Diabetes Metab J.  2021 Jul;45(4):526-538. 10.4093/dmj.2020.0100.

Study on Risk Factors of Peripheral Neuropathy in Type 2 Diabetes Mellitus and Establishment of Prediction Model

• Wu B
• Niu Z
• Hu F

• KMID: 2518895
• DOI: http://doi.org/10.4093/dmj.2020.0100

Abstract

Background
Diabetic peripheral neuropathy (DPN) is one of the most serious complications of type 2 diabetes mellitus (T2DM). DPN increases the risk of ulcers, foot infections, and noninvasive amputations, ultimately leading to long-term disability.
Methods
Seven hundred patients with T2DM were investigated from 2013 to 2017 in the Sanlin community by obtaining basic data from the electronic medical record system (EMRS). From September 2018 to July 2019, 681 patients (19 missing) were investigated using a questionnaire, physical examination, biochemical index test, and follow-up Toronto clinical scoring system (TCSS) test. Patients with a TCSS score ≥6 points were diagnosed with DPN. After removing missing values, 612 patients were divided into groups in a 3:1 ratio for external validation. Using different Lasso analyses (misclassification error, mean squared error, –2log-likelihood, and area under curve) and a logistic regression analysis of the training set, models A, B, C, and D were established. The receiver operating characteristic (ROC) curve, calibration plot, dynamic component analysis (DCA) measurements, net classification improvement (NRI) and integrated discrimination improvement (IDI) were used to validate discrimination and clinical practicality of the model.
Results
Through data analysis, model A (containing four factors), model B (containing five factors), model C (containing seven factors), and model D (containing seven factors) were built. After calibration, ROC curve, DCA, NRI and IDI, models C and D exhibited better accuracy and greater predictive power.
Conclusion
Four prediction models were established to assist with the early screening of DPN in patients with T2DM. The influencing factors in model C and D are more important factors for patients with T2DM diagnosed with DPN.

Keyword

Data analysis; Diabetes mellitus, type 2; Diabetic neuropathies; Logistic models

Figure

• 

  Fig. 1. Flowchart of the procedure used in this study. The flowchart shows the entire process of the study from the acquisition of indicators, diagnosis of patients, handing of missing and abnormal values, statistical analysis, and conclusions. TCSS, Toronto clinical scoring system; LASSO, Lead Absolute Shrinkage and Selection Operator; CLASS, misclassification error; AUC, area under curve; MSE, mean squared error; SBP, systolic blood pressure; FBG, fasting blood glucose; LDL-C, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; TC, total cholesterol; BMI, body mass index; PBG, postprandial blood glucose; HbA1c, glycosylated hemoglobin; TG, triglyceride; UA, uric acid; HDL-C, high-density lipoprotein cholesterol; DPN, diabetic peripheral neuropathy; T2DM, type 2 diabetes mellitus.

• 

  Fig. 2. Demographic and clinical features selected using the Lead Absolute Shrinkage and Selection Operator (LASSO) analysis. (A) Area under curve (AUC): LASSO coefficient profiles of the four features. A coefficient profile plot was produced with the log(lambda) sequence. A vertical line was drawn at the value selected using fivefold cross-validation, where the optimal lambda value resulted in four features with nonzero coefficients. (B) Misclassification error (CLASS): LASSO coefficient profiles of the five features. A coefficient profile plot was produced with the log(lambda) sequence. A vertical line was drawn at the value selected using five-fold cross-validation, where the optimal lambda value resulted in five features with nonzero coefficients. (C) Mean squared error (MSE): LASSO coefficient profiles of the seven features. A coefficient profile plot was produced with the log(lambda) sequence. A vertical line was drawn at the value selected using five-fold cross-validation, where the optimal lambda value resulted in seven features with nonzero coefficients. (D) Deviance: LASSO coefficient profiles of the seven features. A coefficient profile plot was produced with the log(lambda) sequence. A vertical line was drawn at the value selected using five-fold cross-validation, where the optimal lambda value resulted in seven features with nonzero coefficients. (E) CLASS & MSE & deviance & AUC: optimal parameters (lambda) selected in the LASSO model using five-fold cross-validation based on the minimum criteria. The partial likelihood deviance (binomial deviance) curve was plotted versus log(lambda). Dotted vertical lines were drawn at the optimal values using the minimum criteria and the 1-standard error (SE) of the minimum criteria.

• 

  Fig. 3. The nomograms for diabetic peripheral neuropathy (DPN) based on models A, B, C, and D. (A) Model A: The nomogram for DPN in patients with type 2 diabetes mellitus (T2DM) was developed in the cohort by integrating systolic blood pressure (SBP), fasting blood glucose (FBG) levels, low-density lipoprotein cholesterol (LDL-C) levels, and estimated glomerular filtration rate (eGFR). (B) Model B: The nomogram for DPN in patients with T2DM was developed in the cohort by integrating age, disease course, FBG levels, total cholesterol (TC) levels, and body mass index (BMI). (C) Model C: The nomogram for DPN in patients with T2DM was developed in the cohort by integrating age, postprandial blood glucose (PBG) levels, FBG levels, glycosylated hemoglobin (HbA1c) levels, TC levels, uric acid (UA) levels, and waist circumference. (D) Model D: The nomogram for DPN in patients with T2DM was developed in the cohort by integrating age, PBG levels, FBG levels, HbA1c levels, LDL-C levels, high-density lipoprotein cholesterol (HDL-C) levels, and BMI.

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