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<p>Background: Various anthropometric indices have been proposed to assess central obesity and predict metabolic syndrome (MetS). However, their ability to predict MetS has not been evaluated in the Nepali adult population. This study compared the predictive potential of 12 novel and traditional anthropometric indices for MetS and its components among Nepali adults. Methods: This cross-sectional study, conducted between January 2022 and June 2023, involved 1,116 adult participants (424 females, 692 males) aged 30–86&#xa0;years from Gandaki Province, Nepal. Twelve anthropometric indices were calculated from the primary anthropometric and metabolic parameters. MetS was defined according to the modified NCEP-ATP III criteria. Logistic regression models were used to assess the strength of associations between these indices and MetS. Receiver operating characteristic (ROC) curve analysis was used to determine the predictive potential of these indices for MetS and its components. AUC differences between various index pairs were also calculated. Results: The overall prevalence of MetS in our study participants was 52.7%. The VAI demonstrated the best performance in predicting MetS (AUC: 0.865 for females, 0.882 for males), followed by LAP (AUC: 0.848 for females, 0.866 for males). The WHR showed good performance (AUC: 0.749 for females, 0.722 for males). BMI, the well-known traditional measure of body adiposity, demonstrated lower predictive ability (AUC: 0.586 for females, 0.571 for males). The optimal cutoffs were as follows: VAI &gt; 2.37 for females, &gt; 1.71 for males; LAP &gt; 37.21 for females, &gt; 47.74 for males; WHR &gt; 0.97 for females, &gt; 0.98 for males; and BMI &gt; 23.10 for females, &gt; 23.90 for males. BAI exhibited the poorest diagnostic performance for MetS prediction in both sexes (AUC &lt; 0.555). Both the VAI and LAP were strongly positively associated (p &lt; 0.001) with increased odds of MetS in both females (OR: 16.03, 95% CI: 9.77–26.31) and males (OR: 24.88, 95% CI: 16.51–37.48). Conclusion: Among Nepali adults, the VAI and LAP outperform traditional anthropometric indices in predicting MetS and its components, suggesting their potential as effective screening tools for early detection. These findings contribute to the development of population-specific screening strategies for MetS in resource-limited settings such as Nepal, potentially enhancing early detection and prevention of cardiometabolic disorders.</p>

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Evaluation of novel and traditional anthropometric indices for predicting metabolic syndrome and its components: a cross-sectional study of the Nepali adult population

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  • 发表日期：

    2025年4月8日

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  • Daya Ram Pokharel,
  • Abhishek Maskey,
  • Goma Kathayat,
  • Binod Manandhar,
  • Ramchandra Kafle,
  • Krishna Das Manandhar

摘要

Background: Various anthropometric indices have been proposed to assess central obesity and predict metabolic syndrome (MetS). However, their ability to predict MetS has not been evaluated in the Nepali adult population. This study compared the predictive potential of 12 novel and traditional anthropometric indices for MetS and its components among Nepali adults. Methods: This cross-sectional study, conducted between January 2022 and June 2023, involved 1,116 adult participants (424 females, 692 males) aged 30–86 years from Gandaki Province, Nepal. Twelve anthropometric indices were calculated from the primary anthropometric and metabolic parameters. MetS was defined according to the modified NCEP-ATP III criteria. Logistic regression models were used to assess the strength of associations between these indices and MetS. Receiver operating characteristic (ROC) curve analysis was used to determine the predictive potential of these indices for MetS and its components. AUC differences between various index pairs were also calculated. Results: The overall prevalence of MetS in our study participants was 52.7%. The VAI demonstrated the best performance in predicting MetS (AUC: 0.865 for females, 0.882 for males), followed by LAP (AUC: 0.848 for females, 0.866 for males). The WHR showed good performance (AUC: 0.749 for females, 0.722 for males). BMI, the well-known traditional measure of body adiposity, demonstrated lower predictive ability (AUC: 0.586 for females, 0.571 for males). The optimal cutoffs were as follows: VAI > 2.37 for females, > 1.71 for males; LAP > 37.21 for females, > 47.74 for males; WHR > 0.97 for females, > 0.98 for males; and BMI > 23.10 for females, > 23.90 for males. BAI exhibited the poorest diagnostic performance for MetS prediction in both sexes (AUC < 0.555). Both the VAI and LAP were strongly positively associated (p < 0.001) with increased odds of MetS in both females (OR: 16.03, 95% CI: 9.77–26.31) and males (OR: 24.88, 95% CI: 16.51–37.48). Conclusion: Among Nepali adults, the VAI and LAP outperform traditional anthropometric indices in predicting MetS and its components, suggesting their potential as effective screening tools for early detection. These findings contribute to the development of population-specific screening strategies for MetS in resource-limited settings such as Nepal, potentially enhancing early detection and prevention of cardiometabolic disorders.