A novel magnetic adsorption nanomaterial was synthesized by the copolymerization of a deep eutectic solvent (DES) and chitosan (CS)-modified Fe3O4 particles. This material was proposed for the magnetic solid-phase extraction (MSPE) of flavonoids from an aqueous extract solution of herbal Epimedium folium without pretreatment. Coupled with high-performance liquid chromatography (HPLC), a rapid, environmentally friendly, and efficacious method was established and successfully applied for the enrichment, separation, and quantification of five quality marker flavonoids (viz. icariin, epimedin A, epimedin B, epimedin C, and baohuoside Ⅰ, respectively) in herbal E. folium collected from four species and 18 habitats. In addition, the physicochemical properties and morphology of Fe3O4-CS-DES were characterized by Fourier transform infrared spectroscopy, transmission electron microscopy, scanning electron microscopy, thermogravimetric analysis, X-ray diffraction, and vibrating sample magnetometry. Furthermore, key parameters, including adsorbent amount, elution solvent, and desorption factor, were optimized. Under the optimal experimental conditions, the five flavonoids were adsorbed rapidly on MSPE, which were released easily using methanol-acidified formic acid. A reversed-phase C18 column with gradient mobile phases comprising acetonitrile and formic acid water was used for analyte separation and elution. Limits of detection for the five analytes ranged from 0.5 to 2.1 ng mL-1. Coefficients of determination in analyses ranged from 0.99994 to 0.99999. Intra-day and inter-day precision ranged from 0.75 to 5.18% (n = 6) and from 2.88 to 7.52% (n = 3), respectively. Recoveries of the five analytes ranged from 80 to 110%, with relative standard deviation values of less than 10%. To the best of our knowledge, as a new adsorbent, Fe3O4-CS-DES nanoparticles were synthesized and used for the first time for the preconcentration and separation of flavonoids. This study provided a new approach for the enrichment and detection of Epimedium flavonoids in real samples.

We created an original, environmentally friendly, and efficacious method of magnetic dispersive solid-phase extraction (MSPE) grounded in magnetic Fe 3 O 4 nanoparticles altered with deep eutectic solvent (DES), followed by high-performance liquid chromatography (HPLC). We subsequently validated said method via simultaneous extraction/determination of five formally designated flavonoids which includes icariin, epimedin A, epimedin B, epimedin C and baohuoside Ⅰ in Epimedium Folium, from four origins and eighteen habitats. Results demonstrated that the five flavonoids were adsorbed quickly on MSPE. They were easily released using methanol that had been acidified with formic acid. The analytes had been concentrated using an ultrasonication supported MSPE method. The said method was grounded in DES modified magnetic Fe 3 O 4 nanoparticles. We utilized a reversed-phase C 18 column, along with a gradient elution program. Mobile phase consisted of formic acid, water, and acetonitrile to separate and elute the analytes for detection. Detection limits for the analytes ranged from 50 to 210 ng mL -1 . Coefficients of determination in the analyses ranged from 0.9996 to 0.9999. The intra-day and inter-day precision were 0.75–5.18% (n=6) and 2.88–7.52% (n=3), respectively. Recoveries of the five major analytes ranged from 80 to 110% with the corresponding relative standard deviation values of less than 10%.

BACKGROUND:Immunoassays provide a rapid tool for the screening of drugs-of-abuse (DOA). However, results are presumptive and confirmatory testing is warranted. To reduce associated cost and delay, laboratories should employ assays with high positive and negative predictive values (PPVs and NPVs). Here, we compared the results of urine drug screens on cobas 6000 (cobas) and ARCHITECTi2000 (ARCHITECT) platforms for six drugs against LC-MS/MS to assess the analytical performance of these assays.METHODS:Eighty nine residual urine specimens, which tested positive for amphetamine, THC-COOH, benzoylecgonine, EDDP, opiates and/or oxycodone during routine drug testing, were stored frozen until later confirmation by LC-MS/MS. Immunoassays were performed on cobas and ARCHITECT using a split sample. A third aliquot from these samples was tested by LC-MS/MS to assess the percentage of false positive, false negative, true positive and true negative results and calculate the PPVs and NPVs for each immunoassay.RESULTS:The PPVs of THC-COOH and EDDP assays were 100% on both platforms. Suboptimal PPVs were achieved for oxycodone (cobas, 57.1% vs ARCHITECT, 66.7%), amphetamine (77.8 vs. 100%), opiates (80.0 vs. 84.6%) and benzoylecgonine (88.9 vs. 84.2%) assays. The NPV was 100% for cobas and ARCHITECT oxycodone assays. Lower NPVs were achieved for THC-COOH (cobas, 28.6% vs ARCHITECT, 25.0%), EDDP (72.7% for both assays), benzoylecgonine (74.4% vs 73.8%), amphetamine (83.3% vs 82.8%) and opiates (100% vs 85.3%).CONCLUSION:Overall, cobas and ARCHITECT urine drug screens have comparable analytical performance. Confirmatory testing is warranted for positive test results especially for oxycodone, amphetamine, opiates and cocaine. Negative drug screen results must be interpreted with caution especially for THC-COOH, EDDP, benzoylecgonine, amphetamine and opiates.

