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Roux-en-Y gastric bypass (RYGB) surgery is associated with weight loss, improved insulin sensitivity and glucose homeostasis, and a reduction in co-morbidities such as diabetes and coronary heart disease. To generate further insight into the numerous metabolic adaptations associated with RYGB surgery, we profiled serum metabolites before and after gastric bypass surgery and integrated metabolite changes with clinical data. Serum metabolites were detected by gas and liquid chromatography-coupled mass spectrometry before, and 3 and 6 months after RYGB in morbidly obese female subjects (n = 14; BMI = 46.2+/-1.7). Subjects showed decreases in weight-related parameters and improvements in insulin sensitivity post surgery. The abundance of 48% (83 of 172) of the measured metabolites changed significantly within the first 3 months post RYGB (p<0.05), including sphingosines, unsaturated fatty acids, and branched chain amino acids. Dividing subjects into obese (n = 9) and obese/diabetic (n = 5) groups identified 8 metabolites that differed consistently at all time points and whose serum levels changed following RYGB: asparagine, lysophosphatidylcholine (C18:2), nervonic (C24:1) acid, p-Cresol sulfate, lactate, lycopene, glucose, and mannose. Changes in the aforementioned metabolites were integrated with clinical data for body mass index (BMI) and estimates for insulin resistance (HOMA-IR). Of these, nervonic acid was significantly and negatively correlated with HOMA-IR (p = 0.001, R = -0.55). Global metabolite profiling in morbidly obese subjects after RYGB has provided new information regarding the considerable metabolic alterations associated with this surgical procedure. Integrating clinical measurements with metabolomics data is capable of identifying markers that reflect the metabolic adaptations following RYGB.

AbstractObjectivesTo evaluate the diagnostic performance of N-terminal pro-B-type natriuretic peptide (NT-proBNP) thresholds for acute heart failure and to develop and validate a decision support tool that combines NT-proBNP concentrations with clinical characteristics.DesignIndividual patient level data meta-analysis and modelling study.SettingFourteen studies from 13 countries, including randomised controlled trials and prospective observational studies.ParticipantsIndividual patient level data for 10 369 patients with suspected acute heart failure were pooled for the meta-analysis to evaluate NT-proBNP thresholds. A decision support tool (Collaboration for the Diagnosis and Evaluation of Heart Failure (CoDE-HF)) that combines NT-proBNP with clinical variables to report the probability of acute heart failure for an individual patient was developed and validated.Main outcome measureAdjudicated diagnosis of acute heart failure.ResultsOverall, 43.9% (4549/10 369) of patients had an adjudicated diagnosis of acute heart failure (73.3% (2286/3119) and 29.0% (1802/6208) in those with and without previous heart failure, respectively). The negative predictive value of the guideline recommended rule-out threshold of 300 pg/mL was 94.6% (95% confidence interval 91.9% to 96.4%); despite use of age specific rule-in thresholds, the positive predictive value varied at 61.0% (55.3% to 66.4%), 73.5% (62.3% to 82.3%), and 80.2% (70.9% to 87.1%), in patients aged <50 years, 50-75 years, and >75 years, respectively. Performance varied in most subgroups, particularly patients with obesity, renal impairment, or previous heart failure. CoDE-HF was well calibrated, with excellent discrimination in patients with and without previous heart failure (area under the receiver operator curve 0.846 (0.830 to 0.862) and 0.925 (0.919 to 0.932) and Brier scores of 0.130 and 0.099, respectively). In patients without previous heart failure, the diagnostic performance was consistent across all subgroups, with 40.3% (2502/6208) identified at low probability (negative predictive value of 98.6%, 97.8% to 99.1%) and 28.0% (1737/6208) at high probability (positive predictive value of 75.0%, 65.7% to 82.5%) of having acute heart failure.ConclusionsIn an international, collaborative evaluation of the diagnostic performance of NT-proBNP, guideline recommended thresholds to diagnose acute heart failure varied substantially in important patient subgroups. The CoDE-HF decision support tool incorporating NT-proBNP as a continuous measure and other clinical variables provides a more consistent, accurate, and individualised approach.Study registrationPROSPERO CRD42019159407.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive loss of cognitive functions. Today the diagnosis of AD relies on clinical evaluations and is only late in the disease. Biomarkers for early detection of the underlying neuropathological changes are still lacking and the biochemical pathways leading to the disease are still not completely understood. The aim of this study was to identify the metabolic changes resulting from the disease phenotype by a thorough and systematic metabolite profiling approach. For this purpose CSF samples from 79 AD patients and 51 healthy controls were analyzed by gas and liquid chromatography-tandem mass spectrometry (GC-MS and LC-MS/MS) in conjunction with univariate and multivariate statistical analyses. In total 343 different analytes have been identified. Significant changes in the metabolite profile of AD patients compared to healthy controls have been identified. Increased cortisol levels seemed to be related to the progression of AD and have been detected in more severe forms of AD. Increased cysteine associated with decreased uridine was the best paired combination to identify light AD (MMSE>22) with specificity and sensitivity above 75%. In this group of patients, sensitivity and specificity above 80% were obtained for several combinations of three to five metabolites, including cortisol and various amino acids, in addition to cysteine and uridine.

