Urinary tract infections (UTIs) are prevalent; however, comprehensive and current epidemiological data remain scarce. This study examined the global, national, and regional burden of UTIs by sex, age, and socio-demographic index (SDI) from 1990 to 2021. The 2021 Global Burden of Disease study included age-standardised incidence rate (ASIR), age-standardised prevalence rate (ASPR), age-standardised death rate (ASDR), and age-standardised disability-adjusted life years rate (ASDAR). The estimated annual percentage change was used to depict temporal trends, whereas Pearson correlation analysis explore its correlation with the human development index (HDI), the SDI, and age-standardised rates (ASRs). An autoregressive integrated moving average model forecasted the UTI burden trends. From 1990 to 2021, the number of UTI cases increased by 66.45%, reaching 4.49 billion cases, with an ASIR of 5,531.88 per 100,000 population. The greatest incidence of UTIs was seen in women and older adult men. Tropical Latin America and low–middle SDI regions exhibited the highest ASIR, ASPR, ASDR, and ASDAR, while East Asia showed the lowest. ASDR and ASDAR decreased with higher SDI levels. ASR and HDI were weakly positively correlated with ASDR and ASDAR. ASIR, ASPR, and ASDAR are projected to increase until 2050. The global burden of UTIs is rising and is influenced by geographical location, age, sex, and economic development, crucial for guiding medical practices and forming relevant policies.

Chronic Kidney Disease (CKD) has become a significant global public health issue, affecting approximately 10% of adults. Due to the lack of obvious symptoms in the early stages, CKD is often difficult to diagnose in a timely manner, leading to the gradual progression of the disease, which can eventually develop into End-Stage Renal Disease (ESRD). This study applied machine learning (ML) methods to integrate patient clinical data and developed an early CKD prediction model applicable to individuals without diabetes, hypertension, or coronary heart disease. The model aims to enhance the accuracy of early CKD risk assessment, thereby delaying disease progression and improving patient outcomes. This study is a retrospective multicenter study conducted in China, including patients with CKD and healthy individuals who underwent physical examinations from February 2021 to April 2024. Six ML methods, including Decision Tree, Multilayer Perceptron, and XGBoost, were used to predict CKD, integrating different combinations of features such as blood routine, urine analysis, and blood biochemistry. Multiple evaluation metrics, including AUC and F1 score, were used to compare the prediction performance. The SHAP interpretability method was applied to assess feature importance and explain the final model's results. Data from three hospitals were used in this study, with the dataset divided into training and internal validation sets (CKD: 11,436 cases, non-CKD: 10,004 cases) and an external validation set (CKD: 350 cases, non-CKD: 473 cases). Among the six ML models, XGBoost performed the best. Regarding feature combinations, the "blood routine + urinalysis + basic information" combination yielded the best performance (AUC = 0.9235, external validation AUC = 0.8962). Additionally, a web tool was developed in this study to facilitate the application of early CKD risk prediction in clinical practice. This study applied an interpretable ML model to effectively predict early CKD. Even when using the relatively low-cost "blood routine + urinalysis + basic information" combination, the model still demonstrated high prediction accuracy. This method has potential clinical application prospects and may help identify early CKD, reducing the risk of disease progression. This research was supported by the National Key Research and Development Program of China (2023YFC3502903, 2022YFC3502302), National Natural Science Foundation of China (82074580), and Science and Technology Project of Jiangsu Provincial Research Institute of Chinese Medicine Schools (JSZYLP2024011).

The kidney is a typical organ undergoing age and injury. Hyperoside is reported to be useful for preventing aging induced by D-galactose (D-gal). However, therapeutic mechanisms remain unclear. We thereby aimed to verify whether hyperoside, compared to vitamin E (VE), could alleviate renal aging and injury by regulating autophagic activity and its related signaling pathways. In vivo, rats were administered with either hyperoside or VE after renal aging modeling induced by D-gal. Changes in renal aging and injury markers, autophagic activity and AMPK-ULK1 signaling pathway in the kidneys were analysed. In vitro, the NRK-52E cells exposed to D-gal were used to investigate regulative actions of hyperoside and VE on cell viability, renal tubular cellular aging markers, autophagic activity and its related signaling pathways by histomorphometry, immunohistochemistry, immunofluorescence, lentiviral transfection and Western blot. Aging and injury in the kidneys and renal tubular cells induced by D-gal were ameliorated by hyperoside and VE. Hyperoside and VE inhibited autophagic activity through mTOR-independent and AMPK-ULK1 signaling pathways. Hyperoside, as a component of phytomedicine similar to VE, attenuated renal aging and injury induced by D-gal via inhibiting AMPK-ULK1-mediated autophagy. This study provides the first evidence that hyperoside contributes to the prevention of age-associated renal injury.