Objectives: The objective of this study was the investigation of age- and sex-associations in a set of blood plasma metabolites in healthy male and female subjects. Methods: A comparison study design with male and female subjects of various ages was used. Metabolic profiling was performed using electrospray ionization tandem mass spectrometry that yielded 186 metabolite concentrations for each study participant. The key age-related metabolites were identified using an integrative analysis of absolute concentrations, metabolite ratios and the differential correlation of pairwise metabolite concentrations. All of the age-associated metabolites were adjusted prior to the analysis to account for differences in Body Mass Index (BMI). Results: A total of 236 plasma samples from 140 female and 96 male subjects aged 20 to 82 years-old were collected and analyzed in the study. 13 and 14 age-associated metabolites (|r| > 0.33 and p < 6.6×10−5), 438 and 337 age-associated metabolite ratios (|r| > 0.37 and p < 3.5×10−6), and 5 and 10 core metabolites were discovered in the female and male groups, respectively. 80% of the metabolites displaying associations with age belonged to sphingolipids and phosphatidylcholines, and the two sexes shared less than 50% of the age-associated metabolites. Conclusion: The study found that changes in metabolite concentrations, metabolite ratios and differential correlations were age and sex-specific.

OBJECTIVES:To determine whether pre-existing nephropathy impacts urinary KIM-1 levels, urinary KIM-1 were measured in patients with normal kidney filtration function but either with or without proteinuria. The reference intervals of urinary KIM-1 in adults with normal kidney filtration function but without urine proteinuria were established. DESIGN AND METHODS:188 urine samples were obtained from adults with normal kidney filtration. 83 of the 188 showed negative urine protein, erythrocytes and leucocytes were used as normal controls. The remaining 105 samples showed at least one abnormal result suggesting possible pre-existing nephropathy. Urinary KIM-1 concentrations were measured using an enzyme-linked immunosorbent assay. Urinary KIM-1 was normalized with urine creatinine concentration. The reference interval for urinary KIM-1 was determined by non-parametric methodology on 147 individuals. RESULTS:The results showed significantly increased urinary KIM-1 concentration in protein positive (protein +, erythrocyte +/-, leucocyte+/-) samples compared to controls (protein-, erythrocyte -, leucocyte -). Urinary KIM-1 concentrations were significantly higher when proteinuria was at trace concentration (0.25 g/L) and correlated with the severity of proteinuria. The creatinine normalized urinary KIM-1 was significantly higher when urine protein was 1 + to 3+ (0.75-5 g/L). The reference interval for urinary KIM-1 was 0.00 (90%CI: 0-0) to 4.19 (90%CI: 3.11-5.62) μg/L, and for creatinine normalized urinary KIM-1 0.00 (90%CI: 0-0) to 0.58 (90%CI: 0.44-0.74) μg/mmol. CONCLUSIONS:Baseline urinary KIM-1 concentrations were increased when there was detectable urine protein and correlated with its severity. The urinary KIM-1 concentrations should be interpreted with consideration of urine protein levels in individual patients.

Menopause is an endocrine-related transition that induces a number of physiological and potentially pathological changes in middle-aged and elderly women. The intention of this research was to investigate the influence of menopause on the intricate relationships between major biochemical metabolites. The study involved metabolic profiling of 186 metabolic markers measured in blood plasma collected from 120 healthy female participants. We developed a method of network analysis using differential correlation that enabled us to detect and characterize differences in metabolites and changes in inter-relationships in pre- and post-menopausal women. A topological analysis was performed on the differential network that uncovered metabolite differences in pre-and post-menopausal women. In this analysis, our method identified two key metabolites, sphingomyelins and phosphatidylcholines, which may be useful in directing further studies into menopause-specific differences in the metabolome, and how these differences may underlie the body's response to stress and disease following the transition from pre- to post-menopausal status for women.

