Pathophysiological mechanisms explaining poor clinical outcome of older cancer patients with low skeletal muscle mass

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.

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

10.1101/343251 ◽

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.

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The prevalence of CT-defined low skeletal muscle mass in patients with metastatic cancer: a cross-sectional multicenter French study (the SCAN study)

Supportive Care in Cancer ◽

10.1007/s00520-021-06603-0 ◽

2021 ◽

Author(s):

Bruno Raynard ◽

Frederic Pigneur ◽

Mario Di Palma ◽

Elise Deluche ◽

François Goldwasser

Keyword(s):

Skeletal Muscle ◽

Cancer Patients ◽

Muscle Mass ◽

Skeletal Muscle Mass ◽

Metastatic Cancer ◽

Lumbar Vertebrae ◽

Diagnostic Methods ◽

Routine Practice ◽

Nutritional Management ◽

Cross Sectional

Abstract Background Cachexia, characterized by involuntary muscle mass loss, negatively impacts survival outcomes, treatment tolerability, and functionality in cancer patients. However, there is a limited appreciation of the true prevalence of low muscle mass due to inconsistent diagnostic methods and limited oncologist awareness. Methods Twenty-nine French healthcare establishments participated in this cross-sectional study, recruiting patients with those metastatic cancers most frequently encountered in routine practice (colon, breast, kidney, lung, prostate). The primary outcome was low skeletal muscle mass prevalence, as diagnosed by estimating the skeletal mass index (SMI) in the middle of the third-lumbar vertebrae (L3) level via computed tomography (CT). Other objectives included an evaluation of nutritional management, physical activity, and toxicities related to ongoing treatment. Results Seven hundred sixty-six patients (49.9% males) were enrolled with a mean age of 65.0 years. Low muscle mass prevalence was 69.1%. Only one-third of patients with low skeletal muscle mass were receiving nutritional counselling and only 28.4% were under nutritional management (oral supplements, enteral or parenteral nutrition). Physicians highly underdiagnosed those patients identified with low skeletal muscle mass, as defined by the primary objective, by 74.3% and 44.9% in obese and non-obese patients, respectively. Multivariate analyses revealed a lower risk of low skeletal muscle mass for females (OR: 0.22, P < 0.01) and those without brain metastasis (OR: 0.34, P < 0.01). Low skeletal muscle mass patients were more likely to have delayed treatment administration due to toxicity (11.9% versus 6.8%, P = 0.04). Conclusions There is a critical need to raise awareness of low skeletal muscle mass diagnosis among oncologists, and for improvements in nutritional management and physical therapies of cancer patients to curb potential cachexia. This calls for cross-disciplinary collaborations among oncologists, nutritionists, physiotherapists, and radiologists.

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