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Fat-soluble vitamers (FSV) are a class of diverse organic substances important in a wide range of biological processes, including immune function, vision, bone health, and coagulation. Profiling FSV in parents and children enables insights into gene-environment contributions to their circulating levels, but no studies have reported on the population epidemiology of FSV in these groups as of yet. In this study, we report distributions of FSV, their parent-child concordance and variation by key characteristics for 2490 children (aged 11-12 years) and adults (aged 28-71 years) in the Child Health CheckPoint of the Longitudinal Study of Australian Children. Ten A, D, E and K vitamers were quantified using a novel automated LC-MS/MS method. All three K vitamers (i.e., K1, MK-4, MK-7) and 1-α-25(OH)2D3 were below the instrument detection limit and were removed from the present analysis. We observed a strong vitamer-specific parent-child concordance for the six quantifiable A, D and E FSVs. FSV concentrations all varied by age, BMI, and sex. We provide the first cross-sectional population values for multiple FSV. Future studies could examine relative genetic vs. environmental determinants of FSV, how FSV values change longitudinally, and how they contribute to future health and disease.

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BACKGROUND: Multi-omics delivers more biological insight than targeted investigations. We applied multi-omics to patients with heart failure with reduced ejection fraction (HFrEF). METHODS: 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography mass spectrometry (LC-MS/GC-MS) and solid-phase microextraction (SPME) volatilomics in plasma and urine. HFrEF was defined using left ventricular global longitudinal strain, ejection fraction and NTproBNP. A consumer breath acetone (BrACE) sensor validated results in n = 73. RESULTS: 28 metabolites were identified by GCMS, 35 by LCMS and 4 volatiles by SPME in plasma and urine. Alanine, aspartate and glutamate, citric acid cycle, arginine biosynthesis, glyoxylate and dicarboxylate metabolism were altered in HFrEF. Plasma acetone correlated with NT-proBNP (r = 0.59, 95% CI 0.4 to 0.7), 2-oxovaleric and cis-aconitic acid, involved with ketone metabolism and mitochondrial energetics. BrACE > 1.5 ppm discriminated HF from other cardiac pathology (AUC 0.8, 95% CI 0.61 to 0.92, p < 0.0001). CONCLUSION: Breath acetone discriminated HFrEF from other cardiac pathology using a consumer sensor, but was not cardiac specific.

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Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography-mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85-0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.

Lay abstract Multiomics is the integration of multiple sources of health information, for example, genomic, metabolite, etc. This delivers more insight than targeted single investigations and provides an ability to perceive subtle individual differences between people. In this study we applied multiomics to patients with heart failure (HF) using DNA sequencing, metabolomics and machine learning applied to ECG echocardiography. We demonstrated significant differences between subsets of patients with HF using these methods. We also showed that machine learning has significant diagnostic potential in identifying HF patients more efficiently than manual or conventional techniques.

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Wine flavor and quality are determined by the assessment of multiple sensory stimuli, including aroma, taste, and mouthfeel. It is therefore important to consider the contribution of as many metabolites as possible when attempting to relate wine composition to quality. In this study, partial least squares regression of the volatile (untargeted headspace solid-phase microextraction coupled with gas chromatography time-of-flight mass spectrometry), non-volatile (untargeted reverse-phase ultra-high-performance liquid chromatography mass spectrometry), and combined metabolite profiles were used to predict Pinot Noir wine quality ratings as assessed by experts. Non-volatile metabolite profiles predicted wine quality ratings better than volatile metabolite profiles, suggesting that the non-volatile composition of Pinot Noir wines contributes to quality perception to a greater extent than the volatile composition. This was underscored by descriptive sensory analysis, which found that taste and mouthfeel attributes were better correlated with wine quality ratings than aroma attributes. Important predictors of Pinot Noir wine quality were also characterized. Some new relationships between wine metabolites and quality ratings were found: dipeptides and unsaturated fatty acids were positively related to Pinot Noir wine quality, while N-(3-methylbutyl)acetamide and xanthine were negatively associated.

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Introduction: Medicinal mushrooms have been used for the treatment of diseases and general promotion of health for many centuries. Recent pharmacological research into medicinal mushrooms has identified various therapeutic properties, with applications in modern medicine.Aim: To evaluate the anti-cancer activities of Fomitopsis pinicola (F. pinicola) alcoholic extract in an in vivo setting.Methods: The anti-tumour effect of the F. pinicola extract was tested in a xenograft immune-compromised Rag-1 mouse model. This was followed by RT-PCR and metabolomics analyses.Results: There were no observable differences in tumor growth between treated and non-treated groups. The bioactive components were not detected in the mouse plasma or the tumor site.Conclusions: The extract was poorly absorbed; this is likely due to the timing of treatment, dosage levels and modifications made to the extract where the alcohol-based solvent was replaced with water. This, in combination with fractionation studies which identified most anti-cancer compounds to be hydrophobic, largely explained the lack of anti-cancer activities in vivo.

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