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<p>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 (<i>N</i> = 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 (<i>N</i> = 175). However, subfoveal and temporal choroidal area thickness significantly decreased in individuals with plaques (<i>P</i> ≤ 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.</p>

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Choroidal thickness as predictor of subclinical carotid atherosclerosis in adults with type 1 diabetes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月11日

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  • Esmeralda Castelblanco,
  • Ignacio Salvador-Miras,
  • Marc Carbonell,
  • Mònica Gratacòs,
  • Alicia Traveset,
  • Eudald Correig,
  • Marta Hernández,
  • Núria Alonso,
  • Josep Franch-Nadal,
  • Dídac Mauricio

摘要

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.