Key result
Adding lipidomics and glycoprotein profiles fails to improve prediction of subclinical atherosclerosis over traditional risk factors.
Why the study?
It was unclear whether glycoprotein and lipoprotein lipidomics profiles, along with cardiac autonomic neuropathy, could improve clinical predictive models for carotid subclinical atherosclerosis in type 1 diabetes.
Does the addition of advanced serum glycoprotein and lipoprotein lipidomics profiling improve the prediction of subclinical atherosclerosis in adults with Type 1 Diabetes?
Cross-Sectional (n=256)
No
Does the addition of advanced serum glycoprotein and lipoprotein lipidomics profiling improve the prediction of subclinical atherosclerosis in adults with Type 1 Diabetes?
Effect estimate: AUC 0.902 (95% CI 0.863-0.940)
Absolute Event Rate: 0.902% vs 0.88%
p-value: p=0.1
Advanced lipidomics and glycoprotein profiling are associated with subclinical atherosclerosis in patients with Type 1 Diabetes but do not improve diagnostic accuracy beyond simple clinical predictors like age, diabetes duration, and smoking.
Should not yet change subclinical atherosclerosis screening in type 1 diabetes; leaves open lipidomics utility in prospective or refined models.
Introduction: This study aimed to evaluate whether glycoprotein and lipoprotein lipidomics profiles could enhance a clinical predictive model for carotid subclinical atherosclerosis in patients with type 1 diabetes (T1D). Additionally, we assessed the influence of cardiac autonomic neuropathy (CAN) on these predictive models. Methods: We conducted a cross-sectional study including 256 patients with T1D. Serum glycoprotein and lipoprotein lipidomics profiles were determined using 1H-NMR spectroscopy. Subclinical atherosclerosis was defined as carotid intima-media thickness (cIMT) ≥ 1.5 mm. CAN was identified using the Clarke score. Predictive models were built and their performance evaluated using receiver operating characteristic curves and cross-validation. Results: Subclinical atherosclerosis was detected in 32% of participants. Patients with both CAN and atherosclerosis were older, had a longer duration of diabetes, and were more likely to present with bilateral carotid disease. Clinical predictors such as age, duration of diabetes, and smoking status remained the strongest determinants of subclinical atherosclerosis [AUC = 0.88 (95%CI: 0.84–0.93)]. While glycoprotein and lipoprotein lipidomics profiles were associated with atherosclerosis, their inclusion in the clinical model did not significantly improve its diagnostic performance. Stratification by the presence of CAN revealed no impact on the model’s ability to predict subclinical atherosclerosis, underscoring its robustness across different risk subgroups. Conclusions: In a cohort of patients with T1D, subclinical atherosclerosis was strongly associated with traditional clinical risk factors. Advanced glycoprotein and lipoprotein lipidomics profiling, although associated with atherosclerosis, did not enhance the diagnostic accuracy of predictive models beyond clinical variables. The predictive model remained effective even in the presence of CAN, highlighting its reliability as a screening tool for identifying patients at risk of subclinical atherosclerosis.
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Quiñones et al. (2025) conducted a cross-sectional in Type 1 diabetes with potential subclinical atherosclerosis (n=256). Advanced glycoprotein and lipoprotein lipidomics profiles vs. Traditional clinical risk factors alone (age, duration of diabetes, smoking status) was evaluated on Diagnostic performance (Area Under the Curve) for subclinical carotid atherosclerosis (AUC 0.902, 95% CI 0.863-0.940, p=0.1). The addition of advanced glycoprotein and lipoprotein lipidomics profiles to traditional clinical risk factors did not significantly improve the predictive model's diagnostic accuracy for subclinical atherosclerosis (AUC 0.902 vs 0.88; p=0.1).
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