Precision cardiovascular risk prediction in type 1 diabetes: An IMI2 SOPHIA analysis

Q: What is the clinical evidence from this study?

Study design: Cross-Sectional. Population: Type 1 diabetes (n=44212). Intervention: Discordant phenotypic risk profiles vs. Standard SCORE2 risk prediction model. Primary outcome: Prediction of major adverse cardiac events (MACE).

Sofia Pazmino; Stefanie Schmid; Jordi Blanch; Daniel Coral; Nele Steenackers; Nicole Forestier; Laura Gallardo‐Nuell; Naichuan Zhang; Birgit Reinhart-Steininger; Yenny Leal; Amar van Laar; Rafael Ramos; Shreekar Bharadwaj Araveti; Ditte F. Heidemann; Kinga Nowak; Jonathan Rosen; Carmen Hurtado del Pozo; Christian-Dominik Möller; Chantal Mathieu; Carel W. le Roux; Paul W. Franks; José Manuel Fernández‐Real; Stefanie Lanzinger; Bart Van der Schueren

doi:10.1038/s41467-026-72029-z

Key result

Adding discordant phenotypic risk profiles to standard models improves MACE and retinopathy prediction in T1D.

n=44,212

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Why the study?

CVD is the leading cause of morbidity and mortality in T1D, underscoring the urgent need for accurate risk assessment to support tailored CVD prevention.

Does adding discordant phenotypic risk profiles to standard risk models improve the prediction of cardiovascular events in adults with type 1 diabetes?

Key Points

To evaluate the applicability and predictive value of discordant body mass index and cardiometabolic biomarker phenotypic risk profiles for cardiovascular disease assessment in individuals with type 1 diabetes.
Analyzed multicenter cross-sectional data from 44,212 adults (>18 years) with type 1 diabetes followed between 2010 and 2022 across three European registries: KUL in Belgium (N=2,241), DPV in Germany/Austria/Luxembourg/Switzerland (N=25,422), and SIDIAP in Spain (N=16,549).
Calculated discordant scores from population-level associations between body mass index and 12 CVD-related biomarkers, allocating individuals into phenotypic clusters using UMAP and evaluating major adverse cardiac events (MACE) risk prediction compared with SCORE2 models.
The discordant hyperglycemic profile comprised the majority of individuals (55% to 76%), with mean HbA1c% significantly lower (p-value <0.0001) in the concordant cluster across all cohorts: KUL females 7.3±1.5 vs 7.9±1.2, DPV females 7.4±1.8 vs 8.2±1.7, and SIDIAP females 7.6±1.4 vs 8.3±1.7.
Adding profile allocation probabilities significantly improved nested prediction models for male macrovascular complications in KUL, male MACE in SIDIAP, extended female MACE, and retinopathy in KUL males and DPV females.
At a 10% 10-year MACE risk threshold, profile-based stratification yielded a net benefit of 2 additional correct interventions and 5,746 unnecessary interventions correctly avoided per 10,000 tested men in SIDIAP.

Study Design

Type

Cross-Sectional (n=44,212)

Multicenter

Yes

Structured PICO

Does adding discordant phenotypic risk profiles to standard risk models improve the prediction of cardiovascular events in adults with type 1 diabetes?

P

Population

44,212 adults (>18 years) living with type 1 diabetes (T1D) from three European registries (KUL, DPV, SIDIAP).

I

Intervention

Addition of discordant phenotypic risk profile allocation probabilities (based on BMI and cardiometabolic biomarkers) to standard cardiovascular risk prediction models.

C

Comparator

Standard cardiovascular risk prediction models (SCORE2) based solely on traditional biomarkers without phenotypic profile allocation probabilities.

O

Outcome

Major adverse cardiac events (MACE) over a 10-year follow-up period.hard clinical

Limitations

Cross-sectional allocation of risk profiles raises uncertainty about their temporal stability
Limited generalizability given the reliance on predominantly European data with relatively little heterogeneity in ethnicity and health care systems

Implication

Integrating BMI-cardiometabolic discordant risk profiles into standard risk engines improves cardiovascular risk prediction and decision-making in patients with type 1 diabetes.

Abstract

Cardiovascular disease (CVD) is a major long-term complication and the leading cause of morbidity and mortality among individuals with type 1 diabetes (T1D), with a substantially higher prevalence compared to the general population and driven by multiple interrelated risk factors—underscoring the urgent need for accurate risk assessment. To support more tailored approaches to CVD prevention, recently, five discordant phenotypic risk profiles for CVD were identified in the general population in Europe with diverse relationship between body mass index and cardiometabolic biomarkers. Here, we show their applicability in 44,212 people with T1D. Improved glycemic control was linked to a decrease in CVD risk as people with T1D with lower glycated hemoglobin belonged to the baseline concordant cluster. This supports the contention that glycemic control in people with T1D is an integral part of lowering CVD risk.

Discussion

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Cite This Study

Pazmino et al. (2026) conducted a cross-sectional in Type 1 diabetes (n=44,212). Discordant phenotypic risk profiles vs. Standard SCORE2 risk prediction model was evaluated on Prediction of major adverse cardiac events (MACE). Adding discordant phenotypic risk profiles to standard cardiovascular risk models improved the prediction of major adverse cardiac events and retinopathy in adults with type 1 diabetes.

synapsesocial.com/papers/69e3215140886becb65408d1 https://doi.org/10.1038/s41467-026-72029-z

Topics

Cardiovascular prevention

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Precision cardiovascular risk prediction in type 1 diabetes: An IMI2 SOPHIA analysis

Figures

Key Points

Study Design

Structured PICO

Limitations

Implication

Abstract

Discussion

Cite This Study

Topics

Precision cardiovascular risk prediction in type 1 diabetes: An IMI2 SOPHIA analysis

Figures

Key Points

Study Design

Structured PICO

Limitations

Implication

Abstract

Discussion

Cite This Study

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