Ojurongbe Taiwo Adetola, Afolabi Habeeb Abiodun, Oyekale Adesola, Bashiru Kehinde Adekunle, Ayelagbe Olubunmi, Ojurongbe Olusola, Abbasi Saddam Akber, Adegoke Nurudeen A
Department of Statistics Osun State University Osogbo Nigeria.
Department of Chemical Pathology Ladoke Akintola University of Technology Ogbomoso Nigeria.
Health Sci Rep. 2024 Jan 25;7(1):e1834. doi: 10.1002/hsr2.1834. eCollection 2024 Jan.
With the global rise in type 2 diabetes, predictive modeling has become crucial for early detection, particularly in populations with low routine medical checkup profiles. This study aimed to develop a predictive model for type 2 diabetes using health check-up data focusing on clinical details, demographic features, biochemical markers, and diabetes knowledge.
Data from 444 Nigerian patients were collected and analysed. We used 80% of this data set for training, and the remaining 20% for testing. Multivariable penalized logistic regression was employed to predict the disease onset, incorporating waist-hip ratio (WHR), triglycerides (TG), catalase, and atherogenic indices of plasma (AIP).
The predictive model demonstrated high accuracy, with an area under the curve of 99% (95% CI = 97%-100%) for the training set and 94% (95% CI = 89%-99%) for the test set. Notably, an increase in WHR (adjusted odds ratio [AOR] = 70.35; 95% CI = 10.04-493.1, -value < 0.001) and elevated AIP (AOR = 4.55; 95% CI = 1.48-13.95, -value = 0.008) levels were significantly associated with a higher risk of type 2 diabetes, while higher catalase levels (AOR = 0.33; 95% CI = 0.22-0.49, < 0.001) correlated with a decreased risk. In contrast, TG levels (AOR = 1.04; 95% CI = 0.40-2.71, -value = 0.94) were not associated with the disease.
This study emphasizes the importance of using distinct clinical and biochemical markers for early type 2 diabetes detection in Nigeria, reflecting global trends in diabetes modeling, and highlighting the need for context-specific methods. The development of a web application based on these results aims to facilitate the early identification of individuals at risk, potentially reducing health complications, and improving diabetes management strategies in diverse settings.
随着全球2型糖尿病发病率的上升，预测模型对于早期检测至关重要，尤其是在常规体检率较低的人群中。本研究旨在利用关注临床细节、人口统计学特征、生化标志物和糖尿病知识的健康检查数据，开发一种2型糖尿病预测模型。
收集并分析了444名尼日利亚患者的数据。我们将该数据集的80%用于训练，其余20%用于测试。采用多变量惩罚逻辑回归来预测疾病发作，纳入腰臀比（WHR）、甘油三酯（TG）、过氧化氢酶和血浆致动脉粥样硬化指数（AIP）。
预测模型显示出高准确性，训练集的曲线下面积为99%（95%CI = 97%-100%），测试集为94%（95%CI = 89%-99%）。值得注意的是，WHR升高（调整优势比[AOR]=70.35；95%CI = 10.04-493.1，P值<0.001）和AIP水平升高（AOR = 4.55；95%CI = 1.48-13.95，P值 = 0.008）与2型糖尿病风险较高显著相关，而过氧化氢酶水平较高（AOR = 0.33；95%CI = 0.22-0.49，P<0.001）与风险降低相关。相比之下，TG水平（AOR = 1.04；95%CI = 0.40-2.71，P值 = 0.94）与该疾病无关。
本研究强调了使用不同的临床和生化标志物在尼日利亚早期检测2型糖尿病的重要性，反映了糖尿病建模的全球趋势，并突出了针对具体情况的方法的必要性。基于这些结果开发的网络应用程序旨在促进对高危个体的早期识别，潜在地减少健康并发症，并改善不同环境下的糖尿病管理策略。
