Petrascu Flavia-Medana, Matei Sergiu-Ciprian, Margan Mădălin-Marius, Ungureanu Ana-Maria, Olteanu Gheorghe-Emilian, Murariu Marius-Sorin, Olariu Sorin, Marian Catalin
Department of Doctoral Studies, "Victor Babeș" University of Medicine and Pharmacy, 300041 Timișoara, Romania.
Department of Biochemistry, "Victor Babeș" University of Medicine and Pharmacy, 300041 Timisoara, Romania.
Biomedicines. 2024 Nov 4;12(11):2524. doi: 10.3390/biomedicines12112524.
Chronic venous disease (CVD) represents a significant health challenge, particularly in obese individuals. This study focuses on the interplay between inflammation, obesity, and CVD, by analyzing the role of inflammatory markers in the disease progression. Clinical and paraclinical data of 619 patients hospitalized and treated in the Phlebology Department (1stSurgical Department, "Pius Brînzeu" Emergency County Hospital Timișoara, Romania) between 2018 and 2024 were analyzed. The statistical analysis revealed that age, C-reactive protein (CRP), fibrinogen, and absolute neutrophil count (ANC) were key predictors of CVD progression. Specifically, elevated CRP and fibrinogen levels correlated strongly with increased CVD severity, particularly in patients with higher body-mass index (BMI). BMI, while not an independent predictor, contributed indirectly to the disease severity through its association with these inflammatory markers. The logistic regression model incorporating age, BMI, CRP, fibrinogen, and ANC demonstrated a high predictive accuracy, with an area under the curve (AUC) of 0.902, highlighting the models reliability in stratifying patients at risk for severe CVD. This predictive model not only aids in identifying high-risk patients but also reinforces inflammation as a critical therapeutic target in CVD management.
慢性静脉疾病(CVD)是一项重大的健康挑战,在肥胖个体中尤为如此。本研究通过分析炎症标志物在疾病进展中的作用,聚焦于炎症、肥胖与CVD之间的相互作用。对2018年至2024年间在罗马尼亚蒂米什瓦拉“皮乌斯·布林泽乌”蒂米什县急救医院第一外科静脉病科住院治疗的619例患者的临床和辅助临床数据进行了分析。统计分析表明,年龄、C反应蛋白(CRP)、纤维蛋白原和绝对中性粒细胞计数(ANC)是CVD进展的关键预测指标。具体而言,CRP和纤维蛋白原水平升高与CVD严重程度增加密切相关,尤其是在体重指数(BMI)较高的患者中。BMI虽然不是独立的预测指标,但通过与这些炎症标志物的关联间接影响疾病严重程度。纳入年龄、BMI、CRP、纤维蛋白原和ANC的逻辑回归模型显示出较高的预测准确性,曲线下面积(AUC)为0.902,突出了该模型在对重度CVD风险患者进行分层方面的可靠性。这种预测模型不仅有助于识别高危患者,还强化了炎症作为CVD管理中关键治疗靶点的地位。