Quantification of lipoproteins by proton nuclear magnetic resonance spectroscopy (1H-NMRS) improves the prediction of cardiac autonomic dysfunction in patients with type 1 diabetes

• Published in: Journal of endocrinological investigation Vol. 47; no. 8; pp. 2075 - 2085
• Main Authors: Nattero-Chávez, L., Insenser, M., Amigó, N., Samino, S., Martínez-Micaelo, N., Dorado Avendaño, B., Quintero Tobar, A., Escobar-Morreale, H. F., Luque-Ramírez, M.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2024; Springer Nature B.V
• Subjects: Autonomic nervous system; C-reactive protein; Cholesterol; D-Galactosamine; Diabetes; Diabetes mellitus (insulin dependent); Diabetic neuropathy; Endocrinology; Fibrinogen; Glycoproteins; High density lipoprotein; Internal Medicine; Lipoproteins; Magnetic resonance spectroscopy; Medicine; Medicine & Public Health; Metabolic Diseases; N-Acetylglucosamine; Neuropathy; NMR; Nuclear magnetic resonance; Original Article; Prediction models; Spectrum analysis; Statistical analysis; Triglycerides; Tumor necrosis factor-α; Glycoprotein profile; Lipoproteins; Autonomic nervous system; Type 1 diabetes; Proton nuclear magnetic resonance spectroscopy; Lipid profile; Triglycerides; Cardioautonomic neuropathy
• ISSN: 1720-8386; 0391-4097; 1720-8386
• DOI: 10.1007/s40618-023-02289-9

Aims To assess if advanced characterization of serum glycoprotein and lipoprotein profile, measured by proton nuclear magnetic resonance spectroscopy ( 1 H-NMRS) improves a predictive clinical model of cardioautonomic neuropathy (CAN) in subjects with type 1 diabetes (T1D). Methods Cross-sectional study (ClinicalTrials.gov Identifier: NCT04950634 ). CAN was diagnosed using Ewing’s score. Advanced characterization of macromolecular complexes including glycoprotein and lipoprotein profiles in serum samples were measured by 1 H-NMRS. We addressed the relationships between these biomarkers and CAN using correlation and regression analyses. Diagnostic performance was assessed by analyzing their areas under the receiver operating characteristic curves (AUC ROC ). Results Three hundred and twenty-three patients were included (46% female, mean age and duration of diabetes of 41 ± 13 years and 19 ± 11 years, respectively). The overall prevalence of CAN was 28% [95% confidence interval (95%CI): 23; 33]. Glycoproteins such as N-acetylglucosamine/galactosamine and sialic acid showed strong correlations with inflammatory markers such as high-sensitive C-reactive protein, fibrinogen, IL-10, IL-6, and TNF-α. On the contrary, we did not find any association between the former and CAN. A stepwise binary logistic regression model (R 2  = 0.078; P  = 0.003) retained intermediate-density lipoprotein–triglycerides (IDL–TG) [β:0.082 (95%CI: 0.005; 0.160); P  = 0.039], high-density lipoprotein-triglycerides (HDL–TGL)/HDL–Cholesterol [β:3.633 (95%CI: 0.873; 6.394); P  = 0.010], and large-HDL particle number [β: 3.710 (95%CI: 0.677; 6.744); P  = 0.001] as statistically significant determinants of CAN. Adding these lipoprotein particles to a clinical prediction model of CAN that included age, duration of diabetes, and A 1c enhanced its diagnostic performance, improving AUC ROC from 0.546 (95%CI: 0.404; 0.688) to 0.728 (95%CI: 0.616; 0.840). Conclusions When added to clinical variables, 1 H-NMRS-lipoprotein particle profiles may be helpful to identify those patients with T1D at risk of CAN.