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Learning to predict cancer-associated skeletal muscle wasting from 1 H-NMR profiles of urinary metabolites

Learning to predict cancer-associated skeletal muscle wasting from 1 H-NMR profiles of urinary metabolites,10.1007/s11306-010-0232-9,Metabolomics,Roma

Learning to predict cancer-associated skeletal muscle wasting from 1 H-NMR profiles of urinary metabolites   (Citations: 1)
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Cancer-associated muscle wasting is associated with reduction in functional status, in response to treatment and in life expectancy. Methods currently used to assess muscle loss involve diagnostic imaging techniques such as computed tomography (CT), which are costly, inconvenient, invasive, time consuming and have limited ability to detect early or slowly evolving wasting. We present a novel approach using single time-point urinary metabolite profiles to determine whether a patient is experiencing muscle wasting. We analyzed 93 random urine samples from patients with cancer using 1H-NMR. Using two successive CT images we assessed their lumbar skeletal muscle area (cm2) to estimate the rate of muscle change (% loss or gain over time) for each patient. The average muscle change over time was −4.71%/100 days in the muscle-losing group and +3.91%/100 days in the comparator group. Bivariate statistics identified metabolites related with muscle loss, including constituents and metabolites of muscle (creatine, creatinine, 3-OH-isovalerate), amino acids (Leu, Ile, Val, Ala, Thr, Tyr, Gln, Ser) and intermediary metabolites. We also applied machine-learning techniques to identify patterns of urinary metabolites that identify which patients are likely to lose muscle mass. We evaluated the predictive performance of 8 machine-learning approaches using fivefold cross validation and permutation testing, and found that SVM provided the best generalization accuracy (82.2%). These results suggest that 1H-NMR analysis of a single random urine sample may be a fast, cheap, safe and inexpensive tool to screen and monitor muscle loss, and that useful classifiers for predicting related metabolic conditions are possible with the methodology presented.
Journal: Metabolomics , vol. 7, no. 1, pp. 25-34, 2011
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