13 Superoxide dismutase (SOD) activity in liver homogenate was determined according to the method described by Nandi and Chatterjee.14 This method is based on the ability of SOD to inhibit the auto-oxidation of pyrogallol at an alkaline pH. One unit of SOD is described as the amount of enzyme required to cause 50% inhibition of pyrogallol auto-oxidation. The glutathione (GSH) content

in the liver homogenate was determined using the method of Van Dooran et al.15 The basis of the GSH determination method is the reaction of Ellman’s reagent (5,5′-dithiobis-[2-nitrobenzoic acid]) with thiol groups of GSH at pH 8.0 to produce the yellow 5-thiol-2-nitrobenzoate anion. Glutathione S-transferase (GST) activity was determined according to the method of Habig et al.16 In this assay, GST catalyzes the conjugation of GSH with 1-chloro-2,4-dinitrobenzene, producing see more a chromophore at 340 nm. The total protein contents of liver tissues were determined according to the Lowry method as modified by Peterson.17 Absorbances were recorded using a Shimadzu recording

spectrophotometer (UV-160) in all measurements. Liver cancer cell selleck screening library line HepG2 were maintained in Roswell Park Memorial Institute-160 medium with 10% fetal bovine serum and 1% of 100 U/mL penicillin and 100 μg/mL streptomycin at 37°C inside a humidified incubator with 5% CO2 and 95% room air. Cells were subcultured every 4-7 days with trypsin/ethylenediamine tetraacetic acid (1:250; PAA Laboratory, Germany). Cells were treated with several concentrations of saffron extract for several time points. The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, a yellow tetrazole) proliferation assay was used in the HepG2 cell line to assess the effect on cell proliferation of a range of concentrations of saffron extract.

Cells (104) were plated and grown in 200 μL of growth medium in 96-well microtiter plates. After an overnight attachment period, cells were treated with varying concentrations of saffron extract (1.0, 2.0, 4.0, and 6.0 mg/mL) prepared from a 100 medchemexpress mg/mL stock solution dissolved in water. All studies were performed in triplicate and repeated three times independently. Cell growth was quantified by the ability of living cells to reduce the yellow dye MTT to a purple formazan product. Cells were incubated with MTT (Sigma) at 37°C in a humidified 5% CO2 atmosphere for 2 hours. The MTT formazan product was then dissolved in dimethylsulfoxide, and absorbance was measured at 570 nm in a microplate reader. One day before treatment, cells were seeded at a density of 1.2 × 106 cells per plate. After the indicated times, the cells were harvested by trypsin release, washed twice with phosphate-buffered saline, fixed with 70% ethanol, treated with 1% ribonuclease, and finally stained with propidium iodide (100 μg/mL final concentration).

4B). To identify genome-wide hepatic FXR-binding sites in healthy and obese mice, mice were fed normal or high-fat chow for 20 weeks and then treated for a short time (1 hour) with a synthetic FXR agonist, GW4064, to activate FXR signaling. ChIP assays from liver chromatin were performed with FXR antibody or control IgG. The quality of the ChIP assay was confirmed by the increased binding of FXR to known FXR targets, Shp and organic solute transporter beta, and increased levels of Shp mRNA also confirmed Dabrafenib mouse the effectiveness

of the GW4064 treatment (Supporting Fig. 5). The ChIP-seq analysis generated 2.98 and 3.97 million reads for GW4064-treated healthy and obese mice, respectively (Supporting Fig. 6). FXR-binding peak analysis, with stringent FDR cutoffs of <0.001 and the elimination of peaks observed also with control IgG, identified a total of 15,263 and 5,272 FXR-binding sites in GW4064-treated healthy and obese mice, respectively (Fig. 1A, top). Of these sites, 7,440 or 2,344 were uniquely detected in healthy or high-fat dietary obese mice (Fig. 1A, bottom). The number of overlapping sites in the healthy mice was greater than

