Two user-friendly models and a simple cutoff value of Neutrophil to Lymphocyte ratio (NLR) for early Diagnosis of Diabetic Nephropathy in middleelderly aged pa-tients with Type 2 Diabetes: A Cross-sectional study
Yi-Ming Tian1, Tao Li2, Hongyuan Gu3, Rui Wang1*, Chunming Ma1, Rui Wang1
1Department of Endocrinology, the First Hospital of Qinhuangdao
2Department of Nephrology, the First Hospital of Qinhuangdao
3Department of Tumor Radiotherapy, the First Hospital of Qinhuangdao
*Rui Wang, MD, Department of Endocrinology, the First Hospital of Qinhuangdao, 510515, No. 258 Wenhua road, Haigang District, the City of Qinhuangdao, Hebei Province, China
*P < 0.05.
T-test and Fisher’s exact test were performed to evaluate the difference between inde-pendent samples and the distribution of category variables, respectively. DN, diabetic nephropathy; CVD, cardiovascular disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin A1c; FBG, fast blood glucose; PBG2h, postprandial blood pressure of 2 hour; FBI, fast blood insulin; PBI2h, postprandial blood insulin of 2 hour; FBCP, fast blood c-peptide; PBCP2h, postprandial blood c-peptide of 2 hour; LDL-c,
low-density lipoprotein cholesterol; HDL-c, high-density lipoprotein cho-lesterol; Apo-A, apo-lipoprotein A; Apo-B, apo-lipoprotein B; WBC, white blood cell; NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; UACR, urine albumin to creatinine ratio; effective glomerular filtration rate.
Table 2. Independent risk factors of DN prediction tested by binary logistic regression analysis with adjustments of multi-variables.
Binary logistic regression analysis was explored in identifying the predictors of DN. As a result, duration of diabetes, NLR, SBP and Lp(a) were shown to predict the DN diagnosis independently, after adjusted by multi-variables of age, gender, BMI, smoking history, HbA1c and UA. Gender and smoking history entered the logistic model as category variables. DN, diabetic nephropathy; NLR, neutrophil to lymphocyte ratio;
SBP, systolic blood pres-sure; Lp(a), lipoprotein-a; BMI, body mass index; HbA1c, glycated hemoglobin A1c; UA, uric acid.
Table 3: Comparisons of area under the curve (AUC) for DN prediction.
*P < 0.05. NLR, neutrophil to lymphocyte ratio.
Figure 1. (a) Receiver operating characteristics (ROC ) curve analysis indicated that NLR value of 2.04 had a sensitivity of 48.9% and a specificity of 80.8% in predicting DN, with area under the curve (AUC) of 0.666 (P = 0.001, 95%CI: 0.584-0.742). (b) Model 1 had a sensitivity of 76.9% and a specificity of 75.3%, with the AUC of 0.819 (P < 0.001, 95%CI: 0.741-0.881), using the predicted probabilities of discriminant analysis. (c) Model 2 had a sensitivity of 74.4% and a specificity of 78.7%, with the AUC of 0.817 (P < 0.001, 95%CI: 0.739-0.879), using the predicted probabilities of discriminant analysis. (d) Comparisons of AUC for different ROC curves in predicting DN diagnosis.
Supplemental table 1: NLR values in different groups of potential confounders.
To clarify the cross interaction of NLR and other parameters for DN prediction, NLR levels in different groups of potential confounders were analyzed. As a result, no signifi-cant difference was shown between different groups of gender, with or without past his-tory of CVD, family history of diabetes or smoking history. DM, diabetes mellitus; CVD, cardiovascular disease.
Supplemental table 2 : Statistical characteristics of the 2 constructed discriminant equations.
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