Effects of platelet-rich plasma on subchondral bone marrow edema and biomarkers in synovial fluid of knee osteoarthritis.

BACKGROUND: The aim of the study was to investigate the effect of platelet-rich plasma (PRP) on subchondral bone marrow edema (BME) and the level of biomarkers in synovial fluid of the knee osteoarthritis. METHODS: Eighty-one patients with symptomatic knee osteoarthritis were randomly divided into two groups according to the number of inpatients. Forty-five cases were treated with intra-articular injection of PRP (PRP group), 36 cases were treated with sodium hyaluronate (SH group), and the clinical effects were evaluated using the Visual Analogue Scale (VAS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores. The changes of subchondral BME were assessed by magnetic resonance imaging (MRI) before and after treatment. The levels of TNFα, IL-6, MCP-1, MMP-1, MMP-3, and MMP-9 in synovial fluid were also detected. RESULTS: All the patients completed the corresponding treatment and were followed up for 12 months without serious complications. After the treatment, the VAS and WOMAC scores of the two groups were significantly decreased, and the difference was statistically significant at different time points (P < 0.05). The VAS and WOMAC scores of the PRP group were better than those of the SH group (P < 0.05). MRI showed that the subchondral bone edema of the two groups were reduced in varying degrees, and the reduction was more noticeable in the PRP group (P < 0.05). The levels of TNFα, IL-6, MCP-1, MMP-1, MMP-3, and MMP-9 in two groups were decreased, and the difference was statistically significant at different time points (P < 0.05). However, the levels of TNFα, IL-6, MCP-1, MMP-1, MMP-3, and MMP-9 in the PRP group were significantly lower than those in the SH group (P < 0.05). CONCLUSIONS: Intra-articular injection of PRP can significantly reduce the subchondral BME and the level of biomarkers in synovial fluid of the symptomatic knee osteoarthritis.

Molecular phenotyping of a UK population: defining the human serum metabolome.

Phenotyping of 1,200 'healthy' adults from the UK has been performed through the investigation of diverse classes of hydrophilic and lipophilic metabolites present in serum by applying a series of chromatography-mass spectrometry platforms. These data were made robust to instrumental drift by numerical correction; this was prerequisite to allow detection of subtle metabolic differences. The variation in observed metabolite relative concentrations between the 1,200 subjects ranged from less than 5 % to more than 200 %. Variations in metabolites could be related to differences in gender, age, BMI, blood pressure, and smoking. Investigations suggest that a sample size of 600 subjects is both necessary and sufficient for robust analysis of these data. Overall, this is a large scale and non-targeted chromatographic MS-based metabolomics study, using samples from over 1,000 individuals, to provide a comprehensive measurement of their serum metabolomes. This work provides an important baseline or reference dataset for understanding the 'normal' relative concentrations and variation in the human serum metabolome. These may be related to our increasing knowledge of the human metabolic network map. Information on the Husermet study is available at http://www.husermet.org/. Importantly, all of the data are made freely available at MetaboLights (http://www.ebi.ac.uk/metabolights/).

Untargeted metabolic profiling identifies altered serum metabolites of type 2 diabetes mellitus in a prospective, nested case control study.

BACKGROUND: Application of metabolite profiling could expand the etiological knowledge of type 2 diabetes mellitus (T2D). However, few prospective studies apply broad untargeted metabolite profiling to reveal the comprehensive metabolic alterations preceding the onset of T2D. METHODS: We applied untargeted metabolite profiling in serum samples obtained from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort comprising 300 individuals who developed T2D after a median follow-up time of 6 years and 300 matched controls. For that purpose, we used ultraperformance LC-MS with a protocol specifically designed for large-scale metabolomics studies with regard to robustness and repeatability. After multivariate classification to select metabolites with the strongest contribution to disease classification, we applied multivariable-adjusted conditional logistic regression to assess the association of these metabolites with T2D. RESULTS: Among several alterations in lipid metabolism, there was an inverse association with T2D for metabolites chemically annotated as lysophosphatidylcholine(dm16:0) and phosphatidylcholine(O-20:0/O-20:0). Hexose sugars were positively associated with T2D, whereas higher concentrations of a sugar alcohol and a deoxyhexose sugar reduced the odds of diabetes by approximately 60% and 70%, respectively. Furthermore, there was suggestive evidence for a positive association of the circulating purine nucleotide isopentenyladenosine-5'-monophosphate with incident T2D. CONCLUSIONS: This study constitutes one of the largest metabolite profiling approaches of T2D biomarkers in a prospective study population. The findings might help generate new hypotheses about diabetes etiology and develop further targeted studies of a smaller number of potentially important metabolites.