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.

Females and males are known to have different abilities to cope with stress and disease. This study was designed to investigate the effect of sex on properties of a complex inter-linked network constructed of central biochemical metabolites. The study involved the blood collection and analysis of a large set of blood metabolic markers from a total of 236 healthy participants, which included 140 females and 96 males. Metabolic profiling yielded concentrations of 168 metabolites for each subject. A differential correlation network analysis approach was developed for this study that allowed detection and characterization of interconnection differences in metabolites in males and females. Through topological analysis of the differential network that depicted metabolite differences in the sexes, we identified metabolites with high centralities in this network. These key metabolites were identified as 10 phosphatidylcholines (PCaaC34:4, PCaaC36:6, PCaaC34:3, PCaaC42:2, PCaeC38:1, PCaeC38:2, PCaaC40:1, PCaeC34:1, PC aa C32:1 and PC aa C40:6) and 4 acylcarnitines (C3-OH, C7-DC, C3 and C0). Identification of these metabolites may help further studies of sex-specific differences in the metabolome that may underlie different responses to stress and disease in males and females.

Purpose: Age, sex, and BMI can help to predict knee osteoarthritis (OA) patients from healthy individuals (HV). The metabolome, the comprehensive output of metabolic processes occurring within an individual, and the levels of individual metabolites can also be used to help with disease diagnosis. However, metabolite selection methods and modeling algorithms that best identify metabolites capable of predicting OA have not been well established. We sought to determine a method that was capable of effectively identifying metabolite signatures that were predictive of OA in demographically-stratified populations. Methods: Phosphatidylcholine (lysoPC) and lyso(PC) analogues from plasma of 152 OA patients undergoing total knee replacement and 194 HV (346 total individuals) were measured by metabolomics. Cohorts were stratified by age, sex and BMI. Analogue signatures were determined by generating univariate area under the receiver operator curve (UAUC) values from 1000 bootstrapped training and test sets. Metabolites with UAUC > 0.5 at the 2.5% quantile of the empirical distribution were selected as capable of predicting OA from HV within strata. Three multivariate classification algorithms were tested using each signature. The most consistent algorithm was determined by the minimum difference between training and test set AUC values, derived from 1000 resamplings. The effect of diabetes mellitus on signature elements was also determined by identifying metabolites that were significantly changed in diabetic patients and removing those signature elements from multivariate analyses. Results: The metabolite signature from males age > 50 years old encompassed the majority of identified metabolites in other strata, suggesting lysoPCs and PCs were dominant indicators of OA in older males. Principal component regression with logistic regression (PCR) was the most consistent classification algorithm tested. Using this algorithm, the males age > 50 years old signature had fair power to differentiate OA patients from HV. In individuals with diabetes mellitus compared to those without, 3 metabolites within HV individuals > age 50 and one metabolite in HV males age > 50 years were significantly different and corresponded to metabolites within identified signatures from each stratum used for multivariate prediction modeling. Removing these metabolites had a minimal effect on the empirical AUCs generated using PCR modeling compared to using the entire determined signatures. Conclusions: Individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older male populations. In this cohort, diabetes had a minimal effect on our ability to classify individuals with OA. Our metabolite signature modeling method is likely to increase classification power in validation cohorts.

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.

Objective: To identify novel biomarker(s) for knee osteoarthritis (OA) using a metabolomics approach.Method: We utilized a two-stage case-control study design. Plasma samples were collected from knee OA patients and healthy controls after 8-h fasting and metabolically profiled using a targeted metabolomics assay kit. Linear regression was used to identify novel metabolic markers for OA. Receiver operating characteristic (ROC) analysis was used to examine diagnostic values. Gene expression analysis was performed on human cartilage to explore the potential mechanism for the novel OA marker(s).Results: Sixty-four knee OA patients and 45 controls were included in the discovery stage and 72 knee OA patients and 76 age and sex matched controls were included in the validation stage. We identified and confirmed six metabolites that were significantly associated with knee OA, of which arginine was the most significant metabolite (P < 3.5 x 10(-13) ) with knee OA patients having on average 69 mu M lower than that in controls. ROC analysis showed that arginine had the greatest diagnostic value with area under the curve (AUC) of 0.984. The optimal cutoff of arginine concentration was 57 mM with 98.3% sensitivity and 89% specificity. The depletion of arginine in OA patients was most likely due to the over activity of arginine to ornithine pathway, leading to imbalance between cartilage repair and degradation.Conclusion: Arginine is significantly depleted in refractory knee OA patients. Further studies within a longitudinal setting are required to examine whether arginine can predict early OA changes. (c) 2015 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

