Diabetic kidney disease (DKD) is a devastating complication that affects an estimated third of patients with type 1 diabetes mellitus (DM). There is no cure once the disease is diagnosed, but early treatment at a sub-clinical stage can prevent or at least halt the progression. DKD is clinically diagnosed as abnormally high urinary albumin excretion rate (AER). We hypothesize that subtle changes in the urine metabolome precede the clinically significant rise in AER. To test this, 52 type 1 diabetic patients were recruited by the FinnDiane study that had normal AER (normoalbuminuric). After an average of 5.5 years of follow-up half of the subjects (26) progressed from normal AER to microalbuminuria or DKD (macroalbuminuria), the other half remained normoalbuminuric. The objective of this study is to discover urinary biomarkers that differentiate the progressive form of albuminuria from non-progressive form of albuminuria in humans. Metabolite profiles of baseline 24 h urine samples were obtained by gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) to detect potential early indicators of pathological changes. Multivariate logistic regression modeling of the metabolomics data resulted in a profile of metabolites that separated those patients that progressed from normoalbuminuric AER to microalbuminuric AER from those patients that maintained normoalbuminuric AER with an accuracy of 75% and a precision of 73%. As this data and samples are from an actual patient population and as such, gathered within a less controlled environment it is striking to see that within this profile a number of metabolites (identified as early indicators) have been associated with DKD already in literature, but also that new candidate biomarkers were found. The discriminating metabolites included acyl-carnitines, acyl-glycines and metabolites related to tryptophan metabolism. We found candidate biomarkers that were univariately significant different. This study demonstrates the potential of multivariate data analysis and metabolomics in the field of diabetic complications, and suggests several metabolic pathways relevant for further biological studies.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-011-0291-6) contains supplementary material, which is available to authorized users.

CE suffers from an inherent low concentration sensitivity. Analyte detection limits can be improved by combining CE with SPE. This paper presents an overview of coupled SPE-CE systems that have been reported in literature. Attention is paid to fundamental aspects of coupling SPE and CE, as well as to important SPE requirements. Interfaces for inline and online coupling with CE are critically discussed, and their mode of operation is outlined. Advantages and limitations of the interfaces are discussed and typical examples are selected. Finally, some future developments are discussed.

A preconcentration-capillary electrophoresis (CE) system using a small precolumn in combination with an in-line injection valve is presented. The advantage of the present design is the ability to perform the sample preconcentration fully independently from the CE separation and to prevent sample matrix and washing solvents from entering the CE capillary. With a micro injection valve, sample could be effectively introduced into the CE system in an in-line fashion without seriously affecting the CE separation efficiency. Breakthrough volume, desorption efficiency, and elution volume for the C18 microcolumn (5 x 0.5 mm i.d.) were established, yielding values of 750 microL, 70%, and 0.9-1.1 microL, respectively, using enkephalin peptides. The time between the start of the desorption of the analytes from the precolumn and the injection into the CE system was also studied in order to achieve optimal sensitivity and separation efficiency. The performance of the complete system was demonstrated by the preconcentration and separation of an enkephalin mixture. Using a sample volume of 250 microL and a CE injection voltage of -15 kV for 12 s, linearity was observed over 2 orders of magnitude, and detection limits (S/N = 3) were in the 5-10 ng/mL range. A 1000-fold sensitivity enhancement is obtained using this setup, as compared to a regular CE setup. For 100 ng/mL samples, repeatabilities (RSDs) of migration time and peak area were 1.2 and 11%, respectively.

An on-line SPE-CE-MS system has been developed for the analysis of peptides. Analytes are preconcentrated using a C(18) microcolumn (5 x 0.5 mm id), and then introduced into the CE system via a valve interface. The CE system with a Polybrene-poly(vinylsulfonate) bilayer coated capillary is combined with an ion-trap mass spectrometer via ESI using a coaxial sheath-liquid sprayer. The on-line coupling of the SPE and CE step by the valve interface is advantageous because it allows an independent functioning of the system parts. Optimization of the SPE-CE system was performed using UV detection. Subsequently, the SPE-CE system has been coupled to the ion-trap mass spectrometer. Test solutions with enkephalin peptides (50 ng/mL) were used for evaluation of system performance. Repeatability of effective mobility and peak area ratio of the two enkephalins were within 1.2% and 9% RSD, respectively. The analysis of 1:1 v/v diluted cerebrospinal fluid samples spiked with enkephalin peptides showed detection limits (S/N = 3) in the range of 1.5-3 ng/mL (around 5 nM), which were similar to those obtained for enkephalin test solutions. Moreover, the potential of the on-line SPE-CE-MS system was demonstrated by the analysis of a cytochrome C digest. Some hydrophilic peptides did not show sufficient retention on the SPE column, and were lost during preconcentration. Nonetheless, positive identification of the protein was achieved, indicating the feasibility of the system for proteomics.

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