BACKGROUND:Several observational studies have found a strong association between thyroid function and its related disorders and osteoporosis,but the causal relationship is unclear.OBJECTIVE:To ascertain the causal relationship between genetically predicted thyroid function and its associated disorders,as well as osteoporosis,through the Mendelian randomization analysis with extensive pooled genetic data.METHODS:Pooled data from genome-wide association studies were employed to investigate the causal relationship between thyroid function and its associated disorders and osteoporosis.This was achieved through the utilization of the inverse variance weighting method as the primary Mendelian randomization analysis method,in conjunction with the MR-Egger method,weighted median method,simple model method,and weighted model method.A two-step mediated Mendelian randomization analysis was used to calculate the mediating effect of drug-mediated thyroid dysfunction on osteoporosis and the mediating proportion.Subsequently,sensitivity analyses were conducted using the MR-Egger intercept test and MR-PRESSO to detect multiplicity,Cochran's Q test to detect heterogeneity,and leave-one-out to perform sensitivity analyses.RESULTS AND CONCLUSION:(1)The results of the inverse variance weighting method showed that thyroid function had an effect on bone mineral density,and that thyrotropin,free triiodothyronine on bone mineral density,free thyroxine,and subclinical hyperthyroidism all had a causal effect on bone mineral density.(2)In addition,mediation analyses revealed a potential mediating effect of carbimazole in the causal relationship between hyperthyroidism and the risk of developing osteoporosis,as well as a potential mediating effect of levothyroxine sodium in the causal relationship between hypothyroidism and the risk of developing osteoporosis.(3)In conclusion,thyrotropin,which is high in the normal range,has been demonstrated to increase bone mineral density.Conversely,free triiodothyronine and free thyroxine,which are also high within the normal range,as well as subclinical hyperthyroidism,have been shown to decrease bone mineral density.The risk of developing osteoporosis is partially mediated by the pathway of taking the therapeutic medication in the context of pharmacologic treatment of thyroid dysfunction.(4)The present study primarily focuses on European population data.However,given the commonality of the genetic background and the generalizability of genome-wide data analysis methods,it is of significant importance to explore the pathogenesis of osteoporosis in the Chinese population,develop effective interventions,and provide genetic counseling.

BACKGROUND:Several observational studies have found a strong association between thyroid function and its related disorders and osteoporosis,but the causal relationship is unclear.OBJECTIVE:To ascertain the causal relationship between genetically predicted thyroid function and its associated disorders,as well as osteoporosis,through the Mendelian randomization analysis with extensive pooled genetic data.METHODS:Pooled data from genome-wide association studies were employed to investigate the causal relationship between thyroid function and its associated disorders and osteoporosis.This was achieved through the utilization of the inverse variance weighting method as the primary Mendelian randomization analysis method,in conjunction with the MR-Egger method,weighted median method,simple model method,and weighted model method.A two-step mediated Mendelian randomization analysis was used to calculate the mediating effect of drug-mediated thyroid dysfunction on osteoporosis and the mediating proportion.Subsequently,sensitivity analyses were conducted using the MR-Egger intercept test and MR-PRESSO to detect multiplicity,Cochran's Q test to detect heterogeneity,and leave-one-out to perform sensitivity analyses.RESULTS AND CONCLUSION:(1)The results of the inverse variance weighting method showed that thyroid function had an effect on bone mineral density,and that thyrotropin,free triiodothyronine on bone mineral density,free thyroxine,and subclinical hyperthyroidism all had a causal effect on bone mineral density.(2)In addition,mediation analyses revealed a potential mediating effect of carbimazole in the causal relationship between hyperthyroidism and the risk of developing osteoporosis,as well as a potential mediating effect of levothyroxine sodium in the causal relationship between hypothyroidism and the risk of developing osteoporosis.(3)In conclusion,thyrotropin,which is high in the normal range,has been demonstrated to increase bone mineral density.Conversely,free triiodothyronine and free thyroxine,which are also high within the normal range,as well as subclinical hyperthyroidism,have been shown to decrease bone mineral density.The risk of developing osteoporosis is partially mediated by the pathway of taking the therapeutic medication in the context of pharmacologic treatment of thyroid dysfunction.(4)The present study primarily focuses on European population data.However,given the commonality of the genetic background and the generalizability of genome-wide data analysis methods,it is of significant importance to explore the pathogenesis of osteoporosis in the Chinese population,develop effective interventions,and provide genetic counseling.

