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Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator curve (AUC) analysis. Using signatures, multivariate classification modeling was performed using various algorithms to select the most consistent method as measured by AUC differences between resampled training and test sets. Lists of metabolites indicative of OA [AUC > 0.5] were identified for each stratum. The signature from males age > 50 years old encompassed the majority of identified metabolites, suggesting lysoPCs and PCs are dominant indicators of OA in older males. Principal component regression with logistic regression was the most consistent multivariate classification algorithm tested. Using this algorithm, classification of older males had fair power to classify OA patients from healthy individuals. Thus, individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older populations, particularly males. Our metabolite signature modeling method is likely to increase classification power in validation cohorts. [ABSTRACT FROM AUTHOR]

Metabolomics studies use quantitative analyses of metabolites from body fluids or tissues in order to investigate a sequence of cellular processes and biological systems in response to genetic and environmental influences. This promises an immense potential for a better understanding of the pathogenesis of complex diseases. Most conventional metabolomics analysis methods exam one metabolite at a time and may overlook the synergistic effect of combining multiple metabolites. In this article, we proposed a new bioinformatics framework that infers the non-linear synergy among multiple metabolites using a symbolic model and subsequently, identify key metabolites using network analysis. Such a symbolic model is able to represent a complex non-linear relationship among a set of metabolites associated with osteoarthritis (OA) and is automatically learned using an evolutionary algorithm. Applied to the Newfoundland Osteoarthritis Study (NFOAS) dataset, our methodology was able to identify nine key metabolites including some known osteoarthritis-associated metabolites and some novel metabolic markers that have never been reported before. The results demonstrate the effectiveness of our methodology and more importantly, with further investigations, propose new hypotheses that can help better understand the OA disease. [ABSTRACT FROM AUTHOR]

To test whether type 2 diabetic patients have an elevated level of advanced glycation end-products (AGEs) and responsible for altered phosphatidylcholine metabolism, which we recently found to be associated with osteoarthritis (OA) and diabetes mellitus (DM), synovial fluid (SF) and plasma samples were collected from OA patients with and without DM. Hyperglycemia-related AGEs including methylglyoxal (MG), free methylglyoxal-derived hydroimidazolone (MG-H1), and protein bound N-(Carboxymethyl)lysine (CML) and N-(Carboxyethyl)lysine (CEL) levels were measured in both SF and plasma samples using liquid chromatography coupled tandem mass spectrometry methodology. The correlation between these AGEs and phosphatidylcholine acyl-alkyl C34:3 (PC ae C34:3) and C36:3 (PC ae C36:3) were examined. Eighty four patients with knee OA, including 46 with DM and 38 without DM, were included in the study. There was no significant difference in plasma levels of MG, MG-H1, CML, and CEL between OA patients with and without DM. However, the levels of MG and MG-H1, but not CML and CEL in SF were significantly higher in OA patients with DM than in those without (all p ≤0.04). This association strengthened after adjustment for age, body mass index (BMI), sex and hexose level (p<0.02). Moreover, the levels of MG-H1 in SF was negatively and significantly correlated with PC ae C34:3 (ρ = -0.34; p = 0.02) and PC ae C36:3 (ρ = -0.39; P = 0.03) after the adjustment of age, BMI, sex and hexose level. Our data indicated that the production of non-protein bound AGEs was increased within the OA-affected joint of DM patients. This is associated with changes in phosphatidylcholine metabolism and might be responsible for the observed epidemiological association between OA and DM. [ABSTRACT FROM AUTHOR]

Cooperation is pervasive and constitutes the core behavioral principle of human social life. Previous studies have revealed that mutual cooperation was reliably correlated with two reward-related brain regions, the ventral striatum and the orbitofrontal cortex. Using functional magnetic resonance imaging (fMRI), this study sought to investigate how the loss and gain contexts modulated the neural responses to mutual cooperation. Twenty-five female participants were scanned when they played a series of one-shot prisoner’s dilemma games in the loss and gain contexts. Specifically, participants and partners independently chose to either cooperate with each other or not, and each was awarded or deprived of (in the gain context or the loss context, respectively) a sum of money which depended upon the interaction of their choices. Behavioral results indicated that participants cooperated in nearly half of the experiment trials and reported higher level of positive emotions for mutual cooperation in both contexts, but they cooperated more in the gain than in the loss context. At the neural level, stronger activities in the orbitofrontal cortex were observed for mutual cooperation compared with the other three outcomes in both contexts, while stronger activation in ventral striatum associated with mutual cooperation was observed in the gain context only. Together, our data indicated that, even in the one-shot interaction under loss context, participants still exhibited preference for cooperation and the rewarding experience from a mutually cooperative social interaction activated the ventral striatum and the orbitofrontal cortex, but the loss context weakened the association between the ventral striatum activation and mutual cooperation. [ABSTRACT FROM AUTHOR]

Hyperuricemia (HU) often progresses to combine with non-alcoholic fatty liver disease (NAFLD) in the clinical scenario, which further exacerbates metabolic disorders; early detection of biomarkers, if obtained during the HU progression, may be beneficial for preventing its combination with NAFLD. This study aimed to decipher the biomarkers and mechanisms of the development of steatosis in HU. Four groups of subjects undergoing health screening, including healthy subjects, subjects with HU, subjects with HU combined with NAFLD (HU+NAFLD) and subjects with HU initially and then with HU+NAFLD one year later (HU→HU+NAFLD), were recruited in this study. The metabolic profiles of all subjects’ serum were analyzed by liquid chromatography quadruple time-of-flight mass spectrometry. The metabolomic data from subjects with HU and HU+NAFLD were compared, and the biomarkers for the progression from HU to HU+NAFLD were predicted. The metabolomic data from HU→HU+NAFLD subjects were collected for further verification. The results showed that the progression was associated with disturbances of phospholipase metabolism, purine nucleotide degradation and Liver X receptor/retinoic X receptor activation as characterized by up-regulated phosphatidic acid, cholesterol ester (18:0) and down-regulated inosine. These metabolic alterations may be at least partially responsible for the development of steatosis in HU. This study provides a new paradigm for better understanding and further prevention of disease progression. [ABSTRACT FROM AUTHOR]