Nepal is experiencing a rapid epidemiological transition, with recent studies indicating a MetS prevalence of 15–16% in the adult population [4]. Given the significant public health implications of MetS in Nepal, there is a pressing need for simple, cost-effective screening tools for its early detection. While various anthropometric indices have been validated in other populations, comprehensive comparisons in the Nepali population are lacking. This cross-sectional study thus aimed to evaluate and compare the performance of 12 different traditional and novel anthropometric indices in predicting MetS and its components among Nepali adults. To the best of our knowledge, this is the first study to address a critical research gap and provide valuable insights into the diagnostic performance of these indices for clinical practice and public health interventions in the Nepali population.
Our findings indicate that novel indices such as the VAI and LAP outperform traditional measures such as BMI and WHR in identifying MetS and its components among Nepali adults. The VAI exhibited the highest diagnostic accuracy for predicting MetS, with AUCs of 0.866 for females and 0.882 for males, and its three components, viz. dysglycemia, high TG, and low HDL-cholesterol. The optimal cutoff values for MetS were > 1.97 for females (sensitivity 78.7%, specificity 81.4%) and > 2.16 for males (sensitivity 80.8%, specificity 84.4%). These findings are consistent with studies from other Asian, European, and South American populations in which the VAI was the best predictor of MetS [23–26]. The robustness of the VAI can be attributed to its combination of anthropometric (BMI, waist circumference) and metabolic (triglycerides, HDL-cholesterol) parameters, offering a detailed assessment of adiposity dysfunction [17]. Additionally, the VAI correlates well with the visceral fat area measured via CT scan and is linked to insulin resistance and cardiovascular risk [27, 28]. However, the need for blood tests to calculate the VAI may limit its application in resource-limited settings, such as Nepal. A meta-analysis by Bijari et al. further validated the utility of the VAI, showing moderate-to-high accuracy for MetS screening, with a summary AUC of 0.847 [29].
The LAP displayed the second-best performance, with AUCs of 0.839 for females and 0.869 for males. The optimal cutoff value was > 53.4 for both sexes, indicating high sensitivity and specificity. These findings align with those of international studies. For example, an Iranian study by Haghighatdoost et al. reported AUCs of 0.871 for females and 0.897 for males [30]. In China, Li et al. reported that LAP was the strongest predictor of MetS for both men (AUC = 0.87) and women (AUC = 0.85) [31]. In Malaysia, Ching et al. reported that LAP was the best predictor of MetS among vegetarians, with AUCs of 0.91 for males and 0.87 for females [32]. Similarly, a study on Spanish elderly individuals by Alvero-Cruz et al. highlighted the high predictive value of LAP, especially in males (AUC = 0.85) [33]. The effectiveness of LAP likely stems from its combination of waist circumference and triglyceride levels, capturing both anatomical and physiological aspects of lipid accumulation [16]. Moreover, LAP has been shown to correlate with insulin resistance and predict cardiovascular disease risk [34]. However, a blood test for triglyceride measurement is needed, which might not be feasible in all clinical settings in Nepal.
The WHR demonstrated good predictive ability, with AUCs for females 0.749 and males 0.722. The optimal cutoff values were > 0.98 for both sexes, which is higher than the standard cutoffs of 0.90 for men and 0.85 for women [35]. This difference may reflect the unique body composition of South Asians and underscore the need for population-specific cutoffs. The performance of WHR in our study is supported by various international studies, although with some variations. In China, Liu et al. reported that the WHR was a significant predictor of MetS, although with lower AUCs (0.539 for men, 0.552 for women) than other indices [36]. In Nigeria, Raimi et al. reported that the WHR was effective in detecting cardiovascular risk factors but was weaker than other indices, such as the WHtR [37]. In Qatar, Bener et al. reported that the WHR has moderate predictive power for MetS, with an AUC of 0.75 in both men and women [38]. The WHR is linked to abdominal fat distribution and cardiovascular risk but may not effectively reflect changes in body fat over time [39]. Its simplicity and ease of measurement make it suitable for large-scale screening in resource-limited settings such as Nepal.
