{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T13:11:05Z","timestamp":1793279465039,"version":"4.5.0"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To examine the associations of TMI and BMI with bioimpedance-derived adiposity and selected hematological markers in physically active young adults.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In this cross-sectional study, (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f59) participants (male\u202f=\u202f37, female\u202f=\u202f22; age 22.63\u202f\u00b1\u202f2.29\u202fyears) 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 (\n                      <jats:italic>\u03b1<\/jats:italic>\n                      \u202f=\u202f0.05). Physical activity status followed WHO\/ACSM guidelines (\u2265150\u202fmin\u00b7week\n                      <jats:sup>\u22121<\/jats:sup>\n                      of moderate-to-vigorous activity).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      TMI showed a stronger positive correlation with body fat percentage than BMI (\n                      <jats:italic>r<\/jats:italic>\n                      \u202f=\u202f0.50,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001 vs.\n                      <jats:italic>r<\/jats:italic>\n                      \u202f=\u202f0.38,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.003). BMI was positively correlated with HGB (\n                      <jats:italic>r<\/jats:italic>\n                      \u202f=\u202f0.32,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.013) and HCT (\n                      <jats:italic>r<\/jats:italic>\n                      \u202f=\u202f0.26,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.046) and negatively correlated with NLR (\n                      <jats:italic>\u03c1<\/jats:italic>\n                      \u202f=\u202f\u22120.27,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.041) and PLR (\n                      <jats:italic>r<\/jats:italic>\n                      \u202f=\u202f\u22120.30,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.022). TMI was negatively correlated with NLR (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f\u22120.27,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.039). In the multivariable model (predictors: BMI, TMI, HGB, HCT, NLR, PLR), HCT was a significant negative predictor of adiposity (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u202f=\u202f\u22120.34,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.021), whereas TMI showed a positive but marginally non-significant association (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u202f=\u202f0.25,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.073). Assumptions and multicollinearity were acceptable.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2025.1700684","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T06:22:02Z","timestamp":1763101322000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["From BMI to TMI: revisiting adiposity and fitness assessment in young active adults through a historical and contemporary lens"],"prefix":"10.3389","volume":"13","author":[{"given":"Onur Mutlu","family":"Ya\u015far","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veli Volkan","family":"G\u00fcrses","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali 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