Distinguishing risk of curve progression in adolescent idiopathic scoliosis with bone microarchitecture phenotyping: a 6-year longitudinal study.

Low bone mineral density and impaired bone quality have been shown to be important prognostic factors for curve progression in adolescent idiopathic scoliosis (AIS). There is no evidence-based integrative interpretation method to analyze high-resolution peripheral quantitative computed tomography (HR-pQCT) data in AIS. This study aimed to (1) utilize unsupervised machine learning to cluster bone microarchitecture phenotypes on HR-pQCT parameters in girls with AIS, (2) assess the phenotypes' risk of curve progression and progression to surgical threshold at skeletal maturity (primary cohort), and (3) investigate risk of curve progression in a separate cohort of girls with mild AIS whose curve severity did not reach bracing threshold at recruitment (secondary cohort). Patients were followed up prospectively for 6.22 ± 0.33 years in the primary cohort (n = 101). Three bone microarchitecture phenotypes were clustered by fuzzy C-means at time of peripubertal peak height velocity (PHV). Phenotype 1 had normal bone characteristics. Phenotype 2 was characterized by low bone volume and high cortical bone density, and phenotype 3 had low cortical and trabecular bone density and impaired trabecular microarchitecture. The difference in bone quality among the phenotypes was significant at peripubertal PHV and continued to skeletal maturity. Phenotype 3 had significantly increased risk of curve progression to surgical threshold at skeletal maturity (odd ratio [OR] = 4.88; 95% CI, 1.03-28.63). In the secondary cohort (n = 106), both phenotype 2 (adjusted OR = 5.39; 95% CI, 1.47-22.76) and phenotype 3 (adjusted OR = 3.67; 95% CI, 1.05-14.29) had increased risk of curve progression ≥6° with mean follow-up of 3.03 ± 0.16 years. In conclusion, 3 distinct bone microarchitecture phenotypes could be clustered by unsupervised machine learning on HR-pQCT-generated bone parameters at peripubertal PHV in AIS. The bone quality reflected by these phenotypes was found to have significant differentiating risk of curve progression and progression to surgical threshold at skeletal maturity in AIS.

Adolescent idiopathic scoliosis (AIS) is an abnormal spinal curvature that commonly presents during puberty growth. Evidence has shown that low bone mineral density and impaired bone quality are important risk factors for curve progression in AIS. High-resolution peripheral quantitative computed tomography (HR-pQCT) has improved our understanding of bone quality in AIS. It generates a large amount of quantitative and qualitative bone parameters from a single measurement, but the data are not easy for clinicians to interpret and analyze. This study enrolled girls with AIS and used an unsupervised machine-learning model to analyze their HR-pQCT data at the first clinic visit. The model clustered the patients into 3 bone microarchitecture phenotypes (ie, phenotype 1: normal; phenotype 2: low bone volume and high cortical bone density; and phenotype 3: low cortical and trabecular bone density and impaired trabecular microarchitecture). They were longitudinally followed up for 6 years until skeletal maturity. We observed the 3 phenotypes were persistent and phenotype 3 had a significantly increased risk of curve progression to severity that requires invasive spinal surgery (odds ratio = 4.88, p = .029). The difference in bone quality reflected by these 3 distinct phenotypes could aid clinicians to differentiate risk of curve progression and surgery at early stages of AIS.

Conditional generative adversarial network-assisted system for radiation-free evaluation of scoliosis using a single smartphone photograph: a model development and validation study.

