RESUMO
BACKGROUND: The triglyceride/HDL cholesterol (TG/HDL-C) ratio and the Lipoprotein Insulin Resistance (LP-IR) score are lipid markers of insulin resistance. Their associations with carotid intima media thickness (cIMT; subclinical atherosclerosis) and incident cardiovascular disease (CVD) have not been thoroughly investigated. METHODS: In a cross-sectional cohort (89 subjects without type 2 diabetes (T2D) and 81 subjects with T2D we determined cIMT (ultrasound), homeostasis model assessment of insulin resistance (HOMA-IR) and the TG/HDL-C ratio. The LP-IR score, based on 6 lipoprotein characteristics determined by nuclear magnetic resonance spectroscopy, was measured in 123 participants. A prospective study was carried out among 6232 participants (Prevention of REnal and Vascular ENd-stage Disease study). RESULTS: Cross-sectionally, the adjusted associations of HOMA-IR, the TG/HDL-C ratio and the LP-IR score with cIMT were approximately similar (standardized ß = 0.34 (95 % CI 0.19-0.48), 0.24 (95 % CI 0.09-039) and 0.41 (95 % CI 0.23--0.59), respectively). Prospectively, 507 new cases of CVD were observed after a median follow-up of 8.2 (interquartile range 7.5-8.8) years. HOMA-IR, the TG/HDL-C ratio and LP-IR were each associated with incident CVD independent of potential confounders (HR 1.12, 95 % CI 1.02-1.24;1.22, 95 % CI 1.11-1.35 and 1.15. 95 % CI 1.01-1.31, respectively). The association of the TG/HDL-C ratio with incident CVD was somewhat stronger than that of HOMA-IR. CONCLUSION: Lipoprotein-based markers of insulin resistance are at least as strongly associated with subclinical atherosclerosis and clinical atherosclerosis development as HOMA-IR, obviating the need to measure insulin to determine the impact of insulin resistance. For practical purposes, the easily obtainable TG/HDL-C ratio may suffice.
Assuntos
Aterosclerose , Doenças Cardiovasculares , Diabetes Mellitus Tipo 2 , Resistência à Insulina , Humanos , Aterosclerose/diagnóstico , Doenças Cardiovasculares/diagnóstico , Espessura Intima-Media Carotídea , HDL-Colesterol , Estudos Transversais , Lipoproteínas , Estudos Prospectivos , TriglicerídeosRESUMO
Undiagnosed type 2 diabetes (T2D) remains a major public health concern. The global estimation of undiagnosed diabetes is about 46%, being this situation more critical in developing countries. Therefore, we proposed a non-invasive method to quantify glycated hemoglobin (HbA1c) and glucose in vivo. We developed a technique based on Raman spectroscopy, RReliefF as a feature selection method, and regression based on feed-forward artificial neural networks (FFNN). The spectra were obtained from the forearm, wrist, and index finger of 46 individuals. The use of FFNN allowed us to achieve an error in the predictive model of 0.69% for HbA1c and 30.12 mg/dL for glucose. Patients were classified according to HbA1c values into three categories: healthy, prediabetes, and T2D. The proposed method obtained a specificity and sensitivity of 87.50% and 80.77%, respectively. This work demonstrates the benefit of using artificial neural networks and feature selection techniques to enhance Raman spectra processing to determine glycated hemoglobin and glucose in patients with undiagnosed T2D.