From BMI to TMI: Revisiting Adiposity and Fitness Assessment in Young Active Adults through a Historical and Contemporary Lens

Onur Mutlu Yasar¹Veli Volkan Gürses²Ali Erdem Ciğerci³Erdal Bal⁴Yeliz Pehlivan⁴Mustafa Baş⁴Nedim Malkoc⁴Merve Bektaş⁴Gizem Başkaya²Sare Dündar²Omur Fatih Karakullukcu⁵Hamza Küçük^6*

• ¹Faculty of Health Sciences, Izmir Demokrasi University, İzmir, Türkiye
• ²Faculty of Sport Sciences, Bandırma University, Bandırma, Türkiye
• ³Faculty of Sports Sciences, Kastamonu University, Kastamonu, Türkiye
• ⁴Faculty of Life Sciences, University of Health Sciences, Istanbul, Türkiye
• ⁵Ministry of National Education, Ankara, Türkiye
• ⁶Ondokuz Mayıs University, Samsun, Türkiye

Background: Traditional reliance on body mass index (BMI) to assess adiposity may misclassify physically active individuals with high lean mass. The triponderal mass index (TMI) has emerged as a potentially more accurate alternative, but evidence in physically active populations is limited. Purpose: To examine the associations of TMI and BMI with bioimpedance-derived adiposity and selected hematological markers in physically active young adults Methods: In this cross-sectional study, (n=59) participants (male=37, female=22; age 22.63 ± 2.29 years) underwent anthropometry, whole-body bioimpedance and venous blood sampling. TMI and BMI were calculated, and associations with body fat percentage, hemoglobin, hematocrit, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were examined. Normality assumptions were checked; correlations and multiple linear regressions were computed (α=0.05). Physical activity status followed WHO/ACSM guidelines (≥150 min·week⁻¹ of moderate-to-vigorous activity). Results: TMI showed a stronger positive correlation with body fat percentage than BMI (r = .50, p < .001 vs r = .38, p = .003). BMI was positively correlated with HGB (r = .32, p = .013) and HCT (r = .26, p = .046) and negatively correlated with NLR (ρ = −.27, p = .041) and PLR (r = −.30, p = .022). TMI was negatively correlated with NLR (ρ = −.27, p = .039). In the multivariable model (predictors: BMI, TMI, HGB, HCT, NLR, PLR), HCT was a significant negative predictor of adiposity (β = −.34, p = .021), whereas TMI showed a positive but marginally non-significant association (β = .25, p = .073). Assumptions and multicollinearity were acceptable. Conclusion: In physically active young adults, TMI relates more strongly than BMI to bioimpedance-derived adiposity and may aid field-based screening. However, athlete-level decisions should await confirmation in sport-specific, longitudinal studies using criterion methods.