1.Predicting Clinically Significant Prostate Cancer Using Urine Metabolomics via Liquid Chromatography Mass Spectrometry
Chung-Hsin CHEN ; Hsiang-Po HUANG ; Kai-Hsiung CHANG ; Ming-Shyue LEE ; Cheng-Fan LEE ; Chih-Yu LIN ; Yuan Chi LIN ; William J. HUANG ; Chun-Hou LIAO ; Chih-Chin YU ; Shiu-Dong CHUNG ; Yao-Chou TSAI ; Chia-Chang WU ; Chen-Hsun HO ; Pei-Wen HSIAO ; Yeong-Shiau PU ;
The World Journal of Men's Health 2025;43(2):376-386
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.
2.Predicting Clinically Significant Prostate Cancer Using Urine Metabolomics via Liquid Chromatography Mass Spectrometry
Chung-Hsin CHEN ; Hsiang-Po HUANG ; Kai-Hsiung CHANG ; Ming-Shyue LEE ; Cheng-Fan LEE ; Chih-Yu LIN ; Yuan Chi LIN ; William J. HUANG ; Chun-Hou LIAO ; Chih-Chin YU ; Shiu-Dong CHUNG ; Yao-Chou TSAI ; Chia-Chang WU ; Chen-Hsun HO ; Pei-Wen HSIAO ; Yeong-Shiau PU ;
The World Journal of Men's Health 2025;43(2):376-386
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.
3.Predicting Clinically Significant Prostate Cancer Using Urine Metabolomics via Liquid Chromatography Mass Spectrometry
Chung-Hsin CHEN ; Hsiang-Po HUANG ; Kai-Hsiung CHANG ; Ming-Shyue LEE ; Cheng-Fan LEE ; Chih-Yu LIN ; Yuan Chi LIN ; William J. HUANG ; Chun-Hou LIAO ; Chih-Chin YU ; Shiu-Dong CHUNG ; Yao-Chou TSAI ; Chia-Chang WU ; Chen-Hsun HO ; Pei-Wen HSIAO ; Yeong-Shiau PU ;
The World Journal of Men's Health 2025;43(2):376-386
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.
4.Predicting Clinically Significant Prostate Cancer Using Urine Metabolomics via Liquid Chromatography Mass Spectrometry
Chung-Hsin CHEN ; Hsiang-Po HUANG ; Kai-Hsiung CHANG ; Ming-Shyue LEE ; Cheng-Fan LEE ; Chih-Yu LIN ; Yuan Chi LIN ; William J. HUANG ; Chun-Hou LIAO ; Chih-Chin YU ; Shiu-Dong CHUNG ; Yao-Chou TSAI ; Chia-Chang WU ; Chen-Hsun HO ; Pei-Wen HSIAO ; Yeong-Shiau PU ;
The World Journal of Men's Health 2025;43(2):376-386
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.
5.Predicting Clinically Significant Prostate Cancer Using Urine Metabolomics via Liquid Chromatography Mass Spectrometry
Chung-Hsin CHEN ; Hsiang-Po HUANG ; Kai-Hsiung CHANG ; Ming-Shyue LEE ; Cheng-Fan LEE ; Chih-Yu LIN ; Yuan Chi LIN ; William J. HUANG ; Chun-Hou LIAO ; Chih-Chin YU ; Shiu-Dong CHUNG ; Yao-Chou TSAI ; Chia-Chang WU ; Chen-Hsun HO ; Pei-Wen HSIAO ; Yeong-Shiau PU ;
The World Journal of Men's Health 2025;43(2):376-386
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.
6.Interpretation of the China Guidelines for the Prevention and Treatment of Diabetes(2024 Edition)
Jingxia YIN ; Li YU ; Danlan PU ; Xiaoli XU ; Yong LIAO
Journal of Chongqing Medical University 2025;50(5):557-564
In recent years,significant progress has been made in diabetes research both domestically and internationally,new diagno-sis and treatment techniques and methods have been constantly emerging,and clinical research evidence has been continuously accu-mulated and updated.To keep pace with these important developments,the Diabetes Branch of the Chinese Medical Association has ac-tively organized experts to update the China Guidelines for the Prevention and Treatment of Diabetes.This update aims to provide a more comprehensive and scientific guide for diabetes prevention and treatment,greatly promote the standardized and integrated man-agement of diabetes by clinicians,and ensure that patients receive standardized and personalized treatment plans to improve therapeu-tic outcomes and quality of life.
