Baseline characteristics of participants, by appropriate ICD shock.

<p>Baseline characteristics of participants, by appropriate ICD shock.</p

Baseline characteristics of participants, by all-cause mortality.

<p>Baseline characteristics of participants, by all-cause mortality.</p

Adjusted hazard ratios (HR) and 95% confidence interval (CI) for appropriate shock and all-cause mortality associated with each oxylipin-to-precursor ratio.

<p>Models were adjusted for age, sex, race, enrollment center, ejection fraction, NYHA class, cardiomyopathy etiology, atrial fibrillation, diabetes, hypertension, and chronic kidney disease.</p

Adjusted hazard ratios (HR) and 95% confidence interval (CI) for appropriate shock and all-cause mortality associated with each oxylipin.

<p>Models were adjusted for age, sex, race, enrollment center, ejection fraction, NYHA class, cardiomyopathy etiology, atrial fibrillation, diabetes, hypertension, and chronic kidney disease.</p

A Mendelian Randomization Study of Metabolite Profiles, Fasting Glucose, and Type 2 Diabetes.

Mendelian randomization (MR) provides us the opportunity to investigate the causal paths of metabolites in type 2 diabetes and glucose homeostasis. We developed and tested an MR approach based on genetic risk scoring for plasma metabolite levels, utilizing a pathway-based sensitivity analysis to control for nonspecific effects. We focused on 124 circulating metabolites that correlate with fasting glucose in the Erasmus Rucphen Family (ERF) study (n = 2,564) and tested the possible causal effect of each metabolite with glucose and type 2 diabetes and vice versa. We detected 14 paths with potential causal effects by MR, following pathway-based sensitivity analysis. Our results suggest that elevated plasma triglycerides might be partially responsible for increased glucose levels and type 2 diabetes risk, which is consistent with previous reports. Additionally, elevated HDL components, i.e., small HDL triglycerides, might have a causal role of elevating glucose levels. In contrast, large (L) and extra large (XL) HDL lipid components, i.e., XL-HDL cholesterol, XL-HDL-free cholesterol, XL-HDL phospholipids, L-HDL cholesterol, and L-HDL-free cholesterol, as well as HDL cholesterol seem to be protective against increasing fasting glucose but not against type 2 diabetes. Finally, we demonstrate that genetic predisposition to type 2 diabetes associates with increased levels of alanine and decreased levels of phosphatidylcholine alkyl-acyl C42:5 and phosphatidylcholine alkyl-acyl C44:4. Our MR results provide novel insight into promising causal paths to and from glucose and type 2 diabetes and underline the value of additional information from high-resolution metabolomics over classic biochemistry

Selection of the markers significantly different between controls and cases (EO-PE or LO-PE) based on training set.

<p>Student's t-test was used for statistical analysis. Significance value FDR<15%. EO-PE: early-onset preeclampsia; LO-PE: late-onset preeclampsia; MoM: multiple of the median; FDR: false discovery rate; MAP: mean arterial pressure.</p

Studies that assessed preeclampsia screening using metabolomics techniques.

<p>PE: preeclampsia; DR: detection rate; FPR: false positive rate; MC: maternal characteristics; UtA: Uterine artery Doppler measurement; CRL: crown-rump-length.</p

Model predicted early preeclampsia detection rate (95% CI) for FPR of 10% with prior risk, MAP, taurine, asparagine and glycylglycine in control and preeclampsia groups.

<p>DR: detection rate; FPR: false positive rate; MAP: Mean Arterial Pressure; CI: confidence interval; AUC: area under curve, EO-PE: early-onset preeclampsia; LO-PE: late-onset preeclampsia.</p><p>*The best model selected for further validation.</p

Receiver operating characteristic curves (ROCs) with prediction models for early onset preeclampsia and late onset preeclampsia used on the validation set.

<p>Prior risk of preeclampsia containing maternal characteristics (black line) and prior risk for preeclampsia combined with different markers (red line). MAP: mean arterial pressure.</p

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis

<div><p>In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA) respond insufficiently to TNF-α inhibitors (TNFis). The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients’ response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124). The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI). The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28), erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP). Out of 139 metabolites, the best-performing predictors were <i>sn1</i>-LPC(18:3-ω3/ω6), <i>sn1</i>-LPC(15:0), ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01). The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23) and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05). Our study established an accurate prediction model for response to TNFi therapy, containing metabolites and clinical parameters. Associations between metabolites and disease activity may help elucidate additional pathologic mechanisms behind RA.</p></div