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.

This study aimed to investigate the effects of caffeoylquinic acids from Erigeron breviscapus(EBCQA) on cognitive impairment and mitochondrial dysfunction in Alzheimer's disease(AD), and to explore its underlying mechanisms. The impacts of EBCQA on paralysis, β-amyloid(Aβ) oligomerization, and mRNA expression of mitophagy-related genes [PTEN-induced putative kinase 1(PINK1) homolog-encoding gene pink-1, Parkin homolog-encoding gene pdr-1, Bcl-2 interacting coiled-coil protein 1(Beclin 1) homolog-encoding gene bec-1, microtubule-associated protein 1 light chain 3(LC3) homolog-encoding gene lgg-1, autophagic adapter protein 62(p62) homolog-encoding gene sqst-1] were examined in the AD Caenorhabditis elegans CL4176 model, along with mitochondrial functions including adenosine triphosphate(ATP) content, enzyme activities of mitochondrial respiratory chain complexes Ⅰ,Ⅲ, and Ⅳ, and mitochondrial membrane potential. Additionally, the effects of EBCQA on the green fluorescent protein(GFP)/red fluorescent protein from Discosoma sp.(DsRed) ratio, the expression of phosphatidylethanolamine-modified and GFP-labeled LGG-1(PE-GFP::LGG-1)/GFP-labeled LGG-1(GFP::LGG-1), and GFP-labeled SQST-1(GFP::SQST-1) proteins were investigated in transgenic C. elegans strains. The effect of EBCQA on paralysis was further evaluated after RNA interference(RNAi)-mediated suppression of the pink-1 and pdr-1 genes in CL4176 strain. An AD rat model was established through intraperitoneal injection of D-galactose and intragastric administration of aluminum trichloride. The effects of β-nicotinamide mononucleotide(NMN) and EBCQA on learning and memory ability, neuronal morphology, mitophagy occurrence, mitophagy-related protein expression(PINK1, Parkin, Beclin 1, LC3-Ⅱ/LC3-Ⅰ, p62), and mitochondrial functions(ATP content; enzyme activities of mitochondrial respiratory chain complexes Ⅰ, Ⅲ, and Ⅳ; mitochondrial membrane potential) were investigated in this AD rat model. The results showed that EBCQA delayed paralysis onset in the CL4176 strain, reduced Aβ oligomer formation, and upregulated the mRNA expression levels of lgg-1, bec-1, pink-1, and pdr-1, while downregulating sqst-1 mRNA expression. EBCQA also enhanced ATP content, mitochondrial membrane potential, and the activities of mitochondrial respiratory chain complexes Ⅰ, Ⅲ, and Ⅳ. Furthermore, EBCQA improved the PE-GFP::LGG-1/GFP::LGG-1 ratio, reduced GFP::SQST-1 expression, and decreased the GFP/DsRed ratio. Notably, the ability of EBCQA to delay paralysis was significantly reduced following RNAi-mediated suppression of pink-1 and pdr-1 in CL4176 strain. In AD rats, the administration of NMN or EBCQA significantly improved learning and memory, restored neuronal morphology in the hippocampus, increased autophagosome numbers, and upregulated the expression of PINK1, Parkin, Beclin 1, and the LC3-Ⅱ/LC3-Ⅰ ratio, while reducing p62 expression. Additionally, the treatment with NMN or EBCQA both elevated ATP content, mitochondrial respiratory chain complex Ⅰ, Ⅲ, and Ⅳ activities, and mitochondrial membrane potential in the hippocampus. The above findings indicate that EBCQA improves cognitive impairment and mitochondrial dysfunction in AD, possibly through activation of PINK1/Parkin-mediated mitophagy.
Animals ; Alzheimer Disease/psychology* ; Mitophagy/drug effects* ; Mitochondria/genetics* ; Caenorhabditis elegans/metabolism* ; Ubiquitin-Protein Ligases/genetics* ; Cognitive Dysfunction/physiopathology* ; Rats ; Protein Kinases/genetics* ; Humans ; Male ; Disease Models, Animal ; Caenorhabditis elegans Proteins/genetics* ; Drugs, Chinese Herbal/administration & dosage*