ObjectiveThis paper aims to conduct the feature analysis and correlation analysis on the ocular collateral features and differential metabolites in patients with chronic hepatitis B （CHB） complicated by glucose metabolism disorder （GMD），particularly those with the damp-heat syndrome type，by integrating shadowless scleral imaging and metabolomics technologies. MethodsA total of 313 patients were recruited from the Hepatology and Endocrinology Outpatient Departments of Public Health Clinical Center of Chengdu according to the inclusion/exclusion criteria，and they were divided into a CHB group and a CHB complicated by GMD groups （damp-heat syndrome group and non-damp-heat syndrome group）. All patients underwent high-definition ocular image acquisition and feature extraction using an intelligent analysis system for shadowless scleral imaging to analyze the differences in the counting of morphological feature scores of ocular collaterals among groups. By using a digital sampling method，24 patients from each group were randomly selected，along with 20 healthy volunteers，for untargeted metabolomic analysis of peripheral serum. Differential metabolites were identified，statistically analyzed，and subjected to potential biomarker analysis and pathway enrichment. Spearman method was performed to conduct the correlation analysis on the differential ocular collateral features and differential metabolites，followed by correlation network construction. ResultsCompared with those in the CHB group，patients with CHB complicated by GMD showed significant changes in ocular collateral feature scores such as "hillock"，"blood vessels"，and "pale dusky coloration" （P<0.05）. In comparison with those in the healthy group，metabolites including N-acetylglucosamine，acetylhomoserine，and myo-inositol （AUC>0.7） were identified as potential biomarkers for the disease. Compared with those in the CHB complicated by GMD group with non-damp-heat syndrome，patients with damp-heat syndrome exhibited significant changes in feature scores of "plaques"，"yellow coloration"，"spleen"，and "gallbladder" （P<0.05）. In comparison with those in the healthy group，metabolites such as O²′-4a-cyclic tetrahydrobiopterin，theobromine，xanthurenic acid，and L-glutamic acid 5-phosphate （AUC>0.7） were identified as potential biomarkers for the damp-heat syndrome type. The Spearman correlation analysis reveals weak to moderate linear correlations between the differential scleral collateral features and metabolites. By constructing a "disease-syndrome" network of ocular diagnosis and metabolites，"xanthurenic acid-gallbladder" and "theobromine-plaque/yellow coloration" were identified as specific molecular-phenotypic correlated biomarker clusters for CHB complicated by GMD with dampness-heat syndrome. ConclusionPatients with CHB complicated by GMD demonstrate differential ocular diagnostic features and serum metabolites corresponding to disease states and dampness-heat syndrome. These objective biomarkers can guide both clinical syndrome differentiation and medication. The macro-micro integration based on ocular feature clusters and potential metabolic biomarkers offers an innovative approach to a combined traditional Chinese and Western medicine diagnosis and treatment model for this disease.

Objectives: . Idiopathic sudden sensorineural hearing loss (ISSNHL) is a significant cause of hearing loss. Intratympanic steroid injection (ITSI) is commonly used as an initial or salvage treatment; however, the lack of a standardized treatment protocol has resulted in variability in clinical practice. In addition, no efficient prediction model currently exists to support personalized management. Therefore, this study aimed to develop tailored management strategies for ISSNHL using a machine-learning model. Methods: . This retrospective multicenter cohort study was conducted between January 2015 and December 2020, with data analysis performed between January 2021 and March 2024. Patients were selected based on the International Classification of Diseases, 10th Revision criteria for ISSNHL, along with relevant medication and procedure codes. Patients with pure-tone audiogram results not meeting ISSNHL criteria, better initial hearing in the affected ear, an identifiable etiology, no post-treatment audiogram, or delayed treatment (>6 weeks) were excluded. We included 770 patients diagnosed with ISSNHL who received ITSI. The primary outcome was the area under the receiver operating characteristic curve for prediction performance. Recovery status was determined using the last pure-tone audiogram. Modeling was conducted on the Quanta for Medical Care AI platform using five machine-learning algorithms and a nested cross-validation framework, in which feature selection and hyperparameter tuning were performed in the inner folds and model performance was evaluated in the outer folds. Results: . A random forest classifier outperformed the other models in predicting hearing outcomes, achieving an area under the receiver operating characteristic curve of 0.788. Time to ITSI was the most influential treatment-related factor, with ITSI administered within 10 days of hearing loss being associated with better outcomes. This model can be used to provide personalized prognostic estimates under different treatment protocols. Conclusion . The machine-learning-based prediction model facilitates personalized treatment strategies and timely treatment adjustments for ISSNHL, thereby optimizing the likelihood of complete recovery.

