Effects of an Exercise and Nutritional Intervention on Circulating Biomarkers and Metabolomic Profiling During Adjuvant Treatment for Localized Breast Cancer: Results From the PASAPAS Feasibility Randomized Controlled Trial.

PURPOSE: Exercise has been shown to improve physical and psychological conditions during cancer therapy, but mechanisms remain poorly understood. The purpose of the present study was to report the results of cancer-related biomarkers and metabolomics outcomes from the PASAPAS feasibility study. METHODS: In the PASAPAS randomized controlled trial, 61 women beginning adjuvant chemotherapy for localized breast cancer were randomized in a 6-month program of weekly aerobic exercises associated with nutritional counseling versus usual care with nutritional counseling. In the present analysis of 58 women for whom blood samples were available, first, circulating levels of biomarkers (ie, insulin, insulin-like growth factor 1, estradiol, adiponectin, leptin, interleukin-6, and tumor necrosis factor α) were measured at baseline and 6-month follow-up. Changes in biomarkers were compared between exercisers (n = 40) and controls (n = 18) using mixed-effect models. Second, serum metabolites were studied using an untargeted 1H nuclear magnetic resonance spectroscopy, and orthogonal partial least squares analyses were performed to discriminate exercisers and controls at baseline and at 6 months. RESULTS: Over the 6-month intervention, no statistically significant differences were observed between exercisers and controls regarding changes in biomarkers and metabolomic profiles. CONCLUSION: The present analysis of the PASAPAS feasibility trial did not reveal any improvement in circulating biomarkers nor identified metabolic signatures in exercisers versus controls during adjuvant breast cancer treatment. Larger studies preferably in women with poor physical activity level to avoid ceiling effect, testing different doses and types of exercise on additional biological pathways, could allow to clarify the mechanisms mediating beneficial effects of physical exercise during cancer treatment. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01331772. Registered 8 April 2011, https://clinicaltrials.gov/ct2/show/NCT01331772?term=pasapas&rank=1.

Metabolomic Profiling of Body Fluids in Mouse Models Demonstrates that Nuclear Magnetic Resonance Is a Putative Diagnostic Tool for the Presence of Thyroid Hormone Receptor α1 Mutations.

Background: Resistance to thyroid hormone alpha (RTHα) is a rare genetic disease due to mutations in the THRA gene, which encodes thyroid hormone receptor alpha 1 (TRα1). Since its first description in 2012, 46 cases of RTHα have been reported worldwide, corresponding to 26 different mutations of TRα1. RTHα patients share some common symptoms with hypothyroid patients, without significant reduction in thyroid hormone level. The high variability of clinical features and the absence of reliable biochemical markers make the diagnosis of this disease difficult. Some of these mutations have been recently modeled in mice. Methods: In our study, we used four different mouse models heterozygous for frameshift mutations in the Thra gene. Two of them are very close to human mutations, while the two others have not yet been found in patients. We characterized the metabolic phenotypes of urine and plasma samples collected from these four animal models using an untargeted nuclear magnetic resonance (NMR)-based metabolomic approach. Results: Multivariate statistical analysis of the metabolomic profiles shows that biofluids of mice that carry human-like mutations can be discriminated from controls. Metabolic signatures associated with Thra mutations in urine and plasma are stable over time and clearly differ from the metabolic fingerprint of hypothyroidism in the mouse. Conclusion: Our results provide a proof-of-principle that easily accessible NMR-based metabolic fingerprints of biofluids could be used to diagnose RTHα in humans.

A statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study.

Metabolomics is a potentially powerful tool for identification of biomarkers associated with lifestyle exposures and risk of various diseases. This is the rationale of the 'meeting-in-the-middle' concept, for which an analytical framework was developed in this study. In a nested case-control study on hepatocellular carcinoma (HCC) within the European Prospective Investigation into Cancer and nutrition (EPIC), serum (1)H nuclear magnetic resonance (NMR) spectra (800 MHz) were acquired for 114 cases and 222 matched controls. Through partial least square (PLS) analysis, 21 lifestyle variables (the 'predictors', including information on diet, anthropometry and clinical characteristics) were linked to a set of 285 metabolic variables (the 'responses'). The three resulting scores were related to HCC risk by means of conditional logistic regressions. The first PLS factor was not associated with HCC risk. The second PLS metabolomic factor was positively associated with tyrosine and glucose, and was related to a significantly increased HCC risk with OR = 1.11 (95% CI: 1.02, 1.22, P = 0.02) for a 1SD change in the responses score, and a similar association was found for the corresponding lifestyle component of the factor. The third PLS lifestyle factor was associated with lifetime alcohol consumption, hepatitis and smoking, and had negative loadings on vegetables intake. Its metabolomic counterpart displayed positive loadings on ethanol, glutamate and phenylalanine. These factors were positively and statistically significantly associated with HCC risk, with 1.37 (1.05, 1.79, P = 0.02) and 1.22 (1.04, 1.44, P = 0.01), respectively. Evidence of mediation was found in both the second and third PLS factors, where the metabolomic signals mediated the relation between the lifestyle component and HCC outcome. This study devised a way to bridge lifestyle variables to HCC risk through NMR metabolomics data. This implementation of the 'meeting-in-the-middle' approach finds natural applications in settings characterised by high-dimensional data, increasingly frequent in the omics generation.