that in the obese mice, because some of the FXR-binding sites in the obese group overlapped with two or more binding sites in the healthy group. To validate the ChIP-seq data, we randomly selected FXR-binding sites. Neither the size nor position of FXR-binding peaks 上海皓元医药股份有限公司 was considered to select binding sites for validation beta-catenin inhibitor and follow-up studies. ChIP assays revealed that binding to 24 of 27 sites in healthy mice and 20 of 21 in obese mice was enriched by at least 1.8-fold relative to vehicle-treated mice (Supporting Figs. 7 and 8), confirming binding to approximately 90% of these sites and validating the accuracy of the ChIP-seq analysis. The central question of this study was to determine whether FXR regulation might be altered

in obesity, which could underlie abnormal liver function and metabolism. Therefore, we focused on the differences in FXR binding between GW4064-treated healthy and obese mice. Notably, 7,440 of the total 15,632 FXR-binding sites in healthy mice were unique in these mice, whereas 2,344 of the total 5,272 sites in obese mice were unique (Fig. 1A). Potential FXR target genes were identified based on the criteria that an FXR-binding site was within 10 kb of the gene. FXR-binding sites corresponded to 2,583 or 1,566 potential target genes unique in healthy or obese mice (Fig. 1A). These results indicate that nearly half of the total FXR-binding sites are unique in healthy or obese mice, suggesting that transcriptional regulation patterns by FXR are likely altered in obesity. Binding sites of FXR were predominantly distributed in intron (38%) and intergenic (40%) regions in both groups of mice (Fig. 1B).

0097, R = – 0.4). No significant correlation was detected between viral load and METAVIR inflammatory score or fibrosis. Conclusions: Treg in colonic mucosa is associated with liver inflammation and HCV replication. This mechanism could be through inhibition of HCV-spe-cific immune responses and subsequent decrease in liver inflammation and fibrosis. Follow-up analysis of mucosal Treg cells during HCV infection may open the field for a
of immunotherapy to manipulate Treg cells during the course of infection in order to improve responses to therapy

and to prevent liver inflammation. Disclosures: Kenneth E. Sherman – Advisory Committees or Review Panels: Kadmon, Bioline, Janssen/Tibotec, Fibrogen, MedPace, Merck; Grant/Research Support: Merck, Genentech/Roche, Gilead, Anadys, Briston-Myers Squibb, Vertex, Boehringer-Ingelheim, Novartis The following people have nothing to click here disclose: Helal F. selleck kinase inhibitor Hetta, Mohamed A. Mekky, Nasr K. Khalil, Wegdan A. Mohamed, Mohamed A. EL-Feky, Shabaan H. Ahmed, Enas A. Daef, Mahmoud I. Nassar, Ahmed M. Nasr, Mohamed Tarek M. Shata Background & Aims: Thrombocytopenia is a common and costly problem in patients with chronic hepatitis C (CHC). There are many potential factors affecting the platelet count in patients with CHC. The aim of this study was to determine the association between thrombopoietin level, immature platelet fraction (IPF), immunoglobulin G (IgG) level, spleen size, and the platelet count in CHC.

Methods: We retrospectively studied a consecutive sample of patients with CHC, laboratory results of interest and stored serum from a given time point, and imaging of the spleen within one year of that time point. Patients were excluded for medications, toxins, or comorbidities known to affect the platelet count. Clinical laboratory results for ALT, INR, IgG, IPF, and platelet count were obtained from the medical record. Thrombopoietin (TPO), glycocalicin, and von Wille-brand

Factor (vWF) levels were determined MCE公司 by enzyme linked immunosorbent assay on stored sera. Spleen size was measured on ultrasound imaging. Hepatic fibrosis was assessed via transient elastography (TE) when available and missing values were approximated using a simple imputation method. We performed univariate and multivariable analyses of the relationships between predictor variables and the platelet count. Results: The cohort included 105 patients (median age 55 years, 47% female). The median platelet count was 198 K/uL and 16% of the patients with fibrosis data had cirrhosis. On univariate analysis, the following variables were significantly associated with the platelet count: age, ALT, direct bilirubin, total bilirubin, IPF, INR, spleen size, vWF, glycocalicin, fibrosis stage on liver biopsy, and TE (P-values all <0.05). A multivari-able model found the following factors to be independently associated with the platelet count: imputed TE score (coefficient -1.26, P=0.0032), TPO (-0.27, P=0.02), IPF (-10.98, P<0.