Coronavirus disease-2019 (COVID-19) is associated with a wide spectrum of clinical symptoms including acute respiratory failure. Biomarkers that can predict outcomes in patients with COVID-19 can assist with patient management. The aim of this study is to evaluate whether procalcitonin (PCT) can predict clinical outcome and bacterial superinfection in patients infected with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Adult patients diagnosed with SARS-CoV-2 by nasopharyngeal PCR who were admitted to a tertiary care center in Boston, MA with SARS-CoV-2 infection between March 17 and April 30, 2020 with a baseline PCT value were studied. Patients who were presumed positive for SARS-CoV-2, who lacked PCT levels, or who had a positive urinalysis with negative cultures were excluded. Demographics, clinical and laboratory data were extracted from the electronic medical records. 324 patient charts were reviewed and grouped by clinical and microbiologic outcomes by day 28. Baseline PCT levels were significantly higher for patients who were treated for true bacteremia (p = 0.0005) and bacterial pneumonia (p = 0.00077) compared with the non-bacterial infection group. Baseline PCT positively correlated with the NIAID ordinal scale and survival over time. When compared to other inflammatory biomarkers, PCT showed superiority in predicting bacteremia. Baseline PCT levels are associated with outcome and bacterial superinfection in patients hospitalized with SARS-CoV-2.

ObjectivesThere is sparse information on the safety of early primary discharge from the emergency department (ED) after rule-out of myocardial infarction in suspected acute coronary syndrome (ACS). This prospective registry aimed to confirm randomised study results in patients at low-to-intermediate risk, with a broader spectrum of symptoms, across different institutional standards and with a range of local troponin assays including high-sensitivity cTn (hs-cTn), cardiac troponin (cTn) and point-of-care troponin (POC Tn).DesignProspective, multicentre European registry.Setting18 emergency departments in nine European countries (Germany, Austria, Switzerland, France, Spain, UK, Turkey, Lithuania and Hungary)ParticipantsThe final study cohort consisted of 2294 patients (57.2% males, median age 57 years) with suspected ACS.InterventionsUsing the new dual markers strategy, 1477 patients were eligible for direct discharge, which was realised in 974 (42.5%) of patients.Main outcome measuresThe primary endpoint was all-cause mortality at 30 days.ResultsCompared with conventional workup after dual marker measurement, the median length of ED stay was 60 min shorter (228 min, 95% CI: 219 to 239 min vs 288 min, 95% CI: 279 to 300 min) in the primary dual marker strategy (DMS) discharge group. All-cause mortality was 0.1% (95% CI: 0% to 0.6%) in the primary DMS discharge group versus 1.1% (95% CI: 0.6% to 1.8%) in the conventional workup group after dual marker measurement. Conventional workup instead of discharge despite negative DMS biomarkers was observed in 503 patients (21.9%) and associated with higher prevalence of ACS (17.1% vs 0.9%, p<0.001), cardiac diagnoses (55.2% vs 23.5%, p<0.001) and risk factors (p<0.01), but with a similar all-cause mortality of 0.2% (95% CI: 0% to 1.1%) versus primary DMS discharge (p=0.64).ConclusionsCopeptin on top of cardiac troponin supports safe discharge in patients with chest pain or other symptoms suggestive of ACS under routine conditions with the use of a broad spectrum of local standard POC, conventional and high-sensitivity troponin assays.Trial registration number NCT02490969.

