Castelblanco Esmeralda, Salvador-Miras Ignacio, Carbonell Marc, Gratacòs Mònica, Traveset Alicia, Correig Eudald, Hernández Marta, Alonso Núria, Franch-Nadal Josep, Mauricio Dídac
Department of Medicine, Division of Endocrinology, Metabolism and Lipid Research, Washington University School of Medicine, St Louis, MO, 63110, USA.
Department of Endocrinology & Nutrition, Hospital de la Santa Creu i Sant Pau, Sant Quintí, 89, 08041, Barcelona, Spain.
Sci Rep. 2025 Mar 11;15(1):8360. doi: 10.1038/s41598-025-93534-z.
Patients with Type 1 Diabetes (T1DM) have a higher risk of cardiovascular disease. This study used carotid ultrasound to identify subclinical carotid plaques and Optical Coherence Tomography (OCT) to evaluate ophthalmological markers as predictors of carotid plaque presence in 242 adults with T1DM, employing machine learning models for early risk assessment. Individuals with carotid plaques (N = 67) did not show significant differences in retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL) and inner plexiform layer (IPL) complex compared to those without (N = 175). However, subfoveal and temporal choroidal area thickness significantly decreased in individuals with plaques (P ≤ 0.01). Machine learning identified age, hypertension, dyslipidemia, smoking, and diabetic retinopathy as key predictors for plaque presence, while ophthalmological measures made a modest contribution. Choroidal thickness exhibited an inverse relationship with plaque risk. Despite robust accuracy and high specificity (82-85% and 92-98%, respectively), the models were overly conservative in predicting positive instances (balanced accuracy of 0.60 for the left eye and 0.71 for the right eye). If further validated, choroidal thickness could complement traditional risk factors as an early marker of CV risk in T1DM patients. Integrating this measure in specialized clinical settings could help identify individuals who may need additional cardiovascular assessments.
1型糖尿病（T1DM）患者患心血管疾病的风险更高。本研究采用颈动脉超声识别亚临床颈动脉斑块，并使用光学相干断层扫描（OCT）评估眼科标志物，作为242例T1DM成年患者颈动脉斑块存在情况的预测指标，采用机器学习模型进行早期风险评估。与无颈动脉斑块的患者（N = 175）相比，有颈动脉斑块的患者（N = 67）在视网膜神经纤维层（RNFL）、神经节细胞层（GCL）和内网状层（IPL）复合体方面未显示出显著差异。然而，有斑块的患者黄斑下和颞侧脉络膜面积厚度显著降低（P≤0.01）。机器学习确定年龄、高血压、血脂异常、吸烟和糖尿病视网膜病变是斑块存在的关键预测因素，而眼科测量的贡献较小。脉络膜厚度与斑块风险呈负相关。尽管模型具有较高的准确性和特异性（分别为82 - 85%和92 - 98%），但在预测阳性病例时过于保守（左眼平衡准确率为0.60，右眼为0.71）。如果进一步验证，脉络膜厚度可作为T1DM患者心血管风险的早期标志物，补充传统风险因素。在专业临床环境中纳入这一测量指标有助于识别可能需要额外心血管评估的个体。
