The performance of body mass component indices in detecting risk of musculoskeletal injuries in physically active young men and women

Abstract

Background Body composition indices can be related to musculoskeletal injuries (MI), particularly in physically active groups. However, little is known about the accuracy of these diagnoses as potential predictors of musculoskeletal injuries. Therefore, this study aims to indicate the cut-off points of relative body mass (BMI), fat mass index (FMI), skeletal muscle mass (SMI), and muscle to fat ratio (MFR) and establish its reliability in injury prediction for physically active men and women. Methods The sample included 119 physically active individuals aged 23.72 ± 1.12 (66 men body height 1.79 ± 0.07 (m); body weight 80.51 ± 9.85 (kg) and 53 women body height 1.67 ± 0.08 (m); body weight 62 ± 10.72 (kg)), students at university of physical education in Poland. The participants’ physical activity was measured with the International Physical Activity Questionnaire (IPAQ). The relevance of body mass index (BMI), fat mass index (FMI), skeletal muscle mass index (SMI), and muscle to fat ratio (MFR) in detecting injury risk was examined. Musculoskeletal injuries during 1 year before examination were registered using a self-reporting questionnaire. The areas under the curve (AUC) and Youden Index in the receiver operating characteristic curve (ROC) were calculated. Results The cut-off points used to classify the indices among men were BMI = 24.38; FMI = 3.74; SMI = 16.40; MFR = 3.70; and for women BMI = 20.90; FMI = 4.17; SMI = 8.96; MFR = 1.67. Results suggested the greatest reliability in the prediction of musculoskeletal injuries among men had BMI (AUC = 0.623; Youden = 0.30) and SMI (AUC = 0.608; Youden = 0.23) whereas among women, MFR (AUC = 0.628; Youden = 0.29) and FMI (AUC = 0.614; Youden = 0.32). Conclusion BMI and SMI are the most appropriate indices to predict the risk of musculoskeletal injury in physically active men, whereas in women, MFR and FMI are more reliable. These results indicate that the indices with more muscle mass meaning are better in predicting injury among men. In contrast, indices with a higher contribution of fat are better for women. It indicates sex differentiation of injury risk conditions. Men should focus on developing muscle mass, whereas women should reduce body fat to decrease injury risk. However, widespread use seemed to be limited to the specific examined group. Therefore, cut-off points should be used with caution, and calculated values should be verified and confirmed in subsequent studies.

Association between Cardiovascular and Musculoskeletal Health in Firefighters

“…Similarly, a study by O’Leary et al 58 noted that in military, personal females were at higher risk for musculoskeletal injury compared with males, particularly related to the lower limbs. A recent study noted that reduced lean body mass predisposed males to injury, whereas a higher fat mass predisposed females to injury 50 . The current results indicated that a significant difference existed between male and female BF%, which could, possibly, explain the association between sex and injuries seen in the current study.…”

“…The results of the present study indicated that the Framingham risk score and poor CVHI increased the odds of MSIs by 27% and 62%, respectively. As these risk scores are predictive of risk for atherosclerotic diseases, this relationship may help explain the association between these measures and MSIs 17,19,20,50 . Moreover, several factors included in the Framingham risk score and CVHI were independently associated with an increased likelihood of injury in the current results, making the cumulative CVD risk scores association with MSIs unsurprising.…”

Prognostic Potential of the Body Composition Indices in Predicting Positive Changes in Resting Blood Pressure after High-Intensity Interval Training in Adolescents

“…Our previous study showed that body composition indices might help establish the optimal value of BMI and FMI to achieve a positive effect in physical fitness improvement after HIIT. Generally, using a ROC curve method effectively describes the cut-off point indicating the phenomenon’s occurrence, e.g., injury risk or failure in a training program based on chosen factors such as physical fitness [ 28 , 29 ] and body composition indices [ 25 , 26 ]. However, there is a lack of observation considering body composition indices concerning blood pressure parameters.…”

“…The Youden index allows, based on the ROC curve sensitivity and specificity values, the optimal cut-off point to be determined [ 25 , 26 ].…”

“…All of that indices have recently gained significance, owing to several factors, including the similarity of its calculation method to that of BMI [ 23 ]. Their prognostic potential in detecting some phenomena, e.g., the risk of injuries, the risk of osteoporosis, or positive changes in physical efficiency, was positively verified [ 24 , 25 , 26 , 27 , 28 , 29 ].…”