Rafael Garcia-Carretero1,2, Luis Vigil-Medina1, Oscar Barquero-Perez2. 1. Department of Internal Medicine, Mostoles University Hospital, Rey Juan Carlos University, Mostoles, Spain. 2. Department of Signal Theory and Communications and Telematics Systems and Computing, Rey Juan Carlos University, Mostoles, Spain.
Abstract
Background: Certain inflammatory biomarkers, such as interleukin-6, interleukin-1, C-reactive protein (CRP), and fibrinogen, are prototypical acute-phase parameters that can also be predictors of cardiovascular disease. However, this inflammatory response can also be linked to the development of type 2 diabetes mellitus (T2DM). Methods: We performed a cross-sectional, retrospective study of hypertensive patients in an outpatient setting. Demographic, clinical, and laboratory parameters, such as the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), CRP, and fibrinogen, were recorded. The outcome was progression to overt T2DM over the 12-year observation period. Results: A total of 3,472 hypertensive patients were screened, but 1,576 individuals without T2DM were ultimately included in the analyses. Patients with elevated fibrinogen, CRP, and insulin resistance had a significantly greater incidence of progression to T2DM. During follow-up, 199 patients progressed to T2DM. Multivariate logistic regression analyses showed that body mass index [odds ratio (OR) 1.04, 95% confidence interval (CI): 1.01-1.07], HOMA-IR (OR 1.13, 95% CI: 1.08-1.16), age (OR 1.05, 95% CI: 1.03-1.07), log(CRP) (OR 1.37, 95% CI: 1.14-1.55), and fibrinogen (OR 1.44, 95% CI: 1.23-1.66) were the most important predictors of progression to T2DM. The area under the receiver operating characteristic curve (AUC) of this model was 0.76. Using machine learning methods, we built a model that included HOMA-IR, fibrinogen, and log(CRP) that was more accurate than the logistic regression model, with an AUC of 0.9. Conclusion: Our results suggest that inflammatory biomarkers and HOMA-IR have a strong prognostic value in predicting progression to T2DM. Machine learning methods can provide more accurate results to better understand the implications of these features in terms of progression to T2DM. A successful therapeutic approach based on these features can avoid progression to T2DM and thus improve long-term survival.
Background: Certain inflammatory biomarkers, such as interleukin-6, interleukin-1, C-reactive protein (CRP), and fibrinogen, are prototypical acute-phase parameters that can also be predictors of cardiovascular disease. However, this inflammatory response can also be linked to the development of type 2 diabetes mellitus (T2DM). Methods: We performed a cross-sectional, retrospective study of hypertensive patients in an outpatient setting. Demographic, clinical, and laboratory parameters, such as the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), CRP, and fibrinogen, were recorded. The outcome was progression to overt T2DM over the 12-year observation period. Results: A total of 3,472 hypertensive patients were screened, but 1,576 individuals without T2DM were ultimately included in the analyses. Patients with elevated fibrinogen, CRP, and insulin resistance had a significantly greater incidence of progression to T2DM. During follow-up, 199 patients progressed to T2DM. Multivariate logistic regression analyses showed that body mass index [odds ratio (OR) 1.04, 95% confidence interval (CI): 1.01-1.07], HOMA-IR (OR 1.13, 95% CI: 1.08-1.16), age (OR 1.05, 95% CI: 1.03-1.07), log(CRP) (OR 1.37, 95% CI: 1.14-1.55), and fibrinogen (OR 1.44, 95% CI: 1.23-1.66) were the most important predictors of progression to T2DM. The area under the receiver operating characteristic curve (AUC) of this model was 0.76. Using machine learning methods, we built a model that included HOMA-IR, fibrinogen, and log(CRP) that was more accurate than the logistic regression model, with an AUC of 0.9. Conclusion: Our results suggest that inflammatory biomarkers and HOMA-IR have a strong prognostic value in predicting progression to T2DM. Machine learning methods can provide more accurate results to better understand the implications of these features in terms of progression to T2DM. A successful therapeutic approach based on these features can avoid progression to T2DM and thus improve long-term survival.
Entities:
Keywords:
inflammatory biomarkers; metabolic syndrome; prognostic factors; type 2 diabetes
Authors: Ludovica Ilari; Agnese Piersanti; Christian Göbl; Laura Burattini; Alexandra Kautzky-Willer; Andrea Tura; Micaela Morettini Journal: Front Physiol Date: 2022-02-17 Impact factor: 4.566