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

• Published in: Journal of bone and mineral research 2024-08, Vol.39 (7), p.956-966
• Main Authors: Yang, Kenneth Guangpu, Lee, Wayne Yuk-Wai, Hung, Alec Lik-Hang, Kumar, Anubrat, Chui, Elvis Chun-Sing, Hung, Vivian Wing-Yin, Cheng, Jack Chun-Yiu, Lam, Tsz-Ping, Lau, Adam Yiu-Chung
• Format: Article
• Language: English
• Subjects: Adolescent; Bone and Bones - diagnostic imaging; Bone and Bones - pathology; Bone Density; Child; Disease Progression; Female; Humans; Longitudinal Studies; Phenotype; Risk Factors; Scoliosis - diagnostic imaging; Scoliosis - pathology; Tomography, X-Ray Computed
• ISSN: 0884-0431; 1523-4681; 1523-4681
• DOI: 10.1093/jbmr/zjae083

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.