Minimal measurement strategies for cardiometabolic risk classification within the Positive Health framework: A cross-sectional analysis of NHANES 1999-2004 data

Background: Large-scale prevention and population health monitoring require measurement approaches that are both feasible and informative. Although several self-measurable anthropometric and fitness indicators have been associated with cardiometabolic risk, it remains unclear whether combining multiple measurements provides meaningful improvements over simpler approaches. We evaluated whether a parsimonious set of self-measurable indicators can achieve classification performance comparable to a full candidate set and quantified the incremental value of additional measurements. Methods: Using data from 8,275 adults in the NHANES 1999-2004 cohorts, we evaluated a predefined minimal set of four self-measurable anthropometric and fitness indicators (body mass index (BMI), waist-to-height ratio (WHtR), mid-upper arm circumference (MUAC), and VO2max (as a proxy for the 6-minute walk test) as candidate indicators of cardiometabolic risk. Their ability to reflect underlying clinical risk factors related to adiposity, glucose and lipid metabolism, and physical fitness was assessed using nested logistic regression models, likelihood ratio tests, discrimination metrics, and decision tree analyses. Results: WHtR consistently showed the strongest discriminative performance, with {Delta}PR-AUC values for BMI versus WHtR ranging from -0.002 to -0.037, and emerged as the primary splitting variable. Adding BMI to WHtR resulted in small gains in PR-AUC for most outcomes, ranging from 0.000 to 0.008, except for triglycerides where the gain was larger ({Delta}PR-AUC=0.039). Further inclusion of MUAC and VO2max provided limited additional value overall, with evidence of variation across outcomes and sex stratified analyses. Conclusion: Most classification performance was achieved using a limited number of simple self-measurable indicators, with little additional benefit from incorporating further measurements. These findings suggest that parsimonious measurement strategies may provide a feasible approach for cardiometabolic risk classification in population health and prevention settings while reducing measurement burden.