0097, R = – 0.4). No significant correlation was detected between viral load and METAVIR inflammatory score or fibrosis. Conclusions: Treg in colonic mucosa is associated with liver inflammation and HCV replication. This mechanism could be through inhibition of HCV-spe-cific immune responses and subsequent decrease in liver inflammation and fibrosis. Follow-up analysis of mucosal Treg cells during HCV infection may open the field for a
of immunotherapy to manipulate Treg cells during the course of infection in order to improve responses to therapy

and to prevent liver inflammation. Disclosures: Kenneth E. Sherman – Advisory Committees or Review Panels: Kadmon, Bioline, Janssen/Tibotec, Fibrogen, MedPace, Merck; Grant/Research Support: Merck, Genentech/Roche, Gilead, Anadys, Briston-Myers Squibb, Vertex, Boehringer-Ingelheim, Novartis The following people have nothing to Z-VAD-FMK in vivo disclose: Helal F. PD0325901 Hetta, Mohamed A. Mekky, Nasr K. Khalil, Wegdan A. Mohamed, Mohamed A. EL-Feky, Shabaan H. Ahmed, Enas A. Daef, Mahmoud I. Nassar, Ahmed M. Nasr, Mohamed Tarek M. Shata Background & Aims: Thrombocytopenia is a common and costly problem in patients with chronic hepatitis C (CHC). There are many potential factors affecting the platelet count in patients with CHC. The aim of this study was to determine the association between thrombopoietin level, immature platelet fraction (IPF), immunoglobulin G (IgG) level, spleen size, and the platelet count in CHC.

Methods: We retrospectively studied a consecutive sample of patients with CHC, laboratory results of interest and stored serum from a given time point, and imaging of the spleen within one year of that time point. Patients were excluded for medications, toxins, or comorbidities known to affect the platelet count. Clinical laboratory results for ALT, INR, IgG, IPF, and platelet count were obtained from the medical record. Thrombopoietin (TPO), glycocalicin, and von Wille-brand

Factor (vWF) levels were determined MCE by enzyme linked immunosorbent assay on stored sera. Spleen size was measured on ultrasound imaging. Hepatic fibrosis was assessed via transient elastography (TE) when available and missing values were approximated using a simple imputation method. We performed univariate and multivariable analyses of the relationships between predictor variables and the platelet count. Results: The cohort included 105 patients (median age 55 years, 47% female). The median platelet count was 198 K/uL and 16% of the patients with fibrosis data had cirrhosis. On univariate analysis, the following variables were significantly associated with the platelet count: age, ALT, direct bilirubin, total bilirubin, IPF, INR, spleen size, vWF, glycocalicin, fibrosis stage on liver biopsy, and TE (P-values all <0.05). A multivari-able model found the following factors to be independently associated with the platelet count: imputed TE score (coefficient -1.26, P=0.0032), TPO (-0.27, P=0.02), IPF (-10.98, P<0.

