Yinan Zhang

Plasma metabolite profiles of Alzheimer's disease and mild cognitive impairment.

Previous studies have demonstrated altered metabolites in samples of Alzheimers disease (AD) patients. However, the sample size from many of them is relatively small and the metabolites are relatively limited. Here we applied a comprehensive platform using ultraperformance liquid chromatography-time-of-flight mass spectrometry and gas chromatography-time-of-flight mass spectrometry to analyze plasma samples from AD patients, amnestic mild cognitive impairment (aMCI) patients, and normal controls. A biomarker panel consisting of six plasma metabolites (arachidonic acid, N,N-dimethylglycine, thymine, glutamine, glutamic acid, and cytidine) was identified to discriminate AD patients from normal control. Another panel of five plasma metabolites (thymine, arachidonic acid, 2-aminoadipic acid, N,N-dimethylglycine, and 5,8-tetradecadienoic acid) was able to differentiate aMCI patients from control subjects. Both biomarker panels had good agreements with clinical diagnosis. The 2 panels of metabolite markers were all involved in fatty acid metabolism, one-carbon metabolism, amino acid metabolism, and nucleic acid metabolism. Additionally, no altered metabolites were found among the patients at different stages, as well as among those on anticholinesterase medication and those without anticholinesterase medication. These findings provide a comprehensive global plasma metabolite profiling and may contribute to making early diagnosis as well as understanding the pathogenic mechanism of AD and aMCI.

Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection

Metabolomic data analysis becomes increasingly challenging when dealing with clinical samples with diverse demographic and genetic backgrounds and various pathological conditions or treatments. Although many classification tools, such as projection to latent structures (PLS), support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF), have been successfully used in metabolomics, their performance including strengths and limitations in clinical data analysis has not been clear to researchers due to the lack of systematic evaluation of these tools. In this paper we comparatively evaluated the four classifiers, PLS, SVM, LDA, and RF, in the analysis of clinical metabolomic data derived from gas chromatography mass spectrometry platform of healthy subjects and patients diagnosed with colorectal cancer, where cross-validation, R 2/Q 2 plot, receiver operating characteristic curve, variable reduction, and Pearson correlation were performed. RF outperforms the other three classifiers in the given clinical data sets, highlighting its comparative advantages as a suitable classification and biomarker selection tool for clinical metabolomic data analysis.

Circulating Unsaturated Fatty Acids Delineate the Metabolic Status of Obese Individuals

Background Obesity is not a homogeneous condition across individuals since about 25–40% of obese individuals can maintain healthy status with no apparent signs of metabolic complications. The simple anthropometric measure of body mass index does not always reflect the biological effects of excessive body fat on health, thus additional molecular characterizations of obese phenotypes are needed to assess the risk of developing subsequent metabolic conditions at an individual level. Methods To better understand the associations of free fatty acids (FFAs) with metabolic phenotypes of obesity, we applied a targeted metabolomics approach to measure 40 serum FFAs from 452 individuals who participated in four independent studies, using an ultra-performance liquid chromatograph coupled to a Xevo G2 quadruple time-of-flight mass spectrometer. Findings FFA levels were significantly elevated in overweight/obese subjects with diabetes compared to their healthy counterparts. We identified a group of unsaturated fatty acids (UFAs) that are closely correlated with metabolic status in two groups of obese individuals who underwent weight loss intervention and can predict the recurrence of diabetes at two years after metabolic surgery. Two UFAs, dihomo-gamma-linolenic acid and palmitoleic acid, were also able to predict the future development of metabolic syndrome (MS) in a group of obese subjects. Interpretation These findings underscore the potential role of UFAs in the MS pathogenesis and also as important markers in predicting the risk of developing diabetes in obese individuals or diabetes remission after a metabolic surgery.

