Waist-to-Height Ratio: a cardiovascular risk benchmark

Why BMI alone is not enough

Body mass index is a convenient population-level tool, but it does not distinguish fat mass from muscle mass and does not reflect the distribution of adipose tissue across the body. Two people with the same BMI can differ dramatically in their amount of visceral fat — the fat that accumulates around the internal organs and is directly linked to metabolic and cardiovascular risk.

Abdominal obesity with a normal BMI — the so-called "thin outside, fat inside" (TOFI) phenotype — often goes undetected with standard anthropometry. This is where WHtR adds information: it directly assesses the proportion of central obesity relative to body build.

How to calculate waist-to-height ratio

WHtR = waist circumference (cm) ÷ height (cm). With a waist of 82 cm and a height of 175 cm the result is 82 / 175 = 0.47 — below the threshold of 0.5. Waist circumference is measured at the level of the navel while standing, at the end of a gentle exhale, without pulling the tape too tight.

The value 0.5 encodes a simple rule: waist circumference should be less than half of height. Unlike BMI, this threshold does not depend on sex or ethnicity — it applies equally to men and women, across different populations.

Meta-analysis: how WHtR relates to mortality

Abdi Dezfouli et al. (International Journal of Obesity, 2023, PMID 37770574) conducted a systematic review and meta-analysis of 20 cohort studies. The risk of death in the high-WHtR group compared with the low-WHtR group:

• All-cause mortality (categorical): HR 1.23 (95% CI 1.04–1.41)
• CVD mortality (categorical): HR 1.39 (95% CI 1.18–1.59)
• All-cause mortality (per 1 SD increase in WHtR): HR 1.16 (95% CI 1.07–1.25)
• CVD mortality (per 1 SD increase in WHtR): HR 1.19 (95% CI 1.07–1.31)

The authors concluded that WHtR can be used in clinical practice as a simple tool for predicting mortality. An important caveat: the meta-analysis covered heterogeneous studies and did not perform a direct comparison of WHtR with BMI in terms of predictive power.

NHANES: non-linear association and the 0.58 threshold

Wang et al. (Frontiers in Nutrition, 2025, PMID 40860487) analysed data from 47 741 adult participants in NHANES (1999–2018), with mortality tracked through national registries up to December 2019. Median follow-up was 9.16 years.

The key methodological innovation was restricted cubic spline analysis, which reveals the non-linear nature of the association. The inflection point at which risk rises sharply was WHtR 0.58. The shape of the association was U-shaped: both a very low (< 0.48) and a high WHtR were associated with increased mortality. The lowest-risk zone was approximately 0.48–0.58.

At WHtR ≥ 0.58 compared with the reference group:

• CVD mortality: HR 1.36
• Diabetes mortality: HR 1.70
• Cerebrovascular disease mortality: HR 1.10

The authors note that because of the very large sample size, the 95% confidence intervals were unusually narrow — an artefact of statistical precision in this specific sample rather than an indicator of reliability when applied to other populations.

Is WHtR better than BMI?

Patel et al. (PLoS ONE, 2025) conducted a direct comparison in a cohort of 94 401 adults (median age 67 years) with a median follow-up of 21 years; 48 323 deaths were recorded. Risk of death at WHtR ≥ 0.55 compared with WHtR < 0.50:

• Women: HR 1.23 (95% CI 1.17–1.29)
• Men: HR 1.11 (95% CI 1.06–1.17)

However, the authors concluded that WHtR does not substantially outperform either BMI or waist circumference in predictive power in this older cohort. The advantage of WHtR lies elsewhere: Park et al. (Diabetes, Obesity & Metabolism, 2026, PMID 41877357), studying more than 2 million patients with type 2 diabetes, showed that WHtR ≥ 0.5 identifies elevated cardiovascular and mortality risk even in patients without formal abdominal obesity by waist circumference. This is a group that BMI and standard waist circumference thresholds fail to identify.