Lijing Huo

Osteoporosis is a systemic skeletal disorder characterised by reduced bone mass, microarchitectural deterioration and increased fracture risk. Its pathogenesis extends beyond an imbalance between bone formation and resorption and involves osteoimmune dysregulation, vascular insufficiency, oxidative and inflammatory stress, and altered mechanical signalling within the bone microenvironment. Conventional two-dimensional cultures cannot adequately reproduce the three-dimensional matrix organisation, spatial multicellular interactions and mechanical properties of bone tissue, whereas animal models are limited by species-specific differences in bone remodelling, immune regulation and disease progression. Bone organoids integrating human stem/progenitor cells, bone-resident cells, immune and endothelial components, biomimetic matrices and dynamic culture systems provide a promising intermediate platform for modelling osteoporosis-related microenvironmental dysfunction. Unlike previous reviews that broadly summarised bone organoid construction and applications across skeletal diseases, this review focuses specifically on osteoporosis and proposes a mechanism–module–functional readout framework. This framework translates osteogenic suppression, RANKL/RANK/OPG-mediated osteoclast activation, immune–inflammatory amplification, vascular impairment and mechanical unloading into selectable organoid modules with measurable endpoints, including matrix formation, mineralisation, osteoclast resorption, inflammatory and oxidative-stress responses, vascular network function and mechanosensitive signalling. We further discuss how these models could connect candidate molecular signals with human-relevant three-dimensional functional phenotypes, thereby supporting biomarker discovery, drug-response assessment and patient stratification. However, organoid-derived candidates should not be regarded as clinically validated biomarkers without confirmation in independent patient cohorts and prospective clinical studies. Standardised construction, reproducible quality control, functional maturation and clinically anchored validation will therefore be essential for advancing osteoporosis-related bone organoids from experimental disease models towards biomarker translation and precision medicine.

Objective: To evaluate whether serum Gasdermin D (GSDMD) levels are associated with diabetic kidney disease (DKD) and renal function impairment, and to assess its potential diagnostic value. Methods: This cross-sectional observational study included 111 patients with DKD, 100 patients with non-diabetic kidney diseases, and 135 healthy controls. Serum GSDMD levels were measured using a chemiluminescence assay. Associations between GSDMD and clinical parameters were analyzed using Spearman correlation and binary logistic regression. Receiver operating characteristic (ROC) curves were constructed to evaluate diagnostic performance. Results: Serum GSDMD levels were significantly higher in patients with DKD than in healthy controls (P < 0.05). GSDMD levels were positively correlated with glucose (GLU), creatinine (CREA), blood urea nitrogen (BUN), and urinary albumin-to-creatinine ratio (UACR), and negatively correlated with estimated glomerular filtration rate (eGFR) (all P < 0.01). Multivariate logistic regression identified GSDMD as an independent factor associated with DKD. The area under the ROC curve (AUC) for GSDMD in identifying DKD was 0.847 (95% CI: 0.808–0.886), which increased to 0.933 (95% CI: 0.904–0.962) when combined with conventional indicators. Conclusion: Serum GSDMD levels are significantly associated with diabetic kidney disease and renal dysfunction. These findings suggest that GSDMD may serve as a complementary biomarker for DKD assessment; however, longitudinal and multicenter studies are required to confirm its prognostic value and clinical applicability.

Objective: This study analyzes the value of serum Gasdermin D (GSDMD) as a novel biomarker for the early diagnosis and disease progression of heart failure. Methods: We conducted a retrospective analysis of clinical data and laboratory results to compare the GSDMD levels among the heart failure group, other cardiac disease group, and healthy control group. We investigates the correlation between GD and other indicators, as well as the independent risk factors for heart failure. Receiver operating characteristic (ROC) curve analysis was conducted to compare the diagnostic value of serum GSDMD, N-terminal pro-B-type natriuretic peptide (NT-ProBNP), ejection fraction (EF) in patients. Results: There were statistically significant differences in serum GSDMD, EF and other laboratory tests among the three groups (P< 0.05). Among heart failure patients with different cardiac function classifications, the serum levels of NT-ProBNP, GSDMD increased with the increase in cardiac function classification. Serum GSDMD was correlated with all indicators (P< 0.05) in subjects. Serum GSDMD and NT-ProBNP were independent risk factors while EF was an independent protective factor for heart failure. The AUCs of serum GSDMD, NT ProBNP, and EF for diagnosing heart failure were 0.819, 0.970, and 0.831, respectively, while the AUC of their combined diagnosis of heart failure was 0.986. Conclusion: This study suggests that Serum GSDMD, as a novel sensitive biomarker, has the potential for early diagnosis and disease progression assessment of heart failure.