Serum Metabolic Signatures of Four Types of Human Arthritis

Similar symptoms of the different types of arthritis have continued to confound the clinical diagnosis and represent a clinical dilemma making treatment choices with a more personalized or generalized approach. Here we report a mass spectrometry-based metabolic phenotyping study to identify the global metabolic defects associated with arthritis as well as metabolic signatures of four major types of arthritis--rheumatoid arthritis (n = 27), osteoarthritis (n = 27), ankylosing spondylitis (n = 27), and gout (n = 33)--compared with healthy control subjects (n = 60). A total of 196 metabolites were identified from serum samples using a combined gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF MS) and ultraperformance liquid chromatography quadrupole-time-of-flight mass spectrometry (UPLC-QTOF MS). A global metabolic profile is identified from all arthritic patients, suggesting that there are common metabolic defects resulting from joint inflammation and lesion. Meanwhile, differentially expressed serum metabolites are identified constituting an unique metabolic signature of each type of arthritis that can be used as biomarkers for diagnosis and patient stratification. The results highlight the applicability of metabonomic phenotyping as a novel diagnostic tool for arthritis complementary to existing clinical modalities.

The Metabolite Profiles of the Obese Population Are Gender-Dependent

Studies have identified that several amino acids, in particular, branched-chain amino acids (BCAAs), have increased significantly in obese individuals when compared to lean individuals. Additionally, these metabolites were strongly associated with future diabetes, which rendered them prognostic markers suitable for obese populations. Here we report a metabonomic study that reveals new findings on the role of these amino acid markers, particularly BCAAs, in a Chinese cohort including 106 healthy obese and 105 healthy lean participants. We found that the BCAAs were correlated with insulin resistance and differentially expressed in obese men, but not in obese women. The results were verified with two independent groups of participants (Chinese, n = 105 and American, n = 72) and demonstrate that the serum metabolite profiles of the obese population are gender-dependent. The study supports the previous findings of a panel of several key metabolites as prognostic markers of the obese population and highlights the need to take into account gender differences when using these markers for risk assessment.

Serum metabolic signatures of fulminant type 1 diabetes.

Fulminant type 1 diabetes (FT1DM) is a relatively new clinical entity featured by acute destruction of pancreatic beta cells. Clinical consequences of FT1DM could be fatal when timely medications are not provided, suggesting the particular importance of rapid and accurate diagnosis. Here we report a serum metabonomics study of FT1DM patients, together with healthy control subjects (NC), type 2 diabetes (T2DM), classic type 1 diabetes (T1DM), and diabetic ketoacidosis (DKA) patients, with the aim of discovering metabolic markers associated with FT1DM. A total of 79 subjects were enrolled (22 NC, 22 T1DM, 22 T2DM, 8 DKA and 5 FT1DM) and the serum metabolic profiling of fasting blood samples was performed using gas chromatography time-of-flight mass spectrometry (GC-TOFMS) coupled with multivariate and univariate statistical analyses. Serum metabolites differentially expressed in FT1DM relative to NC, or to T2DM, T1DM and DKA were identified. Three metabolite markers, 5-oxoproline, glutamate, and homocysteine, were significantly altered among FT1DM, T2DM, T1DM, and DKA. In addition, the three metabolite markers, 5-oxoproline, glutamate, and homocysteine, presented similar patterns of distribution across groups. The results showed that the metabolic signatures of FT1DM identified in this study could be of potential clinical significance for the accurate diagnosis of FT1DM.

Metabolic profiling reveals disorder of carbohydrate metabolism in mouse fibroblast cells induced by titanium dioxide nanoparticles

As titanium dioxide (TiO2) nanoparticles are widely used commercially, their potential biosafety and metabolic mechanism needs to be fully explained. In this study, the cytotoxicity of homogeneous and weakly aggregated (< 100 nm) TiO2 nanoparticles was investigated by analyzing the changes in metabolite profiles both in mouse fibroblast (L929) cells and their corresponding culture media using gas chromatograph with a time‐of‐flight mass spectrometry (GC/TOFMS)‐based metabolomic strategy. With multivariate statistics analysis, satisfactory separations were observed in principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS‐DA) models. Based on the variable importance in the OPLS‐DA models, a series of differential metabolites were identified by comparison between TiO2 nanoparticle‐treated L929 cells or their corresponding culture media and the control groups. It was found that the major biochemical metabolism (carbohydrate metabolism) was suppressed in TiO2 nanoparticle‐treated L929 cells and their corresponding culture media. These results might account for the serious damage to energy metabolism in mitochondria and the increased cellular oxidation stress in TiO2 nanoparticle‐induced L929 cells. These results also suggest that the metabolomic strategy had a great potential in evaluating the cytotoxicity of TiO2 nanoparticles and thus was very helpful in understanding its underlying molecular mechanisms. Copyright

Metabolomics analysis reveals variation in Schisandra chinensis cetabolites from different origins.

