Rethinking ratio-based normalization: A guide towards model-based approaches in heart weight analysis

Abstract

Heart weight is a critical parameter in cardiology and mouse research, reflecting structural and functional changes linked to cardiac size or hypertrophy and pathophysiological conditions. Normalizing heart weight (HW) to body weight (BW) or tibia length (TL) is a common practice; however, the validity of these ratios has been questioned due to non-proportional relationships between parameters, and this becomes particularly problematic when comparing distinct populations based on such normalized values. Using data from over 25,000 C57BL/6N wildtype mice provided by the International Mouse Phenotyping Consortium (IMPC), we investigated the limitations of ratio-based normalization when comparing different groups, aiming to propose a robust framework for HW analysis. Our findings reveal negligible to weak correlations between HW, BW, and TL across age and sex groups, undermining the validity of ratio-based methods. A modelling study using simulated data demonstrated that ratios could produce misleading results, including reversed or false group differences, when scaling assumptions are violated. Ratios yield accurate and interpretable results only when a truly proportional relationship exists between the variables--specifically, when the regression line passes through the origin--conditions under which ratio-based normalization aligns with outcomes obtained from more robust modelling approaches. These results underscore the superiority of linear models with covariate adjustment and allometric scaling for organ weight analysis, as they more accurately capture biologically relevant scaling relationships. By leveraging the IMPCs large-scale wildtype dataset, we establish the necessity of reassessing normalization practices in quantitative biology traits and propose that ratios should be avoided when comparing normalized values across distinct populations unless key mathematical assumptions are met. This study advances the analytical rigor in phenotyping research, enabling more accurate interpretations of organ mass and function across biological contexts.