Analytica Chimica Acta 650 (2009) 16–22 Contents lists available at ScienceDirect Analytica Chimica Acta journal homepage: www.elsevier.com/locate/aca Metabonomics research of diabetic nephropathy and type 2 diabetes mellitus based on UPLC–oaTOF-MS system Jie Zhang a,∗ , Lijuan Yan b , Wengui Chen c , Lin Lin a , Xiuyu Song c , Xiaomei Yan a , Wei Hang a,∗∗ , Benli Huang a a The Key Laboratory for Chemical Biology of Fujian Province, College of Chemistry and Chemical Engineering, Xiamen University, 361005 Xiamen, China b XiaMen Entry-Exit Inspection and Quarantine Bureau, 361012 Xiamen, China c First Hospital of Xiamen, 361003 Xiamen, China article info Article history: Received 12 December 2008 Received in revised form 4 February 2009 Accepted 12 February 2009 Available online 21 February 2009 Keywords: Diabetic nephropathy Diabetes mellitus Metabonomics Ultra performance liquid chromatography Time-of-flight mass spectrometry abstract Ultra performance liquid chromatography (UPLC) coupled with orthogonal acceleration time-of-flight (oaTOF) mass spectrometry has showed great potential in diabetes research. In this paper, a UPLC–oaTOF- MS system was employed to distinguish the global serum profiles of 8 diabetic nephropathy (DN) patients, 33 type 2 diabetes mellitus (T2DM) patients and 25 healthy volunteers, and tried to find potential biomark- ers. The UPLC system produced information-rich chromatograms with typical measured peak widths of 4 s, generating peak capacities of 225 in 15 min. Furthermore, principal component analysis (PCA) was used for group differentiation and marker selection. As shown in the scores plot, the distinct clustering between the patients and controls was observed, and DN and T2DM patients were also separated into two individual groups. Several compounds were tentatively identified based on accurate mass, isotopic pattern and MS/MS information. In addition, significant changes in the serum level of leucine, dihy- drosphingosine and phytoshpingosine were noted, indicating the perturbations of amino acid metabolism and phospholipid metabolism in diabetic diseases, which having implications in clinical diagnosis and treatment. © 2009 Elsevier B.V. All rights reserved. 1. Introduction Diabetes mellitus (DM) has been described as an “epidemic” of contemporary society. It is estimated that the number of peo- ple worldwide with DM will be about 221 million in 2010, and the number will expand to 300 million by 2025 [1]. DM is a typical heterogeneous metabolic disorder, characterized by abnor- mal metabolism of carbohydrates, lipids, and proteins. Diabetic nephropathy (DN) is the most severe complication of DM, asso- ciated with a high risk of atherosclerotic diseases and premature death [2]. Diabetic nephropathy occurs in 30–40% of type 1 diabetes mellitus (T1DM) after 20 years and in 15–20% of type 2 diabetes mellitus (T2DM) [3]. Therefore, the obvious correlation between DM and DN has led many to speculate that the two diseases may share common pathogenetic processes and metabolic defects. Metabonomics is defined by Nicholson as “quantitative mea- surement of the dynamic multiparametric metabolic response of ∗ Corresponding author. Tel.: +86 592 2184517; fax: +86 592 2184618. ∗∗ Corresponding author. Tel.: +86 592 2184618; fax: +86 592 2184618. E-mail addresses: jiezhang@dicp.ac.cn (J. Zhang), weihang@xmu.edu.cn (W. Hang). living systems to pathophysiological stimuli or genetic modifica- tion” [4]. Metabonomics is able to provide information on disease processes, drug toxicity and gene function, and has showed great potential in diabetes research [5,6]. Much of the original work related with diabetes metabonomics were performed using NMR spectroscopy [7–13], but there has been a trend toward employ- ing chromatographic techniques such as gas chromatography (GC) and liquid chromatography (LC) coupled with mass spectrom- etry (MS) [14–16]. Diabetic disease has been shown to have a direct relationship with a disorder in the lipids and fatty acids [17,18]. Several GC–MS methods have been developed to inves- tigate the change of serum fatty acid profiles between T2DM patients and healthy controls [19–22]. As for the metabolites with low volatility and thermal stability, LC–MS is a powerful alter- native that offers high selectivity and sensitivity, and has large potential in diabetes research [5,23–27]. This potential has been further enhanced following the introduction of UPLC–MS, with its higher resolution separations [28–32]. Animal models were widely used in the metabonomics research, but more experience from human populations was needed [33]. Nevertheless, LC–MS metabonomics of human being is much more difficult to carry out due to the wide variability of health state, diet and phenotype. Recently, some attempts have been made to process clinical sam- 0003-2670/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.aca.2009.02.027