冠心病和 2 型糖尿病患者的 CCN5/WISP2 血清水平及其与炎症和胰岛素抵抗的相关性；一种机器学习方法

IF 2.3 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY

Biochemistry and Biophysics Reports Pub Date : 2024-10-30 DOI:10.1016/j.bbrep.2024.101857

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引用次数: 0

摘要

引言研究表明，CCN5/WISP2对代谢途径有各种影响，但之前的调查尚未确定其血清水平与CAD和/或T2DM之间的联系。方法本病例对照研究调查了 160 人血清中 CCN5、TNF-α、IL-6、脂肪连素和空腹胰岛素的水平，这些人被分为四个相同的组别（T2DM、CAD、CAD-T2DM 和健康对照组）。统计测试包括卡方检验、方差分析、斯皮尔曼相关性和逻辑回归。ROC 曲线用于表示 CCN5 的诊断潜力。疾病状态由机器学习算法预测：决策树、梯度提升树、随机森林、奈夫贝叶斯和 KNN。这些模型的性能通过各种指标进行评估，所有指标都通过应用 10 倍交叉验证来确保其稳健性。结果与健康对照组相比，CAD、T2DM 和 CAD-T2DM 组的 CCN5 浓度明显更高（CAD：336.87 ± 107.36 ng/mL；T2DM：367.46 ± 102.15 ng/mL；CAD-T2DM：404.68 ± 108.15 ng/mL；对照组：205.62 ± 63.34 ng/mL；P <；0.001）。在所有患者组中，CCN5 与细胞因子（IL-6 和 TNF-α）之间均存在明显的正相关性（P < 0.05）。多叉逻辑回归分析表明，即使调整了性别、体重指数和年龄，CCN5 与 T2DM-CAD、T2DM 和 CAD 病症之间仍存在显著关联（P <0.001）。在机器学习模型方面，Naïve Bayes 模型在对 T2DM 病例进行分类时表现最佳，其 AUC 值为 0.938±0.066。在预测 CAD 方面，随机森林分类器的 AUC 值最高，为 0.994±0.020。在预测 CAD-T2DM 时，Naïve Bayes 模型的 AUC 值最高，为 0.981±0.059，准确率为 97.50 % ± 7.91 %，F 值为 96.67 % ± 10.54 %。尽管如此，还需要更多的研究来确定CCN5是否可作为预测标志物。

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CCN5/WISP2 serum levels in patients with coronary artery disease and type 2 diabetes and its correlation with inflammation and insulin resistance; a machine learning approach

Introduction

Studies have shown various effects of CCN5/WISP2 on metabolic pathways, yet no prior investigation has established a link between its serum levels and CAD and/or T2DM. Therefore, this study seeks to explore the relation between CCN5 and the risk factor of CAD and/or diabetes, in comparison to individuals with good health, marking a pioneering endeavor in this field.

Methods

This case-control study investigates serum levels of CCN5, TNF-α, IL-6, adiponectin, and fasting insulin in a population of 160 individuals recruited into four equal groups (T2DM, CAD, CAD-T2DM, and healthy controls). Statistical tests comprise Chi-square tests, ANOVA, Spearman correlation, and logistic regression. ROC curves were used to represent the diagnostic potential of CCN5. Disease states are predicted by machine learning algorithms: Decision Tree, Gradient Boosted Trees, Random Forest, Naïve Bayes, and KNN. These models' performance was evaluated by various metrics, all of which were ensured to be robust by applying 10-fold cross-validation. Analyses were done in SPSS and GraphPad Prism and RapidMiner software.

Results

The CAD, T2DM, and CAD-T2DM groups had significantly higher CCN5 concentrations compared to the healthy control group (CAD: 336.87 ± 107.36 ng/mL, T2DM: 367.46 ± 102.15 ng/mL, CAD-T2DM: 404.68 ± 108.15 ng/mL, control: 205.62 ± 63.34 ng/mL; P < 0.001). A positive and significant correlation was observed between CCN5 and cytokines (IL-6 and TNF-α) in all patient groups (P < 0.05). Multinomial logistic regression analysis indicated a significant association between CCN5 and T2DM-CAD, T2DM, and CAD conditions (P < 0.001) even after adjusting for gender, BMI, and age (P < 0.001). Regarding the machine learning models, the Naïve Bayes model showed the best performance for classifying cases of T2DM, achieving an AUC value of 0.938±0.066. For predicting CAD, the Random Forest classifier achieved the highest AUC value of 0.994±0.020. In the case of CAD-T2DM prediction, the Naïve Bayes model demonstrated the highest AUC of 0.981±0.059, along with an Accuracy of 97.50 % ± 7.91 % and an F-measure of 96.67 % ± 10.54 %.

Conclusion

Our study has revealed, for the first time, a positive connection between CCN5 serum levels and the risk of developing T2DM and CAD. Nonetheless, more research is needed to ascertain whether CCN5 can serve as a predictive marker.

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来源期刊

Biochemistry and Biophysics Reports Biochemistry, Genetics and Molecular Biology-Biophysics

CiteScore

4.60

自引率

0.00%

发文量

191

审稿时长

59 days

期刊介绍： Open access, online only, peer-reviewed international journal in the Life Sciences, established in 2014 Biochemistry and Biophysics Reports (BB Reports) publishes original research in all aspects of Biochemistry, Biophysics and related areas like Molecular and Cell Biology. BB Reports welcomes solid though more preliminary, descriptive and small scale results if they have the potential to stimulate and/or contribute to future research, leading to new insights or hypothesis. Primary criteria for acceptance is that the work is original, scientifically and technically sound and provides valuable knowledge to life sciences research. We strongly believe all results deserve to be published and documented for the advancement of science. BB Reports specifically appreciates receiving reports on: Negative results, Replication studies, Reanalysis of previous datasets.