Activity Prediction Modeling Based on a Combination of Growth Differentiation Factor 15 and Serum Biomarker Levels in Dermatomyositis and Polymyositis

IF 4.7 3区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
Qiong Wu , Wei Wang , Ling Qiu, Wanchan Peng, Yunli Zhang, Jinfang Fu, Siyu Wu
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引用次数: 0

Abstract

Aims

Growth differentiation factor 15 (GDF15) plays an important role in multiple inflammatory disorders. We aimed to analyze serum GDF15 levels in adult patients with idiopathic inflammatory myopathies (IIMs).

Methods

Serum GDF15 levels were measured in 179 adult patients with IIMs and 76 healthy controls (HCs). The association between GDF15 levels and disease variables was analyzed using Spearman's rank correlation. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminatory ability of GDF15 and the GDF15-to-lymphocyte ratio (GLR). Machine learning methods were applied to build predictive models.

Results

GDF15 levels and GLR were significantly elevated in patients with adult IIMs than in HCs. Compared with patients in remission, both GDF15 and GLR were significantly higher in myositis patients in an active phase. GDF15 levels correlated positively with myositis disease activity indices and negatively correlated with lymphocyte and platelet counts. ROC curve analysis revealed that GDF15 levels and GLR outperformed muscle enzymes and distinguished well between patients with active disease and those in remission. Furthermore, even in the normal muscle enzyme group, GDF15 levels and GLR were also well-distinguished between patients with active disease and those in remission. Using machine learning, a logistic regression model of GDF15 combined with creatine kinase and lymphocyte count was constructed and had a reliable predictive value for disease activity.

Conclusions

GDF15, particularly GLR, was significantly correlated with disease activity in adult patients with IIMs. They could serve as useful biochemical markers for evaluating disease activity, monitoring disease progression, and guiding treatment in adult patients with IIMs.

基于生长分化因子 15 与皮肌炎和多发性肌炎血清生物标志物水平组合的活动预测模型
目的:生长分化因子15(GDF15)在多种炎症性疾病中发挥着重要作用。我们旨在分析特发性炎症性肌病(IIMs)成年患者的血清 GDF15 水平:方法:测定了179名特发性炎症性肌病(IIMs)成年患者和76名健康对照组(HCs)的血清GDF15水平。采用斯皮尔曼秩相关分析了GDF15水平与疾病变量之间的关系。接收者操作特征(ROC)曲线分析评估了 GDF15 和 GDF15 与淋巴细胞比值(GLR)的鉴别能力。应用机器学习方法建立预测模型:结果:成人 IIMs 患者的 GDF15 水平和 GLR 均明显高于 HCs 患者。与缓解期患者相比,肌炎活动期患者的GDF15和GLR均明显升高。GDF15水平与肌炎疾病活动指数呈正相关,与淋巴细胞和血小板计数呈负相关。ROC曲线分析显示,GDF15水平和GLR优于肌酶,能很好地区分活动期患者和缓解期患者。此外,即使在肌酶正常组,GDF15水平和GLR也能很好地区分活动期患者和缓解期患者。通过机器学习,构建了一个将GDF15与肌酸激酶和淋巴细胞计数相结合的逻辑回归模型,该模型对疾病活动具有可靠的预测价值:结论:GDF15,尤其是GLR,与成年IIMs患者的疾病活动性有显著相关性。结论:GDF15 特别是 GLR 与成年 IIM 患者的疾病活动性有明显相关性,它们可以作为有用的生化标记物,用于评估 IIM 患者的疾病活动性、监测疾病进展和指导治疗。
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来源期刊
Archives of Medical Research
Archives of Medical Research 医学-医学:研究与实验
CiteScore
12.50
自引率
0.00%
发文量
84
审稿时长
28 days
期刊介绍: Archives of Medical Research serves as a platform for publishing original peer-reviewed medical research, aiming to bridge gaps created by medical specialization. The journal covers three main categories - biomedical, clinical, and epidemiological contributions, along with review articles and preliminary communications. With an international scope, it presents the study of diseases from diverse perspectives, offering the medical community original investigations ranging from molecular biology to clinical epidemiology in a single publication.
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