This study was to investigate how OA patients with metabolic syndrome (MetS) are different metabolically from OA patients without MetS components and healthy individuals. A two-stage case–control study design was utilized. Synovial fluid (SF) and plasma samples were collected from patients undergoing total knee joint replacement due to primary OA and healthy controls (only plasma) and metabolically profiled using UPLC-MS coupled with assay kit which measures 186 metabolites. Orthogonal projection to latent structure-discriminant analysis and linear regression were used to identify metabolic markers for discriminating OA patients with MetS components from those without and healthy individuals. 54 paired SF and plasma samples from knee OA patients and 30 plasma samples from healthy controls were included in the discovery stage, and 143 plasma samples (72 from knee OA patients and 71 from the age, sex, and BMI matched controls) were included in the validation stage. OA patients with MetS can be clearly discriminated from OA patients without MetS based on the metabolite profiles of both SF and plasma and the separation appeared to be driven by type 2 diabetes but not obesity, hypertension, or dyslipidemia. When compared with OA patients with diabetes, OA without diabetes, and healthy controls, phosphatidylcholine acyl-alkyl C34:3 (PC ae C34:3) and phosphatidylcholine acyl-alkyl C36:3 (PC ae C36:3) were identified and confirmed to be associated with the concurrence of OA and diabetes (all p < 0.003). The study demonstrated that altered phosphatidylcholine metabolism was associated with both OA and diabetes mellitus.

Background Accumulating evidence suggests an independent association between osteoarthritis (OA) and type 2 diabetes mellitus (DM). Our recent work [Zhang, et al. Metabolomics, 2015] found phosphatidylcholine acyl-alkyl C34:3 (PC ae C34:3) and phosphatidylcholine acyl-alkyl C36:3 (PC ae C36:3) were associated with both OA and DM. Both synovial and plasma concentrations of these two metabolites were reduced in knee OA and DM patients, and OA patients with DM had lowest concentration of these two metabolites, suggesting the altered unsaturated phosphatidylcholine metabolism may be responsible for the association between OA and DM. Objectives We hypothesized hyperglycemia-related production of advanced glycation end-products (AGEs) was involved in the altered phosphatidylcholine metabolism in OA patients and tested this hypothesis in the current study. Methods Synovial fluid and plasma samples were collected from OA patients with and without DM. Hyperglycemia-related AGEs including methylglyoxal (MG) and methylglyoxal-derived hydroimidazolone (MG-H1) levels were measured in both synovial fluid and plasma samples using UPLC/MS method. The correlation between MG, MG-H1, and PC ae C34:3 and PC ae C36:3 were examined. Results 84 knee OA patients, including 46 with DM and 38 without DM, were included in the study. We did not find a significant difference in plasma MG-H1 concentration between OA with and without DM. However, we found that log transformed synovial concentrations of MG-H1 in the groups of OA with diabetes were 2.56±0.27 ng/ml which was significantly higher than that in the group of OA without diabetes (2.39 ±0.25 ng/ml, P=0.012). Similarly, synovial concentration of MG was 2.05±0.11 ng/ml in the group of OA with diabetes which was significantly higher than 1.99±0.11 ng/ml in the group of OA without diabetes (P=0.046). The significance remained after adjusting the age, BMI and sex. The correlation between MG-H1 and PC ae C34:3, MG-H1 and PC ae C36:3, MG and PC aeC34:3, and MG and PC ae C36:3 were -0.15, -0.32, -0.23 and -0.06, respectively. Conclusions We demonstrated that both MG-H1 and MG concentrations in synovial fluid were elevated in OA patients with DM and associated with the levels of PC ae C34:3 and PC ae C36:3, suggesting that hyperglycemia-related AGEs may be responsible for the altered phosphotidylcholine metabolism in OA. References Weidong Zhang, Guang Sun, Sergei Likhodii, Erfan Aref-Eshghi, Patricia E. Harper, Edward Randell, Roger Green, Glynn Martin, Andrew Furey, Proton Rahman, Guangju Zhai. Metabolomic analysis of human synovial fluid and plasma reveals that phosphatidylcholine metabolism is associated with both osteoarthritis and diabetes mellitus. Metabolomics (2016) 12:24. DOI 10.1007/s11306–015–0937-x Acknowledgement We thank all the study participants who made this study possible, and all the staff who helped us in the collection of samples. The study was funded by Canadian Institutes of Health Research (CIHR), Newfoundland & Labrador RDC, and Memorial University. Disclosure of Interest None declared