7.Interpretation of updated key points in the American Diabetes Association's 2025 Standards of Care in Diabetes
Xiaoying DONG ; Jingxia YIN ; Ling LI ; Li YU ; Danlan PU ; Yong LIAO
Journal of Chongqing Medical University 2025;50(5):565-573
Over the years,the American Diabetes Association(ADA)has been actively committed to the development and promotion of standards for the diagnosis,treatment,and daily care of diabetes.Since 1989,it has updated the diabetes diagnosis and treatment standards every year,which have become one of the most authoritative guidelines in diabetes and have been recognized and adopted by various countries.On December 10,2024,the 2025 Standards of Care in Diabetes were released,incorporating the latest evidence-based medicine content related to diabetes and its complications and comorbidities.It aims to provide guidance on the diagnosis,treat-ment,and management of the condition for clinicians,patients and their families,and researchers.This article interprets the major up-dates from the Standards.
8.Interpretation and reflections on Guideline for the management of diabetes mellitus in the elderly in China(2024 edition)from a nursing perspective
Ping TANG ; Jingxia YIN ; Ling LI ; Li YU ; Yong LIAO ; Danlan PU
Journal of Chongqing Medical University 2025;50(6):765-769
With the acceleration of population aging in China,the management of elderly patients with diabetes has become a critical issue in the field of public health.By integrating international evidence-based medical research data with the specific conditions of China,the 2024 edition of Guideline for the management of diabetes mellitus in the elderly in China proposes individualized diagnosis and treatment strategies for elderly patients with diabetes.This article gives a systematic interpretation of the guideline from a nursing perspective,with a focus on the core contents closely associated with nursing practice,and it also elaborates on the implications for nursing work,summarizes and analyzes the challenges in the management of elderly patients with diabetes,and proposes corresponding strategies,in order to provide a theoretical basis and practical guidance for implementing nursing management of elderly patients with diabetes among clinical nurses.
9.Interpretation of Guideline for the prevention and treatment of diabetes mellitus in China(2024 edition):a nursing practice perspective
Li YU ; Long CUI ; Ling LI ; Yong LIAO ; Danlan PU ; Jingxia YIN
Journal of Chongqing Medical University 2025;50(10):1317-1322
The Guideline for the prevention and treatment of diabetes mellitus in China(2024 edition),released by the Chinese Dia-betes Society on January 20,2025,has updated evidence on diabetes mellitus from various aspects including its epidemiological status in China,diagnosis and treatment progress,and complication management,aiming to guide and facilitate standardized comprehensive management of diabetes mellitus in clinical practice.This paper interprets the guideline from the perspective of nursing practice,focus-ing on nursing care for special diabetic conditions,lifestyle and behavioral interventions,and the procedures of relevant nursing tech-niques.We hope to provide nursing professionals with standardized guidance on diabetes prevention and treatment,thereby further standardizing and refining specialized nursing care for diabetes mellitus,enhancing the quality of patient care,and improving patient prognosis.
10.Relationship between coagulation indicators and early stage prognosis in patients with acute respiratory distress syndrome
Xiaoer JIN ; Yufan PU ; Miao WANG ; Chunmeng XUE ; Qingbo LIAO ; Qi DING
Chongqing Medicine 2024;53(15):2296-2300,2307
Objective To investigate the relationship between coagulation indicators and early prognosis in patients with acute respiratory distress syndrome (ARDS).Methods The data of ARDS patients receiving the treatment in the intensive care unit (ICU) from 2008-2019 were selected from the Critical Care Medicine Open Database (MIMIC-Ⅳ V2.0 version) jointly published by MIT,Beth Israel Deaconess Medical Center,and Philips Medical,the data were categorized according to the severity of the patients' disease and the causes of lung damage.The coagulation indexes and 28 d mortality (m28d) rates were compared among different ARDS patients.The receiver operating characteristic (ROC) curve was drawn.The area under the curve was calculated to evaluate the predictive values of the related indicators.The univariate and multivariate logistic re-gression was adopted to analyze the risk factors affecting m28d in the patients with ARDS.Results Maximum prothrombin time (PTmax) in the patients with pulmonary origin ARDS was significantly lower than that in the patients without pulmonary origin ARDS,and the difference was statistically significant (P<0.05).PLTmin,PLTmax and Sequential Organ Failure Assessment (SOFA) score had statistical difference among dif-ferent severity degrees of ARDS patients (P<0.05).Minimum international normalized ratio (INRmin),maxi-mum international normalized ratio (INRmax),minimum prothrombin time (PTmin),PTmax,maximum activated partial thromboplastin time (APTTmax) and SOFA score had statistical differences between the survival group and death group (P<0.05).AUC of INRmin,INRmax,PTmin,PTmax and APTTmax were 0.607,0.624,0.610,0.620 and 0.648 respectively.The multivariate logistic regression analysis showed that APTTmax (OR=1.011,95%CI:1.001-1.022,P=0.029) was an independent risk factor for affecting m28d in the ARDS patients.Conclu-sion Plasma PLT levels in different severities of ARDS patients have the difference and APTTmax on the first day in ICU is an independent risk factor for affecting early prognosis in ARDS patients.

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