To investigate the association between the Dietary Inflammatory Index (DII), biological aging, and the staging and mortality of cardiovascular-kidney-metabolic (CKM) syndrome. Data of 7,918 participants were derived from the National Health and Nutrition Examination Survey 2005–2018. Cross-sectional analyses using multivariable logistic regression were conducted to evaluate the relationship between DII and CKM staging. Cox proportional hazards models were employed to assess the impact of DII on mortality in CKM patients. Mediation analyses were performed to determine whether biological aging mediated DII-staging and DII-mortality association. Machine learning models were developed to classify CKM stages 3/4 and predict all-cause mortality, with SHapley Additive exPlanations (SHAP) used to interpret the contribution of DII components. Over a median follow-up of 9.3 years, 819 deaths were recorded. Higher DII were associated with an increased risk of advanced CKM stages [OR (95% CI): tertile 2, 1.39 (1.17, 1.65); tertile 3, 1.85 (1.56, 2.20)], and all-cause mortality [(HR (95% CI): tertile 2, 1.20 (1.01–1.43); tertile 3: 1.45 (1.21–1.73)]. The optimal risk stratification threshold for DII to predict all-cause mortality was 1.93. Mediation analyses revealed that biological aging accounted for 23% (95% CI: 18-28%) of the effect of DII on advanced CKM stages and 13% (95% CI: 8-22%) of the effect of DII on all-cause mortality. Furthermore, the Light Gradient Boosting Machine model showed strong performance in predicting advanced CKM staging (AUC: 0.896, 95% CI: 0.882–0.911), while Logistic regression performed better in predicting all-cause mortality (AUC: 0.857, 95% CI: 0.831–0.884). SHAP analysis revealed that intake of magnesium and n-3 fatty acid were associated with reduced risk of both advanced CKM stages and all-cause mortality. DII, a marker of pro-inflammatory dietary patterns, was significantly linked to CKM syndrome progression and mortality, partly by influencing biological aging. This underscores the importance of diet quality in managing CKM staging and mortality risk.

Background: Chronic renal allograft dysfunction (CRAD) continues to limit the long-term survival of transplanted kidneys. Inflammatory cell accumulation during the early phase of CRAD may accelerate fibrotic remodeling and graft dysfunction, although the regulatory mechanisms driving this process have not been fully clarified. Interleukin-11 (IL-11) is increasingly recognized as a fibro-inflammatory mediator, but whether miRNA-dependent regulation of IL-11 participates in early CRAD-associated inflammation remains unknown. This study aimed to investigate whether miR-204-5p regulates early inflammatory injury in CRAD by targeting IL-11. Methods and results: A rat CRAD model was established by orthotopic renal transplantation, and kidney tissues were collected at 12 weeks post-transplantation to assess early inflammatory and fibrotic changes. IL-11 expression was inhibited in vivo using IL-11-shRNA AAV, and miR-204-5p was overexpressed using mmu-miR-204-5p AAV. Histological staining, immunohistochemistry, RT-qPCR, ELISA, and renal function analyses were performed. In vitro , BUMPT cells were stimulated with TGF-β1 to mimic CRAD-associated early inflammatory and pro-fibrotic responses. IL-11/IL-11RA signaling was blocked using neutralizing antibodies or siRNA. The ERK1/2 inhibitor PD98059 was used to assess downstream pathway activation. The direct interaction between miR-204-5p and IL-11 was validated by dual-luciferase reporter assays, and rescue experiments with recombinant IL-11 were performed to determine whether IL-11 mediates the effect of miR-204-5p. Conclusion: miR-204-5p attenuates early inflammatory injury in CRAD by directly targeting IL-11 and suppressing IL-11-associated ERK1/2-NF-κB inflammatory signaling. The miR-204-5p/IL-11 axis may represent a potential regulatory pathway and therapeutic target for early inflammatory injury in CRAD.

Objective: To develop and validate cluster-specific diagnostic models for osteoporosis in patients with chronic kidney disease (CKD) using routine clinical parameters. Methods: Data were obtained from the National Health and Nutrition Examination Survey. A total of 3,806 CKD patients were enrolled and randomly allocated to training (70%, n = 2,667) and internal validation (30%, n = 1,139) cohorts, with an additional external validation cohort from the China Renal Data System or Chinese Renal Disease Data System. K-means clustering was performed to identify distinct subgroups. Weighted univariate logistic regression, Least Absolute Shrinkage and Selection Operator and Boruta algorithm were used for feature selection. Final diagnostic models were constructed using weighted multivariable logistic regression. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), and net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results: Three distinct CKD phenotypic clusters were identified; two were selected for model development. Group A retained six variables (sex, age, body mass index, serum uric acid, serum creatinine, and fracture history) with AUCs of 0.880, 0.839, and 0.815 across training, internal, and external (CRDS) validation. Group B retained seven variables (sex, age, marital status, red blood cell count, body mass index, race, and fracture history) with AUCs of 0.824, 0.806, and 0.808. Both models demonstrated favorable calibration and clinical utility. NRI revealed divergent subgroup effects: gains in Group B (0.0059) versus losses in Group A (-0.0149). Conclusions: Cluster-specific diagnostic models based on weighted logistic regression exhibited robust discriminative performance for osteoporosis diagnosis in CKD patients, with heterogeneous reclassification gains across phenotypic subgroups.