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

TL;DR

This paper shows that normalizing heart weight using ratios can lead to misleading results and suggests using model-based approaches instead.

Contribution

The study demonstrates the limitations of ratio-based normalization and proposes more robust model-based alternatives for heart weight analysis.

Findings

Heart weight, body weight, and tibia length showed negligible to weak correlations in mice.

Ratio-based normalization can produce spurious or reversed group differences when proportionality is violated.

Linear and allometric models offer more reliable frameworks for organ weight analysis.

Abstract

Heart weight (HW) is a critical parameter in cardiology and mouse research, commonly normalized to body weight (BW) or tibia length (TL) to account for size differences. Ratio-based normalization, however, assumes strict proportionality between variables, an assumption that is rarely tested and may bias group comparisons. We analysed HW, BW, and TL measurements from over 25,000 C57BL/6N wildtype mice generated by the International Mouse Phenotyping Consortium. Sex- and age-stratified analyses were combined with simulation-based modelling to evaluate empirical scaling relationships and the statistical behaviour of ratio-based normalization. Across all age and sex groups, correlations between HW, BW, and TL were negligible to weak, indicating substantial deviations from proportionality. Simulations demonstrated that ratio-based normalization can generate misleading results, including spurious or reversed group differences, when proportionality assumptions are violated. Ratios were consistent with linear and allometric models only under strictly proportional conditions, characterized by regression lines passing through the origin. Linear models with covariate adjustment and allometric scaling provide more robust and biologically meaningful frameworks for organ weight analysis. Ratio-based normalization should be avoided unless key mathematical assumptions are met.

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