Anthropometric Factors Associated With Bone Stress Injuries in Collegiate Distance Runners: New Risk Metrics and Screening Tools?

Running Event, Age, and Competitive Level as Predictors of Dual-Energy X-Ray Absorptiometry-Derived Body Composition and Bone Health Markers in Female Runners

ABSTRACT

Wilson, LJ and Curtis, C. Running event, age, and competitive level as predictors of dual-energy X-ray absorptiometry-derived body composition and bone health markers in female runners. J Strength Cond Res 38(7): e366-e372, 2024-The aim of this study was to assess the impact of running discipline, competitive level (COMP), and age on body composition measures in female athletes. A total of n = 51 female runners (age: 30.9 ± 5.7 years, stature: 166.7 ± 5.7 cm, and body mass (BM): 57.1 ± 8.2 kg) completed a full-body dual-energy x-ray absorptiometry (DXA) scan in a cross-sectional design. One-way ANOVA or Kruskal-Wallis was used to identify differences in DXA measures and independent variables. Stepwise regression determined the contribution of independent variables on DXA measures. Body fat percentage (BF%) and fat mass (FM) differed based on COMP (BF%: H (2) = 17.451; FM: H (2) = 17.406, both p ≤ 0.0001). Competitive level modestly predicted BF% and FM (BF%: R2adj = 0.316, F (1,50) = 22.660; FM: R2adj = 0.300, F (1,50) = 21.029, both p ≤ 0.0001). Bone mineral density (BMD) and BMD Z-score (BMD Z ) did not differ between age, running discipline, or COMP (age: BMD: F (2,50) = 2.825, BMD Z : F (2,50) = 2.215; running discipline: BMD: F (3,50) = 1.145, BMD Z : F (3,50) = 1.474; COMP: BMD: F (2,50) = 0.074, BMD Z : F (2,50) = 1.297, all p ≤ 0.05). Age and running discipline modestly predicted BMD and BMD Z (BMD: R2adj = 0.179, F (1,50) = 5.264; BMD Z : R2adj = 0.173, F (1,50) = 4.545, both p ≤ 0.05). These findings indicate COMP may be a predictor of BF% and FM. Age and running discipline appear predictors of bone health markers. Such findings may enable medical and sport science practitioners to tailor interventions relating to realization of training adaptations, performance, and health.

A novel non-invasive method for predicting bone mineral density and fracture risk using demographic and anthropometric measures

Fractures are costly to treat and can significantly increase morbidity. Although dual-energy x-ray absorptiometry (DEXA) is used to screen at risk people with low bone mineral density (BMD), not all areas have access to one. We sought to create a readily accessible, inexpensive, high-throughput prediction tool for BMD that may identify people at risk of fracture for further evaluation. Anthropometric and demographic data were collected from 492 volunteers (♂275, ♀217; [44 ​± ​20] years; Body Mass Index (BMI) = [27.6 ​± ​6.0] kg/m2) in addition to total body bone mineral content (BMC, kg) and BMD measurements of the spine, pelvis, arms, legs and total body. Multiple-linear-regression with step-wise removal was used to develop a two-step prediction model for BMC followed by BMC. Model selection was determined by the highest adjusted R2, lowest error of estimate, and lowest level of variance inflation (α ​= ​0.05). Height (HTcm), age (years), sexm=1, f=0, %body fat (%fat), fat free mass (FFMkg), fat mass (FMkg), leg length (LLcm), shoulder width (SHWDTHcm), trunk length (TRNKLcm), and pelvis width (PWDTHcm) were observed to be significant predictors in the following two-step model (p ​< ​0.05). Step1: BMC (kg) = (0.006 3 × HT) ​+ ​(-0.002 4 × AGE) ​+ ​(0.171 2 × SEXm=1, f=0) ​+ ​(0.031 4 × FFM) ​+ ​(0.001 × FM) ​+ ​(0.008 9 × SHWDTH) ​+ ​(-0.014 5 × TRNKL) ​+ ​(-0.027 8 × PWDTH) - 0.507 3; R2 ​= ​0.819, SE ​± ​0.301. Step2: Total body BMD (g/cm2) = (-0.002 8 × HT) ​+ ​(-0.043 7 × SEXm=1, f=0) ​+ ​(0.000 8 × %FAT) ​+ ​(0.297 0 × BMC) ​+ ​(-0.002 3 × LL) ​+ ​(0.002 3 × SHWDTH) ​+ ​(-0.002 5 × TRNKL) ​+ ​(-0.011 3 × PWDTH) ​+ ​1.379; R2 ​= ​0.89, SE ​± ​0.054. Similar models were also developed to predict leg, arm, spine, and pelvis BMD (R2 ​= ​0.796-0.864, p ​< ​0.05). The equations developed here represent promising tools for identifying individuals with low BMD at risk of fracture who would benefit from further evaluation, especially in the resource or time restricted setting.

