PDF(Pubmed)
Abstract：
UNASSIGNED: Diabetic nephropathy (DN) is a common complication of type 2 diabetes mellitus (T2DM) that significantly impacts the quality of life for affected patients. Dyslipidemia is a known risk factor for developing cardiovascular complications in T2DM patients. However, the association between serum lipoprotein(a) (Lp(a)) and high-density lipoprotein cholesterol (HDL-C) with DN requires further investigation.
UNASSIGNED: For this cross-sectional study, we randomly selected T2DM patients with nephropathy (DN, n = 211) and T2DM patients without nephropathy (T2DM, n = 217) from a cohort of 142,611 patients based on predefined inclusion and exclusion criteria. We collected clinical data from the patients to identify potential risk factors for DN using binary logistic regression and machine learning. After obtaining the feature importance score of clinical indicators by building a random forest classifier, we examined the correlations between Lp(a), HDL-C and the top 10 indicators. Finally, we trained decision tree models with top 10 features using training data and evaluated their performance with independent testing data.
UNASSIGNED: Compared to the T2DM group, the DN group had significantly higher serum levels of Lp(a) (p < 0.001) and lower levels of HDL-C (p = 0.028). Lp(a) was identified as a risk factor for DN, while HDL-C was found to be protective. We identified the top 10 indicators that were associated with Lp(a) and/or HDL-C, including urinary albumin (uALB), uALB to creatinine ratio (uACR), cystatin C, creatinine, urinary ɑ1-microglobulin, estimated glomerular filtration rate (eGFR), urinary β2-microglobulin, urea nitrogen, superoxide dismutase and fibrinogen. The decision tree models trained using the top 10 features and with uALB at a cut-off value of 31.1 mg/L showed an average area under the receiver operating characteristic curve (AUC) of 0.874, with an AUC range of 0.870 to 0.890.
UNASSIGNED: Our findings indicate that serum Lp(a) and HDL-C are associated with DN and we have provided a decision tree model with uALB as a predictor for DN.
UNASSIGNED: For this cross-sectional study, we randomly selected T2DM patients with nephropathy (DN, n = 211) and T2DM patients without nephropathy (T2DM, n = 217) from a cohort of 142,611 patients based on predefined inclusion and exclusion criteria. We collected clinical data from the patients to identify potential risk factors for DN using binary logistic regression and machine learning. After obtaining the feature importance score of clinical indicators by building a random forest classifier, we examined the correlations between Lp(a), HDL-C and the top 10 indicators. Finally, we trained decision tree models with top 10 features using training data and evaluated their performance with independent testing data.
UNASSIGNED: Compared to the T2DM group, the DN group had significantly higher serum levels of Lp(a) (p < 0.001) and lower levels of HDL-C (p = 0.028). Lp(a) was identified as a risk factor for DN, while HDL-C was found to be protective. We identified the top 10 indicators that were associated with Lp(a) and/or HDL-C, including urinary albumin (uALB), uALB to creatinine ratio (uACR), cystatin C, creatinine, urinary ɑ1-microglobulin, estimated glomerular filtration rate (eGFR), urinary β2-microglobulin, urea nitrogen, superoxide dismutase and fibrinogen. The decision tree models trained using the top 10 features and with uALB at a cut-off value of 31.1 mg/L showed an average area under the receiver operating characteristic curve (AUC) of 0.874, with an AUC range of 0.870 to 0.890.
UNASSIGNED: Our findings indicate that serum Lp(a) and HDL-C are associated with DN and we have provided a decision tree model with uALB as a predictor for DN.
摘要：
■糖尿病肾病（DN）是2型糖尿病（T2DM）的常见并发症，显着影响患者的生活质量。血脂异常是T2DM患者发生心血管并发症的已知危险因素。然而,血清脂蛋白(a)(Lp(a))和高密度脂蛋白胆固醇(HDL-C)与DN的相关性需要进一步研究.
■对于这项横断面研究，我们随机选择T2DM肾病患者(DN，n=211）和无肾病的T2DM患者（T2DM，n=217)来自基于预定义的纳入和排除标准的142,611名患者的队列。我们收集了患者的临床数据，使用二元逻辑回归和机器学习来确定DN的潜在危险因素。通过构建随机森林分类器获得临床指标的特征重要性评分后,我们检查了Lp(a)，HDL-C和前10名指标。最后,我们使用训练数据训练了具有前10个特征的决策树模型，并使用独立的测试数据评估了它们的性能。
■与T2DM组相比，DN组血清Lp(a)水平显著升高(p<0.001),HDL-C水平显著降低(p=0.028)。Lp(a)被确定为DN的危险因素，而HDL-C被发现是保护性的。我们确定了与Lp（a）和/或HDL-C相关的前10个指标，包括尿白蛋白（uALB），uALB与肌酐比值（uACR），胱抑素C,肌酐,尿α1-微球蛋白，估计肾小球滤过率(eGFR)，尿β2-微球蛋白，尿素氮,超氧化物歧化酶和纤维蛋白原。使用前10个特征和截止值为31.1mg/L的uALB训练的决策树模型显示受试者工作特征曲线下平均面积(AUC)为0.874,AUC范围为0.870至0.890。
■我们的发现表明血清Lp（a）和HDL-C与DN相关，并且我们提供了以uALB作为DN预测因子的决策树模型。
■对于这项横断面研究，我们随机选择T2DM肾病患者(DN，n=211）和无肾病的T2DM患者（T2DM，n=217)来自基于预定义的纳入和排除标准的142,611名患者的队列。我们收集了患者的临床数据，使用二元逻辑回归和机器学习来确定DN的潜在危险因素。通过构建随机森林分类器获得临床指标的特征重要性评分后,我们检查了Lp(a)，HDL-C和前10名指标。最后,我们使用训练数据训练了具有前10个特征的决策树模型，并使用独立的测试数据评估了它们的性能。
■与T2DM组相比，DN组血清Lp(a)水平显著升高(p<0.001),HDL-C水平显著降低(p=0.028)。Lp(a)被确定为DN的危险因素，而HDL-C被发现是保护性的。我们确定了与Lp（a）和/或HDL-C相关的前10个指标，包括尿白蛋白（uALB），uALB与肌酐比值（uACR），胱抑素C,肌酐,尿α1-微球蛋白，估计肾小球滤过率(eGFR)，尿β2-微球蛋白，尿素氮,超氧化物歧化酶和纤维蛋白原。使用前10个特征和截止值为31.1mg/L的uALB训练的决策树模型显示受试者工作特征曲线下平均面积(AUC)为0.874,AUC范围为0.870至0.890。
■我们的发现表明血清Lp（a）和HDL-C与DN相关，并且我们提供了以uALB作为DN预测因子的决策树模型。
