Afrisham Reza, Farrokhi Vida, Ayyoubzadeh Seyed Mohammad, Vatannejad Akram, Fadaei Reza, Moradi Nariman, Jadidi Yasaman, Alizadeh Shaban
Department of Medical Laboratory Sciences, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Department of Hematology and Transfusion Sciences, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Biochem Biophys Rep. 2024 Oct 30;40:101857. doi: 10.1016/j.bbrep.2024.101857. eCollection 2024 Dec.
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.
引言：研究表明CCN5/WISP2对代谢途径有多种影响，但此前尚无研究证实其血清水平与CAD和/或T2DM之间存在关联。因此，本研究旨在探讨CCN5与CAD和/或糖尿病危险因素之间的关系，并与健康个体进行比较，这在该领域尚属首次。 方法：本病例对照研究调查了160名个体的血清CCN5、TNF-α、IL-6、脂联素和空腹胰岛素水平，这些个体被分为四个相等的组（T2DM组、CAD组、CAD-T2DM组和健康对照组）。统计检验包括卡方检验、方差分析、Spearman相关性分析和逻辑回归分析。ROC曲线用于表示CCN5的诊断潜力。疾病状态通过机器学习算法进行预测：决策树、梯度提升树、随机森林、朴素贝叶斯和KNN。通过各种指标评估这些模型的性能，并通过应用10倍交叉验证确保所有指标的稳健性。分析在SPSS、GraphPad Prism和RapidMiner软件中进行。 结果：与健康对照组相比，CAD组、T2DM组和CAD-T2DM组的CCN5浓度显著更高（CAD组：336.87±107.36 ng/mL，T2DM组：367.46±102.15 ng/mL，CAD-T2DM组：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）之间存在显著正相关（P<0.05）。多项逻辑回归分析表明，即使在调整性别、BMI和年龄后，CCN5与T2DM-CAD、T2DM和CAD状况之间仍存在显著关联（P<0.001）（P<0.001）。关于机器学习模型，朴素贝叶斯模型在分类T2DM病例方面表现最佳，AUC值为0.938±0.066。对于预测CAD，随机森林分类器的AUC值最高，为0.994±0.020。在预测CAD-T2DM时，朴素贝叶斯模型的AUC最高，为0.981±0.059，准确率为97.50%±7.91%，F值为96.67%±10.54%。 结论：我们的研究首次揭示了CCN5血清水平与发生T2DM和CAD风险之间的正相关关系。尽管如此，仍需要更多研究来确定CCN5是否可作为预测标志物。
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