# Cardiovascular risk equations in type 1 diabetes: from generic models to personalized prediction

## TL;DR

Current cardiovascular risk tools are not well-suited for people with type 1 diabetes, but new models like Steno-Risk offer better predictions when combined with imaging techniques.

## Contribution

The paper highlights the inadequacy of generic cardiovascular risk models for T1D and proposes a multi-modal precision medicine approach integrating T1D-specific equations and imaging.

## Key findings

- Generic risk calculators like SCORE2 and ACC/AHA perform poorly in T1D populations.
- T1D-specific models like Steno-Risk show better performance in identifying high-risk individuals.
- Combining risk equations with subclinical atherosclerosis screening improves risk classification.

## Abstract

Cardiovascular disease (CVD) remains the leading cause of premature mortality in type 1 diabetes (T1D). Despite this burden, current prevention guidelines and risk stratification tools are largely extrapolated from type 2 diabetes or general populations, often failing to capture the unique pathophysiological drivers of atherosclerosis in T1D. This narrative review aims to critically evaluate current cardiovascular prevention strategies, identify the limitations of generic risk calculators, and analyse the clinical utility and discriminatory performance of population-specific risk equations.

Current clinical practice guidelines provide heterogeneous and inconsistent recommendations for T1D. Generic risk calculators (e.g. SCORE2, ACC/AHA pooled cohort equations) are limited by short-term prediction horizons and a lack of validation in T1D cohorts, frequently leading to poor diagnostic performances in this population. Conversely, T1D-specific equations, such as Steno Type 1 Risk Engine (Steno-Risk) and the emerging LIFE-T1D model, incorporate disease-specific variables such as diabetes duration or albuminuria. Available evidence indicates that T1D equations as Steno-Risk demonstrate superior discriminatory performance compared to generic scores identifying those individuals with higher cardiovascular risk. However, despite improved calibration, specific tools still classify a substantial proportion of patients with established subclinical atherosclerotic disease as ‘moderate risk,’ creating a grey area that complicates therapeutic decision-making.

Generic cardiovascular risk algorithms are inadequate for the T1D population. While specific equations represent a significant advancement toward accurate stratification, mathematical models alone may be insufficient. Moving toward precision medicine requires a combined approach that integrates validated T1D-specific risk calculators with subclinical atherosclerosis screening (e.g. carotid ultrasound, CAC scoring) and novel biomarkers. This synergistic strategy is essential to refine classification in the ‘moderate risk’ category and guide individualised cardioprotective interventions.

Generic cardiovascular risk prediction tools are inadequate for individuals with type 1 diabetes (T1D), as they show poor predictive performances and fail to account for specific disease-related factors.Population-specific risk equations in T1D, such as Steno-Risk, demonstrate superior discriminatory performance identifying those with a higher cardiovascular risk compared to standard generic guideline risk calculators or classifications.To resolve the uncertainty in the ‘moderate-risk’ category, precision medicine in T1D requires a multi-modal approach that integrates validated specific equations with direct screening for subclinical atherosclerosis using imaging techniques.

Generic cardiovascular risk prediction tools are inadequate for individuals with type 1 diabetes (T1D), as they show poor predictive performances and fail to account for specific disease-related factors.

Population-specific risk equations in T1D, such as Steno-Risk, demonstrate superior discriminatory performance identifying those with a higher cardiovascular risk compared to standard generic guideline risk calculators or classifications.

To resolve the uncertainty in the ‘moderate-risk’ category, precision medicine in T1D requires a multi-modal approach that integrates validated specific equations with direct screening for subclinical atherosclerosis using imaging techniques.