Aim. Patients with diabetic ketosis often exhibit albuminuria. We previously found that acute hyperglycaemia can cause nephrotoxic injury. Here, we explored whether an excessive ketone body level causes kidney injury and the potential underlying mechanism. Methods. Fifty-six type 2 diabetes without ketosis (NDK group), 81 type 2 diabetes with ketosis (DK group), and 38 healthy controls (NC group) were enrolled. Clinical data were collected before and after controlling diabetic ketosis. Beta-hydroxybutyric acid (BOHB), an AKT activator, an AKT inhibitor, or plasmids encoding DAB2 were transformed into human renal proximal tubule epithelial cells (HK-2 cells). Results. The urinary albumin-to-creatinine ratio (ACR), transferrin (TF), immunoglobulin G (IgG), Beta2-microglobulin (β2-MG), retinol-binding protein (RBP), N-acetyl-beta-glucosaminidase (NAG), and Beta-galactosidase (GAL) were higher in the DK than NC and NDK groups. The proportion of patients with an increased urinary level of TF, IgG, β2-MG, RBP, NAG, or GAL was higher in the DK group too. After controlling ketosis, urinary microalbumin, TF, IgG, β2-MG, and RBP decreased significantly. In HK-2 cells, albumin endocytosis and megalin expression decreased with increasing BOHB concentration. Compared with BOHB treatment, BOHB with AKT activator significantly increased the DAB2, megalin levels and albumin endocytosis; the AKT inhibitor treatment exhibited the opposite effects. Compared with BOHB treatment, megalin expression and albumin endocytosis were significantly increased after BOHB with DAB2 overexpression treatment. Conclusions. Patients with diabetic ketosis may suffer from glomerular and tubular injuries that recover after ketosis control. High concentrations of BOHB downregulate megalin expression by inhibiting the AKT/DAB2/megalin signalling pathway and albumin endocytosis in proximal renal tubules.

Background: Although Andrographis paniculata (AP) exhibits various biological functions such as anticancer, anti-inflammatory, antimalarial, antimicrobial, antioxidant, cardioprotective and immunomodulatory, its role in estrogen deficiency-related osteoporosis remains unclear. Methods: Ovariectomy (OVX)-induced estrogen deficiency-related osteoporotic mouse models and sham mouse models were established using 8-week-old female C57BL/6J mice. Micro-computed tomography (µCT) scanning was performed to assess the skeletal phenotype. The differentiation potential of bone marrow mesenchymal stem cells (BMSCs) from the OVX and sham groups was assessed by osteogenic or adipogenic induction medium in vitro. To verify the effects of AP, alizarin red S (ARS) staining, alkaline phosphatase (ALP) staining and oil red O (ORO) staining, reverse transcription assay and quantitative real-time polymerase chain reaction were applied to detect the lineage differentiation ability of BMSCs. Results: µCT scanning showed that AP treatment attenuated the osteoporotic phenotype in OVX-induced estrogen deficiency-related osteoporotic mice. The results of ARS staining, ALP staining, ORO staining and quantitative real-time polymerase chain reaction indicated that BMSCs from OVX-induced osteoporotic mice displayed a significant reduction in osteogenic differentiation and an increase in adipogenic differentiation, which could be reversed by AP treatment. Conclusions: Our findings suggested that AP regulated the differentiation potential of BMSCs and ameliorated the development of estrogen deficiency-related osteoporosis, which might be an effective therapeutic method for estrogen deficiency-related osteoporosis.

Lower extremity deep vein thrombosis (DVT) is a serious complication in elderly patients with hip fracture, contributing to increased morbidity and mortality. Diabetes mellitus, with its prothrombotic state, may further elevate this risk. Early identification of high‑risk patients is important for targeted thromboprophylaxis. The objective of the present study was to develop and validate machine learning (ML) models for predicting DVT using clinical variables available at admission in elderly patients with hip fracture, with a specific focus on diabetes as a key predictor. A retrospective cohort study of elderly patients (≥65 years) with hip fracture who were admitted to a tertiary academic medical center (Tianjin Hospital, China) between January 2020 and December 2025, was conducted. A total of seven ML algorithms were developed and validated using a 70‑30 split with 10‑fold cross‑validation. Model interpretability was enhanced using Shapley Additive exPlanations (SHAP) analysis. Subgroup analyses were performed to evaluate model performance across diabetic and non‑diabetic populations. DVT occurred in 16.5% (66/400) of patients during hospitalization. Diabetes was present in 32.5% of the cohort and was significantly associated with DVT (odds ratio=2.64; 95% CI: 1.56‑4.48). The random forest model demonstrated an improved performance [area under the curve (AUC)=0.92; accuracy=0.87; sensitivity=0.85; specificity=0.88]. SHAP analysis identified diabetes‑associated variables (hemoglobin A1c, diabetes duration and fasting glucose) among the top predictors, along with age, albumin, D‑dimer and immobility on admission. Lipid parameters, including low‑density lipoprotein cholesterol and triglycerides, also contributed to prediction. Model performance remained robust in diabetic (AUC=0.94) and non‑diabetic (AUC=0.90) subgroups. Calibration was good (slope=1.02; intercept=‑0.02; Brier score=0.09; Hosmer‑Lemeshow P=0.45). Decision curve analysis determined clinical net benefit across thresholds of 15‑40%. ML models, particularly random forest, accurately predicted DVT in patients with elderly hip fracture using routinely available admission data. Diabetes therefore emerged as a pivotal risk factor and its inclusion enhanced predictive accuracy. The present model holds promise for early risk stratification and individualized thromboprophylaxis, although rigorous external validation is warranted before any clinical application.