OBJECTIVE:To identify novel biomarker(s) for predicting advanced knee OA.METHODS:Study participants were derived from the Newfoundland Osteoarthritis Study and the Tasmania Older Adult Cohort Study. All knee OA cases were patients who underwent total knee replacement (TKR) due to primary OA. Metabolic profiling was performed on fasting plasma. Four thousand and eighteen plasma metabolite ratios that were highly correlated with that in SF in our previous study were generated as surrogates for joint metabolism.RESULTS:The discovery cohort included 64 TKR cases and 45 controls and the replication cohorts included a cross-sectional cohort of 72 TKR cases and 76 controls and a longitudinal cohort of 158 subjects, of whom 36 underwent TKR during the 10-year follow-up period. We confirmed the previously reported association of the branched chain amino acids to histidine ratio with advanced knee OA (P = 9.3 × 10(-7)) and identified a novel metabolic marker-the lysophosphatidylcholines (lysoPCs) to phosphatidylcholines (PCs) ratio-that was associated with advanced knee OA (P = 1.5 × 10(-7)) after adjustment for age, sex and BMI. When the subjects of the longitudinal cohort were categorized into two groups based on the optimal cut-off of the ratio of 0.09, we found the subjects with the ratio ⩾0.09 were 2.3 times more likely to undergo TKR than those with the ratio <0.09 during the 10-year follow-up (95% CI: 1.2, 4.3, P = 0.02).CONCLUSION:We identified the ratio of lysoPCs to PCs as a novel metabolic marker for predicting advanced knee OA. Further studies are required to examine whether this ratio can predict early OA change.

Osteoarthritis (OA) significantly compromises the life quality of affected individuals and imposes a substantial economic burden on our society. Unfortunately the pathogenesis of the disease is till poorly understood and no effective medications have been developed. OA is a complex disease that involves both genetic and environmental influences. To elucidate the complex interlinked structure of metabolic processes associated with OA, we developed a differential correlation network approach to detecting the interconnection of metabolite pairs whose relationships are significantly altered due to the diseased process. Through topological analysis of such a differential network, we identified key metabolites that played an important role in governing the connectivity and information flow of the network. Identification of these key metabolites suggests the association of their underlying cellular processes with OA and may help elucidate the pathogenesis of the disease and the development of novel targeted therapies.

Objective.To investigate the relationship between plasma and synovial fluid (SF) metabolite concentrations in patients with osteoarthritis (OA).Methods.Blood plasma and SF samples were collected from patients with primary knee OA undergoing total knee arthroplasty. Metabolic profiling was performed by electrospray ionization tandem mass spectrometry using the AbsoluteIDQ kit. The profiling yielded 168 metabolite concentrations. Correlation analysis between SF and plasma metabolite concentrations was done on absolute concentrations as well as metabolite concentration ratios using Spearman’s rank correlation (ρ) method.Results.A total of 69 patients with knee OA were included, 30 men and 39 women, with an average age of 66 ± 8 years. For the absolute metabolite concentrations, the average ρ was 0.23 ± 0.13. Only 8 out of 168 metabolite concentrations had a ρ ≥ 0.45, with a p value ≤ 2.98 × 10−4, statistically significant after correcting multiple testing with the Bonferroni method. For the metabolite ratios (n = 28,056), the average ρ was 0.29 ± 0.20. There were 4018 metabolite ratios with a ρ ≥ 0.52 and a p value ≤ 1.78 × 10−6, significant after correcting multiple testing. Sex-separate analyses found no difference in ρ between men and women. Similarly, there was no difference in ρ between people younger and older than 65 years.Conclusion.Correlation between blood plasma and SF metabolite concentrations are modest. Metabolite ratios, which are considered proxies for enzymatic reaction rates and have higher correlations, should be considered when using blood plasma as a surrogate of SF in OA biomarker identification.