Metabolomics and a Breath Sensor Identify Acetone as a Biomarker for Heart Failure.

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.

Effect of androgen deprivation therapy on serum levels of sclerostin, Dickkopf-1, and osteoprotegerin: a cross-sectional and longitudinal analysis.

Androgen deprivation therapy (ADT) for men with prostate cancer (PCa) results in accelerated bone loss and increased risk of bone fracture. The aim of the present study was to evaluate serum bone markers-sclerostin, Dickkopf-1 (DKK-1) and osteoprotegerin (OPG), in a cohort of 88 PCa patients without known bone metastases, managed with and without ADT, and to analyse their relationship with bone mineral density (BMD) and sex steroids. The cross-sectional analysis between acute-, chronic- and former-ADT groups and PCa controls showed that sclerostin and OPG levels significantly differed between them (p = 0.029 and p = 0.032). Groups contributing to these significant changes were recorded. There were no significant differences in serum DKK-1 levels across the four groups (p = 0.683). In the longitudinal analysis, significant % decreases within groups were seen for DKK-1 [chronic-ADT (- 10.06%, p = 0.0057), former-ADT (- 12.77%, p = 0.0239), and in PCa controls group (- 16.73, p = 0.0022); and OPG levels in chronic ADT (- 8.28%, p = 0.003) and PCa controls group (- 12.82%, p = 0.017)]. However, % changes in sclerostin, DKK-1, and OPG did not differ significantly over 6-months across the evaluated groups. Sclerostin levels showed significant positive correlations with BMD at baseline in the ADT group, while in PCa controls this correlation existed at both baseline and 6-month time points. Sclerostin correlated negatively with testosterone in former ADT users and in PCa controls. Possible prognostic features denoted by parallel increases in sclerostin and BMD are discussed.

Multiomics, virtual reality and artificial intelligence in heart failure.

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.

Effect of Androgen Deprivation Therapy on Bone Mineral Density in a Prostate Cancer Cohort in New Zealand: A Pilot Study.

INTRODUCTION: Reduction in bone mineral density (BMD) is a common side effect of androgen deprivation therapy (ADT). We aimed to examine the cross-sectional and longitudinal variation in BMD and associated bone markers in patients with nonmetastatic prostate cancer (PCa) managed with and without ADT. METHODS: Bone mineral density of the total body, lumbar spine, femoral neck, ultradistal forearm, and one-third distal radius was measured in 88 patients with PCa without bone metastases at baseline and at 6 months. Patients were categorized into 4 groups: (1) acute ADT (≤6 months), (2) chronic ADT (>6 months), (3) former ADT, and (4) no ADT (controls). Serum levels of bone metabolism markers, procollagen type I N-terminal propeptide (PINP) and C-terminal cross-linking telopeptide of type I collagen (CTX), were also measured. RESULTS: In the cross-sectional analysis, men receiving chronic ADT had significantly lower total body BMD as compared with former ADT users and men with no ADT. In longitudinal analysis, a significant reduction in ultradistal forearm BMD was observed in both acute and chronic ADT users after 6 months (4.08% and 2.7%, P = .012 and .026, respectively). A significant reduction in total body BMD was observed in acute ADT users (2.99%, P = .032). Former ADT users had a significant increase in both lumbar spine and femoral neck BMD (2.84% and 1.59%, P = .008 and .002, respectively). The changes in BMD were not significantly different between acute and chronic ADT users. In the cross-sectional analysis, higher levels of PINP and CTX were observed in acute and chronic ADT users than former ADT users or PCa controls. In longitudinal analysis, the level of serum PINP and CTX did not change significantly from baseline to 6 months in acute, chronic, and former ADT users, or PCa controls, and the percentage change did not differ among the 4 groups. CONCLUSIONS: Men on acute ADT had a similar rate of bone loss to men on chronic ADT. Reversibility in ADT-induced bone loss was observed in those who discontinued ADT. Serum levels of PINP and CTX were higher in acute and chronic ADT users and levels returned to the range of PCa controls when treatment was withdrawn.