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.

Purpose: Preoperative localization plays an important role in secondary hyperparathyroidism (SHPT) surgery. The advantages of neck ultrasound (US) include high availability and low cost. However, the reported sensitivity of US is 54%– 76%, and the reason for missed parathyroid glands (PGs) on US has been rarely addressed. Methods: Fifty-four patients who were diagnosed with renal SHPT from September 2020 to March 2022 were included in this retrospective study. Preoperative localization included surgeon-oriented US and technetium 99m-sestamibi singlephoton emission CT (SPECT)/CT. Results: A total of 212 PGs were pathologically confirmed, resulting in a success rate of 96.2% (52 of 54). Using echo, 193 PGs (91.0%) were accurately localized, while 19 glands (9.0%) were not identified, including those in ectopic positions (n = 12, at thymus or intrathyroid or others), of small size (<1 cm, n = 6), or overlapping with an ipsilateral PG (n = 1). US accurately detected 4 PGs in 36 (66.7%) patients, while SPECT/CT localized 4 glands in 19 patients (35.2%). Although the number of US-detectable PGs was not associated with success rate, it showed a significant negative correlation with surgical time (rs = –0.459, P = 0.002). Conclusion US detected 4 glands in 66% of SHPT patients with a sensitivity of 90% for localization. Ectopic position and small size were the most common reasons for the failure to detect PG on US. Complete preoperative echo localization might shorten operating time.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Acute kidney injury (AKI) increases the risk of chronic kidney disease (CKD), and AKI and CKD are seen as interconnected syndromes. Acute kidney disease (AKD) is defined as subacute damage and/or loss of kidney function occurring 7 to 90 days after AKI, during which period key interventions may be initiated to hinder the development of CKD. While AKD is usually under-recognized, it is associated with high morbidity and mortality globally. This review article aims to summarize the current knowledge concerning the epidemiology, pathophysiology, and management of AKD with the aim to develop monitoring strategies and therapeutic agents of AKD. Generally, AKD tends to occur more frequently in the elderly and those with chronic diseases, such as hypertension, diabetes mellitus, and metabolic syndrome. In addition, the severity, duration, and frequency of AKI are independent risk factors for AKD. Investigations of several mechanisms of AKD, such as renal tubular epithelium cell-cycle arrest, epigenetic change, chronic inflammation, mitochondria dysfunction, failed regeneration of tubular cells, metabolic reprogramming, and renin-angiotensin system (RAS) activation, have identified additional potential pharmacotherapy targets. Management of AKD includes prevention of repeated AKI, early and regular follow-up by a nephrologist, resumption and adjustment of essential medication, optimization of blood pressure control and nutrition management, and development of new pharmaceutical agents including RAS inhibitors. Finally, we outline a care bundle for AKD patients based on important lessons learned from studies and registries and identify the need for clinical trials of RAS inhibitors or other novel agents to impede ensuing CKD development.

Anemia is common in patients with chronic kidney disease (CKD) and is mainly caused by insufficient production of erythropoietin from fibrotic kidney. Because anemia impairs quality of life and overall prognosis, recombinant human erythropoietin-related products (erythropoiesis-stimulating agents, ESAs) have been developed to increase hemoglobin level for decades. However, many safety concerns have been announced regarding the use of ESAs, including an increased occurrence of cardiovascular events, vascular access thrombosis, cancer progression, and recurrence. Hypoxia-inducible factor (HIF) is crucial to erythropoietin production, as a result, prolyl hydroxylase domain (PHD) enzyme inhibitors have been new therapeutic agents for the treatment of anemia in CKD. They can be administered orally, which is a preferred route for patients not undergoing hemodialysis. In clinical trials, PHD inhibitor could induce noninferior effect on erythropoiesis and improve functional iron deficiency compared with ESAs. Although no serious adverse events were reported, safety is still a concern because HIF stabilization induced by PHD inhibitor has pleotropic effects, such as angiogenesis, metabolic change, and cell survival, which might lead to unwanted deleterious effects, including fibrosis, inflammation, cardiovascular risk, and tumor growth. More molecular mechanisms of PHD inhibition and long-term clinical trials are needed to observe these pleotropic effects for the confirmation of safety and efficacy of PHD inhibitors.

BACKGROUND: Particulate matter (PM) < 2.5 μm (PM METHODS: We obtained DNA methylation and exercise data of 496 participants (aged between 30 and 70 years) from the Taiwan Biobank (TWB) database. We also extracted PM RESULTS: DLEC1 methylation and PM CONCLUSIONS We found significant positive associations between PM
Adult ; Aged ; Air Pollutants/adverse effects* ; DNA Methylation/drug effects* ; Environmental Exposure/adverse effects* ; Exercise ; Female ; Humans ; Male ; Middle Aged ; Particulate Matter/adverse effects* ; Taiwan ; Tumor Suppressor Proteins/metabolism*