Allometric Scaling of Left Heart Structure and Function in Elite Cyclists: Development of Sport‐Specific Prediction Equations

Background Elite athletes frequently exceed conventional left ventricular (LV) and left atrial thresholds when guideline‐recommended linear scaling for body size is applied, prompting unnecessary investigation. Few athlete‐specific allometric equations yield percentage predicted values or Z scores. We aimed to develop allometric equations for LV and left atrial structure and function in elite cyclists, accounting for body size, sex, discipline, and heart rate, and to compare these with body surface area (BSA) indexing. Methods We evaluated 181 elite cyclists (105 male, 76 female; age 22.7±5.5 years; 128 endurance, 53 sprint) with normal preparticipation cardiac screening. Weighted least squares allometric regression determined optimal scaling exponents for height, weight, and BSA, with models advancing in complexity only when prediction accuracy significantly improved. Equations were externally validated in 62 independent elite male endurance cyclists. Results Men had larger BSA‐scaled LV and left atrial size than women (both P <0.001). Many cyclists exceeded conventional thresholds (American Society of Echocardiography 2015: LV end‐diastolic volume indexed to BSA 69.6%, LV end‐systolic volume indexed to BSA 62.9%; British Society of Echocardiography 2020: LV end‐diastolic volume indexed to BSA 50.8%, left atrial volume indexed to BSA 28.7%). Height provided superior allometric fit versus weight or BSA. Height, sex, discipline, and RR interval were key determinants. On external validation, only 6.5% exceeded the Z score threshold for LV end‐diastolic volume indexed to BSA, compared with 90.3% classified abnormal by ASE guidelines. Conclusions Height‐based allometric scaling outperformed weight or BSA for cardiac dimensions and function in elite cyclists. These sport‐specific equations, validated in an independent cohort, provide clinically usable percentage predicted values and Z scores that permit quantitative assessment rather than binary thresholds and may reduce misclassification of physiological adaptation as disease in preparticipation cardiac screening programs.