Department of Internal Medicine, Mostoles University Hospital, Rey Juan Carlos University, Mostoles, Spain.
Department of Signal Theory and Communications and Telematics Systems and Computing, Rey Juan Carlos University, Mostoles, Spain.
Metab Syndr Relat Disord. 2021 May;19(4):240-248. doi: 10.1089/met.2020.0139. Epub 2021 Feb 16.
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). 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. 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. 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.
某些炎症生物标志物，如白细胞介素-6、白细胞介素-1、C 反应蛋白（CRP）和纤维蛋白原，是典型的急性期参数，也可以预测心血管疾病。然而，这种炎症反应也可能与 2 型糖尿病（T2DM）的发展有关。
我们对门诊高血压患者进行了一项横断面、回顾性研究。记录了人口统计学、临床和实验室参数，如胰岛素抵抗的稳态模型评估（HOMA-IR）、CRP 和纤维蛋白原。结果是在 12 年的观察期内进展为显性 T2DM。
共筛选了 3472 例高血压患者，但最终有 1576 例无 T2DM 的患者纳入分析。纤维蛋白原、CRP 和胰岛素抵抗升高的患者进展为 T2DM 的发生率显著更高。在随访期间，199 例患者进展为 T2DM。多变量逻辑回归分析显示，体重指数[比值比（OR）1.04，95%置信区间（CI）：1.01-1.07]、HOMA-IR（OR 1.13，95%CI：1.08-1.16）、年龄（OR 1.05，95%CI：1.03-1.07）、log（CRP）（OR 1.37，95%CI：1.14-1.55）和纤维蛋白原（OR 1.44，95%CI：1.23-1.66）是进展为 T2DM的最重要预测因素。该模型的受试者工作特征曲线（ROC）下面积（AUC）为 0.76。使用机器学习方法，我们构建了一个包含 HOMA-IR、纤维蛋白原和 log（CRP）的模型，其准确性优于逻辑回归模型，AUC 为 0.9。
我们的结果表明，炎症生物标志物和 HOMA-IR 对预测进展为 T2DM 具有很强的预后价值。机器学习方法可以提供更准确的结果，以更好地理解这些特征对进展为 T2DM 的影响。基于这些特征的成功治疗方法可以避免进展为 T2DM，从而提高长期生存率。
