Background: Osteogenesis imperfecta (OI) is marked by clinical and genetic heterogeneity, and the genotype–phenotype correlation remains not very clear. We conducted a clinical and genetic study in a Chinese OI cohort to determine the spectra of phenotypes and pathogenic variants. Methods: In this study, 298 Chinese families were recruited from 2019 to 2024. Clinical phenotypes including fractures, short stature, skeletal deformities, blue sclera, dentinogenesis imperfecta, and hearing loss were recorded and analyzed. Next-generation sequencing combined with PCR-based techniques was used to detect candidate pathogenic variants. Variant pathogenicity was evaluated via conservation analysis, bioinformatics analysis, and functional studies at the cellular level. In this OI cohort, the spectra of pathogenic variants, clinical phenotypes, and genotype–phenotype correlations were analyzed. Results: Our OI cohort included 71 type I (23.83%), 122 type III (40.94%), 90 type IV (30.20%), and 15 type V (5.03%) probands. The cohort consisted of 196 children (65.77%) and 102 adults (34.23%). For the first time, phenotypic differences between different age groups were confirmed. In total, we identified 231 variants, including 47 novel pathogenic variants. Notable variants include two atypical splicing variants, one small deletion, two small duplications, one gross deletion, and one gross duplication. New genotype–phenotype correlations were observed: patients with SERPINF1 variants had the highest fracture frequency, followed by those with WNT1 variants, compared to patients with other gene variants. Conclusions: We performed the clinical and genetic analysis in a large Chinese OI cohort. The expanded spectra of genetic variants and clinical phenotypes were constructed by identifying 47 novel pathogenic variants and summarizing the skeletal and extra-skeletal manifestations. The current paper will provide important evidence for the precise diagnosis of the disease.

Background: Osteogenesis imperfecta (OI) is a genetic disorder characterized by low bone mass, bone fragility and short stature. There is a significant gap in knowledge regarding the growth patterns across different types of OI, and the prediction of height in individuals with OI was not adequately addressed. In this study, we described the growth patterns and predicted the height of individuals with OI employing multiple machine learning (ML) models. Accurate height prediction enables effective monitoring and facilitates the development of personalized intervention plans for managing OI. Method: This study included cross-sectional data for 323 participants with OI, and the median height Z-score for OI types I, III and IV were − 0.62 (-5.93 ~ 3.24), -3.97 (-10.44 ~ -0.02) and − 1.64 (-6.67 ~ 2.44), respectively. Based on the cross-sectional data of participants, the height curves across different gender and OI types were plotted and compared. Subsequently, feature selection techniques, specifically the filter and wrapper methods, were employed to identify predictive factors for the height of participants. Finally, multiple machine learning (ML) models were constructed for height prediction, and the performance of each model was systematically evaluated. Results: The analysis of height curves revealed that male with OI are significantly taller than female with OI from the age of 14 (p = 0.045), individuals with OI type III are statistically shorter than those with OI types I and IV starting from 3 years old (p = 0.006), and those with OI type IV are statistically shorter than those with OI type I from the age of 10 (p = 0.028). The application of filter and wrapper methods identified gender (p = 0.001), age (p < 0.001), Sillence types (p = 0.007), weight Z-score (p < 0.001) and aBMD Z-score (p = 0.021) as significant predictive factors for height. The optimal performance of predictive models was registered by gradient boosting classifier (GB) (bias = 5.783, accuracy = 92.59%, R² = 0.828), random forest (RF) (bias = 6.155, accuracy = 90.12%, R² = 0.788), ensemble machine learning (EML) (bias = 6.250, accuracy = 91.36%, R² = 0.825) and deep neuron networks (DNNs) (bias = 6.223, accuracy = 90.12%, R² = 0.821). Conclusion: This study analyzed a large cohort of individuals with OI and provided detailed height patterns across different gender and OI types that are crucial for assessing overall growth. Gender, age, Sillence types, weight Z-score and aBMD Z-score were identified as predictive factors for height. The predictive models of GB, RF, EML and DNNs had higher accuracy to evaluate the height of individuals with OI. This study allows guardians and physicians to timely monitor the height parameters, and facilitate the creation of personalized intervention schedules tailored to the needs of individuals with OI.

