OBJECTIVEAcylcarnitines were suggested as early biomarkers even prior to insulin resistance in animal studies, but their roles in predicting type 2 diabetes were unknown. Therefore, we aimed to determine whether acylcarnitines could independently predict type 2 diabetes by using a targeted metabolic profiling approach. RESEARCH DESIGN AND METHODSA population-based prospective study was conducted among 2,103 communityliving Chinese individuals aged 50-70 years from Beijing and Shanghai with a mean follow-up duration of 6 years. Fasting glucose, glycohemoglobin, and insulin were determined at baseline and in a follow-up survey. Baseline plasma acylcarnitines were profiled by liquid chromatography-tandem mass spectrometry. RESULTSOver the 6-year period, 507 participants developed diabetes. A panel of acylcanitines, especially with long chain, was significantly associated with increased risk of type 2 diabetes. The relative risks of type 2 diabetes per SD increase of the predictive model score were 2.48 (95% CI 2.20-2.78) for the conventional and 9.41 (95% CI 7.62-11.62) for the full model including acylcarnitines, respectively. Moreover, adding selected acylcarnitines substantially improved predictive ability for incident diabetes, as area under the receiver operator characteristic curve improved to 0.89 in the full model compared with 0.73 in the conventional model. Similar associations were obtained when the predictive models were established separately among Beijing or Shanghai residents. CONCLUSIONSA panel of acylcarnitines, mainly involving mitochondrial lipid dysregulation, significantly improved predictive ability for type 2 diabetes beyond conventional risk factors. These findings need to be replicated in other populations, and the underlying mechanisms should be elucidated.The escalating global epidemic of type 2 diabetes has contributed considerably to socioeconomic burdens in both developed and developing countries (1). To understand biological mechanisms and improve clinical predictions, it is essential to identify novel biomarkers to enhance the capability to predict early pathophysiological changes (2). As a largely preventable disease, early prediction is the key to control the epidemic trend of type 2 diabetes, particularly in those countries with

Metabolomic analysis of human fecal water recently aroused increasing attention with the importance of fecal metabolome in exploring the relationships between symbiotic gut microflora and human health. In this study, we developed a quantitative metabolomic method for human fecal water based on trimethylsilylation derivatization and GC/MS analysis. Methanol was found to be the best solvent for protein precipitation and extraction of fecal water metabolome. Within the optimized linear range of sampling volume (less than 50 microL), compounds showed a good linearity with a correlation coefficient higher than 0.99. The developed method showed good repeatability for both sample preparation and GC/MS analysis with the relative standard deviations lower than 10% for most compounds and less than 20% for a few other ones. The method was further validated by studying analytical variability using a set of clinical samples as well as a pooled sample. The pH value and matrix effects were the main factors affecting the accuracy of quantitative calibration curves. The increased pH value decreased the loss of short chain fatty acids during lyophilization. Spiking fecal water to a standard mixture significantly enhanced the accuracy of quantitative calibration curves, probably due to the inhibition of volatile loss during lyophilization and the increase of compound solubility in the derivatization medium. A strategy for calibration curve preparation was proposed in order to avoid the effects of pH and matrix. Totally, 133 compounds were structurally confirmed from a set of clinical samples, and 33 of them were quantified, which demonstrates the suitability of this method for a quantitative metabolomic study of human fecal water samples.