Unveiling Silent Atherosclerosis in Type 1 Diabetes: The Role of Glycoprotein and Lipoprotein Lipidomics, and Cardiac Autonomic Neuropathy
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)...
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| Published in | Metabolites Vol. 15; no. 1; p. 55 |
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| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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01.01.2025
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| ISSN | 2218-1989 2218-1989 |
| DOI | 10.3390/metabo15010055 |
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| Abstract | 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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| AbstractList | 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.
We conducted a cross-sectional study including 256 patients with T1D. Serum glycoprotein and lipoprotein lipidomics profiles were determined using
H-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.
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.
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. 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 [sup.1]H-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. 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. 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.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. 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 1 H-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. |
| Audience | Academic |
| Author | Michael Fernández, Antonio Carlos Escobar-Morreale, Héctor F. Quintero Tobar, Alejandra de Lope Quiñones, Sara Quiñones-Silva, Jhonatan Luque-Ramírez, Manuel Martínez García, María Ángeles Insenser Nieto, María Nattero-Chávez, Lía Dorado Avendaño, Beatriz |
| AuthorAffiliation | 2 Department of Endocrinology and Nutrition, Hospital Universitario Ramón y Cajal, 28034 Madrid, Spain; jhonatanboris.quinones@salud.madrid.org (J.Q.-S.) 1 Diabetes, Obesity and Human Reproduction Research Group, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS) & Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Universidad de Alcalá, 28034 Madrid, Spain 3 Department of Radiology, Hospital Universitario Ramón y Cajal, 28034 Madrid, Spain |
| AuthorAffiliation_xml | – name: 1 Diabetes, Obesity and Human Reproduction Research Group, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS) & Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Universidad de Alcalá, 28034 Madrid, Spain – name: 3 Department of Radiology, Hospital Universitario Ramón y Cajal, 28034 Madrid, Spain – name: 2 Department of Endocrinology and Nutrition, Hospital Universitario Ramón y Cajal, 28034 Madrid, Spain; jhonatanboris.quinones@salud.madrid.org (J.Q.-S.) |
| Author_xml | – sequence: 1 givenname: Sara surname: de Lope Quiñones fullname: de Lope Quiñones, Sara – sequence: 2 givenname: Manuel orcidid: 0000-0002-6002-4237 surname: Luque-Ramírez fullname: Luque-Ramírez, Manuel – sequence: 3 givenname: Antonio Carlos orcidid: 0009-0002-5626-0218 surname: Michael Fernández fullname: Michael Fernández, Antonio Carlos – sequence: 4 givenname: Alejandra surname: Quintero Tobar fullname: Quintero Tobar, Alejandra – sequence: 5 givenname: Jhonatan orcidid: 0000-0001-6061-9751 surname: Quiñones-Silva fullname: Quiñones-Silva, Jhonatan – sequence: 6 givenname: María Ángeles orcidid: 0000-0002-8354-7284 surname: Martínez García fullname: Martínez García, María Ángeles – sequence: 7 givenname: María orcidid: 0000-0003-3168-7856 surname: Insenser Nieto fullname: Insenser Nieto, María – sequence: 8 givenname: Beatriz orcidid: 0000-0003-4751-3017 surname: Dorado Avendaño fullname: Dorado Avendaño, Beatriz – sequence: 9 givenname: Héctor F. orcidid: 0000-0002-6890-1644 surname: Escobar-Morreale fullname: Escobar-Morreale, Héctor F. – sequence: 10 givenname: Lía orcidid: 0000-0002-9758-9397 surname: Nattero-Chávez fullname: Nattero-Chávez, Lía |
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| Keywords | proton nuclear magnetic resonance spectroscopy type 1 diabetes mellitus glycoprotein profile metabolomics cardioautonomic neuropathy cardiac autonomic neuropathy lipid profile triglycerides subclinical atherosclerosis carotid plaques lipoproteins |
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| Snippet | Introduction: This study aimed to evaluate whether glycoprotein and lipoprotein lipidomics profiles could enhance a clinical predictive model for carotid... This study aimed to evaluate whether glycoprotein and lipoprotein lipidomics profiles could enhance a clinical predictive model for carotid subclinical... Introduction: This study aimed to evaluate whether glycoprotein and lipoprotein lipidomics profiles could enhance a clinical predictive model for carotid... |
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| SubjectTerms | Arteriosclerosis Atherosclerosis Autonomic nervous system Blood pressure cardiac autonomic neuropathy cardioautonomic neuropathy Cardiovascular disease carotid plaques Diabetes Diabetes mellitus (insulin dependent) Diabetic neuropathy Diabetic retinopathy Electrocardiography Foot diseases Glucose glycoprotein profile Glycoproteins Heart Heart rate High density lipoprotein lipid profile Lipoproteins Magnetic resonance spectroscopy Medical history Medical research Medicine, Experimental Neuropathy NMR Nuclear magnetic resonance Nuclear magnetic resonance spectroscopy Prediction models Risk factors Risk groups Spectrum analysis Statistical analysis Type 1 diabetes Ultrasonic imaging |
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| Title | Unveiling Silent Atherosclerosis in Type 1 Diabetes: The Role of Glycoprotein and Lipoprotein Lipidomics, and Cardiac Autonomic Neuropathy |
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