Osteogenesis imperfecta (OI) is characterized by substantial genetic and functional heterogeneity, yet low-burden objective tools for repeated gait monitoring in routine outpatient care remain limited. This study investigated whether routine smartphone videos combined with markerless pose estimation could capture genotype-associated gait patterns in children with IFITM5- and WNT1-related OI, with COL1-related OI included as a cross-sectional reference cohort. This single-center retrospective cohort study screened 237 pediatric patients with genetically confirmed IFITM5- or WNT1-related OI between 2014 and 2024. The main analytic cohort included 71 patients: 48 with IFITM5 mutations and 23 with WNT1 mutations. During the same period, 107 patients with COL1-related OI were screened, of whom 26 were included as a cross-sectional reference cohort. MediaPipe Pose was used to extract spatiotemporal and postural gait parameters from outpatient walking videos. Algorithmic consistency under simulated video perturbations was assessed, and gait phenotypes were evaluated using multivariable models, linear mixed-effects models, Kaplan-Meier analysis, and exploratory machine-learning analysis. Step_Width and Trunk_Lean showed high algorithmic consistency under perturbation testing, with ICC ranges of 0.966-0.980 and 0.923-0.983, respectively. At baseline, WNT1 patients showed greater Step_Width (0.265 ± 0.073 vs. 0.203 ± 0.062 m), Trunk_Lean (4.53° ± 2.10° vs. 2.72° ± 1.63°), and Normalized_Step_Width (0.377 ± 0.103 vs. 0.270 ± 0.083) than IFITM5 patients (all P < 0.001). These differences remained after adjustment for age, sex, documented medication exposure, and surgical history, with adjusted differences of 0.046 m for Step_Width, 1.612° for Trunk_Lean, and 0.087 for Normalized_Step_Width. In the COL1-related reference analysis, WNT1 patients retained higher Step_Width and Normalized_Step_Width than COL1-related OI patients, whereas Trunk_Lean did not differ significantly between these groups. Longitudinal analysis showed no significant age-related gait changes in the WNT1 group, whereas IFITM5 patients showed mild annual decreases in Step_Width (- 0.0026 m/year) and Normalized_Step_Width (- 0.0046/year). Surgery-free survival declined earlier in the WNT1 group than in the IFITM5 group (log-rank P = 0.007). The exploratory genotype classification model achieved an AUC of 0.804 (95% CI, 0.678-0.914). Routine outpatient smartphone videos combined with markerless pose estimation can capture genotype-associated gait patterns in pediatric OI. WNT1-related OI was characterized by a wider base of support and coronal-plane compensation, whereas IFITM5-related OI showed mild longitudinal changes in spatial gait metrics. Video-derived gait parameters may provide low-burden adjunctive measures for outpatient functional monitoring, although prospective reference-standard validation and larger multi-genotype cohorts are needed.

