A Multivariate Analysis of Anthropometric Indices and their Association with Chronic Health Outcomes

Though body mass index (BMI) has many known limitations and research has shown other anthropometric indices to have better predictive power for various health outcomes, BMI is still the most calculated, reported, and relied on anthropometric index in clinical decision making. We consider 10 non-invasive anthropometric indices and their associations with overall health in non-pregnant U.S. adults.

Using NHANES 2021-2023 survey, we assess the correlational structure among the indices and categorize them into factor groups, with a representative index being identified in each group. We use logistic regression to gauge each representative index's association with eight common health conditions (seven self-reported chronic disease diagnoses and general health status), individually and in combination with the other factor groups' representative indices.

We find the 10 indices are made up of two multi-index groups and two stand-alone indices, with the representative indices being: BMI, conicity index (CI), waist-hip-height ratio, and waist-to-height ratio. Among representative indices, BMI routinely performs the worst as a stand-alone index, while CI has the strongest associations across disease outcomes.

CI should be considered as a potential additional standard measurement included in clinical practice.