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PE: preeclampsia; sFlt-1: soluble FMS-like tyrosine kinase-1; PlGF: placental growth factor; GC-MS: gas chromatography-mass spectrometry; LC-MS: liquid chromatography-mass spectrometry; NMR: nuclear magnetic resonance spectroscopy; HMDB: human metabolome database; RCT: randomized control trial; e-PE: early-onset PE; l-PE: late-onset PE; PLS-DA: partial least-squares-discriminant analysis; CRL: crown-rump length; UtPI: uterine artery Doppler pulsatility index; BMI: body mass index; MAP: mean arterial pressure; OS: oxidative stress; PAPPA: plasma protein A; FTIR: Fourier transform infrared; BCAA: branched chain amino acids; Arg: arginine; NO: nitric oxide.

Chronic obstructive pulmonary disease (COPD), asthma and cystic fibrosis (CF) are characterized by airway obstruction and an inflammatory process. Reaching early diagnosis and discrimination of subtypes of these respiratory diseases are quite a challenging task than other chronic illnesses. Metabolomics is the study of metabolic pathways and the measurement of unique biochemical molecules generated in a living system. In the last decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. In this article, we review the current state of the metabolomics of COPD, asthma and CF with a focus on the different methods and instrumentation being used for the discovery of biomarkers in research and translation into clinic as diagnostic aids for the choice of patient-specific therapies.

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Focal segmental glomerulosclerosis (FSGS) is a common glomerulonephritis, and its rates of occurrence are increasing worldwide. Proteinuria is a clinical defining feature of FSGS which correlates with the severity of podocyte injury in patients with nephrotic-range protein excretion. Metabolite biomarkers corresponding with the level of proteinuria could be considered as non-invasive complementary prognostic factors to proteinuria. The urine samples of 15 patients (n = 6 women and n = 9 men) with biopsy-proven FSGS were collected and subjected to nuclear magnetic resonance (NMR) analysis for metabolite profiling. Multivariate statistical analyses, including principal component analysis and orthogonal projection to latent structure discriminant analysis, were applied to construct a predictive model based on patients with proteinuria >3000 mg/day and <3000 mg/day. In addition, random forest was performed to predict differential metabolites, and pathway analysis was performed to find the defective pathways responsible for proteinuria. Ten metabolites, significant in both statistical methods (orthogonal projection to latent structure discriminant analysis and random forest), were considered as prognostic biomarkers for FSGS: citrulline, dimethylamine, proline, acetoacetate, alpha-ketoisovaleric acid, valine, isobutyrate, D-Palmitylcarnitine, histidine, and N-methylnicotinamide. Pathway analysis revealed impairment of the branched-chain amino acid degradation pathways in patients with massive proteinuria. This study shows that metabolomics can reveal the molecular changes corresponding with disease progression in patients with FSGS and provide a new insight for pathogenic pathways. Copyright © 2016 John Wiley & Sons, Ltd.