Diabetes, obesity & metabolism · 2026
Development of a Parsimonious Sagittal Abdominal Diameter-Based Model for Estimating Visceral Fat Area in Central Obesity Assessment.
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Li Chao, Shi Wen, Qin Jiamin, Dong Peixin, Ran Yuting, Chen Dandan, Wei Hao, Xu Zhuoqi, Du Tao, Li Wangen, Li Yanli, Hu Zhuoqing
Аннотация
BACKGROUND AND AIMS: Visceral fat area (VFA) is a key marker of central obesity and metabolic risk, but its assessment still depends largely on imaging. Existing VFA estimation equations often rely on conventional anthropometric or laboratory indices, and their increasing complexity may compromise model stability, generalizability, and practical implementation. We aimed to develop a parsimonious sagittal abdominal diameter (SAD)-based model for estimating VFA and to evaluate its performance in two independent cohorts.
METHODS: In this cross-sectional study, we analysed two independent cohorts: NHANES 2011-2016 (N = 5655), in which reference VFA was derived from dual-energy X-ray absorptiometry (DXA, N = 3878), and a Chinese adult cohort (N = 3927), in which reference VFA was measured by computed tomography (CT, N = 190) in a subset of participants. Sex-specific regression models were developed separately in each cohort using prespecified anthropometric variables and were evaluated in terms of agreement with reference VFA, discrimination of metabolic risk, internal validation using bootstrap resampling, and sensitivity analyses.
RESULTS: SAD was the strongest anthropometric predictor of VFA, with age providing additional explanatory value in both populations. The final equations differed slightly across cohorts: NHANES models included SAD, age, and height, whereas the Chinese models included SAD, age, and weight. Model fit was good, with R2 values ranging from 0.67 to 0.81. Agreement between estimated VFA and reference VFA was strong, with concordance correlation coefficients of 0.805-0.893 and mean absolute errors of 18.94-26.83 cm2. Estimated VFA showed discrimination comparable to reference VFA for identifying individuals with ≥ 2 non-adipose metabolic syndrome (MetS) components. Sensitivity analyses confirmed the robustness of the primary SAD-based model. We further developed an online calculator based on the SAD-eVFA model to improve its accessibility and real-world applicability.
CONCLUSIONS: We developed parsimonious, cohort-specific SAD-based equations for estimating VFA. These equations demonstrated good agreement with reference VFA and effective discrimination for identifying individuals with ≥ 2 non-adipose components of MetS. This strategy may provide a feasible and potentially scalable framework for central obesity assessment in population screening, primary care, and individual health management.
METHODS: In this cross-sectional study, we analysed two independent cohorts: NHANES 2011-2016 (N = 5655), in which reference VFA was derived from dual-energy X-ray absorptiometry (DXA, N = 3878), and a Chinese adult cohort (N = 3927), in which reference VFA was measured by computed tomography (CT, N = 190) in a subset of participants. Sex-specific regression models were developed separately in each cohort using prespecified anthropometric variables and were evaluated in terms of agreement with reference VFA, discrimination of metabolic risk, internal validation using bootstrap resampling, and sensitivity analyses.
RESULTS: SAD was the strongest anthropometric predictor of VFA, with age providing additional explanatory value in both populations. The final equations differed slightly across cohorts: NHANES models included SAD, age, and height, whereas the Chinese models included SAD, age, and weight. Model fit was good, with R2 values ranging from 0.67 to 0.81. Agreement between estimated VFA and reference VFA was strong, with concordance correlation coefficients of 0.805-0.893 and mean absolute errors of 18.94-26.83 cm2. Estimated VFA showed discrimination comparable to reference VFA for identifying individuals with ≥ 2 non-adipose metabolic syndrome (MetS) components. Sensitivity analyses confirmed the robustness of the primary SAD-based model. We further developed an online calculator based on the SAD-eVFA model to improve its accessibility and real-world applicability.
CONCLUSIONS: We developed parsimonious, cohort-specific SAD-based equations for estimating VFA. These equations demonstrated good agreement with reference VFA and effective discrimination for identifying individuals with ≥ 2 non-adipose components of MetS. This strategy may provide a feasible and potentially scalable framework for central obesity assessment in population screening, primary care, and individual health management.