The objective of the current study was to find a metabolic signature associated with the early manifestations of type-2 diabetes mellitus. Modern metabolic profiling technology (MxP™ Broad Profiling) was applied to find early alterations in the plasma metabolome of type-2 diabetic patients. The results were validated in an independent study. Eicosanoid and single inon monitoring analysis (MxP™ Eicosanoid and MxP™ SIM analysis) were performed in subsets of samples. A metabolic signature including significantly increased levels of glyoxylate as a potential novel marker for early detection of type-2 diabetes mellitus was identified in an initial study (Study1). The signature was significantly altered in fasted diabetic and pre-diabetic subjects and in non-fasted subjects up to three years prior to the diagnosis of type-2 diabetes; most alterations were also consistently found in an independent patient group (Study 2). In Study 2 diabetic and most control subjects suffered from heart failure. In Study 1 a subgroup of diabetic subjects, with a history of use of anti-hypertensive medication further showed a more pronounced increase of glyoxylate levels, compared to a non-diabetic control group when tested in a hyperglycemic state. In the context of a prior history of anti-hypertensive medication, alterations in hexosamine and eicosanoid levels were also found. A metabolic signature including glyoxylate was associated with type-2 diabetes mellitus, independent of the fasting status and of occurrence of another major disease. The same signature was also found to be associated with pre-diabetic subjects. Glyoxylate levels further showed a specifically strong increase in a subgroup of diabetic subjects. It could represent a new marker for the detection of medical subgroups of diabetic subjects.

Aims Telemedical interventions in heart failure patients intend to avoid unfavourable, indication‐related events by an early, individualized care, which reacts to the current patients need. However, telemedical support is an expensive intervention, and usually only patients with high risk for unfavourable follow‐up events will be able to profit from it. Möckel et al. therefore adapted a new design which we call ‘prognostic‐efficacy‐combination design’. This design allows to define a biomarker cut‐off and to perform a randomized controlled trial (RCT) in a biomarker‐selected population within a single study. However, so far, it has not been evaluated if this double use of the control group for biomarker cut‐off definition and efficacy assessment within the RCT leads to a bias in treatment effect estimation. In this methodological research work, we therefore want to evaluate whether the ‘prognostic‐efficacy‐combination design’ leads to biased treatment effect estimates and also compare it to alternative designs. If there is a bias, we further want to analyse its magnitude under different parameter settings. Methods We perform a systematic Monte Carlo simulation study to investigate among others potential bias, root mean square error and sensitivity, and specificity as well as the total treatment effect estimate in various realistic trial scenarios that mimic and vary the true data characteristics of the published TIM‐HF2 Trial. In particular, we vary the event proportion, the sample size, the biomarker distribution, and the lower bound for the sensitivity. Results The results show that indeed the proposed design leads to some bias in the effect estimators, indicating an overestimation of the effect. However, this bias is relatively small in most scenarios. Conclusions The ‘prognostic‐efficacy‐combination design’ can generally be recommended for clinical applications due to its efficiency compared to two separate trials. We recommend a sufficiently large sample size depending on the trial scenario. Our simulation code can be adapted to explore suitable sample sizes for other settings.

