Prediction of Skeletal Muscle and Fat Mass in Patients with Advanced Cancer Using a Metabolomic Approach

Abstract

Urine and plasma metabolites originate from endogenous metabolic pathways in different organs and exogenous sources (diet). Urine and plasma were obtained from advanced cancer patients and investigated to determine if variations in lean and fat mass, dietary intake, and energy metabolism relate to variation in metabolite profiles. Patients (n = 55) recorded their diets for 3 d and after an overnight fast they were evaluated by DXA and indirect calorimetry. Metabolites were measured by NMR and direct injection MS. Three algorithms were used [partial least squares discriminant-analysis, support vector machines (SVM), and least absolute shrinkage and selection operator] to relate patients' plasma/urine metabolic profile with their dietary/physiological assessments. Leave-one-out cross-validation and permutation testing were conducted to determine statistical validity. None of the algorithms, using 63 urine metabolites, could learn to predict variations in individual's resting energy expenditure, respiratory quotient, or their intake of total energy, fat, sugar, or carbohydrate. Urine metabolites predicted appendicular lean tissue (skeletal muscle) with excellent cross-validation accuracy (98% using SVM). Total lean tissue correlated highly with appendicular muscle (Pearson r = 0.98; P < 0.0001) and gave similar cross-validation accuracies. Fat mass was effectively predicted using the 63 urine metabolites or the 143 plasma metabolites, exclusively. In conclusion, in this population, lean and fat mass variation could be effectively predicted using urinary metabolites, suggesting a potential role for metabolomics in body composition research. Furthermore, variation in lean and fat mass potentially confounds metabolomic studies attempting to characterize diet or disease conditions. Future studies should account or correct for such variation.

Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects

“…The reason why differences in the metabolically active mass, and also in REE, are not reflected in urine and plasma metabolites cannot be explained by our study results. Our results are in line with previous findings of Frisard et al and Stretch et al [24,44] who also did not find any associations between energy metabolism and metabolite profiles. Frisard et al examined only markers of oxidative stress in healthy adults [44].…”

“…Frisard et al examined only markers of oxidative stress in healthy adults [44]. Furthermore, Stretch et al only investigated 63 urine metabolites in advanced cancer patients [24]. Our results based on a multi-platform metabolomics approach in urine as well as in plasma revealed that also in healthy subjects there is no distinct metabolite profile directly associated with REE.…”

“…It remains unclear whether the discrimination of LBM is nothing more than the discrimination of sex, because most of the metabolites for sex seem to be identical to the metabolites discriminating LBM [24]. To date, limited data are available from metabolomics studies which determined predictors of REE.…”

“…Results from a cancer patient study revealed that none of the algorithms applied to 63 urine metabolites could predict variations in REE [24]. Whether the REE of healthy humans is associated with distinct metabolite profiles has not been investigated so far.…”

Differences in Urinary Amino Acid Patterns in Individuals with Different Types of Urological Tumor Urinary Amino Acid Patterns as Markers of Urological Tumors

“…According to the Human Urine Metabolome Project (32), only few amino acids have been identified as possible oncological markers -alanine (33) and valine (34), associated with lung cancer. The only investigated and commercially used method of using amino acid concentration in urine is represented by Prostarix (Metabolon Inc.).…”