Background: Patients with coarctation of the aorta (CoA) and an interrupted aortic arch (IAA) may present with small mitral valves (MVs) and a reduced left ventricular (LV) volume. Biventricular repair (BVR) in these patients is dependent on adequate size of the left cardiac structures.Purpose: This study evaluated the impact of the hemodynamic characteristics of atrial septal defects (ASDs) on MV growth following surgical repair. Methods: We retrospectively reviewed the data of patients diagnosed with CoA or IAA between 2007 and 2024. The z score for MV size measured 6 months postoperatively (Z2) was compared with the preoperative MV size (Z1). The factors associated with MV growth were also studied. Results: A total of 161 patients with CoA or IAA were included. Transthoracic echocardiography was used to assess the MV and LV dimensions preoperatively and 6 months postoperatively. Of the cohort, 155 (96.3%) underwent initial BVR and 6 underwent single-ventricle palliation. MV z scores significantly increased following BVR (mean change: +0.45±1.35; P<0.001) but decreased after single-ventricle repair (-0.56±0.49, P=0.04). Multivariate analysis identified the initial MV z score and ASD pressure gradient as independent predictors of MV growth (R2=0.39). Conclusion Annular growth of the MV was not observed in patients who underwent single-ventricle palliation. In contrast, among patients who achieved BVR, those with a small preoperative MV annulus and low ASD pressure gradient demonstrated subsequent catch-up MV growth, suggesting that adequate left-sided preload is essential for MV development.

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: The study aimed to comprehensively analyze testosterone and precursor concentrations in the testicular interstitial fluid (TIF) of men with azoospermia, exploring their significance in the testicular microenvironment and their correlation with testicular sperm retrieval outcomes. Materials and Methods: We analyzed 37 TIF samples, including 5 from men with obstructive azoospermia (OA) and 32 from men with non-obstructive azoospermia (NOA). Liquid chromatography with tandem mass spectrometry quantified testosterone and precursor levels. Comparative assessments of the outcomes of testicular sperm retrieval were performed between the OA and NOA groups as well as among men with NOA. Results: Men with NOA who had not undergone hormone treatment exhibited significantly higher intratesticular concentrations of testosterone (median 1,528.1 vs. 207.5 ng/mL), androstenedione (median 10.6 vs. 1.9 ng/mL), and 17-OH progesterone (median 13.0 vs. 1.8 ng/mL) than men diagnosed with OA. Notably, in the subgroup of patients with NOA subjected to medical treatment, men with successful sperm retrieval had significantly reduced levels of androstenedione (median androstenedione 5.7 vs. 18.5 ng/mL, p=0.004). Upon a more detailed analysis of these men who underwent hormone manipulation treatment, the testosterone/androstenedione ratio (indicative of HSD17B3 enzyme activity) was markedly increased in men with successful sperm retrieval (median: 365.8 vs. 165.0, p=0.008) compared with individuals with NOA who had unsuccessful sperm recovery. Furthermore, within the subset of men with NOA who did not undergo medical treatment before microdissection testicular sperm extraction but achieved successful sperm retrieval, the ratio of 17-OH progesterone/progesterone (indicative of CYP17A1 activity) was substantially higher. Conclusions The study suggests distinct testosterone biosynthesis pathways in men with compromised spermatogenesis and those with normal spermatogenesis. Among NOA men with successful retrieval after hormone optimization therapy, there was decreased androstenedione and increased HSD17B3 enzyme activity. These findings have diagnostic and therapeutic implications for the future.

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: The study aimed to comprehensively analyze testosterone and precursor concentrations in the testicular interstitial fluid (TIF) of men with azoospermia, exploring their significance in the testicular microenvironment and their correlation with testicular sperm retrieval outcomes. Materials and Methods: We analyzed 37 TIF samples, including 5 from men with obstructive azoospermia (OA) and 32 from men with non-obstructive azoospermia (NOA). Liquid chromatography with tandem mass spectrometry quantified testosterone and precursor levels. Comparative assessments of the outcomes of testicular sperm retrieval were performed between the OA and NOA groups as well as among men with NOA. Results: Men with NOA who had not undergone hormone treatment exhibited significantly higher intratesticular concentrations of testosterone (median 1,528.1 vs. 207.5 ng/mL), androstenedione (median 10.6 vs. 1.9 ng/mL), and 17-OH progesterone (median 13.0 vs. 1.8 ng/mL) than men diagnosed with OA. Notably, in the subgroup of patients with NOA subjected to medical treatment, men with successful sperm retrieval had significantly reduced levels of androstenedione (median androstenedione 5.7 vs. 18.5 ng/mL, p=0.004). Upon a more detailed analysis of these men who underwent hormone manipulation treatment, the testosterone/androstenedione ratio (indicative of HSD17B3 enzyme activity) was markedly increased in men with successful sperm retrieval (median: 365.8 vs. 165.0, p=0.008) compared with individuals with NOA who had unsuccessful sperm recovery. Furthermore, within the subset of men with NOA who did not undergo medical treatment before microdissection testicular sperm extraction but achieved successful sperm retrieval, the ratio of 17-OH progesterone/progesterone (indicative of CYP17A1 activity) was substantially higher. Conclusions The study suggests distinct testosterone biosynthesis pathways in men with compromised spermatogenesis and those with normal spermatogenesis. Among NOA men with successful retrieval after hormone optimization therapy, there was decreased androstenedione and increased HSD17B3 enzyme activity. These findings have diagnostic and therapeutic implications for the future.

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.