The WHtR and BRI showed similar performances, with AUCs ranging from 0.687 to 0.697. The optimal cutoff for the WHtR was > 0.56 for males and > 0.638 for females, which exceeds the global cutoff of 0.5 [40]. The BRI, a relatively new index, has shown potential in various populations, and our results support its potential utility in Nepali adults [13]. The similar performance of BRI and WHtR in our study aligns with the international literature. A meta-analysis by Rico-Martín et al. revealed that BRI has good discriminatory power for MetS, similar to WC and WHtR [41]. In China, Wu et al. reported comparable performance for the BRI and WHtR in assessing MetS (AUCs: 0.739 for males, 0.817 for females) [18]. Stefanescu et al. in Peru also reported that the BRI was a useful clinical predictor of MetS [25]. Both indices, accounting for height, may offer an advantage over the WHR in assessing central obesity. They correlate well with body fat percentage and cardiovascular risk factors [40, 42], and incorporating height better reflects body shape changes across different stages.
The WWI showed moderate performance, with AUCs of 0.690 for females and 0.672 for males. The optimal cutoff of > 11.46 for both sexes closely matches the values reported in a Chinese study (11.48 for males and 11.54 for females) [20]. This suggests that WWI could be useful when height measurements are unavailable or unreliable, which is beneficial for certain community settings in Nepal. However, international studies on WWI are limited. Wu et al. reported that the WWI had a weaker ability to identify MetS than other indices [18]. Although WWI is correlated with abdominal fat and metabolic risk factors, further research is needed to confirm its utility across different populations [43].
The CI demonstrated moderate predictive ability, with AUCs of 0.664 for females and 0.694 for males. The optimal cutoffs were > 1.32 for females and > 1.34 for males, which are lower than those reported in other populations [15]. This difference may result from ethnic variations in body composition and fat distribution. The performance of CI in our study aligns with a study in Brazil by Roriz et al., which identified CI as a good indicator of visceral fat in adults and elderly individuals [44]. The CI is correlated with cardiovascular risk factors and insulin resistance, but its complex calculation may limit its practical use in Nepal, as the local setting demands the use of simpler indices [45].
The AVI showed good performance, particularly in males (AUC 0.721). Since AVI is derived from waist circumference, its effectiveness in predicting MetS closely parallels that of waist circumference. The optimal cutoff of > 16.2 for both sexes provides a straightforward clinical threshold. These results align with those of a study in China by Wu et al. that reported that AVI was optimal for identifying MetS (AUC: 0.743 for males, 0.819 for females) [18]. Similarly, a study from Mexico by Guerrero-Romero and Rodríguez-Morán linked AVI to impaired glucose tolerance and type 2 diabetes [11]. The AVI aims to estimate abdominal volume and correlates well with cardiovascular risk factors. However, more research is needed to determine its population-specific cutoff values and validate its performance across different ethnicities.
The CUN-BAE showed moderate predictive ability, with AUCs of 0.614 for females and 0.550 for males. Its lower performance compared with other indices may be due to its development in a Caucasian population, underscoring the need for ethnic-specific body composition equations [16]. This result is consistent with a Spanish study by Gómez-Ambrosi et al. that reported that CUN-BAE was clinically useful for estimating body fat [46]. However, further validation is needed for non-Caucasian populations. While CUN-BAE correlates well with body fat percentage in Caucasians, its accuracy in ethnic groups such as the Nepali population requires further validation [47].
BMI showed relatively poor performance in predicting MetS, with AUCs of 0.586 for females and 0.571 for males. The optimal cutoff values (> 23.2 for females, > 23.9 for males) were very close to the WHO-recommended average threshold of 23 kg/m² for South Asian overweight individuals, supporting the use of lower BMI limits for South Asian populations [48]. This finding aligns with the study by Xu et al. in China, where BMI had lower AUCs than other indices did [49]. However, in South Africa, Sekgala et al. reported that BMI has good predictive ability for MetS [50]. Despite its limitations in assessing body composition and fat distribution, particularly in Asian populations [19], BMI remains widely used because of its simplicity and established cutoffs for obesity, which may still be valuable in Nepal's public health programs.
