SMRT: A smart mass ratio technique that outperforms body mass index (BMI) for predicting waist-to-height ratio
Year: 2026
Type: article
Abstract: Background: Waist-to-height ratio (WHtR) is a superior indicator of central obesity and cardiometabolic risk compared with body mass index (BMI). However, its use in clinical practice is limited because waist circumference is often not measured or may be collected inconsistently. This study aimed to develop and validate SMRT, a simple anthropometric model that estimates WHtR using only height and weight. Methods: Four NHANES cycles (2015-2016, 2017-2018, partial 2017-March 2020, and 2021-2023) were pooled to obtain a nationally representative sample of adults aged ≥18 years with complete anthropometric data (n=22,109). Linear regression was used to derive a height-weight model for estimating WHtR. Model performance was evaluated using Pearson correlation (r), root mean square error (RMSE), mean absolute error (MAE), and agreement across WHtR risk categories, and was compared directly with BMI. Results: The SMRT model was: WHtR_est = 1.271 + 0.00470 × weight (kg) - 0.634 × height (m). SMRT showed a very strong correlation with measured WHtR (r=0.92504), outperforming BMI (r=0.9133). SMRT demonstrated the lowest prediction error (RMSE=0.03899; MAE=0.0291). It correctly classified 78.2% of participants across WHtR risk categories, compared with 64.4% using BMI. Sensitivity for detecting WHtR≥0.50 was markedly higher for SMRT (90.3%) than for BMI (78.1%). Conclusions: SMRT is a simple, robust, and clinically practical model for estimating WHtR using only height and weight. Developed using more than 22,000 adults from multiple NHANES cycles, SMRT provides a valuable screening tool when waist circumference is unavailable and may improve assessment of central adiposity and cardiometabolic risk.
Source: PubMed
DOI: 10.3205/000354
Author: Baspinar, Sukru Mert
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7123816431
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