A Novel Inflammation and Insulin Resistance Related Indicator to Predict the Survival of Patients With Cancer

医学 接收机工作特性 危险系数 内科学 癌症 队列 置信区间 肿瘤科 胰岛素抵抗 C反应蛋白 炎症 胰岛素
作者
Guo‐Tian Ruan,Hailun Xie,Heyang Zhang,Chenan Liu,Yi‐Zhong Ge,Qi Zhang,Ziwen Wang,Xi Zhang,Meng Tang,Mengmeng Song,Xiaowei Zhang,Ming Yang,Yongbing Chen,Kaiying Yu,Li Deng,Yizhen Gong,Wen Hu,Kunhua Wang,Minghua Cong,Hanping Shi
出处
期刊:Frontiers in Endocrinology [Frontiers Media SA]
卷期号:13 被引量:41
标识
DOI:10.3389/fendo.2022.905266
摘要

Background Systemic inflammation and insulin resistance (IR) are closely related in patients with cancer. However, there is no relevant indicator that combines inflammation and IR to predict patient prognosis. Therefore, this study aimed to develop and validate a novel inflammation- and IR-related marker in patients with cancer. Methods The total cohort of this study included 5221 patients with cancer, and the training and validation cohorts were randomized in a 7:3 ratio. C-reactive protein (CRP) and fasting triglyceride glucose (TyG) were used to reflect patients’ inflammation and IR status, respectively. The CRP-TyG index (CTI) was composed of CRP and TyG. The concordance (C)-index, receiver operator characteristic (ROC) curve, and calibration curve reflected the prognostic predictive power of CTI. Univariate and multivariate survival analyses predicted the prognostic value of CTI in patients with cancer. Results The C-indices of CTI in patients with cancer were 0.636, 0.617, and 0.631 in the total, training, and validation cohorts, respectively. The 1-, 3-, and 5-year ROC and calibration curves showed that CTI had a good predictive ability of survival in patients with cancer. Meanwhile, patients with high CTI had a worse prognosis compared to patients with low CTI (total cohort: hazard ratio [HR] = 1.46, 95% confidence interval [95% CI] = 1.33–1.59; training cohort: HR = 1.36, 95% CI = 1.22–1.52; validation cohort: HR = 1.73, 95% CI = 1.47–2.04]. Conclusion The CTI is a useful prognostic indicator of poor prognosis and a promising tool for treatment strategy decision-making in patients with cancer.
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