Predicted Excess Cardiovascular Age and a Reverse Socioeconomic Gradient in a Middle-Income Latin American Country: A Population-Based Analysis of 163,889 Peruvians.

Predicted cardiovascular age (heart age) translates the risk-factor profile into an equivalent age, which may facilitate interpretation of estimated cardiovascular risk. Excess cardiovascular age describes, in years, the integrated burden of modifiable risk factors and its distribution in the population. This study aimed to quantify socioeconomic and geographic inequalities in predicted excess cardiovascular age among Peruvian adults using standardized inequality measures, and to describe its temporal variation from 2014 to 2024. We analyzed ENDES Peru 2014-2024 data for adults aged 30-74 years. Cardiovascular age was estimated using the body mass index (BMI)-based non-laboratory Framingham equation, and excess was defined as the difference between cardiovascular age and chronological age. Weighted means and 95% confidence intervals were estimated accounting for the complex survey design. Socioeconomic inequalities were assessed using absolute and relative gaps between extreme wealth quintiles (Q5-Q1), the Slope Index of Inequality (SII), the Relative Index of Inequality (RII), and the concentration index/curve. Among 163,889 participants, mean excess cardiovascular age was 9.64 years (95% CI: 9.48-9.80), with similar estimates in women (9.73; 95% CI: 9.52-9.94) and men (9.54; 95% CI: 9.33-9.75). Temporal variation was observed, peaking in 2021 (10.91; 95% CI: 10.57-11.25). Excess increased with wealth (Q1: 7.14 vs. Q5: 11.25 years), with an SII of 5.04 years (95% CI: 4.71-5.37) and a concentration index of 0.087. The gradient was steeper in men (SII 6.14) than in women (SII 3.90). Geographically, Metropolitan Lima had higher excess than the Highlands (11.17 vs. 7.45 years), and urban areas exceeded rural areas (10.28 vs. 7.25 years). In Peru, adults aged 30-74 years had a mean predicted excess cardiovascular age of about 10 years, with a consistent pro-rich and urban/coastal concentration pattern, more pronounced among men. Because this metric is derived from a risk prediction equation, these findings should be interpreted as surveillance-oriented evidence of inequalities in estimated risk-factor burden, not as evidence of observed cardiovascular disease, subclinical cardiovascular damage, causal mechanisms, or tested intervention effects.