7B,C). These results indicate that overexpression of both Cryab and 14-3-3ζ promotes the progression of HCC. Here, the majority of our data reinforce the notion that Cryab is a positive regulator of HCC growth and aggressiveness. First, Cryab promoted HCC progression in Selisistat order vivo and in vitro. Second, functional and genetic screens demonstrated that Cryab overexpression fostered HCC progression by inducing EMT. We also demonstrate for the first time that Cryab complexed

with 14-3-3ζ, and elevated expression of Cryab up-regulated 14-3-3ζ protein, which relayed the signal from Cryab to activate the ERK1/2. Clinically, we found that Cryab expression correlated with BCLC staging, patients’ overall survival, and disease recurrence. Moreover, we demonstrated that Cryab overexpression activated the ERK1/2/Fra-1/slug signal to induce HCC cell EMT. The above results support

the notion that Cryab does play an important role in the progression of HCC. Based on a combination of co-IP with subsequent MS or western blot-based identification selleckchem of binding partners, we demonstrated that Cryab physically complexes with 14-3-3ζ. Furthermore, our results showed that the forced expression of Cryab was accompanied by up-regulation of 14-3-3ζ protein, but not 14-3-3ζ mRNA, in HCC cells. In addition, the interference of 14-3-3ζ reverses the mesenchymal phenotype conferred by Cryab overexpression, suggesting that the Cryab can protect 14-3-3ζ protein from degradation. The correlation coefficient between the Cryab and 14-3-3ζ proteins reached 0.760 in HCC tissues, supporting the notion that the Cryab-14-3-3ζ complex functions as a cooperative unit in HCC cells. This notion was further supported by the observation that the patients with overexpression of both Cryab and 14-3-3ζ had the poorest prognosis. The 14-3-3 protein belongs to a family of conserved regulatory molecules expressed in all eukaryotic cells,29 and MCE Cryab is the most abundant sHsp in heart and muscle.30 Because both Cryab and 14-3-3ζ regulate many important proteins that are essential for homeostasis,31,

32 directly targeting Cryab or 14-3-3ζ may be a challenge. Here, we failed to detect the Cryab and 14-3-3ζ complex in normal liver cells L02 (unpubl. data), which indicates that this complex may not exist in normal cells, or may only exist in very small amounts. Thus, our findings may provide an alternative molecular target for HCC therapies by promoting the dissociation of the Cryab and 14-3-3ζ complex. By gene expression analysis, co-IP with MS, bioinformatics analysis, and step-by-step RNA interference, we demonstrated the Cryab overexpression-induced hyperactivity of the ERK signal by forming a complex with 14-3-3ζ. Specifically, this ERK signal hyperactivity was resistant to sorafenib. As one of the 14-3-3 proteins, 14-3-3ζ was first identified to be associated with Raf.

Differential expression of SEMA7A in the liver, which occurs during fibrogenesis, may potentially explain the

increased risk of HCC development in the course of cirrhosis. Disclosures: The following people have nothing to disclose: Samuele De Minicis, Chiara Rychlicki, Laura Agostinelli, Cinzia Candelaresi, Luciano Trozzi, Stefania Saccomanno, Eleonora Mingarelli, Marco Marzioni, Antonio Benedetti, Gianluca Svegliati-Baroni Vascular invasion has been known to be a strong p38 MAPK phosphorylation predictor of hepatocellular carcinoma (HCC) recurrence after liver transplant, but clinically reliable molecular markers for vascular invasion are still not available yet. Here we report a miRNA signature that can distinguish recurrent HCC with vascular invasion from recurrent HCC without vascular invasion. We examined vascular invasion on 124 HCC tumor nodules from a cohort of 77 HCC patients, 45 of whom had recurrent HCC within 3 years of transplant. IWR-1 purchase We performed miRNA expression profiling on all nodules using miRNA microarrays. High value miRNA candidates (most statistically significant and present in the HCC recurrence with macrovascular invasion) were then be validated by qPCR verification. We found that 1 3 miRNAs were differentially expressed with at least 2 fold expression change with p<0.05 (12

downregulated: miR-22, miR-29a, miR-30a, miR-34a, miR-99a, miR-100, miR-126, miR-192, miR-194, miR-195, miR-199a, and miR-497, and 1 upregulated: miR-494). Hierarchical clustering of miRNAs versus patients clearly shows that these miRNAs significantly distinguish patients with and without HCC macrovascular invasion. Further analyses of these miRNAs demonstrates that most of 12 down-regulated

