Age-related changes of micro-morphological subchondral bone properties in the healthy femoral head.

OBJECTIVE: Alterations in the subchondral bone (SCB) are likely to play a decisive role in the development of osteoarthritis (OA). Since aging represents a major risk factor for OA, the aim of the current study was to assess the microstructural changes of the subchondral bone in the femoral head during aging. DESIGN: Femoral heads and matched iliac crest biopsies of 80 individuals (age 21-99 years) were collected post-mortem. The bone microstructure of the subchondral trabecular bone as well as the cartilage thickness (Cg.Th) and subchondral bone plate thickness (SCB.Th) were quantified using histomorphometry. The different subregions of the SCB were also imaged by quantitative backscattered electron imaging (qBEI) in 31 aged cases to assess the bone mineral density distribution (BMDD). RESULTS: The detected linear decline of bone volume per tissue volume (BV/TV) in the femoral head with aging (Slope, 95% CI: -0.208 to -0.109 %/yr.) was primarily due to a decrease in trabecular thickness (Tb.Th, Slope, 95% CI: -0.774 to -0.343 µm/yr). While SCB.Th declined with aging (Slope, 95% CI: -1.941 to -0.034 µm/yr), no changes in Cg.Th were detected (Slope, 95% CI: -0.001 to 0.005 mm/yr). The matrix mineralization of the subchondral bone was lower compared to the trabecular bone and also decreased with aging. CONCLUSIONS: Regular changes of the SCB during aging primarily involve a reduction of Tb.Th, SCB.Th and matrix mineralization. Our findings facilitate future interpretations of early and late OA specimens to decipher the role of the SCB in OA pathogenesis.

A novel COL1A2 C-propeptide cleavage site mutation causing high bone mass osteogenesis imperfecta with a regional distribution pattern.

Osteogenesis imperfecta (OI) is typically characterized by low bone mass and increased bone fragility caused by heterozygous mutations in the type I procollagen genes (COL1A1/COL1A2). We report two cases of a 56-year-old woman and her 80-year-old mother who suffered from multiple vertebral and non-vertebral fractures with onset in early childhood. A full osteologic assessment including dual-energy X-ray absorptiometry (DXA), high-resolution peripheral quantitative computed tomography (HR-pQCT), and serum analyses pointed to a high bone mineral density (BMD) in the hip (DXA Z-score + 3.7 and + 3.9) but low to normal bone mass in the spine and preserved bone microstructure in the distal tibia. Serum markers of bone formation and bone resorption were elevated. Using whole exome sequencing, we identified a novel mutation in the COL1A2 gene causing a p. (Asp1120Gly) substitution at the protein level and affecting the type I procollagen C-propeptide cleavage site. In line with previously reported cases, our data independently prove the existence of an unusual phenotype of high bone mass OI caused by a mutation in the procollagen C-propeptide cleavage with a clinically persistent phenotype through adulthood.

A machine learning approach to identify risk factors for running-related injuries: study protocol for a prospective longitudinal cohort trial.

BACKGROUND: Running is a very popular sport among both recreational and competitive athletes. However, participating in running is associated with a comparably high risk of sustaining an exercise-related injury. Due to the often multifactorial and individual reasons for running injuries, a shift in thinking is required to account for the dynamic process of the various risk factors. Therefore, a machine learning approach will be used to comprehensively analyze biomechanical, biological, and loading parameters in order to identify risk factors and to detect risk patterns in runners. METHODS: The prospective longitudinal cohort study will include competitive adult athletes, running at least 20 km per week and being free of injuries three months before the start of the study. At baseline and the end of the study period, subjective questionnaires (demographics, injury history, sports participation, menstruation, medication, psychology), biomechanical measures (e.g., stride length, cadence, kinematics, kinetics, tibial shock, and tibial acceleration) and a medical examination (BMI, laboratory: blood count, creatinine, calcium, phosphate, parathyroid hormone, vitamin D, osteocalcin, bone-specific alkaline phosphatase, DPD cross-links) will be performed. During the study period (one season), continuous data collection will be performed for biomechanical parameters, injuries, internal and external load. Statistical analysis of the data is performed using machine learning (ML) methods. For this purpose, the correlation of the collected data to possible injuries is automatically learned by an ML model and from this, a ranking of the risk factors can be determined with the help of sensitivity analysis methods. DISCUSSION: To achieve a comprehensive risk reduction of injuries in runners, a multifactorial and individual approach and analysis is necessary. Recently, the use of ML processes for the analysis of risk factors in sports was discussed and positive results have been published. This study will be the first prospective longitudinal cohort study in runners to investigate the association of biomechanical, bone health, and loading parameters as well as injuries via ML models. The results may help to predict the risk of sustaining an injury and give way for new analysis methods that may also be transferred to other sports. TRIAL REGISTRATION: DRKS00026904 (German Clinical Trial Register DKRS), date of registration 18.10.2021.