Background. Traditional Chinese medicine (TCM) has long been used to treat chronic kidney disease (CKD) in Asia. Its effectiveness and safety for CKD treatment have been confirmed in documented studies. However, the prescription rule of formulae for Chinese medicinal herbs is complicated and remains uncharacterized. Thus, we used data mining technology to evaluate the treatment principle and coprescription pattern of these formulae in CKD TCM treatment. Methods. Data on patients with CKD were obtained from the outpatient system of a TCM hospital. We established a Chinese herb knowledge base based on the Chinese Pharmacopoeia and the Chinese Materia Medica. Then, following extraction of prescription information, we deweighted and standardized each prescribed herb according to the knowledge base to establish a database of CKD treatment formulae. We analyzed the frequency with which individual herbs were prescribed, as well as their properties, tastes, meridian tropisms, and categories. Then, we evaluated coprescription patterns and assessed medication rules by performing association rule learning, cluster analysis, and complex network analysis. Results. We retrospectively analyzed 299 prescriptions of 166 patients with CKD receiving TCM treatment. The most frequently prescribed core herbs for CKD treatment were Rhizoma Dioscoreae (Shanyao), Spreading Hedyotis Herb (Baihuasheshecao), Root of Snow of June (Baimagu), Radix Astragali (Huangqi), Poria (Fulin), Rhizoma Atractylodis Macrocephalae (Baizhu), Radix Pseudostellariae (Taizishen), and Fructus Corni (Shanzhuyu). The TCM properties of the herbs were mainly being warm, mild, and cold. The tastes of the herbs were mainly sweet, followed by bitter. The main meridian tropisms were Spleen Meridian of Foot‐Taiyin, Liver Meridian of Foot‐Jueyi, Lung Meridian of Hand‐Taiyin, Stomach Meridian of Foot‐Yangming, and Kidney Meridian of Foot‐Shaoyin. The top three categories were deficiency‐tonifying, heat‐clearing, and dampness‐draining diuretic. Conclusion. Using an integrated analysis method, we confirmed that the primary TCM pathogeneses of kidney disease were deficiency and dampness‐heat. The primary treatment principles were tonifying deficiency and eliminating dampness‐heat.

Introduction: The burden of chronic kidney disease (CKD) varies across regions. This study provides comprehensive global, regional, and national estimates of total CKD and CKD due to four specific etiologies from 1990 to 2021.Methods: Data were extracted from the 2021 Global Burden of Disease study, categorized by sex, 20 age groups, 204 countries or territories, and 5 sociodemographic index (SDI) regions. Age-standardized incidence rates (ASIRs), age-standardized prevalence rates (ASPRs), age-standardized death rates (ASDRs), age-standardized disability-adjusted life year rates (ASDARs) and risk factor burdens for total CKD and four etiology-specific types were analyzed. Temporal trends were assessed using the estimated annual percentage change.Results: In 2021, CKD remained a significant global burden, with 673 million prevalent cases and 1.5 million deaths, primarily due to metabolic risk factors. The fastest growth in the ASPR and ASIR occurred in the middle-SDI regions, whereas the highest ASDR and ASDAR were observed in low-SDI regions. From 1990 to 2021, global ASIR increased for CKD caused by all four specific etiologies. The ASDR and ASDAR increased for CKD due to type 2 diabetes, glomerulonephritis, and hypertension, whereas there was a decline in CKD due to type 1 diabetes.Discussion: Between 1990 and 2021, CKD-related disability-adjusted life years (DALYs) and deaths increased substantially, with type 2 diabetes and hypertension accounting for half of the etiology-specific DALYs in 2021. Effective health policies are urgently needed to address CKD risk factors and implement prevention strategies.

Introduction: This study explored the causal connections between gut microbiota (GM), urinary tract infection (UTI), and potential metabolite mediators using Mendelian randomization (MR).Methods: We utilized summary statistics from the most comprehensive and extensive genome-wide association studies (GWAS) available to date, including 196 bacterial traits for GM, 1,091 blood metabolites, 309 metabolite ratios, alongside UTI data from ukb-b-8814 and ebi-a-GCST90013890. Bidirectional MR analyses were conducted to investigate the causal links between GM and UTI. Subsequently, two MR analyses were performed to identify the potential mediating metabolites, followed by a two-step MR analysis to quantify the mediation proportion.Results: Our findings revealed that out of the total 15 bacterial traits, significant associations with UTI risk were observed across both datasets. Particularly, taxon g_Ruminococcaceae UCG010 displayed a causal link with a diminished UTI risk in both datasets (ukb-b-8814: odds ratio [OR] = 0.9964, 95% confidence interval [CI] = 0.9930–0.9997, P = 0.036; GCST90013890: OR = 0.8252, 95% CI = 0.7217–0.9436, P = 0.005). However, no substantial changes in g_Ruminococcaceae UCG010 due to UTI were noted (ukb-b-8814: β = 0.51, P = 0.87; ebi-a-GCST90013890: β = −0.02, P = 0.77). Additionally, variations in 56 specific metabolites were induced by g_Ruminococcaceae UCG010, with N-acetylkynurenine (NAK) exhibiting a causal correlation with UTI. A negative association was found between g_Ruminococcaceae UCG010 and NAK (OR: 0.8128, 95% CI: 0.6647–0.9941, P = 0.044), while NAK was positively associated with UTI risk (OR: 1.0009; 95% CI: 1.0002–1.0016; P = 0.0173). Mediation analysis revealed that the association between g_Ruminococcaceae UCG010 and UTI was mediated by NAK with a mediation proportion of 5.07%.Discussion: This MR study provides compelling evidence supporting the existence of causal relationships between specific GM taxa and UTI, along with potential mediating metabolites.