Anthropometric Markers as Cardiovascular Predictors: A Comparison Between Conventional Cut-Off Points and Population Percentiles

Background: Cardiovascular diseases (CVD) are the leading cause of mortality worldwide. An ongoing debate exists regarding the most appropriate methods for assessing adiposity-associated cardiovascular risk in working populations. The aim of the study was to evaluate the association between different anthropometric markers and the development of cardiovascular events in Peruvian workers. Methods: This is a retrospective cohort study of 10,300 workers (2014 - 2021). Body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), and the WC-BMI index were evaluated as predictors of CVD (myocardial infarction and/or stroke). Population percentiles (75th and 95th) and conventional cut-off points were used. Additionally, conventional cut-off points were employed, such as 0.5 for WHtR, and according to the Adult Treatment Panel III (ATP-III) (≥ 102 cm in men and ≥ 88 cm in women) or International Diabetes Federation (IDF) (≥ 80 cm in men and ≥ 90 cm in women) for abdominal obesity. Results: Obesity, as measured by BMI, showed a significant association with myocardial infarction (adjusted hazard ratio (aHR): 4.07; 95% confidence interval (CI): 1.08 - 15.4). Very high WC and WHtR (95th percentile) presented a greater risk of total cardiovascular events (aHR: 2.40; 95% CI: 1.12 - 5.12 and aHR: 2.57; 95% CI: 1.17-5.64, respectively), being particularly predictive for stroke (aHR: 4.53; 95% CI: 1.13 - 18.1 and aHR: 4.05; 95% CI: 1.01 - 16.3, respectively). No significant associations were found using conventional cut-off points for WHtR and abdominal obesity. Conclusions: Central adiposity markers, especially WC and WHtR, were evaluated through population percentiles and were better predictors of cardiovascular events than BMI or conventional cut-off points in the Peruvian working population. These findings support reorienting obesity definitions toward cardiovascular risk assessment using population-specific percentiles rather than relying exclusively on universal adiposity thresholds.