Background: Adolescent idiopathic scoliosis (AIS) is the most common spinal disorder in children, characterized by insidious onset and rapid progression, which can lead to severe consequences if not detected in a timely manner. Currently, the diagnosis of AIS primarily relies on X-ray imaging. However, due to limitations in healthcare access and concerns over radiation exposure, this diagnostic method cannot be widely adopted. Therefore, we have developed and validated a screening system using deep learning technology, capable of generating virtual X-ray images (VXI) from two-dimensional Red Green Blue (2D-RGB) images captured by a smartphone or camera to assist spine surgeons in the rapid, accurate, and non-invasive assessment of AIS. Methods: We included 2397 patients with AIS and 48 potential patients with AIS who visited four medical institutions in mainland China from June 11th 2014 to November 28th 2023. Participants data included standing full-spine X-ray images captured by radiology technicians and 2D-RGB images taken by spine surgeons using a camera. We developed a deep learning model based on conditional generative adversarial networks (cGAN) called Swin-pix2pix to generate VXI on retrospective training (n = 1842) and validation (n = 100) dataset, then validated the performance of VXI in quantifying the curve type and severity of AIS on retrospective internal (n = 100), external (n = 135), and prospective test datasets (n = 268). The prospective test dataset included 268 participants treated in Nanjing, China, from April 19th, 2023, to November 28th, 2023, comprising 220 patients with AIS and 48 potential patients with AIS. Their data underwent strict quality control to ensure optimal data quality and consistency. Findings: Our Swin-pix2pix model generated realistic VXI, with the mean absolute error (MAE) for predicting the main and secondary Cobb angles of AIS significantly lower than other baseline cGAN models, at 3.2° and 3.1° on prospective test dataset. The diagnostic accuracy for scoliosis severity grading exceeded that of two spine surgery experts, with accuracy of 0.93 (95% CI [0.91, 0.95]) in main curve and 0.89 (95% CI [0.87, 0.91]) in secondary curve. For main curve position and curve classification, the predictive accuracy of the Swin-pix2pix model also surpassed that of the baseline cGAN models, with accuracy of 0.93 (95% CI [0.90, 0.95]) for thoracic curve and 0.97 (95% CI [0.96, 0.98]), achieving satisfactory results on three external datasets as well. Interpretation: Our developed Swin-pix2pix model holds promise for using a single photo taken with a smartphone or camera to rapidly assess AIS curve type and severity without radiation, enabling large-scale screening. However, limited data quality and quantity, a homogeneous participant population, and rotational errors during imaging may affect the applicability and accuracy of the system, requiring further improvement in the future. Funding: National Key R&D Program of China, Natural Science Foundation of Jiangsu Province, China Postdoctoral Science Foundation, Nanjing Medical Science and Technology Development Foundation, Jiangsu Provincial Key Research and Development Program, and Jiangsu Provincial Medical Innovation Centre of Orthopedic Surgery.

Genetic insight into the putative causal proteins and druggable targets of osteoporosis: a large-scale proteome-wide mendelian randomization study.

Background: Osteoporosis is a major causative factor of the global burden of disease and disability, characterized by low bone mineral density (BMD) and high risks of fracture. We aimed to identify putative causal proteins and druggable targets of osteoporosis. Methods: This study utilized the largest GWAS summary statistics on plasma proteins and estimated heel BMD (eBMD) to identify causal proteins of osteoporosis by mendelian randomization (MR) analysis. Different GWAS datasets were used to validate the results. Multiple sensitivity analyses were conducted to evaluate the robustness of primary MR findings. We have also performed an enrichment analysis for the identified causal proteins and evaluated their druggability. Results: After Bonferroni correction, 67 proteins were identified to be causally associated with estimated BMD (eBMD) (p < 4 × 10-5). We further replicated 38 of the 67 proteins to be associated with total body BMD, lumbar spine BMD, femoral neck BMD as well as fractures, such as RSPO3, IDUA, SMOC2, and LRP4. The findings were supported by sensitivity analyses. Enrichment analysis identified multiple Gene Ontology items, including collagen-containing extracellular matrix (GO:0062023, p = 1.6 × 10-10), collagen binding (GO:0005518, p = 8.6 × 10-5), and extracellular matrix structural constituent (GO:0005201, p = 2.7 × 10-5). Conclusion: The study identified novel putative causal proteins for osteoporosis which may serve as potential early screening biomarkers and druggable targets. Furthermore, the role of plasma proteins involved in collagen binding and extracellular matrix in the development of osteoporosis was highlighted. Further studies are warranted to validate our findings and investigate the underlying mechanism.