Objective: Lung cancer’s high mortality is linked to late diagnosis, with current methods having limitations. This study aimed to evaluate the diagnostic efficacy of Gasdermin D (GSDMD) in lung cancer and explore its biological functions. Methods: A total of 114 lung cancer patients, 87 patients with pulmonary nodules, and 100 healthy controls were enrolled. Clinical data were collected, and venous blood samples were obtained. GSDMD, carcinoembryonic antigen (CEA), neuron - specific enolase (NSE), cytokeratin 19 fragment (CYFRA21 - 1), and other markers were measured using specific assays. Statistical analysis, including variance tests, ANOVA, and logistic regression, was performed to analyze the data. Results: There were no significant differences in age and gender among the three groups. However, GSDMD levels were highest in the lung cancer group, followed by the pulmonary nodule group, and lowest in the healthy control group (P < 0.05). GSDMD was positively correlated with multiple biomarkers such as CEA (r = 0.329, P < 0.001), NSE (r = 0.266, P < 0.001), and CYFRA21 - 1 (r = 0.477, P < 0.001). Multivariate logistic regression analysis indicated that serum GSDMD, CEA, SCC, HE4, and IL - 6 were independent risk factors for lung cancer. The area under the ROC curve (AUC) of plasma GSDMD was 0.860, higher than that of CEA (0.801) and SCC (0.843). At a plasma GSDMD cut - off value of 39.87 pg/mL, the diagnostic sensitivity was 95.6%, and the specificity was 72.2%. When combined with other biomarkers (CEA, SCC, and HE4), the AUC reached 0.959, with a sensitivity of 93.0% and a specificity of 82.9%. Conclusion: GSDMD holds promise as a diagnostic/prognostic biomarker for lung cancer, alone or combined with other markers, enhancing risk assessment accuracy to aid early detection and improve outcomes.

Objective: This study investigates the potential of plasma Gasdermin D (GSDMD) as a novel biomarker for early diagnosis and monitoring of type 2 diabetes mellitus. Methods: We conducted a comparative analysis of clinical indicators among newly diagnosed diabetes patients, those with prediabetes, and healthy controls, finding significantly elevated plasma GSDMD levels in the diabetes group (P < 0.05). Results: Correlation analyses revealed that GSDMD levels were positively associated with inflammatory markers and indicators of insulin resistance, while negatively correlating with HOMA-β (P < 0.001). Logistic regression analysis identified GSDMD, IL-6, and CRP as independent risk factors for type 2 diabetes (P < 0.001). The area under the ROC curve for plasma GSDMD was 0.988, indicating superior diagnostic capability compared to traditional markers like CRP (0.902) and IL-6 (0.857). With a threshold of 17.67 pg/mL, plasma GSDMD exhibited a sensitivity of 93.9% and specificity of 98.0%. Conclusion: These findings suggest that plasma GSDMD not only reflects early metabolic abnormalities associated with type 2 diabetes but also holds promise as a therapeutic target to mitigate inflammatory responses and improve insulin resistance. Further large-scale clinical studies are warranted to validate its diagnostic utility and enhance clinical applications.

Aim: This study was intended to establish the reference intervals of bone turnover markers (BTMs) for healthy populations. Methods: According to the Clinical Laboratory Standards Institute (CLSI) EP28-A3c, we recruited 774 healthy Chinese and investigated their clinical characteristics and relationships among gender, age, season and BTMs. The reference intervals of BTMs for healthy populations in Hebei of China were established through defining the central 95% range of all observations. Results: We found that gender were associated with 25(OH)D, OC, β-CTX, and P1NP (P < 0.05), but not PTH1-84 (P=0.138). All serum BTMs showed differences among different age groups (P < 0.01). The level of 25 (OH) D in winter showed statistical differences with spring, summer, and autumn (P< 0.05). The OC level showed statistical difference between summer and winter (P=0.000). The P1NP levels showed statistical difference between spring and winter (P=0.019), summer and winter (P=0.000), and summer and autumn (P=0.012), respectively. The PTH1-84 levels in winter showed statistical differences with spring, and summer (all P=0.000), while there was no statistically significant difference in β- CTX levels between seasons. Conclusion: We have established the reference intervals of several BTMs for healthy individuals in Hebei of China, which have statistical significance across different age groups and genders, and there are also significant differences between different seasons. Therefore, the Chinese medical laboratories in different locations should group individuals according to gender and age groups in different seasons, and establish corresponding biological reference intervals.