Abstract Background and Aims N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentrations are influenced by age, which may influence the diagnostic performance of this peptide. Machine learning approaches incorporating NT-proBNP and age as continuous measures may have improved diagnostic performance. Methods We pooled individual patient-level data for 10 369 patients [median age 73 years (25th–75th percentile: 59–82)] with suspected acute heart failure across fourteen studies. The diagnostic performance of guideline-recommended NT-proBNP thresholds (uniform rule-out threshold of 300 pg/mL and age-stratified rule-in thresholds of 450, 900, and 1800 pg/mL for patients <50, 50–75, and >75 years, respectively) and the Collaboration for the Diagnosis and Evaluation of Heart Failure (CoDE-HF) machine learning model were evaluated using random effects meta-analysis across age groups. Results Overall, 43.9% (4549/10 369) of patients had an adjudicated diagnosis of acute heart failure. The negative predictive value (NPV) of the rule-out threshold of 300 pg/mL was lower in older patients [NPV 88.7% (confidence interval (CI) 84.2–92.1%) in patients ≥80 years vs 98.9% (97.6–99.5%) <50 years]. Conversely, the positive predictive value (PPV) of age-stratified rule-in thresholds was lower in younger patients [PPV 62.0% (56.2–67.5%) in those <50 years vs 79.6% (70.7–86.3%) ≥80 years]. CoDE-HF was more accurate than guideline-recommended thresholds across all age groups, with NPV and PPV ranging from 96.4% to 99.5% (93.8–99.8% CIs) and 81.1% to 84.2% (74.7–90.4% CIs), respectively. Conclusion The diagnostic performance of guideline-recommended thresholds of NT-proBNP varies significantly with age. A decision-support tool incorporating NT-proBNP with age as a continuous variable provides a more consistent and accurate approach. Graphical Abstract Graphical Abstract

Type 2 diabetes (T2D) is characterized by a variety of metabolic impairments that are closely linked to nonenzymatic glycation reactions of proteins and peptides resulting in advanced glycation end-products (AGEs). Reactive aldehydes derived from sugars play an important role in the generation of AGEs. Using metabolite profiling to characterize human plasma from diabetic versus nondiabetic subjects we observed in a recent study that the reactive aldehyde glyoxylate was increased before high levels of plasma glucose, typical for a diabetic condition, could be measured. Following this observation, we explored the relevance of increased glyoxylate in diabetic subjects and in diabetic C57BLKS/J-Leprdb/db-/- mice in the pathophysiology of diabetes. A retrospective study using samples of long-term blood donors revealed that glyoxylate levels unlike glucose levels became significantly elevated up to 3 years prior to diabetes diagnosis (difference to control P=0.034). Elevated glyoxylate levels impact on newly identified mechanisms linking hyperglycemia and AGE production with diabetes-associated complications such as diabetic nephropathy. Glyoxylate in its metabolic network may serve as an early marker in diabetes diagnosis with predictive qualities for associated complications and as potential to guide the development of new antidiabetic therapies.

The study objective was to evaluate nutritional metabolomics in support of companion animal nutrition. The specific purpose was to identify metabolites that differed significantly ( q -value < 0.05) in the 23 h fasted plasma metabolome of healthy adult female neutered cats ( n  = 14) and dogs ( n  = 14) following 2 weeks adaptation to a single batch of diet, nutritionally complete for both species, with and without dietary glucose supplementation (3.85 % w/w). The study consisted of a two-way, crossover design with samples from individual animals on days 14, 16 and 18 of each diet. Metabolic profiling consistently identified 219 metabolites from cats and 216 metabolites from dogs. Fasted plasma glucose did not change significantly with diet in either species whilst 41 % of cat and 20 % of dog metabolites did change significantly. In general, the two species responded similarly and provided data interpretable within the context of insulin-regulated responses to glucose, for example decreased fatty acid oxidation, increased uptake of branched chain amino acids into muscle, and in cats, decreased glucogenic amino acid catabolism. Other responses, such as increased 1 carbon pool intermediates in cats, were consistent with known nutritional differences between cats and dogs and interpretable within the context of energy metabolism. Data also indicated inter-individual variability and diet-dependent changes in digestion and absorption, providing insights for future study designs. In this proof-of-principle study it was concluded that metabolomics can provide data interpretable to a well-characterised area of nutrition and metabolism and also provide novel insights into metabolic adaptations with relevance to companion animal nutrition.