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Oestereicher, M.A. ; da Silva Buttkus, P. ; Gailus-Durner, V. ; Marschall, S. ; Fuchs, H. ; Hrabě de Angelis, M. ; Schneltzer, E. ; Spielmann, N.

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

Sci. Rep. 16:9231 (2026)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Normalization
ISSN (print) / ISBN 2045-2322
e-ISSN 2045-2322
Zeitschrift Scientific Reports
Quellenangaben Band: 16, Heft: 1, Seiten: , Artikelnummer: 9231 Supplement: ,
Verlag Springer
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Experimental Genetics (IEG)