Aims/hypothesis: We determined whether the time of day of exercise training (morning vs evening) would modulate the effects of consumption of a high-fat diet (HFD) on glycaemic control, whole-body health markers and serum metabolomics.Methods: In this three-armed parallel-group randomised trial undertaken at a university in Melbourne, Australia, overweight/obese men consumed an HFD (65% of energy from fat) for 11 consecutive days. Participants were recruited via social media and community advertisements. Eligibility criteria for participation were male sex, age 30–45 years, BMI 27.0–35.0 kg/m²and sedentary lifestyle. The main exclusion criteria were known CVD or type 2 diabetes, taking prescription medications, and shift-work. After 5 days, participants were allocated using a computer random generator to either exercise in the morning (06:30 hours), exercise in the evening (18:30 hours) or no exercise for the subsequent 5 days. Participants and researchers were not blinded to group assignment. Changes in serum metabolites, circulating lipids, cardiorespiratory fitness, BP, and glycaemic control (from continuous glucose monitoring) were compared between groups.Results: Twenty-five participants were randomised (morning exercisen = 9; evening exercisen = 8; no exercisen = 8) and 24 participants completed the study and were included in analyses (n = 8 per group). Five days of HFD induced marked perturbations in serum metabolites related to lipid and amino acid metabolism. Exercise training had a smaller impact than the HFD on changes in circulating metabolites, and only exercise undertaken in the evening was able to partly reverse some of the HFD-induced changes in metabolomic profiles. Twenty-four-hour glucose concentrations were lower after 5 days of HFD compared with the participants’ habitual diet (5.3 ± 0.4 vs 5.6 ± 0.4 mmol/l,p = 0.001). There were no significant changes in 24 h glucose concentrations for either exercise group but lower nocturnal glucose levels were observed in participants who trained in the evening, compared with when they consumed the HFD alone (4.9 ± 0.4 vs 5.3 ± 0.3 mmol/l,p = 0.04). Compared with the no-exercise group, peak oxygen uptake improved after both morning (estimated effect 1.3 ml min⁻¹ kg⁻¹[95% CI 0.5, 2.0],p = 0.003) and evening exercise (estimated effect 1.4 ml min⁻¹ kg⁻¹[95% CI 0.6, 2.2],p = 0.001). Fasting blood glucose, insulin, cholesterol, triacylglycerol and LDL-cholesterol concentrations decreased only in participants allocated to evening exercise training. There were no unintended or adverse effects.Conclusions/interpretation: A short-term HFD in overweight/obese men induced substantial alterations in lipid- and amino acid-related serum metabolites. Improvements in cardiorespiratory fitness were similar regardless of the time of day of exercise training. However, improvements in glycaemic control and partial reversal of HFD-induced changes in metabolic profiles were only observed when participants exercise trained in the evening.Trial registration: anzctr.org.auregistration no. ACTRN12617000304336.Funding: This study was funded by the Novo Nordisk Foundation (NNF14OC0011493).Graphical

Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent‐variable‐based methods, such as principal component analysis and partial least‐squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state‐of‐the‐art multivariate methods for the analysis of NMR‐based metabolomic data as well as alternative methods, highlighting their strengths and limitations.

Breast cancer (BC) is a leading cause of cancer-related death worldwide. The diverse nature and heterogeneous biology of BC pose challenges for survival prediction, as patients with similar diagnoses often respond differently to treatment. Clinically relevant BC intrinsic subtypes have been established through gene expression profiling and are implemented in the clinic. While these intrinsic subtypes show a significant association with clinical outcomes, their long-term survival prediction beyond 5 years often deviates from expected clinical outcomes. This study aimed to identify naturally occurring long-term prognostic subgroups of BC based on an integrated multi-omics analysis. This study incorporates a clinical cohort of 335 untreated BC patients from the Oslo2 study with long-term follow-up (>12 years). Multi-Omics Factor Analysis (MOFA+) was employed to integrate transcriptomic, proteomic, and metabolomic data obtained from the tumor tissues. Our analysis revealed three prominent multi-omics clusters of BC patients with significantly different long-term prognoses (p = 0.005). The multi-omics clusters were validated in two independent large cohorts, METABRIC and TCGA. Importantly, a lack of prognostic association to long-term follow-up above 12 years in the previously established intrinsic subtypes was shown for these cohorts. Through a systems-biology approach, we identified varying enrichment levels of cell-cycle and immune-related pathways among the prognostic clusters. Integrated multi-omics analysis of BC revealed three distinct clusters with unique clinical and biological characteristics. Notably, these multi-omics clusters displayed robust associations with long-term survival, outperforming the established intrinsic subtypes.