Evaluation of Health Associations With Height‐Normalised Abdominal Body Composition Indices: A Single‐Centre Cross‐Sectional Study

BackgroundTraditional metrics such as body mass index (BMI) and waist circumference (WC) fail to accurately assess the health outcomes associated with abdominal adiposity, because they neglect the intricacies of adipose tissue distribution. Notably, the variability in body composition scaled to height remains underexplored in Chinese demographics. This study introduces height‐normalised indices of abdominal adiposity using computed tomography (CT) scans and further assesses their associations with various health outcomes.MethodsIn a large, diverse Chinese population (n = 1054 healthy individuals; n = 1159 with dyslipidemia; n = 803 with diabetes; n = 1289 with cardio‐cerebrovascular diseases; n = 1108 with cancers; and n = 509 with abnormal bone mas), abdominal CT scans were performed and allometric growth model analyses were used to derive height‐normalised indices (body composition/heightβ). Logistic regression models assessed the associations between these indices and health outcomes.ResultsDistinct scaling powers were observed for visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT) and total abdominal adipose tissue (TAT), as well as for sagittal diameter (SAD), with marked sex differences. Powers for VAT were 1.786 ± 1.270 for males and 1.274 ± 0.692 for females. Powers for SAT were 2.266 ± 0.856 for males and 1.656 ± 0.497 for females. Powers for TAT were 2.141 ± 0.967 for males and 1.438 ± 0.489 for females. Powers for SAD were 0.646 ± 0.217 for males and 0.678 ± 0.141 for females. After controlling for age, BMI and WC, VAT/heightβ, TAT/heightβ and SAD/heightβ retained their significantly positive associations with the odds of health outcomes, whereas SAT/heightβ did not.ConclusionsOur findings endorse the clinical utility of height‐normalised indices, particularly VAT/heightβ, TAT/heightβ and SAD/heightβ, in health outcomes assessment. These indices, grounded in robust empirical data, underscore the necessity of a nuanced approach in obesity‐related health evaluations, advocating for a departure from conventional methods like BMI.