Conicity Index: NHANES Microdata Derivation
No published study provides sex- and age-stratified Conicity Index percentile tables for US adults. The CDC NHANES body measures examination collects waist circumference (BMXWAIST), standing height (BMXHT), and weight (BMXWT), the three inputs to the Conicity Index introduced by Valdez (1991). It compares a person's waist to the circumference of a cylinder of the same weight and height: a perfectly cylindrical body scores near 1, and higher values indicate more central fat. Percentiles were derived as follows:
- Formula:
CI = WC / (0.109 × √(weight_kg / height_m)), with waist circumference (WC) and standing height in metres and weight in kilograms. Dimensionless; the US adult range is about 1.10 to 1.51. - Data: three pooled NHANES cycles: 2015-2016 (
BMX_I / DEMO_I), 2017-March 2020 (P_BMX / P_DEMO), and 2021-2023 (BMX_L / DEMO_L). Adults aged 20 and over with complete waist, height, and weight measurements were included (n = 18,889). - Weights: MEC examination weights (
WTMEC2YR, andWTMECPRPfor the pre-pandemic 2017-March 2020 block) were applied. To pool, each cycle's weight was divided by three. The measured post-filter cycle weight totals were 221.8M (2015-2016), 229.7M (2017-2020), and 232.8M (2021-2023), a relative spread of 4.8%, so the divide-by-three rule gives approximately equal contribution from each cycle. The divide-by-N rule is a FitnessNorms design choice, not official CDC guidance, and would not force equal contribution if the cycle totals diverged. - Why pooled: unlike Body Roundness Index, Conicity Index is stable across these cycles. The 2021-2023 cycle was compared against the full 2015-2023 pool at every published quantile (not just the median): 8 of 130 sex/age/quantile cells differed by more than 0.02 index units (largest difference 0.033). Those cells are scattered across both sexes and several quantiles with mixed sign, consistent with sampling noise rather than a systematic shift across the pandemic break. Each is listed with a reason in the derivation script's accepted-deviations allow-list, and the script refuses to overwrite the published table (it exits with an error) if any cell breaches the threshold without being listed. Pooling gives tighter, more reliable tails than the single most-recent cycle.
- Quantiles: weighted empirical quantiles (P5, P25, P50, P75, P95) were computed for each sex × 5-year age bracket from 20-24 through 80+ using linear interpolation on the weighted cumulative distribution. Survey-design standard errors were not computed.
- No pregnancy exclusion: pregnant participants are included as measured, consistent with the waist-to-height ratio, waist-to-hip ratio, and BRI derivations on this site.
This is an internally derived dataset, not a peer-reviewed publication. The derivation method and its limitations are summarised on the reference page. See also waist-to-height ratio methodology for the related body-composition derivation.
Population and clinical context
Because these norms come from US adults, they reflect a population with a measured adult obesity rate of 42.4% by BMI in 2017-2018 (Hales et al. 2020). Conicity Index rises with central fat, so in a leaner population the same value would be expected to rank at a higher percentile than it does here. We did not find a directly comparable non-US Conicity percentile table; the published non-US Conicity literature reports cardiovascular-risk cut-offs rather than sex- and age-stratified percentile distributions. We therefore describe the direction of the effect rather than a specific magnitude. This is an inference from the US obesity context above, not a measured cross-population comparison.
Conicity Index does not have a single widely accepted clinical cut-off. The original work (Valdez 1991) proposed the index as a population comparison tool and did not establish a universal threshold; later studies report outcome- and population-specific cut-offs that do not agree. Because no single threshold maps cleanly across outcomes, this site reports percentile distributions rather than risk categories.