Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Background: AMP-activated protein kinase (AMPK) is a key enzyme for cellular energy homeostasis and improves metabolic disorders. Brown and beige adipose tissues exert thermogenesis capacities to dissipate energy in the form of heat. Here, we investigated the beneficial effects of the antioxidant alpha-lipoic acid (ALA) in menopausal obesity and the underlying mechanisms. Methods: Female Wistar rats (8 weeks old) were subjected to bilateral ovariectomy (Ovx) and divided into four groups: Sham (n=8), Ovx (n=11), Ovx+ALA2 (n=10), and Ovx+ALA3 (n=6) (ALA 200 and 300 mg/kg/day, respectively; gavage) for 8 weeks. 3T3-L1 cells were used for in vitro study. Results: Rats receiving ALA2 and ALA3 treatment showed significantly lower levels of body weight and white adipose tissue (WAT) mass than those of the Ovx group. ALA improved plasma lipid profiles including triglycerides, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Hematoxylin & eosin staining of inguinal WAT showed that ALA treatment reduced Ovx-induced adipocyte size and enhanced uncoupling protein 1 (UCP1) expression. Moreover, plasma levels of irisin were markedly increased in ALA-treated Ovx rats. Protein expression of brown fat-specific markers including UCP1, PRDM16, and CIDEA was downregulated by Ovx but markedly increased by ALA. Phosphorylation of AMPK, its downstream acetyl-CoA carboxylase, and its upstream LKB1 were all significantly increased by ALA treatment. In 3T3-L1 cells, administration of ALA (100 and 250 μM) reduced lipid accumulation and enhanced oxygen consumption and UCP1 protein expression, while inhibition of AMPK by dorsomorphin (5 μM) significantly reversed these effects. Conclusion ALA improves estrogen deficiency-induced obesity via browning of WAT through AMPK signaling.

Background: AMP-activated protein kinase (AMPK) is a key enzyme for cellular energy homeostasis and improves metabolic disorders. Brown and beige adipose tissues exert thermogenesis capacities to dissipate energy in the form of heat. Here, we investigated the beneficial effects of the antioxidant alpha-lipoic acid (ALA) in menopausal obesity and the underlying mechanisms. Methods: Female Wistar rats (8 weeks old) were subjected to bilateral ovariectomy (Ovx) and divided into four groups: Sham (n=8), Ovx (n=11), Ovx+ALA2 (n=10), and Ovx+ALA3 (n=6) (ALA 200 and 300 mg/kg/day, respectively; gavage) for 8 weeks. 3T3-L1 cells were used for in vitro study. Results: Rats receiving ALA2 and ALA3 treatment showed significantly lower levels of body weight and white adipose tissue (WAT) mass than those of the Ovx group. ALA improved plasma lipid profiles including triglycerides, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Hematoxylin & eosin staining of inguinal WAT showed that ALA treatment reduced Ovx-induced adipocyte size and enhanced uncoupling protein 1 (UCP1) expression. Moreover, plasma levels of irisin were markedly increased in ALA-treated Ovx rats. Protein expression of brown fat-specific markers including UCP1, PRDM16, and CIDEA was downregulated by Ovx but markedly increased by ALA. Phosphorylation of AMPK, its downstream acetyl-CoA carboxylase, and its upstream LKB1 were all significantly increased by ALA treatment. In 3T3-L1 cells, administration of ALA (100 and 250 μM) reduced lipid accumulation and enhanced oxygen consumption and UCP1 protein expression, while inhibition of AMPK by dorsomorphin (5 μM) significantly reversed these effects. Conclusion ALA improves estrogen deficiency-induced obesity via browning of WAT through AMPK signaling.

Background: AMP-activated protein kinase (AMPK) is a key enzyme for cellular energy homeostasis and improves metabolic disorders. Brown and beige adipose tissues exert thermogenesis capacities to dissipate energy in the form of heat. Here, we investigated the beneficial effects of the antioxidant alpha-lipoic acid (ALA) in menopausal obesity and the underlying mechanisms. Methods: Female Wistar rats (8 weeks old) were subjected to bilateral ovariectomy (Ovx) and divided into four groups: Sham (n=8), Ovx (n=11), Ovx+ALA2 (n=10), and Ovx+ALA3 (n=6) (ALA 200 and 300 mg/kg/day, respectively; gavage) for 8 weeks. 3T3-L1 cells were used for in vitro study. Results: Rats receiving ALA2 and ALA3 treatment showed significantly lower levels of body weight and white adipose tissue (WAT) mass than those of the Ovx group. ALA improved plasma lipid profiles including triglycerides, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Hematoxylin & eosin staining of inguinal WAT showed that ALA treatment reduced Ovx-induced adipocyte size and enhanced uncoupling protein 1 (UCP1) expression. Moreover, plasma levels of irisin were markedly increased in ALA-treated Ovx rats. Protein expression of brown fat-specific markers including UCP1, PRDM16, and CIDEA was downregulated by Ovx but markedly increased by ALA. Phosphorylation of AMPK, its downstream acetyl-CoA carboxylase, and its upstream LKB1 were all significantly increased by ALA treatment. In 3T3-L1 cells, administration of ALA (100 and 250 μM) reduced lipid accumulation and enhanced oxygen consumption and UCP1 protein expression, while inhibition of AMPK by dorsomorphin (5 μM) significantly reversed these effects. Conclusion ALA improves estrogen deficiency-induced obesity via browning of WAT through AMPK signaling.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is an increasingly common liver disease worldwide. MAFLD is diagnosed based on the presence of steatosis on images, histological findings, or serum marker levels as well as the presence of at least one of the three metabolic features: overweight/obesity, type 2 diabetes mellitus, and metabolic risk factors. MAFLD is not only a liver disease but also a factor contributing to or related to cardiovascular diseases (CVD), which is the major etiology responsible for morbidity and mortality in patients with MAFLD. Hence, understanding the association between MAFLD and CVD, surveillance and risk stratification of MAFLD in patients with CVD, and assessment of the current status of MAFLD management are urgent requirements for both hepatologists and cardiologists. This Taiwan position statement reviews the literature and provides suggestions regarding the epidemiology, etiology, risk factors, risk stratification, nonpharmacological interventions, and potential drug treatments of MAFLD, focusing on its association with CVD.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.