A machine learning based approach to identify carotid subclinical atherosclerosis endotypes

Qiao Sen Chen, Otto Bergman, Louise Ziegler, Damiano Baldassarre, Fabrizio Veglia, Elena Tremoli, Rona J. Strawbridge, Antonio Gallo, Matteo Pirro, Andries J. Smit, Sudhir Kurl, Kai Savonen, Lars Lind, Per Eriksson, Bruna Gigante*

*Corresponding author voor dit werk

OnderzoeksoutputAcademicpeer review

1 Citaat (Scopus)
12 Downloads (Pure)

Samenvatting

Aims: To define endotypes of carotid subclinical atherosclerosis. 

Methods and results: We integrated demographic, clinical, and molecular data (n = 124) with ultrasonographic carotid measurements from study participants in the IMPROVE cohort (n = 3340). We applied a neural network algorithm and hierarchical clustering to identify carotid atherosclerosis endotypes. A measure of carotid subclinical atherosclerosis, the c-IMTmean-max, was used to extract atherosclerosis-related features and SHapley Additive exPlanations (SHAP) to reveal endotypes. The association of endotypes with carotid ultrasonographic measurements at baseline, after 30 months, and with the 3-year atherosclerotic cardiovascular disease (ASCVD) risk was estimated by linear (β, SE) and Cox [hazard ratio (HR), 95% confidence interval (CI)] regression models. Crude estimates were adjusted by common cardiovascular risk factors, and baseline ultrasonographic measures. Improvement in ASCVD risk prediction was evaluated by C-statistic and by net reclassification improvement with reference to SCORE2, c-IMTmean-max, and presence of carotid plaques. An ensemble stacking model was used to predict endotypes in an independent validation cohort, the PIVUS (n = 1061). We identified four endotypes able to differentiate carotid atherosclerosis risk profiles from mild (endotype 1) to severe (endotype 4). SHAP identified endotype-shared variables (age, biological sex, and systolic blood pressure) and endotype-specific biomarkers. In the IMPROVE, as compared to endotype 1, endotype 4 associated with the thickest c-IMT at baseline (β, SE) 0.36 (0.014), the highest number of plaques 1.65 (0.075), the fastest c-IMT progression 0.06 (0.013), and the highest ASCVD risk (HR, 95% CI) (1.95, 1.18-3.23). Baseline and progression measures of carotid subclinical atherosclerosis and ASCVD risk were associated with the predicted endotypes in the PIVUS. Endotypes consistently improved measures of ASCVD risk discrimination and reclassification in both study populations. 

Conclusions: We report four replicable subclinical carotid atherosclerosis-endotypes associated with progression of atherosclerosis and ASCVD risk in two independent populations. Our approach based on endotypes can be applied for precision medicine in ASCVD prevention.

Originele taal-2English
Pagina's (van-tot)2594-2606
Aantal pagina's13
TijdschriftCardiovascular Research
Volume119
Nummer van het tijdschrift16
DOI's
StatusPublished - nov.-2023

Vingerafdruk

Duik in de onderzoeksthema's van 'A machine learning based approach to identify carotid subclinical atherosclerosis endotypes'. Samen vormen ze een unieke vingerafdruk.

Citeer dit