(32) ESTABLISHING CARDIOVASCULAR DISEASE RISK CUTOFF VALUES AMONG FIRST RESPONDERS

Purpose: This study determined if biomarkers of cardiovascular disease (CVD) risk, fitness, and body composition can be used to delineate between low-risk (LOW) and moderate-risk (MOD) CVD risk classifications. Methods: One hundred and seventy-one male (n=171) first responders (73 LEOS; 98 FFs) were analyzed. Fasted (≥12h) blood and saliva bio-samples were collected to assess traditional (i.e., blood glucose and lipids) and non-traditional (i.e., oxidative stress and inflammation) CVD risk biomarkers. Dual-energy x-ray absorptiometry and waist and hip circumferences metrics were taken. A symptom-limited maximal cardiopulmonary exercise test (CPXT) was completed and VO_2max was estimated via the Foster equation. LOW and MOD CVD risk classifications were based on the American College of Sports Medicine risk stratification scoring standards. Shapiro-Wilk Test assessed for normality. Independent sample T-tests or Mann-Whitney U tests (if normality was violated) assessed for differences in CVD risk biomarkers, fitness, and body composition between LOW and MOD. Effect sizes were calculated as Cohen’s d. ROC Curves were performed to establish cutoff values to distinguish LOW from MOD. An area under the curve (AUC) less than 0.5 denotes poor ability to discern LOW from MOD, while 0.7-0.8 is acceptable, 0.8-0.9 is excellent, and >0.9 is outstanding. Cutoff values were derived by using the minimum distance from the left upper corner of the unit square to the appropriate point on the ROC curve. Results: The LOW group was younger (p=0.002, d=-0.474), weighed less (p< 0.001, d=-0.871), lasted longer on the CPXT (p< 0.001, d=0.647), had greater VO_2max values (p< 0.001, d=0.664), and performed more push-ups (p=0.028, d=0.358), in addition to displaying better body composition parameters and indices of CVD risk (Table 1, Panel A). Several significant (p< 0.05) but poor or near acceptable ROC curve AUCs were identified (Table 1 Panel B), while significant and acceptable ROC curve AUCs were found for the body roundness index (BRI; AUC=0.703, p=0.003), waist-to-hip ratio (WHR; AUC=0.701, p=0.004), android body fat (AUC=0.750, p< 0.001), triglycerides (TAG; AUC=0.746, p< 0.001), and advanced oxidation protein products (AOPP; AUC=0.703, p=0.003). As such, cutoff values were estimated for each of these parameters (Table 1 Panel B). Conclusions: These results provide evidence of the importance of assessing body composition and CVD risk biomarkers among first responders – namely, BRI, WHR, android body fat, TAG, and AOPP. These findings align with previous data demonstrating that first responders tend to have adverse health profiles, often illustrated by poor body composition, blood lipids, and increased oxidative stress. These findings suggest practitioners should emphasize tracking body composition and CVD risk biomarkers among first responders. Practical Applications: Practitioners should consider these cutoff values for body composition and CVD risk biomarkers when monitoring first responders in exercise and resistance training programs. Acknowledgements: None