The BAI and ABSI displayed the poorest performance among all indices, with AUCs close to 0.5, indicating minimal predictive ability for MetS. This finding contrasts with some studies that reported that BAI and ABSI are useful for predicting cardiometabolic risk [12, 51]. However, the poor performance of the BAI and ABSI in our study is consistent with some international findings. Zhang et al. in China also reported that BAI had inferior predictive power for MetS compared with BMI and WC [52], and Haghighatdoost et al. in Iran reported ABSI as a weak predictor for MetS [53]. These discrepancies might stem from population differences or the specific outcome assessed. While the BAI estimates body fat percentage without weight, and the ABSI aims to be a sensitive indicator of abdominal adiposity, BAI performance varies across populations [54]. Our results suggest that these methods are not suitable for MetS screening in Nepali adults.
The varying performance of these anthropometric indices highlights the complex relationships among body composition, fat distribution, and metabolic health. The superior performance of the VAI and LAP suggests that incorporating both anthropometric and metabolic parameters may provide a more accurate assessment of metabolic risk. However, the strong performance of simpler indices such as WHR and WHtR indicates that these measures can still serve as valuable screening tools, especially in resource-limited settings such as Nepal. The differences in optimal cutoff values between our study and those reported in other populations emphasize the importance of population-specific thresholds and differences in the use of MetS-defining criteria. Factors such as genetics, lifestyle, and environmental influences can affect body composition and fat distribution, leading to variations in the relationships between anthropometric measures and metabolic risk across different ethnic groups [55]. For example, the lower optimal BMI cutoffs found in our study support previous research suggesting that South Asians may have greater cardiometabolic risk at lower BMI levels than Caucasians [5].
This study has several strengths, including its relatively large sample size, comprehensive evaluation of multiple anthropometric indices, and focus on an understudied population. This is the first study of its kind in Nepal, providing critical data on the performance of various anthropometric indices. The inclusion of both traditional and novel indices allows for a thorough comparison of their predictive abilities, whereas sex-specific cutoff values enhance clinical applicability in the Nepali context. However, there are several limitations as well. The cross-sectional design prevents the establishment of causal relationships between anthropometric indices and MetS. The study population was limited to only Gandaki Province, which may restrict generalizability to other provinces or ethnic groups within Nepal. Future longitudinal studies involving diverse Nepali populations are necessary to confirm and expand upon these results.
These findings will have significant public health and clinical implications in Nepal. The identified optimal cutoff values for various indices can serve as reference points for screening and risk assessment in Nepali adults. Indices such as the VAI, LAP, and WHR showed strong performance and should be considered for routine health assessments and MetS screening programs. The selection of indices should consider resource availability, ease of measurement, and specific population characteristics in the Nepali healthcare context. Future research should validate these findings in larger, more diverse Nepali populations and assess the predictive ability of these indices for MetS and cardiovascular events. Evaluating the cost-effectiveness of using these indices in population-wide screening programs would also provide valuable insights. Finally, investigating the underlying biological mechanisms linking these anthropometric measures to metabolic dysfunction in South Asian populations, including Nepali subgroups, would contribute to a deeper understanding of the observed relationships.
In conclusion, this first-of-its-kind study in Nepal provides a comprehensive evaluation of various anthropometric indices for predicting MetS in Nepali adults. While the VAI and LAP demonstrated the strongest overall performance, simpler measures such as the WHR and WHtR also showed good predictive ability. The identified optimal cutoff values offer clinically useful thresholds for risk assessment in the Nepali population. These findings contribute to the growing body of evidence on population-specific anthropometric indices and their relationship with metabolic health, potentially informing more effective strategies for MetS screening and prevention in Nepal and similar South Asian populations.