miRNAs can inhibit HCC cell survival, proliferation, MCE公司 and angiogenesis via suppressing IGF, WNT, and VEGF signaling pathways, while the up-regulated miR-494 is a convergent downstream of oncogenic transcriptional factors such as H-Ras, c-Jun, and E2F. Our study discovers a miRNA signature distinguishing between recurrent HCC with and without macrovascular invasion. This miRNA signature may serve as a prognostic biomarker and also help direct therapeutic interventions for HCC. Disclosures: Christa L. Whitney-Miller – Grant/Research Support: Genentech The following people have nothing to disclose: KuangHsiang Chuang, Mark S. Orloff, Matthew N. McCall, Anthony Almudevar, Christopher T. Barry Introduction Sorafenib, a multi-tyrosine kinase inhibitor, is the only FDA approved chemotherapeutic agent for metastatic hepatocellular carcinoma (HCC). We have previously shown that triptolide enhances apoptosis in HuH-7 HCC cells. In this study, we examined the effects of these agents and their combination on HCC in vitro and in vivo. Methods HuH-7 cells were treated with triptolide (T – 50 nM), sorafenib (S – 1.

17 begins to shed some light in this area. They found that patients who improved their insulin sensitivity by 40% or greater had a significant improvement in hepatocyte find more ballooning. Alternatively, those with less of a response did not do as well, corroborating the experience seen in the bariatric surgery field.24 These findings highlight the particular need to better characterize patients who may or may not respond to TZD therapy, similar to how we characterize patients with hepatitis C prior to initiating therapy with pegylated interferon and ribavirin. Does gender, ethnicity, or the presence or absence of diabetes

mellitus among patients with NASH influence the degree of insulin sensitivity improvement seen with the TZDs? Does the degree of baseline insulin resistance influence histopathologic response rates? Is there more to the story than simply improving insulin resistance? The TZDs clearly do not improve histology in every patient. With further knowledge of response factors, clinicians may be able to distinguish or even predict responders from nonresponders

FXR agonist prior to initiating therapy with a TZD or within a few weeks to months of starting therapy depending on the insulin response curve. Finally, the results from the PIVENS trial presented at the recent 60th annual meeting of the American Association for the Study of Liver Diseases suggest that treatment strategies other than insulin sensitivity improvement may also improve histopathology.15 The multiple mechanisms leading to NASH may require that more than one therapeutic approach be considered in the treatment of this disease. The future may involve either targeted monotherapy based on specific patient demographics and baseline laboratory data or combination therapy that is aimed at improving key pathogenic abnormalities

seen MCE in patients with NASH, such as oxidative stress, insulin resistance, apoptosis, or other, as-yet clarified, manifestations of lipotoxicity. For now, lifestyle intervention remains the cornerstone of therapy for NAFLD. However, for those patients who are unable to exercise or lose weight through diet, or for those patients with more advanced NASH, studies with adjuvant pharmacologic therapy targeting insulin resistance appears indicated. More recent data suggest that patients with NAFLD are not just at risk for progression to cirrhosis and hepatocellular carcinoma, but also are at significant risk for progression to diabetes and cardiovascular disease. Given this information, it may be myopic for Hepatologists to simply focus on the histopathologic effects of NASH therapies. The beneficial effects of the TZDs to prevent or delay the progression to diabetes and the potential beneficial effects of pioglitazone on cardiovascular disease should be considered concurrently with the potential for histopathologic improvement.