Abnormal morphological features of osteocyte lacunae in adolescent idiopathic scoliosis: A large-scale assessment by ultra-high-resolution micro-computed tomography.

AIM: Abnormal osteocyte lacunar morphology in adolescent idiopathic scoliosis (AIS) has been reported while the results were limited by the number of osteocyte lacunae being quantified. The present study aimed to validate previous findings through (a) comparing morphological features of osteocyte lacunae between AIS patients and controls in spine and ilium using a large-scale assessment, and (b) investigating whether there is an association between the acquired morphological features of osteocyte lacunae and disease severity in AIS. METHOD: Trabecular bone tissue of the facet joint of human vertebrae on both concave and convex sides at the apex of the scoliotic curve were collected from 4 AIS and 5 congenital scoliosis (CS) patients, and also at the same anatomic site from 3 non-scoliosis (NS) subjects intraoperatively. Trabecular bone tissue from ilium was obtained from 12 AIS vs 9 NS subjects during surgery. Osteocyte lacunae were assessed using ultra-high-resolution micro-computed tomography. Clinical information such as age, body mass index (BMI) and radiological Cobb angle of the major curve were collected. RESULTS: There was no significant difference between density of osteocyte lacuna and bone volume fraction (BV/TV) between groups. A total of 230,076 and 78,758 osteocyte lacunae from facet joints of apical vertebra of scoliotic curve and iliac bone were included in the analysis, respectively. In facet joint bone biopsies, lacunar stretch (Lc.St) was higher, and lacunar equancy (Lc.Eq), lacunar oblateness (Lc.Ob), and lacunar sphericity (Lc.Sr) were lower in AIS and CS groups when compared with NS group. CA side was associated with higher Lc.St when compared with CX side. In iliac bone biopsies, Lc.Ob was higher and lacunar surface area (Lc.S) was lower in AIS group than NS group. Median values of Lc.St, Lc.Eq and Lc.Sr were significantly associated with radiological Cobb angle with adjustment for age and BMI (R-squared: 0.576, 0.558 and 0.543, respectively). CONCLUSIONS: This large-scale assessment of osteocyte lacunae confirms that AIS osteocyte lacunae are more oblate in iliac bone that is less influenced by asymmetric loading of the deformed spine than the vertebrae. Shape of osteocyte lacunae in iliac bone is associated with radiological Cobb angle of the major curve in AIS patients, suggesting the likelihood of systemic abnormal osteocyte morphology in AIS. Osteocyte lacunae from concave side of scoliotic curves were more stretched in both AIS and CS groups, which is likely secondary to asymmetric mechanical loading.

Upregulation of microRNA-96-5p is associated with adolescent idiopathic scoliosis and low bone mass phenotype.

Bone densitometry revealed low bone mass in patients with adolescent idiopathic scoliosis (AIS) and its prognostic potential to predict curve progression. Recent studies showed differential circulating miRNAs in AIS but their diagnostic potential and links to low bone mass have not been well-documented. The present study aimed to compare miRNA profiles in bone tissues collected from AIS and non-scoliotic subjects, and to explore if the selected miRNA candidates could be useful diagnostic biomarkers for AIS. Microarray analysis identified miR-96-5p being the most upregulated among the candidates. miR-96-5p level was measured in plasma samples from 100 AIS and 52 healthy girls. Our results showed significantly higher plasma levels of miR-96-5p in AIS girls with an area under the curve (AUC) of 0.671 for diagnostic accuracy. A model that was composed of plasma miR-96-5p and patient-specific parameters (age, body weight and years since menarche) gave rise to an improved AUC of 0.752. Ingenuity Pathway Analysis (IPA) indicated functional links between bone metabolic pathways and miR-96-5p. In conclusion, differentially expressed miRNAs in AIS bone and plasma samples represented a new source of disease biomarkers and players in AIS etiopathogenesis, which required further validation study involving AIS patients of both genders with long-term follow-up.