Abstract:
Diabetes is a condition accompanied by the alteration of body parameters, including those related to lipids like triglyceride (TG), low-density lipoproteins (LDLs), and high-density lipoproteins (HDLs). The latter are grouped under the term dyslipidemia and are considered a risk factor for cardiovascular events. In the present work, we analyzed the complex relationships between twelve parameters (disease status, age, sex, body mass index, systolic blood pressure, diastolic blood pressure, TG, HDL, LDL, glucose, HbA1c levels, and disease onset) of patients with diabetes from Romania. An initial prospective analysis showed that HDL is inversely correlated with most of the parameters; therefore, we further analyzed the dependence of HDLs on the other factors. The analysis was conducted with the Code Interpreter plugin of ChatGPT, which was used to build several models from which Random Forest performed best. The principal predictors of HDLs were TG, LDL, and HbA1c levels. Random Forest models were used to model all parameters, showing that blood pressure and HbA1c can be predicted based on the other parameters with the least error, while the less predictable parameters were TG and LDL levels. By conducting the present study using the ChatGPT Code Interpreter, we show that elaborate analysis methods are at hand and easy to apply by researchers with limited computational resources. The insight that can be gained from such an approach, such as what we obtained on HDL level predictors in diabetes, could be relevant for deriving novel management strategies and therapeutic approaches.
摘要:
糖尿病是一种伴随着身体参数改变的疾病,包括与脂质相关的甘油三酯(TG),低密度脂蛋白(LDLs),和高密度脂蛋白(HDLs)。后者被归类为术语血脂异常,被认为是心血管事件的危险因素。在目前的工作中,我们分析了十二个参数(疾病状态,年龄,性别,身体质量指数,收缩压,舒张压,TG,HDL,LDL,葡萄糖,HbA1c水平,和疾病发作)来自罗马尼亚的糖尿病患者。最初的前瞻性分析表明,HDL与大多数参数呈负相关;因此,我们进一步分析了HDLs对其他因素的依赖性。分析是使用ChatGPT的代码解释器插件进行的,用于构建随机森林表现最佳的几个模型。HDLs的主要预测因子是TG,LDL,和HbA1c水平。使用随机森林模型对所有参数进行建模,表明血压和HbA1c可以基于误差最小的其他参数来预测,而不可预测的参数是TG和LDL水平。通过使用ChatGPT代码解释器进行本研究,我们表明,精细的分析方法是在手,易于应用的研究人员有限的计算资源。从这种方法中可以获得的洞察力,比如我们在糖尿病的HDL水平预测因子上获得的结果,可能与得出新的管理策略和治疗方法有关。