Preseason Vertical Center of Mass Displacement During Running and Bone Mineral Density Z-Score Are Risk Factors for Bone Stress Injury Risk in Collegiate Cross-country Runners

OBJECTIVES: To (1) assess relationships between running biomechanics, bone health, and bone stress injuries (BSIs), and (2) determine which variables constitute the most parsimonious BSI risk model among collegiate cross-country runners. DESIGN: Prospective, observational cohort study. METHODS: Running gait and bone mineral density (BMD) data from healthy collegiate cross-country runners were collected at preseason over 6 seasons. A generalized estimating equation model with backward selection was used to develop the most parsimonious model for estimating BSI risk, controlling for sex, running speed, and prior BSI. The variables assessed were spatiotemporal, ground reaction force, and joint kinematics, based on previous literature. Quasi-likelihood under the independence model criterion values and R2 values were used to select the best-fitting model. RESULTS: Data from 103 runners were included in the analysis. The best-fitting model included vertical center of mass (COM) displacement and BMD z-score. Injury risk increased with greater vertical COM displacement (unit = 0.5 cm; relative risk [RR] = 1.14; 95% confidence interval [CI]: 1.01, 1.29; P = .04) and decreased with greater BMD z-score (unit = 0.5; RR = 0.83; 95% CI: 0.72, 0.95; P = .007). The model performed similarly when step rate was included instead of vertical COM displacement. CONCLUSION: Vertical COM displacement and BMD z-score contributed to the best model for estimating risk the risk of bone stress injury in cross-country runners. Step rate was also an important variable for assessing injury risk. J Orthop Sports Phys Ther 2023;53(12):1-8. Epub 20 October 2023. doi:10.2519/jospt.2023.11860.

A hierarchical clustering approach for examining the relationship between pelvis-proximal femur geometry and bone stress injury in runners

Bone stress injury (BSI) risk in runners is multifactorial and not well understood. Unsupervised machine learning approaches can potentially elucidate risk factors for BSI by identifying groups of similar runners within a population which differ in BSI incidence. Here, a hierarchical clustering approach is used to identify groups of collegiate cross country runners based on 2-dimensional frontal plane pelvis and proximal femur geometry, which was extracted from dual-energy X-ray absorptiometry scans and dimensionally reduced by principal component analysis. Seven distinct groups were identified using the cluster tree, with the initial split being highly related to female-male differences. Visual inspection revealed clear differences between groups in pelvis and proximal femur geometry, and groups were found to differ in lower body BSI incidence during the subsequent academic year (Rand index = 0.53; adjusted Rand index = 0.07). Linear models showed between-cluster differences in visually identified geometric measures. Geometric measures were aggregated into a pelvis shape factor based on trends with BSI incidence, and the resulting shape factor was significantly different between clusters (p < 0.001). Lower shape factor values, corresponding with lower pelvis height and ischial span, and greater iliac span and trochanteric span, appeared to be related to increased BSI incidence. This trend was dominated by the effect observed across clusters of male runners, indicating that geometric effects may be more relevant to BSI risk in males, or that other factors masked the relationship in females. More broadly, this work outlines a methodological approach for distilling complex geometric differences into simple metrics that relate to injury risk.