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.

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.

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.

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.

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.

AIM:To investigate the effects of early high-fat diet(HFD)on cognitive function and hippocam-pal lipidomic profile in transgenic mice bearing five familial Alzheimer disease mutant genes(5×FAD).METHODS:Eight-week-old SPF grade female wild-type(WT)mice were used as the contorl group,and 5×FAD mice were randomly divided into model(5×FAD)group and 5×FAD+HFD group,with 10 mice in each group.The 5×FAD+HFD group was orally given high-fat chow and the remaining 2 groups were given control chow for 12 weeks,and the change in body weight of the mice were recorded.Y-maze and Morris water maze tests were performed to measure the learning memory ability of the mice.Serum total cholesterol(TC),triglyceride(TG),low-density lipoprotein cholesterol(LDL-C)and high-density lipoprotein cholesterol(HDL-C)levels were measured using a biochemical analyzer.Immunohistochemistry was per-formed to visualize amyloid β-protein(Aβ)plaques in brain tissues.Hippocampal levels of tumor necrosis factor-α(TNF-α),interleukin-1β(IL-1β),IL-6,and Aβ were measured by enzyme-linked immunosorbent assay(ELISA).Non-tar-geted lipidomic technology was used to measure the changes of hippocampal lipids.RESULTS:Compared with WT group,the mice in 5×FAD group lost significantly less weight(P<0.01)and spent significantly less time exploring the new arm of the Y-maze and the target quadrant of the water maze(P<0.05 or P<0.01).Brain Aβ plaques were significant-ly increased(P<0.01).Hippocampal levels of Aβ1-40,Aβ1-42,IL-1β and TNF-α were significantly elevated(P<0.05 or P<0.01).Compared with the 5×FAD group,the mice in the 5×FAD+HFD group showed significant increase in body weight(P<0.01)and time spent exploring the new arm of the Y-maze and the target quadrant of the water maze(P<0.01).Biochmeical analysis showed serum TC,LDL-C,HDL-C levels and HDL/TC ratio were significantly increased(P<0.05).Brain Aβ plaques were significantly reduced(P<0.05)and hippocampal Aβ1-40,Aβ1-42 and IL-1β levels were sig-nificantly decreased(P<0.05).Compared with the WT group,27 lipids were increased and 9 lipids were decreased in the 5×FAD group,involving the pathways such as cholesterol metabolism,fat digestion and absorption,regulation of lipolysis processes in adipocytes,and glycerophospholipid metabolism.Eighteen lipids were increased and 47 lipids were de-creased in the 5×FAD+HFD group compared to the 5×FAD group.Cardiolipin and TG were important lipids for separating the lipid profiles of the WT and 5×FAD groups,and TG was an important lipid for separating the lipid profiles of the 5×FAD and 5×FAD+HFD groups.Differential lipid enrichment analysis showed significant increase in TG lipid in the 5×FAD group compared with the WT group and significant decrease in TG lipid in the 5×FAD+HFD group compared with the 5×FAD group.CONCLUSION:Early HFD ameliorates cognitive function in 5×FAD mice by modifying TG metabolic disorder and attenuating neuroinflammation.