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AIM: To identify plasma metabolites used as biomarkers in order to distinguish cirrhotics from controls and encephalopathics.METHODS: A clinical study involving stable cirrhotic patients with and without overt hepatic encephalopathy was designed. A control group of healthy volunteers was used. Plasma from those patients was analysed using 1H- nuclear magnetic resonance spectroscopy. We used the Carr Purcell Meiboom Gill sequence to process the sample spectra at ambient probe temperature. We used a gated secondary irradiation field for water signal suppression. Samples were calibrated and referenced using the sodium trimethyl silyl propionate peak at 0.00 ppm. For each sample 128 transients(FID’s) were acquired into 32 K complex data points over a spectral width of 6 KHz. 30 degree pulses were applied with an acquisition time of 4.0 s in order to achieve better resolution, followed by a recovery delay of 12 s, to allow for complete relaxation and recovery of the magnetisation. A metabolic profile was created for stable cirrhotic patients without signs of overt hepatic encephalopathy and encephalopathic patients as well as healthy controls. Stepwise discriminant analysis was then used and discriminant factors were created to differentiate between the three groups.RESULTS: Eighteen stabled cirrhotic patients, eighteen patients with overt hepatic encephalopathy and seventeen healthy volunteers were recruited. Patients with cirrhosis had significantly impaired ketone body metabolism, urea synthesis and gluconeogenesis. This was demonstrated by higher concentrations of acetoacetate(0.23 ± 0.02 vs 0.05 ± 0.00, P < 0.01), and b-hydroxybutarate(0.58 ± 0.14 vs 0.08 ± 0.00, P < 0.01), lower concentrations of glutamine(0.44 ± 0.08 vs 0.63 ± 0.03, P < 0.05), histidine(0.16 ± 0.01 vs 0.36 ± 0.04, P < 0.01) and arginine(0.08 ± 0.01 vs 0.14 ± 0.02, P < 0.03) and higher concentrations of glutamate(1.36 ± 0.25 vs 0.58 ± 0.04, P < 0.01), lactate(1.53 ± 0.11 vs 0.42 ± 0.05, P < 0.01), pyruvate(0.11 ± 0.02 vs 0.03 ± 0.00, P < 0.01) threonine(0.39 ± 0.02 vs 0.08 ± 0.01, P < 0.01) and aspartate(0.37 ± 0.03 vs 0.03 ± 0.01). A five metabolite signature by stepwise discriminant analysis could separate between controls and cirrhotic patients with an accuracy of 98%. In patients with encephalopathy we observed further derangement of ketone body metabolism, impaired production of glycerol and myoinositol, reversal of Fischer’s ratio and impaired glutamine production as demonstrated by lower b-hydroxybutyrate(0.58 ± 0.14 vs 0.16 ± 0.02, P < 0.0002), higher acetoacetate(0.23 ± 0.02 vs 0.41 ± 0.16, P < 0.05), leucine(0.33 ± 0.02 vs 0.49 ± 0.05, P < 0.005) and isoleucine(0.12 ± 0.02 vs 0.27 ± 0.02, P < 0.0004) and lower glutamine(0.44 ± 0.08 vs 0.36 ± 0.04, P < 0.013), glycerol(0.53 ± 0.03 vs 0.19 ± 0.02, P < 0.000) and myoinositol(0.36 ± 0.04 vs 0.18 ± 0.02, P < 0.010) concentrations. A four metabolite signature by stepwise discriminant analysis could separate between encephalopathic and cirrhotic patients with an accuracy of 87%.CONCLUSION: Patients with cirrhosis and patients with hepatic encephalopathy exhibit distinct metabolic abnormalities and the use of metabonomics can select biomarkers for these diseases.
AIM: To identify plasma metabolites used as biomarkers in order to distinguish cirrhotics from controls and encephalopathics. METHODS: A clinical study involving stable cirrhotic patients with and without overt hepatic encephalopathy was designed. A control group of healthy volunteers was used. Plasma from those patients was analyzed using 1H-nuclear magnetic resonance spectroscopy. We used the Carr Purcell Meiboom Gill sequence to process the sample spectra at ambient probe temperature. We used a gated secondary irradiation field for water signal suppression. Samples were calibrated and referenced using the sodium trimethyl silyl Propionate peak at 0.00 ppm. For each sample 128 transients (FID’s) were acquired into 32 K complex data points over a spectral width of 6 KHz. 30 degree pulses were applied with an acquisition time of 4.0 s in order to achieve better resolution, followed by a recovery delay of 12 s, to allow for complete relaxation and recovery of the magnetisation. A metabol ic profile was created for stable cirrhotic patients without signs of overt hepatic encephalopathy and encephalopathic patients as well as healthy controls. Stepwise discriminant analysis was then used and discriminant factors were created to differentiate between the three groups .RESULTS: Eighteen stabled cirrhotic patients, eighteen patients with overt hepatic encephalopathy and seventeen healthy volunteers were recruited. Patients with significantly impaired ketone body metabolism, urea synthesis and gluconeogenesis. This was demonstrated by higher concentrations of acetoacetate (0.23 ± 0.02 vs. 0.05 ± 0.00, P <0.01), and b (0.58 ± 0.14 vs 0.08 ± 0.00, P <0.01), lower concentrations of glutamine (0.44 ± 0.08 vs 0.63 ± 0.03, P <0.05) and histidine (0.16 ± 0.01 vs 0.36 ± 0.04, (0.08 ± 0.01 vs. 0.14 ± 0.02, P <0.03) and higher concentrations of glutamate (1.36 ± 0.25 vs 0.58 ± 0.04, P <0.01), lactate (1.53 ± 0.11 vs 0.42 ± 0.0 5, P <0.01),A five metabolite signature by stepwise discriminant analysis could separate (0.11 ± 0.02 vs 0.03 ± 0.00, P <0.01) threonine (0.39 ± 0.02 vs 0.08 ± 0.01, P <0.01) and aspartate (0.37 ± 0.03 vs 0.03 ± 0.01) between controls and cirrhotic patients with an accuracy of 98%. In patients with encephalopathy we observed further derangement of ketone body metabolism, impaired production of glycerol and myoinositol, reversal of Fischer’s ratio and impaired glutamine production as demonstrated by lower b-hydroxybutyrate (0.58 ± (0.23 ± 0.02 vs 0.41 ± 0.16, P <0.05), leucine (0.33 ± 0.02 vs 0.49 ± 0.05, P <0.005) and isoleucine (0.12 ± 0.02 vs 0.27 ± 0.02, P <0.0004) and lower glutamine (0.44 ± 0.08 vs 0.36 ± 0.04, P <0.013), glycerol (0.53 ± 0.03 vs 0.19 ± 0.02, P <0.000) and myoinositol (0.36 ± 0.04 vs 0.18 ± 0.02, P < 0.010) concentrations. A four metabolite signature by stepwise discriminant analysis could separa te between encephalopathic and cirrhotic patients with an accuracy of 87% .CONCLUSION: Patients with cirrhosis and patients with hepatic encephalopathy exhibit distinct metabolic abnormalities and the use of metabonomics can select biomarkers for these diseases.