Wu Wei Zi (Schisandra chinensis), an important herbal medicine, is mainly distributed in the northeast of China. Its phytochemical compositions, which depend on geographical origin, climatic conditions and cultural practices, may vary largely among Wu Wei Zi from different areas. In this study, we applied a comprehensive metabolite profiling approach using GC-TOF-MS, ultra-performance LC (UPLC) quadrupole TOF (QTOF) MS and inductively coupled plasma MS to systematically investigate the metabolite variations of S. chinensis from four different areas including Heilongjiang, Liaoning, Jilin, and Shanxi of China. A total of 65 primary metabolites, 35 secondary metabolites and 64 inorganic elements were identified. Several primary metabolites, including shikimic acid and tricarboxylic acid cycle intermediates, were abundant in those located in Heilongjiang, Jilin, and Liaoning. Besides, bioactive lignans are also highly abundant in those from northeastern China than those from northwestern China. Inorganic elements varied significantly among the different locations. Our results suggested that the metabolite profiling approach using GC-TOF-MS, ultra-performance LC quadrupole TOF MS, and inductively coupled plasma MS is a robust and reliable method that can be effectively used to explore subtle variations among plants from different geographical locations.

Metabonomic Profiling of Human Placentas Reveals Different Metabolic Patterns among Subtypes of Neural Tube Defects

Neural tube defects (NTDs) are one of the most common types of birth defects with a complex etiology. We have previously profiled serum metabolites of pregnant women in Lvliang prefecture, Shanxi Province of China, which revealed distinct metabolic changes in pregnant women with NTDs outcome. Here we present a metabonomics study of human placentas of 144 pregnant women with normal pregnancy outcome and 115 pregnant women affected with NTDs recruited from four rural counties (Pingding, Xiyang, Taigu, and Zezhou) of Shanxi Province, the area with the highest prevalence worldwide. A panel of 19 metabolites related to one-carbon metabolism was also quantitatively determined. We observed obvious differences in global metabolic profiles and one-carbon metabolism among three subtypes of NTDs, anencephaly (Ane), spina bifida (SB), and Ane complicated with SB (Ane & SB) via mass-spectrometry-based metabonomics approach. Disturbed carbohydrate, amino acid, lipid, and nucleic acid metabolism were identified. Placental transport of amino acids might be depressed in Ane and Ane & SB group. Deficiency of choline contributes to Ane and Ane & SB pathogenesis via different metabolic pathways. The formation of NTDs seemed to be weakly related to folates. The metabonomic analysis reveals that the physiological and biochemical processes of the three subtypes of NTDs might be different and the subtype condition should be considered for the future investigation of NTDs.

Prevalence of Type 2 Diabetes among High-Risk Adults in Shanghai from 2002 to 2012

Objective The objective of this study was to evaluate the trend and prevalence of prediabetes and diabetes among high-risk adults in Shanghai from 2002 to 2012. Methods From 2002 to 2012, 10043 subjects with known risk factors for diabetes participated in the diabetes-screening project at the Shanghai Sixth People’s Hospital of Shanghai Jiao Tong University. All participants were asked to complete a nurse-administered standard questionnaire concerning age, sex, smoking status, and personal and family histories of diabetes, cardiovascular disease, stroke, hypertension and other diseases. The participants’ body mass index scores, blood pressures and blood glucose levels at 0, 30, 60, 120 and 180 min were measured in response to a 75 g oral glucose tolerance test. Results The overall prevalence of diabetes increased from 27.93% to 34.78% between 2002 and 2012 in high-risk subjects. The study also showed that the prevalence increased much faster in male compared to female subjects. Specifically, an increased rate was seen in middle-aged men, with no change observed in middle-aged females over the eleven-year period. Conclusion This study showed that sex, age, parental diabetic history, and being overweight were associated with an increased risk for diabetes in high-risk people. Therefore, as prediabetes and diabetes are highly prevalent in people with multiple diabetes risk factors in Shanghai, screening programs targeting these individuals may be beneficial.