[1] Regarding chemotherapy, HCC and CGC are among the tumors with the highest refractoriness. Although some drugs, such as doxorubicin, can achieve a partial effect in some cases, no relevant survival benefit has been obtained.[2] Chemoresistance is often present before the treatment, but it can be further enhanced in response to the pharmacological challenge.[3] Mechanisms of chemoresistance (MOCs) have been classified based on their role in drug uptake (MOC-1a) or efflux (MOC-1b), intracellular

drug metabolism (MOC-2), changes in the expression/function of molecular targets (MOC-3), changes in LDK378 solubility dmso the DNA repair machinery (MOC-4), reduced activation of apoptosis (MOC-5a), and enhanced expression/activity of antiapoptotic proteins (MOC-5b).[4] MOCs may involve changes in the expression levels of specific proteins and the presence of genetic variants affecting their function.[5] One of the most promising strategies to overcome chemoresistance of primary liver cancer is the development of

tyrosine kinase inhibitors (TKIs), such as sorafenib. This drug has been approved for the treatment of HCC, although the beneficial effect, regarding the inhibition of tumor progression and the enhancement of overall survival, is rather modest.[6] Sorafenib has been reported to be effective in vitro against cells derived from CGC,[7, 8] although its efficacy in 上海皓元 CGC patients is low.[9]

The mechanism of action of sorafenib Everolimus depends on its access to the intracellular targets, which may be affected by changes in the expression and activity of transporters accounting for its uptake. The organic cation transporter-1 (OCT1, gene symbol SLC22A1), located at the basolateral membrane of healthy hepatocytes, is one of these transporters. OCT1 mediates the uptake of endogenous and exogenous organic cations,[10] including drugs such as metformin,[11] platinum derivatives,[12] anthracyclines,[10] and TKIs.[13] The response to drugs whose hepatic uptake depends on this transporter, such as metformin, is affected by changes in OCT1 expression and by the appearance of less functional variants.[14] In HCC and CGC, a decreased expression of OCT1 has been found.[3, 15] Moreover, a relationship between the presence of inactivating mutations in the SLC22A1 gene and a lower response to imatinib in patients with chronic myeloid leukemia has been reported.[16] In the present study we investigated the expression of aberrant OCT1 variants in HCC and CGC and evaluated in vitro their potential impact on the sensitivity of these tumors to sorafenib. Tumor samples from 23 HCC and 15 CGC (see patient and tumor information in Supporting Table 1) were obtained with written consent of patients from surgically removed tumors. None of these patients had received chemotherapy prior to the resection.

[1] Regarding chemotherapy, HCC and CGC are among the tumors with the highest refractoriness. Although some drugs, such as doxorubicin, can achieve a partial effect in some cases, no relevant survival benefit has been obtained.[2] Chemoresistance is often present before the treatment, but it can be further enhanced in response to the pharmacological challenge.[3] Mechanisms of chemoresistance (MOCs) have been classified based on their role in drug uptake (MOC-1a) or efflux (MOC-1b), intracellular

drug metabolism (MOC-2), changes in the expression/function of molecular targets (MOC-3), changes in Ku-0059436 research buy the DNA repair machinery (MOC-4), reduced activation of apoptosis (MOC-5a), and enhanced expression/activity of antiapoptotic proteins (MOC-5b).[4] MOCs may involve changes in the expression levels of specific proteins and the presence of genetic variants affecting their function.[5] One of the most promising strategies to overcome chemoresistance of primary liver cancer is the development of

tyrosine kinase inhibitors (TKIs), such as sorafenib. This drug has been approved for the treatment of HCC, although the beneficial effect, regarding the inhibition of tumor progression and the enhancement of overall survival, is rather modest.[6] Sorafenib has been reported to be effective in vitro against cells derived from CGC,[7, 8] although its efficacy in medchemexpress CGC patients is low.[9]

