Mass ◽  
Daily Practice ◽  
Smoothing Splines

Abstract BACKGROUND:Body composition and its changes affect cancer patient outcomes. Its determination requires specific and expensive devices. We designed a study to evaluate machine learning approaches to predict fat and skeletal muscle mass using daily practice clinical variables.METHODS:We designed a cross-sectional study in advanced gastrointestinal cancer patients. Response variables were skeletal muscle mass and body fat mass, measured by bioimpedance analysis. Predictors were laboratory and anthropometric variables. Imputation methods were applied. Six approaches were analyzed: (1) multicollinearity analysis, best subset selection (BSS) and multiple linear regression; (2) multicollinearity, BSS and generalized additive models (GAM); (3) multicollinearity, lasso to perform variable selection and GAM; (4) ridge regression; (5) lasso regression; (6) random forest. Model selection was performed evaluating the Mean Squared Error calculated by leave-one-out cross-validation.RESULTS:We included 101 patients under chemotherapy treatment. For skeletal muscle mass, the best approach was the combination of multicollinearity analysis followed by BSS and GAM using smoothing splines with 6 variables (albumin, Hb, height, weight, sex, lymphocytes). The adjusted R2 was 0.895. The best approach for fat mass was multicollinearity analysis, variable selection by lasso, and GAM using smoothing splines with 3 variables (waist-hip ratio, weight, sex). The adjusted R2 was 0.917.CONCLUSION:We developed the first accurate predictive models for body composition in cancer patients applying daily practice clinical variables. This study shows that machine learning is a useful tool to apply in body composition. This is a starting point to evaluate these approaches in research and clinical practice.

scholarly journals Impact of a ketogenic diet intervention during radiotherapy on body composition: III. An interim analysis of the KETOCOMP study

2018 ◽  
Author(s):  
Rainer J. Klement ◽  
Gabriele Schäfert ◽  
Reinhart A. Sweeney
Keyword(s):  
Breast Cancer ◽  
Skeletal Muscle ◽  
Amino Acids ◽  
Rectal Cancer ◽  
Body Composition ◽  
Cancer Patients ◽  
Muscle Mass ◽  
Skeletal Muscle Mass ◽  
Essential Amino Acids ◽  
Ketogenic Diets

AbstractBackgroundKetogenic therapy (KT) in the form of ketogenic diets (KDs) and/or supplements that induce nutritional ketosis have gained interest as a complementary treatment for cancer patients. Besides putative anti-tumor effects, preclinical and preliminary clinical data indicate that KT could induce favorable changes in body composition of the tumor bearing host. Here we present first results of our ongoing KETOCOMP study (NCT02516501) study concerning body composition changes among rectal, breast and head & neck cancer (HNC) patients who underwent concurrent KT during standard-of-care radiotherapy (RT).MethodsEligible patients were assigned to one of three groups: (i) a standard diet group; (ii) a ketogenic breakfast group taking 50-250 ml of a medium-chain triglyceride (MCT) drink plus 10 g essential amino acids in the morning of RT days; (iii) a complete KD group supplemented with 10 g essential amino acids on RT days. Body composition was to be measured prior to and weekly during RT using 8-electrode bioimpedance analysis. Longitudinal data were analyzed using mixed effects linear regression.ResultsA total of 17 patients underwent KT during RT thus far (rectal cancer: n=6; HNC: n=6; breast cancer: n=5). All patients consuming a KD (n=14) reached nutritional ketosis and finished the study protocol with only minor problems reported. Compared to control subjects, the ketogenic intervention in rectal and breast cancer patients was significantly associated with a decline in fat mass over time (−0.3 and −0.5 kg/week, respectively), with no significant changes in skeletal muscle mass. In HNC patients, concurrent chemotherapy was the strongest predictor of body weight, fat free and skeletal muscle mass decline during radiotherapy, while KT showed significant opposite associations. Rectal cancer patients who underwent KT during neoadjuvant RT had significantly better tumor response at the time of surgery as assessed by the Dworak regression grade (median 3 versus 2, p=0.04483).ConclusionsWhile sample sizes are still small our results already indicate some significant favorable effects of KT on body composition. These as well as a putative radiosensitizing effect on rectal tumor cells need to be confirmed once the final analysis of our study becomes possible.

scholarly journals Let-7e-5p Regulates IGF2BP2, and Induces Muscle Atrophy

2021 ◽  
Vol 12 ◽  
Author(s):  
Takuro Okamura ◽  
Hiroshi Okada ◽  
Yoshitaka Hashimoto ◽  
Saori Majima ◽  
Takafumi Senmaru ◽  
...  
Keyword(s):  
Glucose Uptake ◽  
Myosin Heavy Chain ◽  
Muscle Mass ◽  
Muscle Atrophy ◽  
Heavy Chain ◽  
Mitochondrial Function ◽  
C2c12 Cells ◽  
Androgen Depletion

Background and AimsTo understand the role of microRNAs in muscle atrophy caused by androgen-depletion, we performed microarray analysis of microRNA expression in the skeletal muscles of Sham, orchiectomized (ORX), and androgen-treated ORX mice.MethodsTo clarify role and mechanisms of let-7e-5p in the muscle, the effect of let-7e-5p overexpression or knockdown on the expression of myosin heavy chain, glucose uptake, and mitochondrial function was investigated in C2C12 myotube cells. Moreover, we examined serum let-7e-5p levels among male subjects with type 2 diabetes.ResultsWe found that the expression of the miRNA, lethal (let)-7e-5p was significantly lower in ORX mice than that in Sham mice (p = 0.027); however, let-7e-5p expression in androgen-treated ORX mice was higher (p = 0.047). Suppression of let-7e-5p significantly upregulated the expression of myosin heavy chain, glucose uptake, and mitochondrial function. Real-time PCR revealed a possible regulation involving let-7e-5p and Igf2bp2 mRNA and protein in C2C12 cells. The serum let-7e-5p levels were significantly lower, which might be in compensation, in subjects with decreased muscle mass compared to subjects without decreased muscle mass. Let-7e-5p downregulates the expression of Igf2bp2 in myotube cells and inhibits the growth of the myosin heavy chain.ConclusionsBased on our study, serum level of let-7e-5p may be used as a potential diagnostic marker for muscle atrophy.

scholarly journals Prognostic role of body composition parameters in gastric/gastroesophageal junction cancer patients from the EXPAND trial

2019 ◽  
Vol 11 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Ulrich T. Hacker ◽  
Dirk Hasenclever ◽  
Nicolas Linder ◽  
Gertraud Stocker ◽  
Hyun‐Cheol Chung ◽  
...  
Keyword(s):  
Body Composition ◽  
Cancer Patients ◽  
Gastroesophageal Junction ◽  
Prognostic Role ◽  
Gastroesophageal Junction Cancer
Sign in / Sign up

Export Citation Format

Share Document