Targeted and untargeted serum NMR metabolomics to reveal initial kidney disease in diabetes mellitus.

Serum 1H NMR metabolomics has been used as a diagnostic tool for screening type 2 diabetes (T2D) with chronic kidney disease (CKD) as comorbidity. This work aimed to evaluate 1H NMR data to detect the initial kidney damage and CKD in T2D subjects, through multivariate statistical analysis. Clinical data and biochemical parameters were obtained for classifying five experimental groups using KDIGO guidelines: Control (healthy subjects), T2D, T2D-CKD-mild, T2D-CKD-moderate, and T2D-CKD-severe. Serum 1H NMR spectra were recorded to follow two strategies: one based on metabolite-to-creatinine (Met/Cr) ratios as targeted metabolomics, and the second one based on untargeted metabolomics from the 1H NMR profile. A prospective biomarkers panel of the early stage of T2D-CKD based in metabolite-to-creatinine ratio (ornithine/Cr, serine/Cr, mannose/Cr, acetate/Cr, acetoacetate/Cr, formate/Cr, and glutamate/Cr) was proposed. Later, a statistical model based on non-targeted metabolomics was used to predict initial CKD, and its metabolic pathway analysis allowed identifying the most affected pathways: phenylalanine, tyrosine, and tryptophan biosynthesis; valine, leucine, and isoleucine degradation; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; and histidine metabolism. Nonetheless, further studies with a larger cohort are advised to precise ranges in metabolite-to-creatinine ratios and evaluate the prediction pertinency to detect initial CKD in T2D patients in both statistical models proposed.

Using nuclear magnetic resonance urine metabolomics to develop a prediction model of early stages of renal disease in subjects with type 2 diabetes.

Type 2 diabetes mellitus (DM2) is a multimorbidity, long-term condition, and one of the worldwide leading causes of chronic kidney disease (CKD) -a silent disease, usually detected when non-reversible renal damage have already occurred. New strategies and more effective laboratory methods are needed for more opportune diagnosis of DM2-CKD. This study comprises clinical parameters and nuclear magnetic resonance (NMR)-based urine metabolomics data from 60 individuals (20-65 years old, 67.7% females), sorted in 5 experimental groups (healthy subjects; diabetic patients without any clinical sign of CKD; and patients with mild, moderate, and severe DM2-CKD), according to KDIGO. DM2-CKD produces a continuous variation of the urine metabolome, characterized by an increase/decrement of a group of metabolites that can be used to monitor CKD progression (trigonelline, hippurate, phenylalanine, glycolate, dimethylamine, alanine, 2-hydroxybutyrate, lactate, and citrate). NMR profiles were used to obtain a statistical model, based on partial least squares analysis (PLS-DA) to discriminate among groups. The PLS-DA model yielded good validation parameters (sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve (ROC) plot: 0.692, 0.778 and 0.912, respectively) and, thus, it can differentiate between subjects with DM2-CKD in early stages, from subjects with a mild or severe condition. This metabolic signature exhibits a molecular variation associated to DM2-CKD, and data suggests it can be used to predict risk of DM2-CKD in patients without clinical signs of renal disease, offering a new alternative to current diagnosis methods.

Enhanced chromatographic fingerprinting of herb materials by multi-wavelength selection and chemometrics.

A strategy for multi-wavelength chromatographic fingerprinting of herbal materials, using high performance liquid chromatography with a UV-Vis diode array detector is presented. Valeriana officinalis was selected to show the proposed methodology since it is a widely used commercially available herbal drug, and because misfit with other valerian species is a current issue. The enhanced fingerprints were constructed by compiling into a single data vector the chromatograms from four wavelengths (226, 254, 280 and 326 nm), at which characteristic chemical constituents of studied herbs presented maximum absorbance. Chromatographic data pretreatment included baseline correction, normalization and correlation optimized warping. A simplex optimization was performed to retrieve the optimal values of the parameters used in the warping. General success rates of a classification above 90% were achieved by soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The sensitivity and specificity of constructed models were above 94%. Tests on laboratory-made mixtures showed that it is possible to detect adulterations or counterfeits with 5% foreign herbal material, even if it is from the Valerianaceae family. The results suggest that the proposed enhanced fingerprinting approach can be used to authenticate herb materials with complex chromatographic profiles.

Multi-wavelength high-performance liquid chromatographic fingerprints and chemometrics to predict the antioxidant activity of Turnera diffusa as part of its quality control.

The determination of the antioxidant activity of Turnera diffusa using partial least squares regression (PLSR) on chromatographic data is presented. The chromatograms were recorded with a diode array detector and, for each sample, an enhanced fingerprint was constructed by compiling into a single data vector the chromatograms at four wavelengths (216, 238, 254 and 345 nm). The wavelengths were selected from a contour plot, in order to obtain the greater number of peaks at each of the wavelengths. A further pretreatment of the data that included baseline correction, scaling and correlation optimized warping was performed. Optimal values of the parameters used in the warping were found by means of simplex optimization. A PLSR model with four latent variables (LV) explained 52.5% of X variance and 98.4% of Y, with a root mean square error for cross validation of 6.02. To evaluate its reliability, it was applied to an external prediction set, retrieving a relative standard error for prediction of 7.8%. The study of the most important variables for the regression indicated the chromatographic peaks related to antioxidant activity at the used wavelengths.