The mechanism of action of sorafenib click here depends on its access to the intracellular targets, which may be affected by changes in the expression and activity of transporters accounting for its uptake. The organic cation transporter-1 (OCT1, gene symbol SLC22A1), located at the basolateral membrane of healthy hepatocytes, is one of these transporters. OCT1 mediates the uptake of endogenous and exogenous organic cations,[10] including drugs such as metformin,[11] platinum derivatives,[12] anthracyclines,[10] and TKIs.[13] The response to drugs whose hepatic uptake depends on this transporter, such as metformin, is affected by changes in OCT1 expression and by the appearance of less functional variants.[14] In HCC and CGC, a decreased expression of OCT1 has been found.[3, 15] Moreover, a relationship between the presence of inactivating mutations in the SLC22A1 gene and a lower response to imatinib in patients with chronic myeloid leukemia has been reported.[16] In the present study we investigated the expression of aberrant OCT1 variants in HCC and CGC and evaluated in vitro their potential impact on the sensitivity of these tumors to sorafenib. Tumor samples from 23 HCC and 15 CGC (see patient and tumor information in Supporting Table 1) were obtained with written consent of patients from surgically removed tumors. None of these patients had received chemotherapy prior to the resection.

All other participants adhered to the study protocol follow-up schedule. Postintervention liver biopsy was completed in 28 of 31 (90%) participants, 18 of 21 (86%) in the lifestyle intervention group and 10 of 10 (100%) in the control group. The reasons for the lack of follow-up biopsy were anticoagulation therapy (n = 1), technical difficulty (n = 1), and withdrawal from the study (n = 1). The mean weight change over the 48-week period was −8.7 kg (95% CI, −11.7 to −5.6) in the lifestyle intervention group as compared with −0.5 kg (95% CI, −4.8 to 3.8) in the control group (P = 0.005) (Table 2). Percent weight reduction (standard

deviation [SD]) of participants in the lifestyle group was significantly greater than that in participants in the control group at 24 weeks (8.9 [6.3]% versus 0.1 [3.7]%, P < see more 0.001) and at 48 weeks (9.3 [7.5]% versus 0.2 [6.1]%, P = 0.003) (Fig. 2A) Eight participants (40%) in the lifestyle intervention group achieved a 10% or greater weight reduction, whereas no participant (0%) in the control group achieved this degree of weight reduction (P = 0.02). There was a nonsignificant trend for greater percent weight reduction in participants without underlying diabetes (n = 16) compared with those with diabetes (n = 14) (8.5 [9.5]% versus 3.8 [5.7]%, P = 0.12), and in participants who were not on metformin (n = 21) compared with those on

metformin (n = 9) (8.1 [8.4]% versus 2.1 [6.3]%, P = 0.07). A subgroup analysis within the lifestyle intervention group, after correction for heterogeneity of variance, found greater percent weight reduction (P = 0.01) for those without diabetes (13.6 [8.3]%) LY294002 cost versus those with diabetes (5.1 [3.1]%), and also for those not using metformin (11.4 [7.9]%) versus those using metformin (4.4 [3.1]%). There

was no significant difference in the degree of weight loss among participants who had baseline overweight (BMI, 25–29.9 kg/m2), class I (BMI 30–34.9 kg/m2), or class II obesity (BMI, 35–40 kg/m2). Participants in the lifestyle intervention group who had baseline overweight, class I and class II obesity lost 8.7 (6.3)%, 11.5 (7.1)%, and 6.9 (9.3)% of their body weight, respectively (P = 0.56). The mean waist circumference change over the 48-week period was −7.4 cm (95% CI, −10.3 to −4.6) in the lifestyle 上海皓元 intervention group as compared with +0.3 cm (95% CI, −3.2 to 3.8) in the control group (P = 0.004). The overall disease activity of nonalcoholic steatohepatitis (NAS [SD]) improved significantly in the lifestyle intervention group (−2.4 [1.6]) in comparison with the control group (−1.4 [2.1]) (P = 0.05) (Table 3). Steatosis score also improved to a significantly greater degree in the lifestyle group as compared with the control group (−1.1 [0.8] versus −0.3 [0.8], P = 0.02). Ballooning injury score improved in both groups, whereas fibrosis score did not change in either group.