Background: Osteogenesis imperfecta (OI) is a clinically and genetically heterogeneous skeletal dysplasia characterized by systemic bone fragility. Beyond skeletal manifestations, OI is a multisystem condition involving dentinogenesis imperfecta, hearing loss, joint hyperlaxity, and cardiopulmonary complications. The diagnostic paradigm for OI has undergone a revolutionary shift from traditional clinical phenotyping to a molecular etiology-based framework. Driven by advancements in high-throughput sequencing, mutations in over 20 implicated genes have expanded the current OI nosology to at least 23 distinct types. Data sources: This narrative review was conducted by searching for papers using PubMed/MEDLINE. Relevant publications were identified using single and/or combined keywords including: osteogenesis imperfecta, genes (such as COL1A1 and COL1A2 ), animal models, phenotypes (such as osteopenia and femoral bowing), and drug therapies (such as bisphosphonate and denosumab), mesenchymal stem cells, gene therapy, and pluripotent stem cells. Results: This review systematically delineates the molecular pathogenesis of OI, categorizing defects into disrupted collagen biosynthesis, impaired post-translational modification, aberrant procollagen trafficking, and dysfunctional osteoblast differentiation or mineralization pathways. Of note, we highlight the unique genetic landscape and clinical characterization of OI within Asian populations. Furthermore, we evaluate the transition towards precision management. Surgical management of OI has been optimized by telescopic rodding and a multidisplinary approach. Bisphosphonates remain a cornerstone of medical therapy, though responses vary with OI genotypes. Emerging targeted biologics include denosumab, romosozumab, setrusumab, and fresolimumab. Finally, we discuss the transformative potential of cell-based therapies, particularly mesenchymal stem cells. Precision gene editing technologies, such as CRISPR-Cas9, offer a strategy in addressing the root genetic causes of OI. Conclusions: Integrating molecular diagnostics with robust genotype–phenotype correlations is essential for advancing personalized care. Future breakthroughs will rely on the translation of gene and cell therapies to ultimately improve the long-term clinical outcomes for patients with OI.

Objective: To evaluate the surgical treatment strategies and clinical efficacy in patients with cutaneous skeletal hypophosphatemia syndrome (CSHS). Methods: A retrospective analysis was conducted on three cases of CSHS treated at our institution. Clinical data included medical history, physical examination, laboratory tests (hypophosphatemia-related biomarkers and genetic testing), and imaging studies (x-ray and CT). Pre- and postoperative limb deformity correction and functional recovery were assessed. Results: All three patients presented with multiple skeletal deformities and cutaneous melanocytic nevi. Laboratory tests confirmed persistent hypophosphatemia, while imaging revealed widespread osseous abnormalities and long-bone bowing deformities. Following corrective osteotomy with internal fixation, significant improvement in mechanical alignment was achieved. Conclusions: Surgical intervention can effectively correct limb deformities, restore biomechanical alignment, and improve function in patients with CSHS. Hence, it represents a critical component of multidisciplinary management.

Objective: Osteogenesis imperfecta (OI) is a group of connective tissue disorders with significantly clinical and genetic heterogeneity, which is characterized by low bone mineral density, recurrent fractures and skeletal deformities. This study aimed to conduct clinical and genetic analyses in a Chinese OI cohort to expand the spectrum of pathogenic variants and provide evidence for precise genetic counseling and prenatal genetic diagnosis. Methods: A total of 77 Chinese families with clinically suspected OI were enrolled in this study. Clinical assessments at enrollment included physical examinations, X-ray imaging, and bone mineral density testing. Whole exome sequencing (WES) combined with Sanger sequencing was used to detect candidate pathogenic variants. Variant pathogenicity was evaluated via bioinformatics analysis and familial co-segregation analysis. In this OI cohort, the spectra of pathogenic variants, clinical phenotypes, and genotype-phenotype correlations were analyzed. Results: A 100% detection rate for pathogenic variants was achieved in the 77 families, with 79 variants identified in total. Among the 79 variants, 21 (26.6%) were novel variants founded across six OI-associated genes. Interestingly, apart from the correlation between different pathogenic genes and clinical phenotypes, we also discovered that the severity and phenotype of patients associated with the location of pathogenic variants within the type I collagen domain, exhibiting an aggravating trend from the amino terminus to the carboxyl terminus. Conclusion: Based on previous studies of large OI cohorts, we expanded the spectrum of pathogenic variants by identifying 21 novel ones. Meanwhile, we discovered that the location of pathogenic variants, particularly missense variants, in type I procollagen is correlated with the clinical manifestations and severity of patients. These findings will provide important evidence for the precise diagnosis and genetic counseling of the disease.