OBJECTIVE: To explore the therapeutic effects and underlying mechanisms of Dendrobium officinale (Tiepi Shihu) extract (DOE) on insomnia. METHODS: Forty-two male Sprague-Dawley rats were randomly divided into 6 groups (n=7 per group): normal control, model control, melatonin (MT, 40 mg/kg), and 3-dose DOE (0.25, 0.50, and 1.00 g/kg) groups. Rats were raised in a strong-light (10,000 LUX) and -noise (>80 db) environment (12 h/d) for 16 weeks to induce insomnia, and from week 10 to week 16, MT and DOE were correspondingly administered to rats. The behavior tests including sodium pentobarbital-induced sleep experiment, sucrose preference test, and autonomous activity test were used to evaluate changes in sleep and emotions of rats. The metabolic-related indicators such as blood pressure, blood viscosity, blood glucose, and uric acid in rats were measured. The pathological changes in the cornu ammonis 1 (CA1) region of rat brain were evaluated using hematoxylin and eosin staining and Nissl staining. Additionally, the sleep-related factors gamma-aminobutyric acid (GABA), glutamate (GA), 5-hydroxytryptamine (5-HT), and interleukin-6 (IL-6) were measured using enzyme linked immunosorbent assay. Finally, we screened potential sleep-improving receptors of DOE using polymerase chain reaction (PCR) array and validated the results with quantitative PCR and immunohistochemistry. RESULTS: DOE significantly improved rats' sleep and mood, increased the sodium pentobarbital-induced sleep time and sucrose preference index, and reduced autonomic activity times (P<0.05 or P<0.01). DOE also had a good effect on metabolic abnormalities, significantly reducing triglyceride, blood glucose, blood pressure, and blood viscosity indicators (P<0.05 or P<0.01). DOE significantly increased the GABA content in hippocampus and reduced the GA/GABA ratio and IL-6 level (P<0.05 or P<0.01). In addition, DOE improved the pathological changes such as the disorder of cell arrangement in the hippocampus and the decrease of Nissel bodies. Seven differential genes were screened by PCR array, and the GABA_A receptors (Gabra5, Gabra6, Gabrq) were selected for verification. The results showed that DOE could up-regulate their expressions (P<0.05 or P<0.01). CONCLUSION DOE demonstrated remarkable potential for improving insomnia, which may be through regulating GABA_A receptors expressions and GA/GABA ratio.
Animals ; Dendrobium/chemistry* ; Rats, Sprague-Dawley ; Male ; Sleep Initiation and Maintenance Disorders/blood* ; Plant Extracts/therapeutic use* ; Receptors, GABA-A/metabolism* ; Noise/adverse effects* ; Light/adverse effects* ; gamma-Aminobutyric Acid/metabolism* ; Sleep/drug effects* ; Rats ; Receptors, GABA/metabolism*

OBJECTIVES: Depressive and anxiety disorders are among the most common mental disorders worldwide and are associated with unhealthy lifestyle behaviors. The Life's Simple 7 (LS7) guideline proposed by the American Heart Association aims to reduce cardiovascular risk by improving behaviors such as diet and physical activity, but its impact on mental health is not yet fully clear. This study examined the association between LS7 scores and symptoms of anxiety and depression in adults undergoing routine health examinations. METHODS: Data were collected from individuals who underwent health examinations from May 2015 to December 2024 at the Health Management Center of the Third Xiangya Hospital. All participants completed the LS7 assessments, the Self-Rating Depression Scale (SDS), and the Self-Rating Anxiety Scale (SAS). Participants were categorized into 4 LS7 score groups: Low (≤7), average (8-9), good (10), and excellent (11-14). Those with SDS or SAS≥50 were classified as having mental disorder symptoms; with this group, SAS≥50 indicated anxiety, SDS≥50 indicated depression, and SDS and SAS≥50 indicated comorbid anxiety-depression. Binary logistic regression was used to assess associations between LS7 score and mental symptoms, calculating odds ratio (OR) and 95% confidence interval (CI). A restricted cubic spline (RCS) regression model was used to analyze the dose-response relationship between LS7 score (continuous variable) and the risk of mental symptoms. Nodes were set at the 5th, 35th, 65th, and 95th percentiles of the LS7 score, with the 5th percentile as the reference point. All models were adjusted for covariates such as gender, age, living alone, drinking status, education level, and sleep quality. Logistic regression framework was used to fit and calculate the adjusted OR (aOR) and 95% CI. Nonlinear relationship tests were also conducted. Subgroup analysis was performed to explore the interaction between gender, age, drinking habits, education level, and other factors and the LS7 score in influencing the risk of mental symptoms. RESULTS: A total of 5 449 participants were included; 1 363 (25.01%) had depressive symptoms, 398 (7.30%) had anxiety symptoms, and 259 (4.75%) had comorbid anxiety-depression. The prevalence of mental symptoms decreased significantly as LS7 scores increased. Univariate and multivariate Logistic regression indicated that LS7 score≥8 was protective against mental symptoms. Multivariate Logistic regression demonstrated moderate discriminative ability (AUC=0.672). Among individuals with anxiety, depression, or comorbid symptoms, LS7 score distributions showed a graded decrease from poor to excellent groups. After adjustment, an excellent LS7 score was associated with a 39% lower risk of depression (aOR=0.61, 95% CI 0.47 to 0.78, P<0.001), a 63% lower risk of anxiety (aOR=0.37, 95% CI 0.22 to 0.59, P<0.001), and a 66% lower risk of comorbid anxiety-depression (aOR=0.34, 95% CI 0.17 to 0.62, P=0.001). The AUC values of the anxiety model, depression model, and comorbid anxiety and depression model were 0.632, 0.672, and 0.619, respectively. All models demonstrated moderate discriminatory ability, which was statistically significant, but their capacity to distinguish cases from non-cases was limited. RCS analysis confirmed a linear inverse relationship between LS7 score and mental symptom risk. Not smoking and regular physical activity were the strongest protective behaviors. Subgroup analysis suggested stronger protective effects in men, younger adults (≤60), non-drinkers, and those with higher education levels, and revealed a significant interaction between alcohol use and LS7 score (P for interaction=0.021), indicating that alcohol consumption may weaken the protective effect of LS7. CONCLUSIONS Ideal healthy lifestyle behaviors, as reflected by higher LS7 scores, are associated with lower risks of anxiety and depression in adults. Promoting LS7-based lifestyle practices may serve as a practical and effective strategy for the prevention and management of anxiety and depression in both clinical and daily life settings.
Humans ; Cross-Sectional Studies ; Depression/epidemiology* ; Anxiety/epidemiology* ; Adult ; Male ; Female ; Middle Aged ; Healthy Lifestyle ; Risk Factors ; Anxiety Disorders/epidemiology* ; Exercise ; Physical Examination ; Aged